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S0001457519316926 | Provisional drivers do not always comply with graduated driver licensing restrictions and road laws . The aim of this study was to explore the effect of parenting style on young driver compliance with licensing restrictions . Two surveys the first a sample of parents of provisionally licensed drivers from Queensland and the Australian Capital Territory while the second and separate sample was of provisionally licensed drivers from Queensland . A series of regression analyses identified that parents who reported higher levels of control were more likely to feel responsible for their childs driving and to have a child that complied with licensing restrictions . Parents who reported higher levels of support were more likely to offer their child practical assistance in order to help them to comply with licensing restrictions . Young drivers who reported that their parents had higher levels of control were more likely to indicate that they complied with restrictions . Given that parenting style appears to influence provisional driver compliance with licensing requirements there may be an opportunity to develop interventions to enhance compliance . | Surveys with parents and young drivers indicates that parenting styles affect young driver compliance with provisional driving laws. Parents with higher levels of control are more likely to have children that comply with provisional driving laws. Parents with higher levels of support are more likely to offer practical driving assistance. Those whose parents had higher levels of control and lower levels of support were more likely to have additional driving restrictions. |
S0001457519317026 | Many road authorities in Canada have been contemplating the use of wider longitudinal pavement markings to enhance road safety and driver comfort . However conclusive evidence on the safety impacts of wider LPMs has not been available . To address this gap in the literature this study was conducted to investigate the safety impacts of wider LPMs . The study adopted the Full Bayes approach to conduct a before and after safety evaluation using data collected from 38 treatment sites from three Canadian jurisdictions . Collision and traffic data were obtained from the 38 sites over a period of eight years . The widths of LPMs at all sites were increased between 2012 and 2013 which enables a before and after safety evaluation to investigate the impact of the wider markings on the collision frequency . The results showed an overall significant reduction in both total collisions and target collisions by 12.3 and 19.0 respectively after implementing the wider LPMs . Total collisions were reduced by 11.1 27.5 and 1.1 in Alberta British Columbia and Quebec respectively . Similarly a reduction in the run off the road collisions that ranged between 22.7 and 28.9 were observed in the three jurisdictions . The results suggest that wider longitudinal pavement markings can reduce collisions and improve safety on Canadian highways . As such road authorities should consider using this intervention to enhance road safety particularly at locations that experience a high frequency of run off the road collisions . | This study adopted the FB approach to evaluate the safety impacts of wider longitudinal pavement markings using data from BC AB and QC. An overall reduction in total and run off road collisions by 12.3 and 19.0 respectively after implementing the wider markings was observed. As well the total and run off road collisions were significantly reduced in each of the three jurisdictions. The results suggest that wider longitudinal pavement markings can reduce collisions and improve safety on Canadian highways. It is strongly recommended to implement this intervention particularly at locations with a high frequency of run off road collisions. |
S0001457519317099 | With only 20 of bicycling crashes captured in official databases studies on bicycling safety can be limited . New datasets on bicycling incidents are available via crowdsourcing applications with opportunity for analyses that characterize reporting patterns . Our goal was to characterize patterns of injury in crowdsourced bicycle incident reports from BikeMaps.org . We extracted 281 incidents reported on the BikeMaps.org global mapping platform and analyzed 21 explanatory variables representing personal trip route and crash characteristics . We used a balanced random forest classifier to classify three outcomes collisions resulting in injury requiring medical treatment collisions resulting in injury but the bicyclist did not seek medical treatment and collisions that did not result in injury . Results indicate the ranked importance and direction of relationship for explanatory variables . By knowing conditions that are most associated with injury we can target interventions to reduce future risk . The most important reporting pattern overall was the type of object the bicyclist collided with . Increased probability of injury requiring medical treatment was associated with collisions with animals train tracks transient hazards and left turning motor vehicles . Falls right hooks and doorings were associated with incidents where the bicyclist was injured but did not seek medical treatment and conflicts with pedestrians and passing motor vehicles were associated with minor collisions with no injuries . In Victoria British Columbia Canada bicycling safety would be improved by additional infrastructure to support safe left turns and around train tracks . Our findings support previous research using hospital admissions data that demonstrate how non motor vehicle crashes can lead to bicyclist injury and that route characteristics and conditions are factors in bicycling collisions . Crowdsourced data have potential to fill gaps in official data such as insurance police and hospital reports . | Characterized patterns of injuries in crowdsourced bicycling safety data. Type of object the bicycle collided with was most important reporting pattern. Non motor vehicle crashes can lead to injury yet are typically underreported. Crowdsourced data have potential to fill gaps in official data. |
S0001457519317166 | The crash prediction model is a useful tool for traffic administrators to identify significant risk factors estimate crash frequency and screen hazardous locations but some jurisdictions interested in traffic safety analysis can collect only limited or low quality data . Existing crash prediction models can be transferred if calibrated but the current aggregate calibration method limits prediction accuracy and the disaggregate method is resource consuming . Transfer learning is another approach to calibration that acquires knowledge from old data domains to solve problems in new data domains . An instance based transfer learning technique TrAdaBoost.R2 is adopted in this study since it meets the requirement of site based crash prediction model transfer . TrAdaBoost.R2 was compared with AdaBoost.R2 using a simply pooled data set to examine the efficiency in extracting knowledge from a spatially outdated source data domain . The target data domain was sampled to test the techniques adaptability to small sample size . The calibration factor method based on a negative binomial model was employed to compare its predictive performance with that of the transfer learning technique . Mean square error was calculated to evaluate the prediction accuracy . Two cities in China Shanghai and Guangzhou were taken mutually as source data domain and target data domain . Results showed that the models constructed with TrAdaBoost.R2 had better prediction accuracy than the conventional calibration method . The TrAdaBoost.R2 is recommended due to its predictive performance and adaptability to small sample size . Crash prediction models are proposed to construct for peak and off peak hours separately . | Decision Tree is preferred to Support Vector Machine when applying TrAdaBoost.R2. TrAdaBoost.R2 extracts knowledge efficiently from spatially outdated source data. TrAdaBoost.R2 is adaptable for small samples. TrAdaBoost.R2 has better prediction accuracy than conventional calibration methods. A discrepancy of transferability in different time periods is observed. |
S0001457519317294 | This paper introduces a framework for modelling the cyclists comfort zone . Unlike the drivers comfort zone little is known about the cyclists . The framework draws on existing literature in cognitive science about driver behaviour to explain experimental results from cycling field trials and the modelling of these results . We modelled braking and steering manoeuvres from field data of cyclists obstacle avoidance within their comfort zone . Results show that when cyclists avoided obstacles by braking they kept a constant deceleration as speed increased they started to brake earlier farther from the obstacle maintaining an almost constant time to collision . When cyclists avoided obstacles by steering they maintained a constant distance from the object independent of speed . Overall the higher the speed the more the steering manoeuvres were temporally delayed compared to braking manoeuvres . We discuss these results and other similarities between cyclist and driver behaviour during obstacle avoidance . Implications for the design of acceptable active safety and infrastructure design are also addressed . | We modelled cyclist comfortable braking and steering using field experimental data. Cyclists braked with constant deceleration and steered at constant distance. The higher the speed the more steering came after braking. We exemplify how the models can inform active safety and infrastructure design. |
S0001457519317300 | Road user behaviour and personality traits are important determinants of driver crash risk . While a great deal of research has been undertaken to understand the relationships between crash involvement behaviours and personality traits for motor vehicle drivers comparatively few studies have considered these factors for cyclists . This manuscript presents the findings of a study conducted amongst a sample of six hundred and fifteen Australian cyclists investigating these issues . | Cyclists infrequently engaged in aberrant behaviours. Aberrant behaviours decreased with age and cycling frequency. Extroversion was associated with errors and violations. Errors were related to self reported crashes. |
S0001457519317348 | Nowadays intelligent transportation system planning has been often integrated into transportation planning stage . As a component of ITS traffic enforcement cameras have been found to reduce dangerous behaviors such as red light running and speeding . However with limited resource it is important to understand the effects of enforcement cameras on macro level safety so that traffic policy makers can better allocate those resources to improve traffic safety from the planning stage . In this paper we examined the effects of various traffic enforcement cameras on regional traffic crash risk considering their interactions with roadway and land use characteristics . The Kunshan city in Suzhou China was selected in this study and a spatial modeling analysis was applied . According to the modeling results several conclusions can be drawn 1 . Interaction effects on regional injury PDO crash risk were found between traffic enforcement cameras and roadway land use factors 2 . Traffic enforcement cameras were found to be associated with decreased regional crash risk . Among them red light running and speeding cameras were associated with the reduction of injury PDO crash frequency which can be further enhanced when being installed in certain area and on certain roadways . Illegal lane changing cameras were associated with the decrease in PDO crash frequency while such effect on reducing injury crashes was only found as significant on major arterials 3 . The main effects of certain land use and roadway factors appeared to be mediated by traffic enforcement interaction terms . For example the main effect of industrialized land use was found as insignificant while the interaction term between industrial area and speeding cameras showed a significant effect of reducing injury PDO crash frequency . Based on those findings traffic enforcement cameras as one of the major components of ITS need to be carefully considered at the transportation planning stage . In general this study provides valuable information for policy makers and transportation planners to improve regional traffic safety by properly allocating traffic enforcement resources . | With limited resource it is important to understand the effects of enforcement cameras on macro level safety so that traffic policy makers can better allocate those resources to improve traffic safety from the planning stage. In this paper we examined the effects of various traffic enforcement cameras on regional traffic crash risk considering their interactions with roadway and land use characteristics. Interaction effects were found between traffic enforcement cameras and roadway land use factors. The effects of enforcement cameras on reducing crash risk can be magnified when being installed in certain area on certain roadways. The main effects of certain land use and roadway factors appeared to be mediated by traffic enforcement interaction terms. |
S0001457519317403 | Anger is a common behaviour exhibited by road users when ones goals are perceived to have been blocked by another . Recent research has demonstrated that generally cyclists tend to deal with anger in constructive ways . However when anger does manifest it can result in behaviours that increase their crash risk . Amongst motor vehicle drivers mindfulness levels have been associated with less anger and appear to mediate anger and associated aggression . The current study sought to understand whether mindfulness has similar associations with anger and aggression in a sample of cyclists . A total of 583 cyclists completed an online questionnaire that sought information on their levels of mindfulness current mindfulness practices and tendencies for anger and aggression while cycling . The relationships between these were then examined using structural equation modelling . The results showed that cyclists with higher mindfulness levels tended to report less anger across a range of situations . Both direct and indirect relationships were found between mindfulness and aggression again showing that more mindful cyclists tended to engage in less frequent aggression . These findings align with recent research investigating this relationship amongst motor vehicle drivers and suggest that mindfulness may be a promising strategy to reduce or avoid anger and aggression in cyclists . | The relationships between trait mindfulness anger and aggression while cycling were examined. Cyclists with higher mindfulness reported less anger while cycling. Cyclists with higher mindfulness engaged in less aggression. |
S0001457519317415 | To make safe road crossing decisions the pedestrians need to estimate the distance and speed of oncoming vehicles in order to make conclusions about the available time gap they need for their road crossing . Since the speed represents combination of distance and time we focused on pedestrians ability to estimate the speed of the oncoming vehicles accurately . The aim of this study was to find some characteristics important for the speed mis estimation tendencies and its values . Seventy participants estimated speed 3920 times in total . Research included three experiments . One vehicle participated in the first experiment while second and third experiments involved two vehicles with various combinations of vehicle positions and speeds . Initially it was determined that the pedestrians had tendencies to speed underestimation rather than its overestimation and accurate estimation . When the participants estimated the speed of one vehicle they were more inclined to underestimation of higher speeds . On the other hand in the situations where the participants estimated the speed of two vehicles they showed a serious tendency towards underestimation of lower speeds which was completely opposite . The factors such as driving experience age and gender were identified as statistically important in terms of speed underestimation value . We determined that an increase in task complexity with introduction of a larger number of vehicles resulted in more severe speed underestimation . Finally we identified some of the most risky traffic situations in terms of speed underestimation tendencies showed by our participants . | The participants had tendencies to speed underestimation. There were different speed mis estimation rules depending on the number of vehicles. One vehicle more severe underestimation of higher speeds over 50km h . Two vehicles more severe underestimation of lower speeds under 50km h . Driving experience age and gender were significant in speed underestimation value. |
S0001457519317427 | Overtaking cyclists is challenging for drivers because it requires a well timed safe interaction between the driver the cyclist and the oncoming traffic . Previous research has investigated this manoeuvre in different experimental environments including naturalistic driving naturalistic cycling and simulator studies . These studies highlight the significance of oncoming trafficbut did not extensively examine the influence of the cyclists position within the lane . | 18 drivers overtook a robot cyclist on a test track with an oncoming robot vehicle. We varied time gap to the oncoming traffic and position of cyclist within the lane. Safety margins to the cyclist decreased as the situation became more critical. Drivers risk compensation may explain safety margins during the whole manoeuvre. We created Bayesian models that may improve acceptance of active safety systems. |
S0001457519317439 | One of the more hazardous situations for a bicyclist is to go straight on in an intersection where a motor vehicle is turning right and especially so when heavy vehicles are involved . The aim of this study was to investigate truck drivers speed choice gaze behaviour and interaction strategies in relation to vulnerable road users when turning right in signalised and non signalised intersections . Truck drivers experienced or inexperienced with urban traffic drove a 15 km long test route in an urban environment . To guarantee the presence of VRUs a confederate cyclist with the task to cycle straight on was present in three intersections . Overall the results suggest that the specific experience of driving a truck in the city has little effect on the strategies employed when interacting with cyclists in a right turn scenario . Neither gaze nor strategic placement or speed related variables differed significantly between the groups though the drivers inexperienced with urban traffic tended to be more cautious . Glance and driving behaviour were more related to the preconditions afforded by the infrastructure and to interaction type which is a combination of those infrastructural preconditions and the truck drivers own choice of action . The likelihood of a favourable interaction should be increased where the truck remains behind the VRUs on the approach to the intersection something which eliminates the potential for a collision . Education of truck drivers infrastructure design and improved traffic light sequences are potential ways to reduce the occurrence of more demanding and dangerous interaction types . | Two truck driver groups with different urban driving experience drove an urban route. Level of experience had little effect on interaction strategies speed and attention. Lower urban driving experience tended to lead to more cautious behaviour. Intersection design affected interaction type and gaze behaviour. Maximising the likelihood for safe interaction types could improve safety. |
S000145751931752X | Bicycling at night is dangerous with vehicle passing distances being a key concern given that the main cause of night time bicycling fatalities is from motorists hitting bicyclists from behind . However little is known about vehicle passing distances at night or how they are affected by bicyclist visibility . This study assessed the impact of different bicyclist visibility configurations on vehicle passing distances at night time . Fourteen licenced drivers with normal vision drove an experimental vehicle with low beam headlights around a 1 km section of a closed road circuit at night . Each lap involved passing two bicyclists displaying one of four visibility configurations Control Handlebars Helmet and Leg Retro reflectors . Participants were instructed to pass each bicyclist at a distance of 1 metre at a speed no greater than 50km hr consistent with Queenslands Minimum Passing Distance rule . Participants completed eight laps two for each configuration in a randomised sequence . Passing distance was measured using a vehicle mounted ultra sonic sensor . Following each lap participants rated the difficulty level in judging the 1 metre passing distance as well as their estimated passing distance . Visibility configuration significantly affected passing distance with wider passing distances for the Handlebar configuration followed by the Helmet Leg Retro reflectors which were all significantly greater than the Control but not significantly different from each other . There was also a significant effect of visibility configuration on difficulty rating with the Control rated as the most difficult followed by Helmet Handlebars and Leg Retro reflectors . Overall additional visibility aids resulted in wider vehicle passing distances likely due to enhanced visual cues for drivers . The findings suggest that bicyclists should incorporate additional visibility aids to encourage safer passing distances of vehicles at night time . | Poor visibility of bicyclists to drivers is a key contributor to collisions at night time. Vehicle passing distance is a key concern for bicyclists yet little is known about passing distances at night. This experimental study explored the impact of bicycle visibility aids on vehicle passing distances on a closed road circuit. Additional bicycle visibility aids resulted in wider passing distances likely due to enhanced visual cues for drivers. Bicyclists should incorporate additional visibility aids to encourage safer passing distances of vehicles at night time. |
