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S0968090X19305200 | This paper extends a real time Connected Vehicle based traffic signal control algorithm at isolated intersections to balance between two competing intersection objectives efficiency and equity . In this approach a central controller is used to collect real time locations of connected vehicles at regular intervals which can then be used to also identify the existence of some of the non connected vehicles . The control algorithm then aims to optimize the discharge sequence of naturally occurring platoons of vehicles based on their proximity . Specifically the strategy selects the platoon discharge sequenceand associated signal phase and timing planthat minimizes average vehicle delay while limiting the maximum delay any individual vehicle may experience . The latter objective is only possible with detailed vehicle level information available from connected vehicles . The results show that without the threshold on maximum individual vehicle delay average delay is often minimized at the expense of very large delays imposed onto some vehicles . By implementing a threshold both the maximum vehicle delay and the distribution of individual vehicle delaysas measured by the standard deviation and a common measure of population inequity the Gini coefficientcan be improved often with only negligible impacts to intersection efficiency . However the tradeoff between equity and efficiency becomes more significant as the maximum vehicle delay threshold decreases . Sensitivity tests show that this control algorithm works well for different total traffic demands and different demand patterns . The proposed algorithm is also effective under imperfect connected vehicle penetration rates when the connected vehicles make up more than 40 of the traffic stream . The results suggest that the proposed strategy can help significantly reduce long delays and inequitable treatment of vehicles at an intersection when vehicle level information is available to a signal controller . | Real time Connected Vehicle based traffic signal control algorithm. Algorithm seeks to balance between efficiency and equity. Algorithm minimizes average delay while limiting the maximum individual delay. Equity at intersections can be improved with minimum loss of efficiency. |
S0968090X19305418 | This paper presents a machine learning approach to evaluate the performance of aircrafts using on board sensor information on commercially scheduled flights with the aim to further improve system health monitoring strategies in air transportation . Logarithmic multivariate Gaussian models are trained to evaluate the performance of aircrafts at different flight phases separately . By including a forward synchronization feature selection and mini batch training process this model overcomes challenges introduced by the large size and high dimensionality of flight datasets . This framework also addresses the re sampling issue in existing literature causing difficulties in handling time series signals with different lengths . For demonstration and validation the developed model is applied to analyze performance anomalies associated with the mechanical system and pilot operation in a historical flight dataset . Compared with existing literature focusing on similar datasets this evaluation methodology shows promise in detecting performance anomalies especially at approach and takeoff phases . Therefore the developed model is expected to be an effective addition to the current anomaly analysis and monitoring technologies for scheduled commercial flights . Applications include assisting transportation management systems by handling large amounts of historical flight datasets to analyze mechanical and operational anomalies which may potentially improve future aeronautical system design and pilot training . | Scheduled commercial flight monitoring is challenging due to complex flight dataset. Logarithmic multivariate Gaussian model fuses information from on board sensors. Unimodal hypothesis handles time series information without need of re sampling. Mini batch Gaussian learning eliminates the restriction on data size. Mechanical and operational anomalies in an airline flight dataset are analyzed. |
S0968090X19305686 | Connected automated vehicles have been currently considered as promising solutions for realization of envisioned autonomous traffic management systems in the future . CAVs can achieve high desired traffic efficiency and provide safe energy saving and comfortable ride experience for passengers . However in order to practically implement such autonomous systems based on CAVs there exist several significant challenges to be dealt with such as coupled spatiotemporal constraints on CAVs trajectories at unsignalized intersections multiple objectives for trajectory optimization in road segments and heterogeneous decision making behaviors of CAVs in road networks with highly dynamic traffic demand . In this paper we propose a cooperative autonomous traffic organization method for CAVs in multi intersection road networks . The methodological framework consists of threefold components an autonomous crossing strategy based on a conflict