S0001457519317713 | Traffic crash detection is a major component of intelligent transportation systems . It can explore inner relationships between traffic conditions and crash risk prevent potential crashes and improve road safety . However there exist some limitations in current studies on crash detection The commonly used machine learning methods can not simulate the evolving transitions of traffic conditions before crash occurrences Current models collected traffic data of only one temporal resolution which can not fully represent traffic trends in different time intervals . Therefore this study proposes a Long short term memory based framework considering traffic data of different temporal resolutions for crash detection . LSTM is an effective deep learning method to capture the long term dependency and dynamic transitions of pre crash conditions . Three LSTM networks considering traffic data of different temporal resolutions are constructed which can comprehensively indicate traffic variations in different time intervals . A fully connected layer is used to combine the outputs of three LSTM networks and a dropout layer is used to reduce overfitting and improve prediction performance . The LSTMDTR model is implemented on datasets of I880 N and I805 N in California America . The results indicate that the LSTMDTR model can obtain satisfactory performance on crash detection with the highest crash accuracy of 70.43 . LSTMDTR models constructed on one freeway can be transferred to other similar freeways with 65.12 of crash accuracy on transferability . Compared with machine learning methods and LSTM models with one or two temporal resolutions the LSTMDTR model has been validated to perform better on crash detection and transferability . A proper number of neurons in the LSTMDTR model should be determined in real applications considering acceptable detection performance and computation time . The dropout technique can reduce overfitting and improve the generalization ability of the LSTMDTR model increasing crash accuracy from 64.49 to 70.43 . | A deep learning based framework is proposed for crash detection. Traffic data of different temporal resolutions are used to improve performance. The framework obtains desirable performance on crash detection and transferability. The framework performs better than machine learning methods and LSTM models. The effect of important parameters on model performance is discussed. |
S0001457519317749 | This paper proposes a machine learning approach the random survival forest for competing risks to investigate highway rail grade crossing crash severity during a 29 year analysis period . The benefits of the RSF approach are that it is a special type of survival analysis able to accommodate the competing nature of multiple event outcomes to the same event of interest is able to conduct an event specific selection of risk factors has the capability to determine long term cumulative effects of contributors with the cumulative incidence function provides high prediction performance and is effective in high dimensional settings . The RSF approach is able to consider complexities in HRGC safety analysis e.g . non linear relationships between HRGCs crash severities and the contributing factors and heterogeneity in data . Variable importance technique is adopted in this research for selecting the most predictive contributors for each crash severity level . Moreover marginal effect analysis results real several HRGC countermeasures effectiveness . Several insightful findings are discovered . For examples adding stop signs to HRGCs that already have a combination of gate standard flashing lights and audible devices will reduce the likelihood of property damage only crashes for up to seven years but after the seventh year the crossings are more likely to have PDO crashes . Adding audible devices to crossing with gates and standard flashing lights will reduce crash likelihood PDO injury and fatal crashes by 49 52 46 and 50 respectively . | Random survival forest method in highway rail grade crossing safety analysis is introduced. Long term time effects on cumulative probability of crash severity and occurrence over 29 years is evaluated. Contributors long term and instantaneous effects on crash severity and occurrence behave very different. Adding stop sign to active controlled crossings will reduce crash risk up to 7 years. Audible device to active crossings will reduce crash PDO injury fatal likelihoods by 49 52 46 and 50 respectively. |
S0001457519317762 | Transportation agencies utilize Active traffic management systems to dynamically manage recurrent and non recurrent congestion based on real time conditions . While these systems have been shown to have some safety benefits their impact on injury severity outcomes is currently uncertain . This paper used full Bayesian mixed logit models to quantify the impact that ATM deployment had on crash severities . The estimation results revealed lower severities with ATM deployment . Marginal effects for ATM deployments that featured hard shoulder running revealed lower likelihoods for severe and moderate injury crashes of 15.9 and for minor injury crashes of 10.1 . The likelihood of severe and moderate injury crashes and minor injury crashes reduced by 12.4 and 8.33 with ATM without HSR . The models were observed to be temporally transferable and had forecast error of 0.301 and 0.304 for the two models revealing better performance with validation data . These results have implications for improving freeway crash risk at critical locations . | Crash Severity effects of Active Traffic Management ATM systems were assessed. A Bayesian modeling framework for mixed logit models with random parameters was proposed. Model results revealed a reduction of 12.4 and 15.9 for ATM with and without hard shoulder running. The models were observed to be temporally transferable. |
S0001457519317798 | The fastest growing demographic in the United States is people aged 65 and over . Because elderly drivers may experience decline in the physical and mental faculties required for driving it is critical to determine whether elderly drivers are more likely than younger drivers to be at fault in a crash . This study uses Kentucky crash data and linked hospital and emergency department records to evaluate whether linked data can more accurately estimate the crash propensity of elderly drivers to be at fault in injury crashes . The Kentucky crash data is edited to conform to the General Use Model with crash propensities for linked data compared to propensities developed using the GUM dataset alone . The quasi induced exposure method is used to determine crash exposure . Factors such as age gender and crash location are explored to assess their influence on the risk of a driver being at fault in an injury crash . The overall findings are consistent with previous research elderly drivers are more likely than younger drivers to be at fault in a crash . Linking crash with hospital and emergency department records could also establish a clearer understanding of the injury crash propensity of all age groups . Equipped with this knowledge transportation practitioners can design more targeted and effective countermeasures and safety programs to improve the safety of all motorists . | Quasi induced exposure technique is used to estimate drivers crash exposure. Elderly drivers are more likely to be at fault in a crash than younger drivers. Hospital linked data CODES better predict injury crash propensity. Research findings can be used to design safety programs. |
S0001457519317889 | Worldwide efforts have been made to deploy connected vehicle technologies in practice . The Korean government has also conducted various projects to fully exploit the benefits of CVs . This study attempted to estimate the safety benefits achievable of CVs based on crash risk analyses which is a part of a pre deployment project for CVs on freeways . A nice feature of the CVs in this project is that they are equipped with in vehicle forward collision warning systems that are capable of providing both the speed of a preceding vehicle and the spacing between the preceding vehicle and a subject vehicle . This technical support enables us to systematically analyze vehicle interactions in terms of traffic safety . The crash potential index which is able to analyze vehicle interactions in terms of crash risks was adopted to quantify the crash potential of CVs when the forward hazardous situation warning information was either provided or not provided . The results of this study show that the average speed decreased by 10.2 and the time to collision increased by 5.3 when warning information was provided . In addition the achievable reduction in the CPI was approximately 20.7 due to the provision of warning information . An illustrative demonstration of identifying freeway hazardous spots was also presented as a further application of the CPI analysis . The outcomes of this study will be useful for the establishment of relevant policies to promote CV technologies . | Safety benefits obtainable by providing in vehicle warnings are evaluated using the connected vehicle data. Both the probe vehicle data PVD and advanced driving assistance systems ADAS data are used for crash risk analyses. Inter vehicle crash risks are analyzed based on the crash potential index CPI . The achievable reduction in the CPI is approximately 20.7 . |
S0001457519317968 | Reducing nonmotorized crashes requires a profound understanding of the causes and consequences of the crashes at the facility level . Generally existing literature on bicyclists and pedestrian crash models suffers from two distinct problems lack of exposure volume data and inadequacy in capturing potential correlations across various crash aspects . To develop a robust framework for pedestrian crash analysis this research employed a multivariate model across multiple pedestrian crash severities incorporating a crucial piece of information pedestrian exposure . A multivariate spatial Poisson lognormal model in a Bayesian framework was developed to examine the significant factors influencing the fatal incapacitating injury and non incapacitating injury pedestrian crashes at 409 signalized intersections in the Austin area . Various explanatory variables were used to examine the pedestrian crashes including traffic characteristics road geometry built environment features and pedestrian exposure volume at intersections which was estimated through a direct demand model as part of the study . Model results revealed valuable insights . The superior performance of the multivariate model over the univariate model emphasized the need to jointly model multiple pedestrian crash severities . The results showed the significant positive influence of speed limit on fatal pedestrian crashes and revealed that both incapacitating and non incapacitating injury crashes increase with increasing motorized traffic volume . Bus stop presence was found to have a negative influence on incapacitating injury crashes and a positive influence on non incapacitating injury crashes . Moreover the pedestrian volume at intersections positively influences non incapacitating injury crashes . The difference in influence across crash types warrants careful and focused policy design of intersections to reduce pedestrian crashes of all severity types . | Explored pedestrian crash severities at intersections through a Bayesian multivariate spatial Poisson lognormal model. Developed a direct demand model to estimate annual average daily pedestrian volume which was used as an exposure measure. Reported superior performance of the multivariate model over the univariate model. Observed notable variation in the influence of traffic and intersection design related variables across crash severities. |
S0001457519318160 | Rear end crashes are closely related to car following situation of vehicles . Speeding and insufficient headway are the major reasons as the drivers have not enough time to react to a sudden brake from the leading vehicle . Perceptual countermeasures like speed reduction markings are widely used in practice for accident prevention and are verified with substantial effectiveness . However compared with its practical application the perceptual countermeasures are rarely analyzed in depth from the perspective of drivers visual perception where the meaning of perceptual actually dwells . In addition its effect on drivers headway choice is almost ignored in previous research . Given this the present study explored the effects of a certain type of perceptual treatment i.e . the peripheral transverse line markings on drivers choice of speed and headway in car following by a series of on road experiments . In the on road experiments temporary line markings were installed on a real world freeway in China to shape the PTLMs . The intersection angle | A series of peripheral transverse line markings PTLMs were installed on a freeway. The effects of PTLMs on driving behaviors were analyzed considering visual perception. PTLMs could impact drivers speed and headway distance choice. General and sectional relative differences of vehicle flow parameters were introduced. We provide theoretical support for the perceptual countermeasures on accident prevention. |
S0001457519318184 | The Family Climate for Road Safety Scale was developed to measure parenting behaviors specific to the driving context . The original validation study found a scale structure composed of seven factors . However this structure has not been consistently replicated . Two and six factor structures have also been identified . Further this measure has not been validated in the U.S. and has not been subjected to measurement invariance testing to determine the factor structures suitability across sex . Additionally its ability to predict the driving style of emerging adults with varied driving experience has not been directly examined . The current study utilized exploratory and confirmatory factor analytic procedures to identify the factor structure of the FCRSS in a sample of emerging adults in the U.S . The sample consisted of 4392 students recruited from six universities . The sample was predominantly female and was 83.5 White 6.1 Black or African American 5.1 Asian American 4.6 biracial or multiracial 0.4 American Indian or Alaskan Native and 0.2 Pacific Islander or Hawaiian . Results indicated that a five factor model of the FCRSS provided the best fit to the data compared to one two six and seven factor models . The five factors identified for the model were Noncommitment Monitoring Feedback Communication and Modeling . Further invariance testing revealed that the five factor model fit equally well for males and females . Some factors of the FCRSS predicted driving outcomes and driving styles in the expected directions . These findings have implications for family parenting based driving interventions for adolescents and young adults . | Evidence of a Five factor solution for the FCRSS in emerging adults in the U.S. Factors include Noncommitment Monitoring Feedback Communication and Modeling. The five factor model fit equally well with males and females. Noncommitment and Modeling predicted driving behavior in emerging adults. |
S0001457519318391 | Both crash count and severity are thought to quantify crash risk at defined transport network locations . Crash count is a measure of the likelihood of occurring a potential harmful event whereas crash severity is a measure of the societal impact and harm to the society . As the majority of safety improvement programs are focused on preventing fatal and serious injury crashes identification of high risk sitesor blackspotsshould ideally account for both severity and frequency of crashes . Past research efforts to incorporate crash severity into the identification of high risk sites include multivariate crash count models equivalent property damage only models and two stage mixed models . These models however often require suitable distributional assumptions for computational efficiency neglect the ordinal nature of crash severity and are inadequate for capturing unobserved heterogeneity arising from possible correlations between crash counts of different severity levels . These limitations can ultimately lead to inefficient allocation of resources and misidentification of sites with high risk of fatal and serious injury crashes . Moreover the implication of these models in blackspot identification is an important unanswered question . | Incorporation of crash severity into crash count improves the accuracy of crash count prediction. Correlation between crash counts of different severities captures unobserved heterogeneity by extra variation in crash counts. A new weighted risk score metric is proposed to aggregate crash counts by severity levels at a site. The weighted risk score approach identifies higher number of fatal and serious injury crashes in the blackspots. |
S0001457519318408 | The current guiding philosophies in road safety have stated aims of zero deaths and serious injuries . Speed has previously been highlighted as a key factor in the outcome of a crash but the literature to date has yet to provide a robust relationship between impact speed and the risk of serious injury for crashes other than pedestrian crashes . This study aimed to determine the relationship between impact speed and the risk of serious injury in light vehicle crashes . | Impact speed taken from Event Data Recorders for high accuracy. Risk curves produced for risk of serious injury vs impact speed by impact type. Head on impacts have the lowest impact speed for a given level of risk. Measures to reduce impact speeds are important to reduce serious injuries. Head on crashes need to be prevented due to their high risk at low speeds. |
S0001457519318512 | Alcohol involved riders tend to engage in other risk taking behaviours such as un helmeted riding which could further increases injury severity . The combined effect of alcohol involved and un helmeted riding on fatal injuries is rarely investigated . This study investigated the interaction effect between blood alcohol concentration and helmet use on fatal injuries . This study used the National Taiwan Traffic Crash Dataset for the period from 2011 to 2015 . Data on road crashes involving a motorcycle and an automobile were extracted and analysed . Multiple logistic regression models were used to calculate the adjusted odds ratio . We calculated an interaction effect for blood alcohol concentration and helmet use based on STROBE guidelines . There were a total of 669 292 motorcyclist casualties among these casualties 3459 motorcyclists sustained fatal injuries . Alcohol involved riders were 9.47 times more likely than sober ones to sustain fatal injuries . Alcohol involved and un helmeted riders were approximately 18 times more likely to sustain fatal injuries than sober and helmeted riders . Riders involved in head on crashes and approach turn motorcycle crashes had an increased probability of sustaining fatal injuries by 240 and 132 respectively . This study found that alcohol involved riding acts synergistically with un helmeted riding to increase motorcyclist injury severity . | National Taiwan Traffic Crash Dataset for the period from 2011 to 2015 were used in this study. Alcohol involved and un helmeted riders were approximately 18 times more likely to sustain fatal injuries than sober and helmeted riders. The other risk factors of motorcyclist fatalities include head on crashes and approach turn motorcycle crashes. |
S0001457519318561 | More than 1500 U.S. law enforcement personnel fatalities occurred from 2007 to 2016 with 39 of these related to automobile crashes . This study looked at various types of lighting on police vehicles to determine if changes made to the visibility of a police vehicle can impact the surrounding traffic behavior and increase safety for both law enforcement and the general public . Unmarked and marked police vehicles were positioned behind a civilian vehicle on the shoulder of five different multi lane highways in Virginia simulating a routine traffic stop . The data collected indicated that more lighting and the use of red in a light bar impact traffic behavior in terms of merging and speed when passing a police vehicle . The benefits may be attributed to the symbolic influence of red as denoting a different type of emergency than a traffic stop in addition to reds chromatic contrast against the blue sky during the daytime . | Observational data indicates that 95 of drivers observe the Move Over law in Virginia. The lighting configuration of a police vehicle impacts when drivers merge and how they adjust their speed. Red plus blue was shown to be beneficial for merge behavior and speed over an all blue light bar. Speed behavior is difficult to assess as independent speed adjustments can be dependent on clusters on high speed roadways. |
S0001457519318615 | Pedestrian road crossing strategy is one of the most important pedestrian road crossing behaviors . The safety of the pedestrians often depends on it . Among the road crossing strategies rolling gap crossing strategy is the riskiest one . The objective of this research was to explore the factors that influenced pedestrians decision to cross the road by rolling gap crossing at intersection . Data regarding road crossing strategy of the pedestrians their characteristics their road crossing behavior intersection geometry and traffic environmental condition were collected through videography survey method on site observation and secondary source from six intersections of Dhaka Bangladesh . A binary logistic regression model was developed in this study by using the collected data . Results of the developed model showed that seven statistically significant factors strongly influenced pedestrians decision to cross the road by rolling gap crossing at intersections . These factors were intersection control type median width vehicle flow available gap on the road age group of the pedestrians their crossing group size and their behavior of crosswalk usage . The results of this study would help the policymakers to take proper interventions to alleviate pedestrian safety problems . | This study focused on pedestrians decision making process regarding pedestrian road crossing strategy. This study identified factors that pushed pedestrians to cross the road by risky rolling gap crossing strategy at intersection. A binary logistic regression model was developed to identify significant contributing factors. Significant factors were intersection control type median width vehicle flow available gap on the road age group of the pedestrians their crossing group size and their behavior of crosswalk usage. |
S0001457519318627 | Raised pavement markers are among the common safety features of roads playing an important role in preventing and reducing traffic crashes . RPMs are regarded as an effective measure for reducing the high crash rate and mortality in freeway tunnels in China . In this study a driving simulator experiment was conducted to investigate the safety of RPMs in a freeway tunnel . Two different RPM layouts were designed and compared to a control with no RPMs and 32 drivers participated in the driving simulator experiments . The speed relative speed difference lateral position accelerator power acceleration and pupil area were used as indicators of the response characteristics of drivers to RPMs and the interaction of tunnel length tunnel zone and RPM alternatives was discussed . The results indicate that a significant interaction effect exists between tunnel length tunnel zone and RPM alternatives . RPMs could help reduce driver anxiety boredom and fatigue caused by the dark and monotonous tunnel driving environment and improve driver alertness and consciousness of speed . Also the driving risk increases with increasing tunnel length . | Conduct a driving simulator experiment to study driving behavior subjected to RPMs in tunnels. Various layouts of RPMs were studied and compared to a control with no RPMs. RPMs could help reduce driver anxiety boredom and fatigue caused by the dark and monotonous tunnel driving environment and improve driver alertness and consciousness of speed. The findings based on the driving simulator provide a feasible technical route for exploring the utility of reflective facilities. |
S0001457519318718 | Highway horizontal curves provide a smooth transition between two tangent sections of roadways . They allow vehicles to adjust their travel directions gradually . However the geometry changes of the highway sections with H curves often raise safety concerns . In order to deploy effective safety countermeasures a critical task is to understand the risk factors associated with H curves . Existing studies have made efforts to probe the safety issues associated with H curves whereas they were limited to relatively small scale examinations because of the challenges in identifying H curves from large road networks . In addition due to the lack of well archived traffic and roadway information gathering other data associated with the H curves is also difficult . Regarding to these gaps this study aims to leverage open source data to analyze the crash risk of highway sections with H curves . In particular the present study highlights itself from two main aspects a H curve extraction tool was developed to facilitate large scale curve data collection through the analytics of different open source data and a crash modeling framework was developed to quantify H curve crash risk . A case study based on a statewide road network was performed to test the developed crash risk models with the collected curve data . The results show the opportunities of using the developed tool for large scale data collection and analyze the safety impacts of H curve geometric properties elevation change traffic exposure among others . Findings of this study provide insights into the improvement of H curve data collection and safety evaluation . | Developed a H curve extraction tool for large scale data collection. Used random parameter logistic regression models for H curve crash analysis. Enhanced H curve crash analysis with detailed elevation and roadway geometry data. Compared crash risks of H curves on different types of highways. |
S0001457519318895 | Mobile phone use while driving presents significant risks potentially leading to injury or death through distracted driving . Using a case study of Vietnam this research aimed to understand the effect of problematic mobile phone use attitudes and beliefs and perceived risk on the frequency of mobile phone use among motorcyclists and car drivers . A self administered questionnaire was distributed to motorcyclists n | This study explore the effect of problematic mobile phone use attitudes and beliefs and perceived risk on mobile phone use. A self administered questionnaire was distributed to motorcyclists n 529 and car drivers n 328 in Vietnam. Attitudes and beliefs had the strongest effect on mobile phone use among motorcyclists. Problematic mobile phone use had the largest effect on mobile phone use among car drivers. |
S0001457519319062 | Traditional methods for identifying crash prone roadways are mainly based on historical crash data . It usually requires more than three years to collect a sufficient amount of dataset for road safety assessment . However the emerging connected vehicles technology generates rich instantaneous information which can be used to identify dangerous road sections proactively . Information about the identified crash prone intersections can be shared with the surrounding vehicles via CVs communication technology to promote cautious driving behaviors in the longer term such information will guide the implementation of countermeasures to prevent potential crashes . This study proposed a deep learning based method to predict the risk level at intersections based on CVs data from the Michigan Safety Pilot program and historical traffic and intersection crash data in areas around Ann Arbor Michigan USA . One month of data by CVs at intersections were used for analyses which accounts for about 3 12 of overall trips . The risk levels of 774 intersections are determined by the annual crash rates . Feature extraction process is applied to both CVs data and traffic data at each intersection and 24 features are extracted . Two black box deep learning models multi layer perceptron and convolutional neural network are trained with the extracted features . A number of hyperparameters that affect prediction performance are fine tuned using Bayesian optimization algorithm for each model . The performance of the two deep learning models which are black box models were also compared with a decision tree model a white box type of simple machine learning model . The results showed that the accuracies of deep learning models were slightly better than the decision tree model . This indicated that the DL models were capable of uncover the inherent complexity from the dataset and therefore provided higher accuracy than the traditional machine learning model . CNN model achieves slightly higher accuracy and is recommended as the classifier to predict the risk level at intersections in practice . The interpretability analysis of the CNN model is conducted to confirm the validity of the model . This study shows that combination of CVs data and deep learning networks is promising to determine crash risks at intersections with high time efficiency and at low CV penetration rates which help to deploy countermeasures to reduce the crash rates and resolve traffic safety problems . | Identify and mitigate the risk of crash prone intersections with connected vehicles and deep learning. CVs achieved rapid data collection even under a low market penetration rate of CVs. Deep learning models trained with preprocessed dataset and optimized with Bayesian algorithm. CNN outperformed MLP to predict the risk level at intersections in practical application. Interpretability with SHAP method properly identified important features contributing to risk. |
S0001457520300208 | The use of an appropriate driving exposure measure is essential to calculate traffic crash rates and risks . Commonly used exposure measures include driving distance and the number of licensed drivers . These measures have some limitations including the unavailability of disaggregated estimates for consecutive years low data quality and the failure to represent the driving population when the crash occurred . However the length of driving time available annually from the American Time Use Survey can be disaggregated by age gender time of day and day of week and addresses the temporal discontinuity limitation of driving distance on the United States national scale . The objective of this study is to determine if the length of driving time as a driving exposure measure is comparable to driving distance by comparing distance based and time based fatal crash risk ratios by driver age category gender time of day and day of week . The 20162017 National Household Travel Survey provided driving distance and 20162017 Fatality Analysis Reporting System provided the number of drivers in fatal crashes . The distributions of driving distance and length of driving time by driver age category gender time of day day of week were compared . Two negative binomial regression models were used to compute the distance based and time based fatal crash risk ratios . The distributions of driving distance were not different from the length of driving time distributions by driver age category gender time of day and day of week . Driving distance and the length of driving time provide similar fatal crash risk ratio estimates . The length of driving time can be an alternative to driving distance as a measure of driving exposure . The primary advantage of driving time over driving distance is that starting from 2003 the disaggregated estimates of the length of driving time are available from ATUS over consecutive years curtailing the discontinuity limitation of driving distance . Furthermore the length of driving time is related to drivers perceived risks about their driving conditions and as a result may be a better exposure measure than driving distance in comparing crash risks between drivers whose likelihood of traveling in hazardous driving conditions varies substantially . | Comparison of driving time based and population based fatal crash risk ratios was conducted. Time based fatal crash risk ratios are consistent with distance based ones. Using the length of driving time as a driving exposure measure can curtail the discontinuity limitation with driving distance. |
S0001457520300233 | Previous studies related to bus crash frequencies modeling are limited and the statistical models are usually developed at the road segment or zonal level . This study focuses on modeling crash frequencies specifically at the bus service route level which is useful and important to policymakers and bus operation companies toward the improvement of the safety level of bus networks especially for developing countries where buses are still a major mode of urban travels . Using the observed data adopted from one of the bus operating companies in Beijing China we proposed a spatiotemporal random effect zero inflated negative binomial model to investigate bus crash occurrence and identity key influential factors at the bus service route level . The model was motivated to accommodate the special statistical characteristics of the excessive zeros and more importantly the potential spatiotemporal correlations of the data . Three degenerated versions of this model were also developed for comparison purposes . Results indicate that the proposed spatiotemporal ZINB model is statistically superior to the others according to a comprehensive judgment based on the EAIC EBIC and RMSE criteria . The estimated coefficients reveal the impacts of related factors on the likelihood of bus involved crashes from bus operation factors including total passengers number of drivers and proportion of male drivers as well as planning factors including route length and stop density . On the other hand the standard deviations of the introduced structured and unstructured spatiotemporal random effects are statistically significant indicating that the observations are correlated within each route between neighbor routes and across years . Corresponding policy and practical implications are provided for bus operating companies and planning departments toward the improvement of bus safety . | Bus involved crashes were aggregated for each bus service route each year. A spatiotemporal random effect zero inflated negative binomial model was proposed. Two bus routes are treated as neighbors as long as they have overlapped sections. Bus service route level factors related to bus operation and planning were investigated. |
S0001457520300348 | Exposure measures are always among the explanatory variables of any crash model . Regardless of the technique used to model crash the mean crash frequency will increase with an increase in exposure since more crashes are likely to occur at higher exposure . For cyclist vehicle crash models bike and vehicle exposure measures are essential for an accurate and reliable estimate of the cyclist crash risk . However traffic exposure measures are an example of variables that are measured with error . Generally measurement error in regression estimates has three effects 1 produce bias in parameter estimation for statistical models 2 lead to a loss of explanation power 3 mask important features of the data . This study proposes a full Bayesian Poisson Lognormal crash models that account for measurement error in traffic exposure measures . The underlying approach is to adjust the traffic exposure measures for measurement error to improve the accuracy of the crash model and crash model estimates . The full Bayesian models are developed using data for 134 traffic analysis zones in the city of Vancouver Canada . The results show that Poisson Lognormal models that account for measurement error have a better fit for the modeled cyclist vehicle crash data compared to traditional Poisson Lognormal models . The estimates of the Poisson Lognormal model that accounts for measurement error are consistent with traditional Poisson Lognormal models estimates except for the BKT and VKT estimates . Estimates of the BKT and VKT increased after introducing measurement error which indicates an underestimation to BKT and VKT estimates in case of overlooking measurement error . | Full Bayesian Poisson Lognormal crash models are proposed to account for measurement error in traffic exposure. Two models are proposed to account for the measurement error. Measurement error models offered a better fit for the modeled cyclist vehicle crash data. The coefficients of the bike and vehicle exposure increased after accounting for the measurement error. |
S0001457520300488 | Urban planners frequently neglect the role of subjective risk perception during urban cycling . Several findings suggest a complex relationship between the risk of being involved in a crash and the subjective anticipation of this risk . We investigate the relation of objective risks and subjective risk perception in a medium sized German city . Using GIS methods these datasets are linked to various infrastructure and traffic properties that have been found relevant for cycling safety . Despite a generally high alignment of objective and subjective risk our findings highlight that the subjective risk perception at a given location can deviate significantly from the actual crash risk . For example the subjective perception of high risk on one way streets with bikeways in opposing direction is not matched by a high level of objective risk . Vice versa some rather dangerous situations are not perceived as particularly dangerous . Understanding why and where cyclists over or underestimate the actual crash risk may provide a foundation for the design of safer cycling infrastructures as well as for promoting cycling as a comfortable mode of transportation . | The relation of accident risk and risk perception during cycling is understudied. We compared accident statistics with crowd sourced information on risk perception. GIS methods are used to link both datasets to various infrastructure elements. In general we found a high alignment of objective and subjective risk. Some infrastructure elements lead to over or underestimations of accident risks. |
S0001457520300506 | This interdisciplinary study explores factors that contribute to the perseverance of participants in an organizational no phone use while driving road safety intervention . The study sample comprised 200 employees 96 males from 8 organizations in Israel . Subjects completed a 4 month organizational intervention using a smartphone application that monitored smartphone use operationalized as taps per minute where each tap represents a single instance of contact with the screen . The app also silenced notifications during the intervention stage . Changes over time in tapping while driving behavior were examined through self report questionnaires and objectively through the applications monitoring function . Validated measures were used to examine factors associated with perseverance in the program . Organizational safety climate and gender were positively related to perseverance in the intervention . Contrary to our hypothesis safety motivation was not found to influence perseverance . The present intervention is most effective for employees with high safety climate perceptions and for male employees . The organizational intervention presented in the current study was shown to be effective in reducing smartphone use while driving . Our findings show that people will download and use an app that actively reduces their incentive to use their phones at the wheel by silencing incoming notifications . The findings support calls to harness the positive potential of information and communications technologies for organizational interventions . | An organizational intervention reduced smartphone use while driving. Men persevered in the intervention to a greater degree than women. Organizational safety climate perceptions predicted perseverance in an organizational health intervention. |
S0001457520300610 | The study of aggressive driving is an important step in the reduction of crashes due to this behavior . However even though various measures of aggressive driver behavior have been proposed a more thorough examination of what the driving public perceives as aggressive driving behavior can be performed . A nationally representative sample of 198 American adults saw and rated the aggressiveness of various driving behaviors in videos . The videos were shown from a first second or third person perspective . Some videos depicted close following varying in speed and distance from the car ahead . Participants also saw illegal passing videos and collision or near collision videos . A number of variables that might influence judgments of aggressive driving were included as controls . Following other drivers closely was rated as aggressive especially when viewed from a third person perspective . Illegal passes were viewed as more aggressive than speeding . Faster speeds didnt increase aggressive ratings much regardless of perspective . Aggressiveness ratings were especially high for acts that could be considered road rage . People high in trait anger have a bias to view many driving behaviors as intentionally aggressive . | Short videos of driving behaviors can be used to quantify aggressive driving. The observers perspective first second third influences aggressive ratings. Close car following is perceived to be more aggressive than speeding. Those high in trait anger are predisposed to judge close following as aggressive. |
S0001457520300713 | Cervical spine injury is a common result of traffic crashes and such injuries range in severity from minor Nationwide Emergency Department Sample and the Nationwide Inpatient Sample . It is estimated that there are approximately 869 000 traffic crash related cervical spine injuries seen in hospitals in the US annually including around 841 000 sprain strain injuries 2800 spinal disk injuries 23 500 fractures 2800 spinal cord injuries and 1500 dislocations . Because of a highly restrictive inclusion criteria for both crash and injury types as well as a very small sample size the NASS CDS underestimated all types of crash related cervical spine injuries seen in US hospital emergency departments by 84 . The injury type with the largest degree of underestimation in the NASS CDS was cervical disk injuries which were estimated at an 88 lower frequency than in the NEDS . National insurance claim data which include cases of cervical disk injury diagnosed both in and outside of the ED indicate that the NEDS likely undercounts cervical disk injuries by 92 and thus the NASS CDS correspondingly undercounts such injuries by 99 or more . Because of a limited sample size and restrictive criteria for both crash and injury inclusion the NASS CDS can not be used to estimate the number of crash related spinal injuries of any type or severity in the US . The most inappropriate use of the database is for estimating the number of spinal injuries resulting from low speed rear impact collisions as the NASS CDS samples fewer than 1 in 100 000 of the cervical spine injuries of any type occurring in low speed rear impact collisions . | There are no reliable estimates of the number of neck injuries occurring in the US traffic crashes. Data from the NASS CDS were analyzed and compared with national hospital data in order to estimate injury frequencies. There are approximately 869 000 traffic crash related cervical spine injuries seen in hospitals in the US annually.. The annual counts of whiplash and spinal disk injuries in the US likely exceed 1.2 million and 33K respectively. |
S0001457520300816 | Speeding behaviour has been shown to account for a large number of deaths and serious injuries on Australian roads . Vehicle impoundment is one countermeasure which has been implemented to discourage drivers from engaging in high range speeding . Despite this countermeasure being used as a sanction in all Australian jurisdictions to combat high range speeding offences limited research has examined the effectiveness of vehicle impoundments in Australia . The purpose of this research was to examine the effectiveness of vehicle impoundment for high range speeding offences on subsequent offence and crash rates . Data were collected from drivers with an eligible excessive speeding offence in Victoria Australia between 1 July 2006 and 31 December 2014 . During this time there were 17 440 impoundment eligible offences 6 883 of which resulted in vehicle impoundment . The analysis revealed that drivers who had a vehicle impounded were more likely to be male younger hold a probationary licence and to have a court offence . In terms of the effectiveness of vehicle impoundment among high range offenders re offence rates for those who had their vehicle impounded were statistically significantly lower for all licence periods compared with offenders who did not have their vehicle impounded . There was evidence of an effect of impoundment on reducing speeding re offence rates during the impoundment period as well as some evidence that the impact of licence suspension was greater for those who experienced impoundment . Given that vehicle impoundment is a sanction which aims to discourage and or incapacitate drivers from engaging in on road risk taking behaviour in this case high range speeding behaviour the longer term positive effects of this sanction may assist with the on going effort to reduce on road risk taking behaviours . | Examined impact of vehicle impoundment on recidivism and subsequent crash rates among speeding offenders in Victoria Australia. Drivers who had a vehicle impounded were more likely to be male younger and hold a probationary licence compared to drivers who did not. Lower offence rates were identified during the impoundment period compared to pre impoundment and post licence restoration periods. Some evidence that the impact of licence suspension was greater for those who experienced impoundment. The on going use of vehicle impoundments may improve road safety outcomes. |
S0001457520300828 | Risk perception plays an important role in driver behaviour particularly for speed choice . Risk perception studies use a range of techniques from on road data collection to ratings of still photos however participants ratings differ depending on the study methodology possibly due to their perception of control . To explore this we conducted a multiple methods study to investigate drivers perceptions of risk on rural roads . One group of participants drove a 180km route along rural roads and provided verbal risk ratings at thirteen locations of interest . A second group provided ratings at the same points when travelling as a passenger in a vehicle . The third group were shown videos of the same rural roads and also provided risk ratings at the same locations . A week later participants were invited to the laboratory to review the video footage and comment on factors that contributed to the risk ratings . Overall the Observers gave the highest risk ratings and Drivers the lowest . The Observers also provided twice the number of comments to justify their risk rating compared to the other two groups . The results suggest that control and on road experience play a significant role in how perceptions of driving risk are formed and the degree of risk experienced . These findings also bring into question the accuracy of using video based tasks to assess drivers risk perception particularly if the findings are used to inform on road safety interventions . | Risk perception is important in driver behaviour and is influenced by perceived control. Drivers Passengers and Observers rated the risk at 13 locations on rural roads. Observers gave the highest risk ratings and Drivers the lowest. Risk ratings from video based tasks do not reflect on road ratings. |
S000145752030083X | The illegal use of a smartphone while driving increases the risk of crashes . As such road authorities rely on countermeasures to reduce illegal smartphone use . Deterrence based methods dominate road safety however perceptions and impact of formal and informal methods to deter illegal smartphone use in Australia have not yet been explored . The current study reports on a survey of 2774 drivers that own and regularly use a smartphone . The survey analysed the self reported frequency of illegal smartphone use while driving perceptions of formal and informal deterrence mechanisms differences between perceived and informed deterrence and deterrent predictors of illegal use . The findings revealed that illegal smartphone use is increasing in Victoria Australia . Drivers that break the law perceive deterrent mechanisms significantly different from drivers that abide by the law however both groups view the prospect of hurting oneself as most impactful . Additionally drivers tend to underestimate the consequences of illegal use yet overestimate the certainty of apprehension . A binary logistic regression analysis revealed that only age gender and informal sanctions such as social loss internal loss and physical loss were significant predictors of illegal use . None of the formal mechanisms were significant . Based on these findings road safety interventions and future research should consider exploring the psychological characteristics of young peoples perceptions of informal sanctions such as social loss and internal loss . | Survey of 2774 Victorian drivers reveals 37.1 use smartphone illegally. Age gender and informal deterrent mechanisms predictors of illegal smartphone use. No formal deterrent mechanisms are significant predictors of illegal use. Countermeasures should focus on leveraging informal sanctions to reduce illegal use. |
S0001457520300877 | Work zone traffic safety under adverse weather conditions has been a serious concern for drivers and transportation agencies . Existing studies on work zone traffic safety with statistical approaches are limited by the availability of data from historical crashes . To date there is no comprehensive simulation framework to assess work zone traffic safety under adverse driving environments by considering both multi vehicle and single vehicle crashes . To fill this gap this paper presents an integrated framework to evaluate traffic safety in work zone under adverse driving conditions by considering specific work zone configuration weather and road surface conditions . A new risk index is introduced to assess the traffic safety risk of work zones by integrating the risks of multi vehicle crashes and single vehicle crashes . Traffic safety of a typical work zone under different weather conditions is studied to demonstrate the proposed framework . The impacts of the differential speed limits and truck proportion on the work zone traffic safety are also investigated . Results show that adverse weather may increase the crash risk in work zones . The effect of DSL on the work zone traffic safety is found to be insignificant while the truck ratio influences the work zone safety in the rainy and snowy weather by primarily affecting the multi vehicle crash risks . | We present an integrated framework to evaluate traffic safety in work zone. Simultaneous consideration of specific work zone configuration weather and road surface conditions. Introduction of a new risk index by integrating the risks of multi vehicle crashes and single vehicle crashes. |
S0001457520300944 | Managed lanes have been implemented as a vital strategy for traffic safety and management improvements . Previous studies involving MLs didnt examine the impact of connected vehicles lane configuration on freeway facilities with managed toll lanes . CVs are quickly expanding in the transportation industry and are among the most recent promising developments in traffic and safety engineering . In this study several scenarios were tested using microscopic traffic simulation to determine the optimal CV lane configuration strategies while taking into consideration the market penetration rate of CVs and traffic conditions . Both safety and operational performance measures were included in the analyses . A Negative Binomial model was developed for investigating the factors that affect the safety measures . Tobit models were used to evaluate traffic operation . The results of the safety and operational analyses suggested that an MPR between 10 and 30 was recommended when the CVs were only allowed in MLs . By converting one of the general purpose lanes to a managed lane the MPR could reach 60 . It was also concluded that restricting CVs to only the CV lane was not recommended . Lastly the findings suggested that by allowing CVs to use all the lanes in the network the optimal MPR could reach between 70 and 100 . This study has major implications for improving MLs by recommending the optimal CV lane configuration and market penetration rate for each design . | Conduct extensive simulation studies to explore safety and operational performance of connected vehicles. Apply statistical models to explore the significant scenarios related to facility designs traffic conditions and market penetration rate of connected vehicles. Recommend the optimal connected vehicle lane configuration strategies considering the market penetration rate of connected vehicles and traffic conditions. |
S0001457520301020 | Connected and Automated Vehicle technology although in the development stage is quickly expanding throughout the vehicle market . However full market penetration will most likely require considerable planning as key stakeholders manufacturers consumers and governing agencies work together to determine optimal deployment strategies . Specifically road safety is a critical challenge to the widespread deployment and adoption of this disruptive technology . During the transition period fleets will be composed of a combination of CAVs and conventional vehicles and therefore it is imperative to investigate the repercussions of CAVs on traffic safety at different penetration rates . Since crash severity and frequency in conjunction reflect traffic safety this study attempts to investigate the effect of CAVs on both crash severity and frequency through a microsimulation modelling exercise . VISSM microsimulation platform is used to simulate a case study of the M1 Geelong Ring Road network in Victoria Australia . Network performance is evaluated using performance metrics and kinematic variables . Surrogate safety measures are examined to inspect the safety in the network . The results indicate that the introduction of CAVs does not achieve the expected decrease in crash severity and rates involving manual vehicles despite the improvement in network performance given the demand and the set of parameters used in our operational CAV algorithm are intact . Additionally the study identifies that the safety benefits of CAVs are not proportional to CAV penetration and full scale benefits of CAVs can only be achieved at 100 CAV penetration . Further considering network efficiency as a performance metric and total crash rate involving conventional vehicles as a safety metric a Pareto frontier is extracted for varying CAV operational behaviour . The results presented in this study provide insights into the impacts of CAVs on traffic safety valuable for insurance companies and other industry participants enabling safety related services and more enterprising business models . | The distribution of speed suggests speed. with increasing CAV penetration. Robustness of the driving behaviour increases with increased CAV penetration. Increased CAV penetration leads to. of critical crash events conflicts but a. in crash severity. The. of CAVs can only be achieved at. CAV penetration. Pareto optimality is identified under multi criterion evaluation. |
S0001457520301044 | Even with automated vehicles driving situations with short time headways and extreme vehicle dynamics may arise when unpredictable events occur . If drivers take back control under such conditions it is uncertain how they behave and how well they can cope with the situation . This issue has not been investigated yet and is subject to our study . In a driving simulator non distracted participants | Time headway and traction usage were varied to create brake situations of different objective criticality. With increasing objective criticality criticality ratings increased decelerations were higher and more lane changes occurred. Participants overreacted and increased accident risk when taking over in critical brake situations. |
S0001457520301135 | Dockless electric scooters have emerged as a popular micro mobility mode for urban transportation . This new form of mobility offers riders a flexible option for massive first last mile trips . Despite the popularity the limited regulations of E Scooters raise numerous safety concerns among the public and agencies . Due to the unavailability of well archived crash data it is difficult to understand and characterize current state quo of E Scooter involved crashes . This paper aims to shorten the gap by analyzing a set of reported crash data to describe the patterns of crashes related to E Scooter use . Specifically massive media reports were searched and investigated for constructing the crash dataset . Key crash elements such as rider demographics crash type and location were organized in an information table for analysis . From 2017 to 2019 there were 169 E Scooter involved crashes identified from the news reports across the country . Through the descriptive analysis and cross tabulation analysis the distinct characteristics of these reported crashes were highlighted . Overall there was a growing trend for the reported E Scooter involved crashes unevenly distributed among the States . The distribution of the crashes across different groups of users facilities time periods and severity levels also showed skewed patterns toward a subset of categories . The quantitative analyses also provide some supportive evidences for warranting the discussion on key issues including helmet use riding under influence vulnerable riders and data deficiency . This study highlights the importance of public awareness and timely developing safety countermeasures to mitigate crashes involving E Scooters . | One of the first studies to quantitatively analyze E Scooter crash characteristics. Leveraged massive news reports to collect nationwide E Scooter crash data. Analyzed key attributes and their interactions related to E Scooter crash outcome. Discussed critical issues related to E Scooter crash risk and needed efforts. |
S0001457520301159 | The effect of ambient light level on road traffic collisions involving a motorcycle was investigated . Data were drawn from the STATS19 database of UK reported RTCs for the period 20052015 . To isolate the effect of ambient light an odds ratio was used to compare RTCs at specific times of day in the weeks either side of the Spring and Autumn clock changes . This work extended previous studies by using a more precise method for distinguishing between RTCs in daylight and after dark thus avoiding the ambiguity of twilight . Data for four wheel motor vehicle RTCs were also investigated to provide a datum . As expected the risk of an RTC occurring was significantly higher after dark compared to daylight for both motorcycles and FWMVs . Investigation of contextual factors suggests that risk after dark is significantly higher for motorcycles compared to FWMVs for RTCs with two vehicles on roads with low speed limits at T junctions and junctions controlled by a give way sign . These are the situations where visual aids for increasing conspicuity after dark have the greater potential for reducing motorcycle RTCs . | The effect of ambient light level on motorcycle collisions was investigated. A more precise method for distinguishing between RTCs in daylight or dark was used than past studies. This suggests darkness is of greater detriment than suggested in past studies. There is increased risk at T junctions and at give way controlled junctions. |
S0001457520301354 | Driving is a complex task that consists of several physical and physiological processes occurring simultaneously . The complexity of the task depends on several factors but this research focuses on work zone configurations and their effect on driver performance and gaze behavior . The increase in work zone fatalities in the United States between 2015 and 2018 coupled with the limited literature of driver behavior in these complex environments requires a more comprehensive study . Given the nature of these crashes typically lane departures gaze behavior provided an additional physiological dimension to the present research . A framework that comprises of the interactions between driver characteristics mental workload and situation awareness with longitudinal control lateral control and gaze behavior is proposed . Crash analysis and a simulator study with 90 participants were carried out to investigate the performance and gaze based changes with respect to various work zone configurations . Distracted driving was also studied by including a secondary task . The results showed a significant interaction between the longitudinal control and the standard deviation of horizontal gaze position in predicting lateral control . Also significant differences in lateral control and horizontal gaze variations were observed between genders . Female drivers showed lower lateral position deviations and lower horizontal gaze variability . This was a key finding given the inherently higher number of work zone crashes involving male drivers . Placing work zone barriers further away by up to one meter from pavement edges could significantly decrease mental workload and improve safety in work zones . | Work zone crashes are inherently higher in male drivers. A simulator study captured mental workload from various work zone configurations. Crash statistics were compared with clustered participant demographics. Longitudinal and lateral control along with gaze distributions were modeled. |
S0001457520301482 | Driving anger increases risk taking in traffic and road traffic accident involvement . Herein we examine the links between self reported and observed driving anger self reported and observed aggressive driving and personality traits . Specifically sixty drivers drove in an anger inducing simulated driving scenario . A video camera recorded their verbal and gestural expression during the simulator drive . Two weeks before the simulator drive we assessed participants basic personality traits driving anger expression and aberrant driving behaviour via an online survey . State anger was measured immediately before and after the simulator drive . From recorded simulator and video data we obtained four measures the number of accidents an aggressive driving score verbal expression of driving anger and related gestures and headshakes . Verbal and gestural expression while driving were related to an increase in state anger in the simulator drive and different self reported measures While observed verbal expression was positively related to lapses and negatively related to constructive expression gestural expression was positively related to both self reported violations and self reported aggressive expression . The traits Emotionality and Honesty Humility were related to an increase in state anger and to verbal expression in the simulator drive yet age and gender modified the relation to personality traits . Results can support the development of personalised anger management interventions and anger mitigating in vehicle devices . | Driving anger was successfully induced in a driving simulator scenario. Verbal expression in the simulator was negatively related to self reported constructive expression. Gestural expression was positively related to self reported aggressive expression. Gestural expression may be useful for automated in vehicle road anger detection. Personality was related to anger increase and commenting on others while driving. |
S0001457520301494 | Safety performance function has been a vital tool in traffic safety evaluation including finding contributing factors to crashes identifying hotspots and assessing safety effects of countermeasures . In the United States the Highway Safety Manual provides a number of SPFs for a variety of road facilities . Due to the limited availability of traffic data in many regions the transferability of SPFs has been an important topic in traffic safety analysis and has been evaluated by several studies . Nevertheless the international transferability of freeway SPFs and the applicability of transferred SPFs on hotspot identification has been rarely investigated . Based on data from two Chinese cities Shanghai and Suzhou and three U.S. states Texas New York and Florida this study analyzes the transferability of freeway SPFs between Chinese and U.S. regions . These SPFs are then transferred to the other country and their performance on hotspot identification is investigated . SPFs were developed in the frameworks of Poisson Poisson lognormal and negative binomial regressions for the five localities separately and were calibrated using the calibration functions before being transferred . Without calibration the poor model transferability was found between the two countries while after calibration the transferred SPFs between Shanghai Suzhou and Texas New York showed satisfactory performance on both model fitting and hotspot identification . However the transferability of SPFs between Florida and the Chinese cities turned out to be unsatisfactory regardless of whether being calibrated or not which was attributable to the considerable difference in traffic flow . The findings of this study are expected to be a good reference for researchers and practitioners who want to understand the transferability and applicability of SPFs in the international context . | The transferability of freeway SPFs between the U.S. and China was investigated. The capability of transferred SPFs for hotspot identification was further analyzed. SPFs were transferable between Texas New York and Shanghai Suzhou but not between Florida and Shanghai Suzhou. The poor transferability was attributable to the considerable difference in traffic volume. |
S0001457520301640 | Crash data from the state of Kentucky for the 2015 2016 period show that per capita crash rates and increases in crash related fatalities were higher than the national average . In an effort to explain why the U.S. Southeast experiences higher crash rates than other regions of the country previous research has argued the regions unique socioeconomic conditions provide a compelling explanation . Taking this observation as a starting point this study examines the relationship between highway safety and socioeconomic and demographic characteristics using an extensive crash dataset from Kentucky . Its focus is single and two unit crashes that involve commercial motor vehicles and automobiles . Using binary logistic regression and the quasi induced exposure technique to analyze data on the socioeconomic and demographic attributes of the zip codes in which drivers reside factors are identified which can serve as indicators of crash occurrence . Variables such as income education level poverty level employment age gender and rurality of the drivers zip code influence the likelihood of a driver being at fault in a crash . Socioeconomic factors exert a similar influence on CMV and automobile crashes irrespective of the number of vehicles involved . Research findings can be used to identify groups of drivers most likely to be involved in crashes and develop targeted and efficient safety programs . | Sociodemographic factors of CMV drivers residence affect crash occurrence. Quasi induced exposure technique is used to estimate drivers crash exposure. Research findings can be used to identify at risk CMV driver groups. |
S0001457520301664 | A penalty mechanism is usually considered as a powerful means to reduce the probability of traffic violations and accidents by encouraging drivers to comply with traffic regulations . Penalty point and fine strategies are often used in parallel . Different degrees of penalty points and or fines are imposed according to the specific violation behavior of drivers . However the question of whether each penalty produces positive effects in maintaining a drivers compliance with traffic regulations and promoting the drivers traffic safety is still unanswered . This study focuses on quantifying the effects of penalty point and fine strategies on violation recurrences and accident occurrences of drivers . A frailty survival analysis method is conducted to jointly model the occurrence of violation and accident events of each individual . The frailty term in the model is leveraged to address the unobserved heterogeneity among drivers . Personal characteristics and penalty status are also incorporated as covariates in the model . Actual violation and accident data from a province in China are utilized to calibrate the model . The results show that penalty point strategy exhibits deterrent and binding effects however penalty fine strategy does not show the expected effects . The number of years of driving is also a significant factor that influences violation recurrence and accident occurrence . The present study provides insightful information for improving violation penalty mechanisms . | Investigating the deterrent effects of penalty points and fines with empirical data. Quantifying the effects of penalty mechanism on violations and accidents. Modeling violation recurrence and accident occurrence with a joint frailty model. Considering personal characteristics and unobserved heterogeneity. |
S000145752030169X | The overtaking maneuver performed by motorcyclists is one of the primary causes of motorcycle accidents . However few studies in the literature deal with this topic and there are no studies modeling the total overtaking duration i.e . the time during which extreme hazards are manifest . The present paper aims to analyze the motorcyclists behavior during overtaking and to model the total overtaking duration . A field experiment using instrumented motorcycles was performed to collect data and a survival analysis was carried out to model the total overtaking duration . Twenty young motorcyclists drove their own motorcycles which were instrumented with a camera and a global positioning system device onto a two lane suburban road in Rome . A total of 101 overtaking maneuvers were recorded . A methodology based on video and GPS analyses was developed to obtain data describing the motorcyclists behavior . The obtained results showed that the mean values of the main parameters of the overtaking maneuver were consistent with the few data available in the revised literature . The total overtaking duration was modelled using a hazard based duration model . The parametric accelerated failure time duration model with a log logistic distribution which was the best fitted distribution identified the covariates which affected in a statistically significant way the total overtaking duration . The obtained model revealed that the overtaking duration depends on several covariates . The greater average impact was found for the initial distance and speed difference while the initial lateral distance and final distance produced a minor impact . When performing a multiple overtaking the duration of the maneuver tended to increase by 31 . This research can be considered as a pilot study and a starting point for future advances on motorcyclists behavior during overtaking maneuver and for modeling the total overtaking duration . In addition the findings of this study could contribute to the development of advanced rider assistance systems for the overtaking maneuver based on current driving conditions . | A field experiment using instrumented motorcycles was performed. Data on motorcyclists behavior during overtaking maneuver were collected. A survival analysis was carried out to model the total overtaking duration. The log logistic distribution provided the best fit to the total overtaking duration. The model identified the significant covariates that affected the overtaking duration. |
S0001457520301792 | Mountainous highways suffer from high crash rates and fatality rates in many countries and single vehicle crashes are overrepresented along mountainous highways . Route familiarity has been found greatly associated with driver behaviour and traffic safety . This study aimed to investigate and compare the contributory factors that significantly influence the injury severities of the familiar drivers and unfamiliar drivers involved in mountainous highway single vehicle crashes . Based on 3037 cases of mountainous highway single vehicle crashes from 2015 to 2017 the characteristics related to crash environment vehicle and driver are included . Random effects generalized ordered probit models were applied to model injury severities of familiar drivers and unfamiliar drivers that are involved in the single vehicle crashes on the mountainous highways given that the single vehicle crashes had occurred . The results of REGOP models showed that 8 of the studied factors are found to be significantly associated with the injury severities of the familiar drivers and 10 of the studied factors are found to significantly influence the injury severities of unfamiliar drivers . These research results suggest that there is a large difference of significant factors contributing to the injury severities between familiar drivers and unfamiliar drivers . The results shed light on both the similar and different causes of high injury severities for familiar and unfamiliar drivers involved in mountainous highway single vehicle crashes . These research results can help develop effective countermeasures and proper policies for familiar drivers and unfamiliar drivers targetedly on the mountainous highways and alleviate injury severities of mountainous highway single vehicle crashes to some extent . Based on the results of this study some potential countermeasures can be proposed to minimize the risk of single vehicle crashes on different mountainous highways including tourism highways with a large number of unfamiliar drivers and other normal mountainous highways with more familiar drivers . | Differences in injuries of familiar and unfamiliar drivers in single vehicle crashes on mountainous highways are studied. Using data from Yunnan Province of China random effects generalized ordered probit models are applied. Model estimations show that determinants of injury severity vary considerably between familiar and unfamiliar drivers. The study provides insights on injury prevention of familiar and unfamiliar drivers. |
S0001457520302475 | China is the world s largest automotive market and is ambitious for autonomous vehicles development . As one of the key goals of AVs pedestrian safety is an important issue in China . Despite the rapid development of driverless technologies in recent years there is a lack of researches on the adaptability of AVs to pedestrians . To fill the gap this study would discuss the adaptability of current driverless technologies to China urban pedestrians by reviewing the latest researches . The paper firstly analyzed typical Chinese pedestrian behaviors and summarized the safety demands of pedestrians for AVs through articles and open database data which are worked as the evaluation criteria . Then corresponding driverless technologies are carefully reviewed . Finally the adaptability would be given combining the above analyses . Our review found that autonomous vehicles have trouble in the occluded pedestrian environment and Chinese pedestrians do not accept AVs well . And more explorations should be conducted on standard human machine interaction interaction information overload avoidance occluded pedestrians detection and nation based receptivity research . The conclusions are very useful for motor corporations and driverless car researchers to place more attention on the complexity of the Chinese pedestrian environment for transportation experts to protect pedestrian safety in the context of AVs and for governors to think about making new pedestrians policies to welcome the upcoming driverless cars . | Three bad behaviors of Chinese pedestrians are summarized through abundant data. The key technical demands of pedestrians for autonomous vehicles are analyzed. Driverless technologies to protect pedestrians are carefully reviewed. The adaptability of driverless technologies to Chinese pedestrians is analyzed. |
S0001457520302487 | Human necks are vulnerable in train collision accidents . To design a safer cab workspace the driver neck injury mechanism should be investigated first . In this study this issue is addressed by investigating how neck injuries are influenced by the cab workspace dimensions . The driver console seat dynamic models are developed to quantify the neck injuries . The three pivot head neck upper torso model is used to evaluate the relative rotation angle between head and upper torso | A three pivot head neck upper torso model is used to evaluate the neck dynamics. Four injury mechanisms are identified by different driver dynamic responses. Injury mechanisms with large head rotation angles cause severe neck injuries. Chest first impact mechanism is more dangerous than knee first impact mechanism. Distance between console edge and knee bolster determines the mechanism type. |
S0001457520302621 | Among the three major safety assessment methods for highly automated driving systems test tracks provide high fidelity and a safe and controllable testing environment . However due to the lack of realistic background traffic scenarios that can be tested in test tracks are usually static and limited . To address this limitation a new safety assessment framework is proposed in this paper which integrates an augmented reality testing platform and a testing scenario library generation method . The AR testing platform generates simulated background traffic in test tracks which interact with subject ADS under test to create a realistic traffic environment . The TSLG method can systematically generate a set of critical scenarios under each operational design domain and the critical scenarios generated from the TSLG method can be imported into the AR testing platform . The proposed framework has been implemented in the Mcity test track at the University of Michigan with a Level 4 ADS . Field test results show that the proposed framework can accurately and efficiently evaluate the safety performance of highly ADS in a cost effective fashion . In the cut in case study the proposed framework is estimated to accelerate the assessment process by | This paper presents a new safety assessment framework for highly automated driving systems in test tracks. The framework integrates an augmented reality testing platform and a testing scenario library generation method together. The framework has been implemented in Mcity test facility with a SAE Level 4 ADS vehicle. The framework can accelerate the assessment process by multiple orders of magnitude comparing to the on road test approach. |
S0001457520302633 | Since motorcycle taxi drivers often work long hours fatigue would affect their riding abilities impacting crash risks . However there is limited understanding about motorcycle taxi drivers fatigue related crashes . This study investigates self reported fatigue related crashes among motorcycle taxi drivers in Hanoi Vietnam . Results from a survey showed that approximately 16 of the motorcycle taxi drivers reported fatigue related crash involvement . It was also found that nearly 37 of all crashes reported by motorcycle taxi drivers were related to fatigue while riding a motorcycle taxi . Results of the heterogeneity in means random parameter logistic model suggested that working fulltime more delivery trips and overweight conditions were associated with increased likelihoods of fatigue related crash involvement . Hybrid taxi drivers who operate as either traditional or ride hailing taxi drivers at different times and most ride hailing taxi drivers had a reduced likelihood of fatigue related crash involvement when compared to traditional taxi drivers . Overall this study has revealed a significant issue of fatigue related crashes among motorcycle taxi drivers . Immediate interventions via publicity or educational campaigns should be considered by authorities to address this important issue . Ride hailing companies should contribute by sending warnings of excessive riding hours to ride hailing taxi drivers . | This paper investigated fatigue related crashes among motorcycle taxi drivers. Approx. 16 of motorcycle taxi drivers reported fatigue related crash involvement. Logistic regression with heterogeneity in means random parameters was adopted. Delivery trips and overweight conditions increased the risk of fatigue related crashes. Hybrid taxi drivers and most ride hailing drivers had a lower risk of fatigue related crashes |
S0001457520302827 | Turn signal neglect is considered to be a key contributor to crashes at intersections yet relatively little research has been undertaken on this topic particularly in developing countries . Using a case study of Vietnam this research aimed to explore the role of environmental characteristics perceived risk beliefs and lifestyle behaviours on the frequency of turn signal use at intersections . A self administered questionnaire was distributed to motorcyclists and car drivers using online and offline methods . Using partial least squares structural equation modelling key findings indicate that perceived risk beliefs and environmental characteristics play a significant role in affecting the frequency of turn signal use among motorcycle riders and car drivers at intersections . While lifestyle behaviours were not found to be a good predictor of turn signal use among car drivers they were found to indirectly affect turn signal use among motorcycle riders through the mediation of beliefs and perceived risk . The findings can help inform the development of more targeted measures to increase turn signal use . | Turn signal neglect is considered to be a key contributor to crashes at intersections. This research investigates factors affecting the frequency of turn signal use at intersections. A self administered questionnaire was distributed to motorcyclists n 527 and car drivers n 326 . Perceived risk beliefs and environmental characteristics are associated with turn signal use. |
S000145752030289X | Although the fatal rate of passenger vehicle involved crashes has decreased in the United States the fatal rate of truck involved crashes has increased . This has in recent years become a more severe problem than that caused by passenger vehicle involved crashes . More studies need to be conducted in order to investigate factors that impact the severity of truck involved crashes within specific scenarios . This study identifies and evaluates the factors that affect the severity of the truck involved crashes at cross and T intersections in North Carolina from 2005 to 2017 . A latent class clustering for data segmentation is implemented to mitigate unobserved heterogeneity inherent in the crash data . Four partial proportional odds models which include fixed and unfixed parameters are developed considering the heterogeneous and ordinal nature inherent in severities . Estimated parameters and marginal effects are further investigated for better interpreting the impacts . Results show heterogeneous explanatory variables and associated coefficients for different classes and severity levels which indicate the superiority of this combined approach to obtaining more specific factors and accurate coefficients that are estimated in different scenarios . Many factors are found to contribute to the severities and crossroad scenarios are found to be more severe than T intersections . The top five driving behaviors at intersections that contribute to the severity include disregarded signs improper lane use followed too closely ignored signals and failure to yield . These behaviors arouse a necessity to amend the traffic laws and strengthen drivers education while giving further insights to engineering practitioners and researchers . | Truck involved severities at cross and T intersections in North Carolina are modeled. Latent class clustering is applied to reduce the heterogeneity of the crash dataset. Partial proportional odds models are developed considering the ordinal nature and heterogeneity. Factors that affect the crash severity are identified and analyzed under different latent classes. Driving behaviors are specifically analyzed and suggestions for countermeasures are also given. |
S0001457520303006 | This study estimates the effects of lane and shoulder widths on occurrence of head on and single vehicle accidents on rural two lane undivided roads in Norway while considering the differences between winter and non winter accidents and their severity levels . A matched case control method was applied to calculate the odds ratios for lane and shoulder width categories while controlling for the effects of AADT and adjusting for the effects of region speed limit segment length share of long vehicles in AADT and horizontal alignment . The study used a sample of 71 999 roadway segments identified in GIS and 1886 related accidents recorded by the police in five year period . The results suggest that it is relevant to consider winter and non winter accidents as well as severe and slight accidents separately when studying the effects of lane and shoulder widths on the occurrence of head on and single vehicle accidents . When examining lane and shoulder widths for all related accidents the lane widths 1.502.50m and shoulder widths 0.500.75m were relatively safer than other categories on Norwegian two lane rural undivided roads . | Sample of 71 999 roadway segments of rural two lane roads was identified with GIS. Models calculated separately for winter non winter and severe slight accidents. A non monotonous link between risk and shoulder width categories. An increased risk corresponds with increasing lane width categories. |
S0001457520303092 | The connected environment provides surrounding traffic information to drivers via different driving aids that are expected to improve driving behavior and assist in avoiding safety critical events . These driving aids include speed advisory car following assistance lane changing support and advanced information about possible unseen hazards among many others . While various studies have attempted to examine the effectiveness of different driving aids discretely it is still vague how drivers perform when they are exposed to a connected environment with vehicle to vehicle and vehicle to infrastructure communication capabilities . As such the objective of this study is to examine the effects of the connected environment on driving behavior and safety . To achieve this aim an innovative driving simulator experiment was designed to mimic a connected environment using the CARRS Q Advanced Driving Simulator . Two types of driving aids were disseminated in the connected environment continuous and event based information . Seventy eight participants with diverse backgrounds drove the simulator in four driving conditions baseline perfect communication communication delay and communication loss . Various key driving behavior indicators were analyzed and compared across various routine driving tasks such as car following lane changing interactions with traffic lights and giving way to pedestrians at pedestrian crossings . Results suggest that drivers in the perfect communication scenario maintain a longer time to collision during car following a longer time to collision to pedestrian a lower deceleration to avoid a crash during lane changing and a lower propensity of yellow light running . Overall drivers in the connected environment are found to make informed decisions towards safe driving . | An innovative and comprehensive driving simulator experiment for examining driving behaviors in the connected environment. Drivers maintain higher safety margins during car following and lane changing maneuvers in the connected environment. Driver interactions with pedestrians and traffic lights in the connected environment are safer. Communication delay and communication loss deteriorate driving performances and safety. |
S0001457520303298 | Road crashes impose an important burden on health and the economy . Numerous efforts have been undertaken to understand the factors that affect road collisions in general and the severity of crashes in particular . In this literature several strategies have been proposed to model interactions between parties in a crash including the use of variables regarding the other party in the collision data subsetting and estimating models with hierarchical components . Since no systematic assessment has been conducted of the performance of these strategies they appear to be used in an ad hoc fashion in the literature . The objective of this paper is to empirically evaluate ways to model party interactions in the context of crashes involving two parties . To this end a series of models are estimated using data from Canada s National Collision Database . Three levels of crash severity are analyzed using ordered probit models and covariates for the parties in the crash and the conditions of the crash . The models are assessed using predicted shares and classes of outcomes and the results highlight the importance of considering opponent effects in crash severity analysis . The study also suggests that hierarchical specifications and subsetting do not necessarily perform better than a relatively simple single level model with opponent related factors . The results of this study provide insights regarding the performance of different modelling strategies and should be informative to researchers in the field of crash severity . | Strategies to model opponent effects in the analysis of severity of crashes involving two parties. A data workflow is presented for preparing data for modelling party interactions. Single level and hierarchical models are compared. Full sample and sub sample models are compared. The results provide information useful to select a modelling strategy for crash severity. |
S0001457520303511 | Hotspot identification is one of the most important components in the highway safety management process . Previous research has found that hazardous sites identified with different methods are not consistent . It is therefore necessary to evaluate the performance of various HSID methods . The existing evaluation criteria are limited to two consecutive periods and do not consider the temporal instability of crashes . In addition one existing criterion does not precisely evaluate HSID method under given circumstances . This paper proposed three generalized criteria to evaluate the performance of HSID methods High Crashes Consistency Test is proposed to evaluate HSID methods in terms of their reliabilities of identifying sites with high crash counts Common Sites Consistency Test is proposed to gauge HSID methods in consistently identifying a set of common sites as hazardous sites and Absolute Rank Differences Test is proposed to measure the consistency of HSID methods in measuring the absolute differences in rankings . Further three commonly used HSID methods are applied to estimate crashes on Texas rural two lane roadway segments with eight years of crash data . The performance of these three HSID methods were evaluated to validate the proposed criteria . Comparisons between the existing criteria and the generalized criteria revealed that the generalized criteria are capable of evaluating different HSID methods over multiple periods and the generalized criteria are enhanced with a consistent result and with less discrepancy in scores of the best identified HSID method . | Three HSID method evaluation tests are generalized and enhanced HCCT CSCT and ARDT. HCCT evaluates a HSID methods reliability in identifying sites with high crash counts. CSCT measures a HSID methods consistency in identifying common hazardous sites. ARDT assesses a HSID methods consistency in measuring the absolute differences in rankings. The generalized tests are capable of evaluating HSID methods over multiple periods and the results are more consistent. |
S0001457520303614 | In Vietnam motorcycle riders comprise about three quarters of road traffic fatalities the most common cause of which is head injuries that can be prevented by wearing a helmet . This study aims to assess helmet wearing behaviors in Ho Chi Minh City the largest city in Vietnam . Eight rounds of observational studies were conducted in six randomly selected locations between July 2015 and April 2019 . Given the multinomial nature of the outcome measure a multinomial model was developed to estimate the level and trend of helmet use and identify the related individual and environmental factors . A total of 479 892 motorcycle riders were observed over 90 of whom were wearing helmets . However the prevalence of correct helmet use gradually declined from 80.8 in round 155.6 in round 8 . Results from a multinomial model showed the probability of wearing a strapped standard helmet had declined by 22.4 percentage points from round 3 to round 8 while holding other factors constant . The prevalence of correct use is 11.3 percentage points higher for adults than for children . During the same period unstrapped standard helmet use increased by 24.5 percentage points substandard helmet use declined but remained high . The upward trend of incorrect helmet wearing behaviors and wearing substandard helmets sends a rallying call for multisectoral interventions . | Eight rounds of observational studies were conducted on city level representative samples in HCMC. A total of 479 892 motorcycle riders were observed over 90 of whom were wearing helmets. The prevalence of correct helmet use gradually declined from 80.8 in round 1 July 2015 to 55.6 in round 8 April 2019 . The prevalence of correct use is 11.3 percentage points higher for adults than for children. Substandard helmet use declined but remained high. |
S0001457520303791 | Numerous studies have developed intersection crash prediction models to identify crash hotspots and evaluate safety countermeasures . These studies largely considered only micro level crash contributing factors such as traffic volume traffic signals etc . Some recent studies however have attempted to include macro level crash contributing factors such as population per zone to predict the number of crashes at intersections . As many intersections are located between multiple zones and thus affected by factors from the multiple zones the inclusion of macro level factors requires boundary problems to be resolved . In this study we introduce an advanced multilevel model the multiple membership multilevel model for intersection crash analysis . Our objective was to reduce heterogeneity issues between zones in crash prediction model while avoiding misspecification of the model structure . We used five years of intersection crash data for the City of Regina Saskatchewan Canada and identified micro and macro level factors that most affected intersection crashes . We compared the fitting performance of the MMMM with that of two existing models a traditional single model and a conventional multilevel model . The MMMM outperformed the SM and CMM in terms of fitting capability . We found that the MMMM avoided both the underestimation of macro level variance and the type I statistical error that tend to occur when the crash data are analyzed using a SM or CMM . Statistically significant micro level and macro level crash contributing factors in Regina included major roadway AADT four legs traffic signals speed young drivers and different types of land use . | This paper developed the Multiple Membership Multilevel Model MMMM to estimate intersection crashes with both micro and macrolevel input factors. The proposed MMMM can let researchers properly estimate crash frequencies along the boundary of study zones reduced boundary problem . This paper demonstrated that the Multiple Membership Multilevel Model avoided both the underestimation of macrolevel variance and the type I statistical error that tend to occur when the crash data are analyzed using a Single Level Model or Conventional Multilevel Model. |
S0001457520303936 | Extreme Weather Events are currently not well understood by the maritime community even though the shipping industry is not immune to their potential disastrous consequences . This is critical for the Arctic supply chains considering the serious lack of experience data communication facilities and that rules and regulations governing the region are at the embryonic stage . Understanding such the study develops an effective risk assessment model in the context of the maritime supply chain and quantifies the risks associated with EWEs in the Arctic . The model is developed based on a Bayesian Belief Network that reflects a probabilistic risk priority index based on Failure Mode Effects and Criticality Analysis . Here we introduce a new index based on a weighted combination of the likelihood visibility and consequence of risk factors . The model is quantified by 51 respondents based on their sailing experience with cargo carriers along the Northwest Passage . Our findings suggest that dense fog and ice accretion are distinctly critical risk factors followed by thunderstorm hail and or waterspouts extreme coldness and blizzard . The study offers useful insight to all right and stakeholders in the Arctic . Moreover it presents an effective tool to develop high resolution maps for maritime routes considering important shipping elements . | A novel effective risk assessment model for maritime supply chain is developed. The model is used for quantifying risks associated with extreme weather events in the Arctic. A new index based on a weighted combination of the likelihood visibility and consequence of risk factors is presented. The model is developed based on a Bayesian belief network. The tool is effective for developing high resolution maps for maritime routes. |
S0001457520303948 | Adaptive traffic signal control systems improve traffic efficiency but their impacts on traffic safety vary among different implementations . To improve the traffic safety pro actively this study proposes a safety oriented ATSC algorithm to optimize traffic efficiency and safety simultaneously . A multi objective deep reinforcement learning framework is utilized as the backend algorithm . The proposed algorithm was trained and evaluated on a simulated isolated intersection built based on real world traffic data . A real time crash prediction model was calibrated to provide the safety measure . The performance of the algorithm was evaluated by the real world signal timing provided by the local jurisdiction . The results showed that the algorithm improves both traffic efficiency and safety compared with the benchmark . A control policy analysis of the proposed ATSC revealed that the abstracted control rules could help the traditional signal controllers to improve traffic safety which might be beneficial if the infrastructure is not ready to adopt ATSCs . A hybrid controller is also proposed to provide further traffic safety improvement if necessary . To the best of the authors knowledge the proposed algorithm is the first successful attempt in developing adaptive traffic signal system optimizing traffic safety . | A safety oriented adaptive traffic signal control ATSC algorithm optimizing traffic efficiency and safety was proposed. The algorithm was evaluated in simulation and it outperforms the field benchmark in terms of both safety and efficiency. The control rules abstracted from the ATSC could also help the traditional signal controllers to improve traffic safety. To the best of the authors knowledge it is the first successful attempt in developing an ATSC optimizing traffic safety. |
S0001457520303973 | Time to collision index has been extensively utilized to evaluate rear end collision risks but few studies have focused on the special transition process that vehicles change from a safe to a dangerous situation . This study conducts an in depth analysis of the transition condition of rear end collisions . Realistic vehicle trajectory data were extracted from the Federal Highway Administrations Next Generation Simulation datasets . The TTC index was utilized to pinpoint dangerous and transition conditions . A total of 13 types of transition conditions were categorized and a novel indicator the derivative of TTC is proposed to evaluate changing rate of TTCs . Three types of TTCDs corresponding to different time point or interval were further analyzed based on developed regression models . The results indicate that although theoretically there are a total of 13 types of transition conditions three types are dominant in practice the | Transition conditions are analyzed for rear end collisions using time to collision index. Three types of transition conditions dominate among all 13 types in practice. Time to collision derivative index is proposed to evaluate the risk change rate. Time to collision value has the quickest reduction at the transition start point. |
S0001457520304206 | In the geometric design of roundabouts safety oriented approaches are required rather than specification design ones that simply determine the dimensions of the geometric structural elements . We herein propose a risk index that combines the invisibility probability and the crash impact as a performance measure for evaluating the safety of the geometric designs of roundabouts and we also describe a method for calculating this index . Invisibility probability represents the probability that an entering vehicle can not view a vehicle coming from the upstream in a circulatory roadway and crash impact represents the amount of lost kinetic energy at the time of collision corresponds to the impact of the crash . A numerical simulation to model this RI on the basis of various geometric conditions is further presented . It is demonstrated that the invisibility probability is large when the entry angle is small and that the crash impact increases when the deviation angle decreases . The proposed approach is expected to help resolve issues with currently existing roundabouts and improve the design of future roundabouts to enhance their safety performance . | Risk index as a safety performance measure for roundabout designing was proposed. Risk index combines the invisibility probability and the crash impact. Model formula of risk index was presented through the numerical simulation. Evaluation with risk index is expected to help improvement of roundabout designing. |
S0001457520304231 | This paper investigates factors that significantly contribute to the injury severity of different drivers of different nationality backgrounds . Using the data from Riyadh Saudi Arabia a random parameters multinomial logit model of driver injury severity was estimated to explore the effects of a wide range of variables on driver injury severity outcomes . With three possible outcomes only single vehicle crashes are considered and crashes involving domestic and international drivers were modeled separately . Model estimation results show that a wide range factors significantly affect the injury severity outcomes in single vehicle crashes including driver attributes vehicle characteristics driver actions and other factors and that the influence that these variables have on injury severity probabilities vary considerably between Saudi and non Saudi drivers . While Saudi Arabia is rather unique because of the large numbers of non national drivers the results suggest that different nationalities with their different cultural educational and behavioral backgrounds may affect risk taking behavior and resulting crash injury severities . | Differences in crash injuries of Saudi and non Saudi drivers are studied. Using data from Riyadh random parameters logit models are estimated. Model estimations show that determinants of injury severity vary considerably between Saudi and non Saudi drivers. Findings show different nationalities may affect risk taking behavior and resulting crash injury severities. |
S0001457520304309 | Scenario based testing is crucial for considering the intended functional safety of automated driving vehicles . For the first time pre crash scenario mining research was conducted using worldwide accident data obtained from the Initiative for the Global Harmonization of Accident Data . First data from the IGLAD database were analyzed and divided into four categories based on differences in traffic environments among countries and regions . Second according to actual accident characteristics fields and methods of clustering were selected and 21 typical pre crash scenarios were obtained using clustering and analysis . Finally the typical scenarios were analyzed and compared in detail . | Countries in 3 G are an ideal data source for the international scenario research. Scenarios mined in this paper highly consistent with Euro NCAP 2025 Roadmap. Some critical scenario elements are missing in Euro NCAP 2025 Roadmap. Some critical information is missing in IGLAD database. |
S0001457520305200 | The 402 mile of Interstate 80 in Wyoming was selected by the U.S. Department of Transportation to develop test and deploy a suite of Connected Vehicle applications . It is expected that after full deployment of CV technology the pilot will improve safety and mobility under adverse weather conditions by creating new ways to communicate road and travel information to both drivers and fleet managers . In this regard this research employed an integrated microsimulation modeling approach to assess the safety performance of the WYDOT CV Pilot . A 23 mile representative I 80 corridor was selected for developing the microsimulation models . Traffic flow and driving behavior data under winter snowy weather condition were collected to calibrate the baseline microsimulation model . A driving simulator experiment was conducted to quantitatively investigate the impacts of CV technology on driving behavior accordingly the driving behavior data under CV environment were employed to properly update the calibrated CV microsimulation models . The safety effectiveness of the WYDOT CV Pilot were assessed for various demand levels and CV penetration rates . It was concluded that WYDOT CV applications increased drivers situation awareness under adverse weather conditions and thus reduced the crash risk . The reductions in conflicts displayed a decreasing trend with the increase of CV penetration rates but the reduction was not significant when CV penetration was lower than 10 percent . The maximum reduction in conflicts was 85 percent when all trucks were equipped with CV technology . | This study assessed the safety performance of the Wyoming Connected Vehicle pilot deployment program under adverse weather conditions. An integrated field data and driving simulator experiment method for investigating the impacts of CV on truck driver behavior. Microsimulation modeling was employed for assessing the safety performance of CV under various demand levels and CV penetration rates. The reductions in conflicts displayed a decreasing trend with the increase of CV penetration rates. The maximum reduction in conflicts was 85 percent when all trucks were equipped with CV technology. |
S0001457520305297 | Traffic accident statistics have shown the necessity of risk assessment when driving in the dynamic traffic environment . If the risk associated with different traffic elements | Combines data from naturalistic driving study and driver attitude questionnaire. Evaluates driving risks of different influence factors in dynamic environments. Establishes the internal and external field to describe the risk influence. Develops a driving risk coupling model to output dynamic risk assessment |
S0001457520305315 | Increasing automation calls for evaluating the effectiveness and intelligence of automated vehicles . This paper proposes a framework for quantitatively evaluating the intelligence of automated vehicles . Firstly we establish the evaluation environment for automated vehicles including test field test task and evaluation index . The test tasks include the single vehicle decision making and the maneuver execution of multi vehicle interaction . The intelligence evaluation index is the action amount of driving process considering the safety efficiency rationality and comfort . Then we calculate the actual action amount of the automated vehicle in different scenarios in the test field . Finally the least action calculated theoretically corresponds to the highest intelligence degree of the automated vehicle and is employed as a standard to quantify the performance of other tested automated vehicles . The effectiveness of this framework is verified with two naturalistic driving datasets that contain the normal driving scenarios and high risk scenarios . Specifically the naturalistic lane changing data filters 40 416 frames and 179 similar lane changing trajectories . Compared with the lane changing behavior of a large number of drivers experimental results verify that the proposed algorithm can achieve the intelligence degree of drivers in the lane change scenario . Meanwhile in 253 reconstructed high risk scenarios the intelligent risk avoidance ability of the proposed intelligence degree evaluation algorithm can be verified by comparing with the driver behavior and TTC algorithm . These experimental results show that the proposed framework can effectively quantify intelligence and evaluate the performance of automated vehicles under various scenarios . | Proposes a framework for quantitatively evaluating the intelligence of automated vehicles. Develops the symmetric intelligence evaluation index based on principle of least action. Completes evaluation process by testing different scenarios including normal driving and high risk scenarios. |
S0001457520305339 | Real time crash potential prediction could provide valuable information for Active Traffic Management Systems . Fixed infrastructure based vehicle detection devices were widely used in the previous studies to obtain different types of data for crash potential prediction . However it was difficult to obtain data in large range through these devices due to the costs of installation and maintenance . This paper introduced a novel connected vehicle emulated data for real time crash potential prediction . Different from the fixed devices data CV emulated data have high flexibility and can be obtained continuously with relatively low cost . Crash and CV emulated data were collected from two urban arterials in Orlando USA . Crash data were archived by the Signal for Analytics system while the CV emulated data were obtained through the data collection API with a high frequency . Different data cleaning and preparation techniques were implemented while various speed related variables were generated from the CV emulated data . A Long Short term Memory neural network was trained to predict the crash potential in the next 510min . The results from the model illustrated the feasibility of using a novel CV emulated data to predict real time crash potential . The average and 50th percentile speed were the two most important variables for the crash potential prediction . In addition the proposed LSTM outperformed Bayesian logistics regression and XGBoost in terms of sensitivity Area under Curve and false alarm rate . With the rapid development of the connected vehicle systems the results from this paper can be extended to other types of vehicles and data which can significantly enhance traffic safety . | This paper utilizes novel connected vehicle CV emulated data to predict real time crash potential for arterials. The CV emulated data are flexible to obtain. Different speed related variables are estimated based on the CV emulated data to depict continuous traffic conditions. The results proved the feasibility of using CV emulated data for real time crash potential prediction. The proposed methods can be applied to other types of vehicles |
S0001457520305388 | Adaptive traffic signal control is a promising technique to improve the efficiency of signalized intersections especially in the era of connected vehicles when real time information on vehicle positions and trajectories is available . Numerous ATSC algorithms have been proposed to accommodate real time traffic conditions and optimize traffic efficiency . The common objective of these algorithms is to minimize total delay decrease queue length or maximize vehicle throughput . Despite their positive impacts on traffic mobility the existing ATSC algorithms do not consider optimizing traffic safety . This is most likely due to the lack of tools to evaluate safety in real time . However recent research has developed various real time safety models for signalized intersections . These models can be used to evaluate safety in real time using dynamic traffic parameters such as traffic volume shock wave characteristics and platoon ratio . Evaluating safety in real time can enable developing ATSC strategies for real time safety optimization . In this paper we present a novel self learning ATSC algorithm to optimize the safety of signalized intersections . The algorithm was developed using the Reinforcement Learning approach and was trained using the simulation platform VISSIM . The trained algorithm was then validated using real world traffic data obtained from two signalized intersections in the city of Surrey British Columbia . Compared to the traditional actuated signal control system the proposed algorithm reduces traffic conflicts by approximately 40 . Moreover the proposed ATSC algorithm was tested under various market penetration rates of CVs . The results showed that 90 and 50 of the algorithms safety benefits can be achieved at MPR values of 50 and 30 respectively . To the best of the authors knowledge this is the first self learning ATSC algorithm that optimizes traffic safety in real time . | A novel self learning ATSC algorithm is developed to optimize safety in real time. The developed algorithm adapts traffic signals using real time CVs data. The algorithm is validated using real traffic data of two existing intersections. Various market penetration rates of CVs are investigated. The results show a considerable reduction in rear end traffic conflicts. |
S0001457520305522 | In this study two novel fuzzy decision approaches where the fuzzy logic model was revised with the C4.5 decision tree algorithm were applied to the classification of cyclist injury severity in bicycle vehicle accidents . The study aims to evaluate two main research topics . The first one is investigation of the effect of road infrastructure road geometry street accident atmospheric and cyclist related parameters on the classification of cyclist injury severity similarly to other studies in the literature . The second one is examination of the performance of the new fuzzy decision approaches described in detail in this study for the classification of cyclist injury severity . For this purpose the data set containing bicycle vehicle accidents in 20132017 was analyzed with the classic C4.5 algorithm and two different hybrid fuzzy decision mechanisms namely DT based converted FL and novel DT based revised FL . The model performances were compared according to their accuracy precision recall and F measure values . The results indicated that the parameters that have the greatest effect on the injury severity in bicycle vehicle accidents are gender vehicle damage extent road type as well as the highly effective parameters such as pavement type accident type and vehicle movement . The most successful classification performance among the three models was achieved by the DT RFL model with 72.0 F measure and 69.96 Accuracy . With 59.22 accuracy and 57.5 F measure values the DT CFL model rules of which were created according to the splitting criteria of C4.5 algorithm gave worse results in the classification of the injury severity in bicycle vehicle accidents than the classical C4.5 algorithm . In light of these results the use of fuzzy decision mechanism models presented in this study on more comprehensive datasets is recommended for further studies . | Handling the classification of accident injury severity with a fuzzy approach can significantly improve model performance. A Hybrid model is created by combining different machine learning models can give better results than classical algorithms. As an alternative to ANN FLs DT RFL model can be used to reduce or eliminate the expert opinion in the rule creation task. |
S0001457520305558 | One of the main aims of introducing automation in transport is to improve safety by reducing or eliminating human errors it is often argued however that this may induce new types of errors . There is different level of maturity with automation in different transport modes however no systematic research has been conducted on the lessons learned in different sectors so that they can be exploited for the design of safer automated systems . The aim of this paper is to review the impact of key human factors on the safety of automated transport systems with focus on relevant experiences from different transport sectors . A systematic literature review is carried out on the following topics the level of trust in automation in particular the impact of mis aligned trust i.e . mistrust vs overreliance the resulting impact on operator situation awareness the implications for takeover control from machine to human and the role of experience and training on using automated transport systems . The results revealed several areas where experiences from the aviation and road domain can be transferable to other sectors . Experiences from maritime and rail transport although limited tend to confirm the general patterns . Remarkably in the road sector where higher levels of automation are only recently introduced there are clearer and more quantitative approaches to human factors while other sectors focus only on mental modes . Other sectors could use similar approaches to define their own context specific metrics . The paper makes a synthesis of key messages on automation safety in different transport sectors and presents an assessment of their transferability . | Research on automation and safety is in silos for each transport mode. There are several opportunities for transfer of knowledge between transport modes. Lessons from aviation on automation trust SA and the role of training are transferable. The quantitative approach of the road sector to SA and takeover performance can be transferable. Relevant research in maritime and rail is limited. |
S0001457520305807 | Traffic accident management is a critical issue for advanced intelligent traffic management . The increasingly abundant crowdsourcing data and floating car data provide new support for improving traffic accident management . This paper investigates the methods to predict the complicated behavior of traffic flow evolution after traffic accidents using crowdsourcing data . Based on the available data source the traffic condition is divided into four levels by congestion delay index severely congested congested slow moving and uncongested . Four types of accidents are consequently defined based on the occurrence of each level . A hierarchical scheme is designed for identifying the most congested level and sequentially predicting duration of each level . The proposed model is validated using traffic accident data in 2017 from an anonymous source in Beijing China by embedding three machine learning algorithms random forest support vector machine and neural network in the scheme . The results show NN outperforms the other two models when the assessment is conducted in absolute differences . Meanwhile RF has a slightly better performance than SVM especially when predicting the short period congestion of severely congested level at the first time . By continuously updating the traffic condition information significant improvement in accuracy can be acquired regardless of the exact model used . This study shows that emerging crowdsourcing data can be used in a real time analysis of traffic accidents and the proposed model is effective to analyze such data . | Traffic accident and flow data obtained simultaneously from crowdsourcing approach. Entire traffic accidents post impact from occurrence to recovery can be predicted. Machine learning models were applied and good performance was achieved. Results improved when updated information added into the model sequentially. |
S0001457520305819 | The novel semi autonomous vehicles are becoming a reality in our roads being a very important technological advance with promising operational and safety improvements . However road infrastructure must be ready to host them . The technologies of these driving automation systems require certain road conditions that are not always fulfilled causing the systems to fail . These failures generally transfer negotiation control to drivers which may induce a crash if they were not aware of road and traffic conditions . | AV disengagements are highly related to road horizontal geometry. Automated speed is defined as the maximum speed that an AV can attain at a curve. A framework is proposed to analyse how AVs perform in function of road geometry. AV consistency is defined as the difference between automated and operating speeds. A new Level of Service for Automated Driving is introduced. |
S0001457520305844 | Approximately one third of car trips involve one or more passengers and yet we know little about how the presence of a passenger helps or hinders safety and efficiency . To date research in this area has focused on the possible distractive effects of passengers . Although we know that drivers conversing on a mobile phone is distracting and unsafe epidemiological studies suggest that driving with a passenger has a lower crash risk than driving alone . This paper describes two studies into how drivers and passengers interact during a journey a survey regarding the most common actions of passengers and how drivers view their helpfulness and an on road study of driver and passenger interactions . The results indicated several areas that drivers felt passenger assistance was quite helpful but in some cases was exhibited very rarely . The on road study revealed some interesting gender differences in who offers driving support and who requests it . By understanding how passengers can contribute to safer journeys we can provide that information to drivers at risk such as those very early or late in their driving careers . | Passengers often viewed as distracting but safety data suggests they are protective. We conducted a mixed methods study to understand the ways passengers help drivers. On line survey indicated some things drivers wished their passengers would do more. On road sample found gender differences in the support offered by passengers. Promoting the idea of a co driver changes passenger role to an active participant. |
S000145752030587X | Adaptive traffic signal control is a novel traffic management system that is often deployed at high volume intersections in order to mitigate traffic congestion and improve travel time reliability . While past studies have demonstrated its operational effectiveness relatively few have focused on safety performance . Those that have tend to suffer from limitations including small sample sizes insufficient study designs or the lack of consideration of potential temporal and corridor effects after ATSC installation . Furthermore results from previous studies are mixed while many studies point to a safety improvement more recent studies seem to indicate that ATSC systems might increase crash frequency . In light of this a comprehensive Empirical Bayes before after observational study was conducted using ATSC data collected throughout Pennsylvania . Crash modification factors were estimated based on the following different case scenarios crash severity levels and crash types intersection locations and intersection configurations . Temporal trends for intersection level CMFs were examined using annual crash data in the after period . Corridor level CMFs were also developed to quantify changes in safety performance along corridors with ATSC installed . The results suggest that ATSC is associated with a nominal increase in total and angle crashes and an expected decrease in fatal plus injury crashes and rear end crashes . However the results were not statistically significant . The safety effect estimates are similar when considering intersection locations and configurations . In addition the temporal trend analysis indicates that the safety effectiveness does not vary annually in the after period suggesting no obvious novelty effect associated with ATSC . Finally the magnitude of the corridor level CMFs are slightly lower than the intersection level CMFs except for rear end crashes . | CMFs for adaptive traffic signal control ATSC systems developed using Empirical Bayes before after study. CMFs for ATSC system impacts at individual intersections and along entire adaptive corridors are estimated. Temporal trends in safety performance of ATSC system also studied. Results reveal minor increase in total crash frequency and decrease in fatal injury crash frequency. |
S000145752030590X | Guardrails were designed to deter vehicle access to off road areas and consequently prevent hitting rigid fixed objects alongside the road . However guardrails cause 10 of deaths in vehicle to fixed object crashes which recently attracted attention in the highway safety community on the vehicle based injury criteria used in regulations . The objectives of this study were to investigate both full body and body region driver injury probabilities using finite element simulations to quantify the influence of pre impact conditions on injury probabilities and to analyze the relationship between the vehicle based crash severity metrics currently used in regulations and the injury probabilities assessed using dummy based injury criteria . | The car to end terminal crash finite element FE simulations involving a dummy model were performed for the first time in this study. Injuries assessed by vehicle based crash severity metrics showed to be more conservative than the ones determined by dummy based injury criteria. The current vehicle based injury prediction methods which are accepted by U.S and European standards cannot be used to predict head neck and thigh injuries. The results showed that the end terminal accepted under NCHRP guidelines should be re evaluated under new MASH guidelines. |
S000145752030600X | In Germany every year 66 000 road crashes lead to death or injury of young novice drivers . This makes them twice as likely to be involved in or cause vehicle crashes compared to their older and more experienced counterparts . This study aims to address this societal issue by developing a better understanding of the German young driver problem . For this purpose we created an updated 55 item strong version of the Behaviour of Young Novice Drivers Scale originally developed by Scott Parker et al . in 2010 . To make the new version of the BYNDS understandable for German young novice drivers this research used a new method of translation in combination with extensive pre testing . As a result we identified possible threats for response errors such as retrospective formulated questions or double negations . Due the adjustment of the possible sources of error the presented version of the BYNDS is semantically and conceptually different from the original . However due to the application of the updated version of the BYNDS in a robust sample of 700 participants this paper presents the first reliable and validated tool to measure novices risky driving behaviour in Germany . Moreover it offers an updated and extended version of the BYNDS that allows practitioners but also researchers to broaden their understanding of young driver risk . | The inclusion of sixteen new items creates a timely commensurate BYNDS. In particular a broader picture of smartphone use is introduced. The paper presents an innovative translation process easy to adopt. Pitfalls for response errors are addressed and semantics are improved. The questionnaire was applied to a representative German sample of young drivers. |
S0001457520306618 | Health economic evaluation studies can provide insight into which injury prevention interventions maximize available resources to improve health outcomes . A previous systematic review summarized 48 unintentional injury prevention economic evaluations published during 19982009 providing a valuable overview of that evidence for researchers and decisionmakers . The aim of this study was to summarize the content and quality of recent economic evaluations of unintentional injury prevention interventions and compare to the previous publication period . Peer reviewed English language journal articles describing public health unintentional injury prevention economic evaluations published January 1 2010 to December 31 2019 were identified using index terms in multiple databases . Injury causes interventions study methods and results were summarized . Reporting on key methods elements was assessed . Reporting quality was compared between the recent and previous publication periods . Sixty eight recent economic evaluation studies were assessed . Consistent with the systematic review on this topic for the previous publication period falls and motor vehicle traffic injury prevention were the most common study subjects . Just half of studies from the recent publication period reported all key methods elements although this represents an improvement compared to the previous publication period . Most economic evaluations of unintentional injury prevention interventions address just two injury causes . Better adherence to health economic evaluation reporting standards may enhance comparability across studies and increase the likelihood that this type of evidence is included in decision making related to unintentional injury prevention . | Two decades of unintentional injury prevention economic evaluations are summarized. Falls and motor vehicle traffic injuries were the most common study subjects. Only half of recent economic evaluations reported on key methods elements. Standardized and complete reporting can improve comparisons across studies. |
S0001457520306680 | The identification of accident hot spots is a central task of road safety management . Bayesian count data models have emerged as the workhorse method for producing probabilistic rankings of hazardous sites in road networks . Typically these methods assume simple linear link function specifications which however limit the predictive power of a model . Furthermore extensive specification searches are precluded by complex model structures arising from the need to account for unobserved heterogeneity and spatial correlations . Modern machine learning methods offer ways to automate the specification of the link function . However these methods do not capture estimation uncertainty and it is also difficult to incorporate spatial correlations . In light of these gaps in the literature this paper proposes a new spatial negative binomial model which uses Bayesian additive regression trees to endogenously select the specification of the link function . Posterior inference in the proposed model is made feasible with the help of the Plya Gamma data augmentation technique . We test the performance of this new model on a crash count data set from a metropolitan highway network . The empirical results show that the proposed model performs at least as well as a baseline spatial count data model with random parameters in terms of goodness of fit and site ranking ability . | New spatial count data model with flexible link function specification. Additive regression trees enable endogenous partitioning of predictor space. MCMC algorithm for fully Bayesian inference. New method offers excellent goodness of fit and site ranking ability. |
S0001457520306692 | A significant portion of pedestrian accidents occurs in the outskirts areas due to the high vehicle speed and lack of safety facilities for pedestrians . Behavioral study on drivers and pedestrians is the key to better understand the causes of pedestrian accidents in order to develop safety models . | The present study focuses on evaluating the safety of pedestrian crossing in urban and outskirt areas based on fixed videography FV and in motion videography IMV to determine the differences of drivers and pedestrians behaviors in these areas. The logistic and linear regression models based on both approaches showed that pedestrians exhibited relatively similar behavior in both areas. Pedestrian gender pedestrian willingness to cross without waiting on the side of the roads the tendency of pedestrians to move in groups and the time required to cross the road were influential factors on pedestrian crossing behaviors. The distance between the vehicle and the pedestrian as well as the speed of the approaching vehicle were significant factors in choosing an accepted gap by pedestrian. |
S0001457520306709 | Earlier research on injury severity of truck involved crashes focused primarily on single truck and multi vehicle crashes with truck involvement or investigated truck involved injury severity based on rural and urban locations time of day variations lighting conditions roadway classification and weather conditions . However the impact of different vehicle truck collisions on corresponding occupant injury severity is lacking . Therefore this paper advances the current research by undertaking an extensive assessment of the occupant injury severity in truck involved crashes based on vehicle types and identifies the major occupant crash and geometric related contributing factors . A series of log likelihood ratio tests were conducted to justify that separate model by vehicle and occupant types are warranted . Injury severity models were developed using 10 years of crash data on I 80 in Wyoming through binary logistic modeling with a Bayesian inference approach . The modeling results indicated that there were significant differences between the influences of a variety of variables on the injury severities when the truck involved crashes are broken down by vehicle types and separated by occupant types . The age and gender of occupants truck driver occupation driver residency sideswipes presence of junctions downgrades curves and weather conditions were found to have significantly different impacts on the occupant injury severity in different vehicle truck crashes . Finally with the incorporation of the random intercept in the modeling procedure the presence of intra crash and intra vehicle correlations in injury severities were identified among persons within the same crash and same vehicle . | Truck involved crashes result in more severe injuries. Different factors contribute to injury severity of occupants in truck involved crashes. Separate models needed for each occupant of each vehicle type. There is a correlation of occupant injury severity in the same crash and the same vehicle. Actions of car and SUV pickup drivers have more significant impacts on crash severity. |
S0001457520306746 | Current automated driving technology can not cope in numerous conditions that are basic daily driving situations for human drivers . Previous studies show that profound understanding of human drivers capability to interpret and anticipate traffic situations is required in order to provide similar capacities for automated driving technologies . There is currently not enough a priori understanding of these anticipatory capacities for safe driving applicable to any given driving situation . To enable the development of safer more economical and more comfortable automated driving experience expert drivers anticipations and related uncertainties were studied on public roads . First driving instructors expertise in anticipating traffic situations was validated with a hazard prediction test . Then selected driving instructors drove in real traffic while thinking aloud anticipations of unfolding events . The results indicate sources of uncertainty and related adaptive and social behaviors in specific traffic situations and environments . In addition the applicability of these anticipatory capabilities to current automated driving technology is discussed . The presented method and results can be utilized to enhance automated driving technologies by indicating their potential limitations and may enable improved situation awareness for automated vehicles . Furthermore the produced data can be utilized for recognizing such upcoming situations in which the human should take over the vehicle to enable timely take over requests . | The prospective thinking aloud method is introduced. With the method expert drivers anticipations can be analyzed. Anticipations revealed uncertainties and related adaptive and social behaviors. The method can show prototypical traffic events and their information requirements. The results indicate automated vehicles limitations as compared to expert drivers. Data can be used to recognize events where human should take over automated vehicle. The method and results can be used to enhance automated driving technology safety. |
S0001457520306837 | A novel automatic incident detection method for freeways based on the use of data provided by Bluetooth sensors and an unsupervised anomaly detection approach is presented . The two main advantages of the proposed AID system are the use of Bluetooth sensors offers several practical advantages over inductive loop detectors which is one of the preferred sensing technology for traffic flow and the unsupervised anomaly detection approach builds a model without the need of incident information . A common problem when designing an AID system is that incident information i.e . ground truth data with enough accuracy is seldom available . Isolation forest is the unsupervised anomaly detection approach adopted in this work . This method is based on characterizing anomalous traffic conditions by exploiting the fact that anomalies tend to be isolated . The most remarkable feature of this anomaly detection method is its high detection performance while having a very simple tuning procedure and an extremely low computational demand . Finally the effectiveness of the presented AID method is demonstrated using real traffic data collected by a network of Bluetooth sensors installed in Ayalon Highway Tel Aviv . | Developing an automatic incident detection AID method for freeways based on Bluetooth data and an unsupervised anomaly detection approach. Demonstrating the developed AID method using real traffic data collected by a network of Bluetooth sensors installed in Ayalon Highway Tel Aviv. Special emphasis is made in using data provided by Bluetooth sensors without assuming knowledge about incident information. |
S0001457520307442 | The era of Big Data has arrived . Recently under the environment of intelligent transportation systems and connected automated vehicles Big Data has been applied in various fields in transportation including traffic safety . In this study we review recent research studies that employed Big Data to analyze traffic safety under the environment of ITS and CAV . The particular topics include crash detection or prediction discovery of contributing factors to crashes driving behavior analysis crash hotspot identification etc . From the reviewed studies employing advanced analytics for Big Data has a great potential for understanding and enhancing traffic safety . Big Data application in traffic safety integrates and processes massive multi source data breaks through the limitations of the traditional data analytics and discovers and solves the problems which can not be solved by the traditional safety analytics . Lastly suggestions are provided for future Big Data safety analytics under the environment of ITS and CAV . | This paper reviews Big Data safety analytics for intelligent transportation systems and connected automated vehicles. The data models techniques and applications of 57 safety studies are discussed. Types of analytics including descriptive predictive and perspective analysis are summarized. Challenges and future research suggestions are offered based on the above analysis. |
S0001457520307521 | This study presents an effort to investigate the determinants of driver injury severity in run off road crashes . In order to account for unobserved heterogeneity a random parameter ordered probit with heterogeneity in the means approach is applied . The police reported ROR crash data that occurred from 2014 to 2017 in the state of North Carolina is used . Four injury severity levels are defined property damage only possible injury non incapacitating and F I . The driver crash roadway environmental characteristics that potentially affect the driver injury severity are explored . Besides the temporal stability of modeling results among the four time periods is investigated using a series of likelihood ratio tests . Significant temporal instability is found indicating underestimating the temporal instability might result in unreliable conclusions . Estimation results demonstrate that the indicators including male driver alcohol and curved roadways increase the possibility of fatal and incapacitating injuries in the ROR crash in most of the year periods . | Injury severity of run off road crashes was studied and affecting factors on the driver crash severity were investigated. Random parameters ordered probit with heterogeneity in the means approach was used. Unobserved heterogeneity was accounted for and temporal stability of factors was examined. Significant temporal instability was found using the likelihood ratio tests. Marginal effects of specific contributing variables varied over time. |
S0001457520307934 | The present study has investigated the relationship between traffic volume and crash numbers by means of meta analysis based on 521 crash prediction models from 118 studies . The weighted pooled volume coefficient for all crashes and all levels of crash severity is 0.875 . The most important moderator variable is crash type . Pooled volume coefficients are systematically greater for multi vehicle crashes than for single vehicle crashes . Regarding crash severity the results indicate that volume coefficients are smaller for more fatal crashes than for injury crashes but no systematic differences were found between volume coefficients for injury and property damage only crashes . At higher levels of volume and on divided roads volume coefficients tend to be greater than at lower levels of volume and on undivided roads . This is consistent with the finding that freeways on average have greater volume coefficients than other types of road and that two lane roads are the road type with the smallest average volume coefficients . The results indicate that results from crash prediction models are likely to be more precise when crashes are disaggregated by crash type crash severity and road type . Disaggregating models by volume level and distinguishing between divided and undivided roads may also improve the precision of the results . The results indicate further that crash prediction models may be misleading if they are used to predict crash numbers on roads that differ from those that were used for model development with respect to composition of crash types share of fatal or serious injury crashes road types and volume levels . | Meta analysis of the relationship between traffic volume and crash numbers. Crashes increase with increasing volumes but mostly at a lower rate. The relationship is strongest for multi vehicle crashes at high volumes and on freeways. The relationship is weaker for fatal than for less serious crashes. Crash prediction models may improve from disaggregation by crash and road type and volume level. |
S0001457520308149 | The safety of signalized intersections has traditionally been evaluated at an aggregate level by relating historical collision records for several years to the annual trac volume and the geometric characteristics of the intersection . This is a reactive and macroscopic approach that gives little insight into how important dynamic signal cycle related variables can affect intersection safety such as the arrival type and the shock wave characteristics . The objective of this study is to develop traffic conflict based real time safety models for signalized intersections using several state of the art techniques . Traffic conflicts were measured by multiple indicators including time to collision modified time to collision and deceleration rate to avoid collision . Traffic conflict rate was employed as independent variable while traffic volume queue length shock wave area shock wave speed and platoon ratio of each cycle were used as covariates in the safety models . Four candidate Tobit models were developed and compared under the Bayesian framework conventional Tobit model grouped random parameters Tobit model random intercept Tobit model and random parameters Tobit model . The results showed that the GRP Tobit model performs best with lowest Deviance Information Criteria indicating that accounting for the unobserved heterogeneity across sites can significantly improve the model fit . The model estimation results showed that higher conflict rates were associated with various shock wave characteristics and higher traffic volume . Lower conflict rates were related with higher platoon ratio . The developed models can have potential applications in real time safety evaluation real time optimization of signal control and connected and autonomous vehicles trajectories planning . | Conflict based real time safety performance functions are developed for signalized intersections. Several Tobit models including GRP Tobit RI Tobit and GRP Tobit models are compared. Traffic volume queue length shock wave speed and area and the platoon ratio are used as covariates. Higher conflict rates were associated with shock wave area and speed and higher traffic volume. Lower conflict rates were related with higher platoon ratio. |
S0001457520308265 | If the information on freeway exits is not effective or driver vigilance is not adequate the driver may not be able to obtain the information in time resulting in missing the exit or making a forcible lane change that could cause an accident . To allow the driver to obtain sufficient exit information in time and get off the freeway safely this study proposes the creation of a guardrail painted with a yellow color and located prior to the exit . The yellow color guardrail belt aims at informing the drivers that there is an exit ahead and to pay attention to the exit information reminding them to adjust vehicle state and driving behavior in time . A driving simulator experiment with two different scenarios were used to explore the effectiveness of the YCB . Data on eye movement electroencephalograph and driving behavior of the participants were obtained . The results showed that compared with the baseline scenario in the YCB scenario the fixation points were mainly distributed in front of the road and the fixation duration on the guide signs was relatively longer the EEG ratio was smaller the driver decelerated more smoothly and the steering wheel angle was smaller . In addition the statistical analysis showed that there were significant differences in the fixation duration the EEG ratio and steering wheel angle between the two scenarios . This indicated that participants vigilance in the YCB scenario was significantly improved where the participants paid more attention to the guide signs and had better control of the vehicle . This study recommends a new device for reminding drivers to pay attention to freeway exits which would greatly stimulate driver s sense of space on the exit and improve traffic safety on freeways . | Yellow color guardrail belt was designed to improve driver s vigilance near the freeway exit. A simulation experiment was conducted to evaluate the effectiveness of the yellow color guardrail belt. The participants were more focused on searching for exit sign information in the yellow color guardrail belt scenario than the baseline scenario. In the yellow color guardrail belt scene the participants tend to reduce the speed in advance near the freeway exit. |
S000145752030854X | Although tires maintain the only contact between the vehicle and the ground tire failures are still underrepresented in traffic safety assessments . Vehicle stability and safety can deteriorate significantly by a sudden tire failure . The current body of literature on tire failure related crashes is limited and no previous study was found to extensively investigate the factors associated with tire failures and the corresponding injury severity . The contributions of this study include investigating the factors affecting tire failures assessing the impacts of tire failures on occupant injury severity and demonstrating the necessity of statewide tire inspection regulations . An extensive exploratory analysis was performed using ten years of historical crash data along I 80 in Wyoming . Binary logistic regression with the Bayesian inference approach was applied to develop two separate models tire failure and injury severity model . The results from the tire failure model showed that vehicle speeds greater than 75 mph commercial motor vehicles summer season daytime the presence of rough surface downgrades and concrete pavement are all related to higher tire failure occurrences . On the other hand the incidence of a tire failure in a crash significantly contributed to more severe injuries when combined with any of the following instances fire or explosion rollover guardrail hits runoff road angle rear end clear weather speeding downgrades and curved segments . With the incorporation of the random intercept in the modeling procedure the injury severity analysis found a strong presence of intra crash correlation in occupant injury severity within the same crash . Finally based on the findings of the study recommendations are provided to alleviate tire related problems . | There is a need for comprehensive assessment of tire failure and impacts on injury severity. Speed of more than 75 mph summer months and heavy vehicles contribute to tire failures. Tire failures lead to crashes with significant consequences on occupants. Crash reporting systems need to provide more information on tire failures. |
S0001457520309611 | This study firstly aimed to describe bicyclists return to cycling after a hospitalisation crash . Secondly it aimed to determine factors associated with reduced cycling post crash . A study of 83 cyclists hospitalised due to an on road crash was undertaken in Perth Western Australia . Participants completed a questionnaire shortly following the crash and were followed up approximately 12 months after the crash . Injury information was obtained from the WA State Trauma Registry . A binary logistic regression model was used to examine factors associated with | A longitudinal study examined return to cycling after a hospitalisation crash. Sixty percent of participants had. one year post crash. Group riders had a lower risk of. at follow up. Psychological services following a crash may assist with return to cycling. Interventions promoting safe return to cycling should target non group riders |
S0001457520309994 | The consequences of crashes including injury loss of lives and damage to properties are further worsened when buses plying expressways are involved in the crash . Previous studies have separately analyzed crash severity in terms of monetary cost injuries and loss of lives and the size of crashes in terms of the number of vehicles involved . However as both outcome variables are correlated it is imperative to perform a combined analysis using an appropriate econometric model to achieve a better model fit . This study contributes to the literature by jointly exploring the factors influencing the severity and size of express bus involved crashes that occur on expressways and characterizes the dependence between both outcome variables by employing a more plausible copula regression framework . Likelihood ratio tests were also conducted to investigate the temporal stability of the factors that affect both crash severity and size . Based on the goodness of fit statistics the Frank copula model proved superior to the independent ordered probit model . The estimate of the underlying dependence between the outcome variables provided a better comprehension of the correlation between them . Temporal instability was detected for the individual parameters in the models and is attributed to the changing driving behavior due to the heightened road safety campaigns . The results suggest that traffic exposure measures are significantly associated with a higher propensity of observing increased bus crash severity and size . Insights into the factors influencing the size and severity of express bus crashes are discussed and appropriate engineering enforcement and education related countermeasures are proposed . | Copula based multivariate model was proposed for jointly modeling express bus involved crash severity and crash size. Dependency between both outcome variables was accounted for and the temporal stability of factors was investigated. Positive dependence was observed between the outcome variables and temporal instability of crash factors was established. Major significant contributory factors include AADT truck proportion negligence and mainline segment crashes. Countermeasures related to expressway engineering enforcement and education are recommended. |
S0001457520310149 | This study develops bicycle vehicle safety performance functions for five facilities in the Highway Safety Manual . These are urban two lane undivided segments urban four lane divided undivided segments rural two lane undivided segments urban four leg and three leg signalized intersections and urban four leg and three leg stop controlled intersections . Two modeling techniques were explored the Conway Maxwell Poisson model and a machine learning technique the multivariate adaptive regression splines . MARS is a non black box model and can effectively handle non linear crash predictors and interactions . A total of 1 311 bicycle vehicle crashes from 2011 through 2015 in Alabama were collected and their respective police reports were reviewed in details . Results from the SPFs for roadway segments using COM Poisson showed that bicycle vehicle crash frequencies were reduced along curved and downgrade upgrade stretches and when having heavy traffic flow . For urban signalized intersections the absence of right turn lanes on minor roads the presence of bus stops and the increase in the major road annual average daily traffic were significant factors contributing to the increase in the number of bicycle vehicle crashes . However the presence of divided medians on major approaches was found to reduce bicycle vehicle crashes at USG and UST intersections . MARS outperformed the corresponding COM Poisson models for all five facilities based on mean absolute deviance mean square prediction error and generalized R square . MARS is recommended as a promising technique for effectively predicting bicycle vehicle crashes on segments and intersections . | We develop bicycle vehicle specific safety performance functions SPFs in Alabama. Five segment and intersection facilities in the Highway safety Manual were modeled. Two techniques were used and compared the COM Poisson and MARS. MARS outperformed the corresponding COM Poisson models for all five facilities. MARS is recommended as promising technique for effective bicycle crash prediction. |
S0001457520311908 | In March 2020 the World Health Organization declared COVID 19 a world wide pandemic . Countries introduced public health measures to contain and reduce its spread . These measures included closures of educational institutions non essential businesses events and activities as well as working from and staying at home requirements . These measures have led to an economic downturn of unprecedented proportions . Generally as economic activity declines travel decreases and drivers are exposed to a lower risk of collisions . However research on previous economic downturns suggests economic downturns differentially affect driver behaviours and situations . COVID 19 pandemic effects on road safety are currently unknown . However preliminary information on factors such as the increased stress and anxiety brought about by the COVID 19 pandemic more free time increased consumption of alcohol and drugs and greater opportunities for speeding and stunt driving might well have the opposite effect on road safety . Using an interactionist model we identify research questions for researchers to consider on potential person and situation factors associated with COVID 19 that could affect road safety during and after the pandemic . Collaborative efforts by researchers and public and private sectors will be needed to gather data and develop road safety strategies in relation to the new reality of COVID 19 . | Effects of COVID 19 pandemic on road safety is unknown. Previous research indicates economic downturns lead to declines in collisions. COVID 19 has led to an economic downturn of unprecedented proportions. Differential person and situation factors effects should be examined. |
S0001457520311982 | Understanding who heeds the driving related COVID 19 restrictions is critical for assisting public health professionals improve response to this and future pandemic events . The purpose of the current study was to characterize driving behavior changes among adolescents as a function of COVID 19 restrictions . It was hypothesized that adolescent driving would be reduced by COVID 19 restrictions especially for younger teens non minorities females non working teens and those with higher prosocial tendencies . Participants were licensed drivers in REACT a longitudinal study of adolescent driving attention . Upon enrollment in REACT drivers were required to be age 16 or 18 have been issued a drivers license within the last two weeks and be fluent in written spoken English . The current observational cohort study was of drivers reporting driving exposure between February 8 and April 22 2020 . Linear mixed effects models estimated differences in driving changes between COVID 19 periods . Results indicated a decrease across pre COVID 19 period in days driven per week and vehicle miles driven was explained by the change of slope post COVID 19 restrictions . Post COVID 19 driving days per week decreased by 37 and VMD decreased by 35 . This decrease was lower in ethnic minorities older adolescents and employed adolescents . Those with greater dire prosocial tendencies showed greater post COVID 19 driving decline . Findings provide early evidence of COVID 19 restriction related adolescent driving changes suggesting older employed minority teens and teens with lower prosocial tendencies are less likely to reduce driving behavior . These observations provide a foundation for more extensive studies of adolescent drivers during various driving and contact restrictions and inform future public health campaigns for social distancing . | Post COVID 19 restrictions driving days per week decreased 37 among adolescents. Vehicle miles driven in past two weeks decreased 35 among adolescents. Decrease was lower among ethnic minorities older and employed adolescents. Those with greater dire prosocial tendencies showed greater driving decline. |
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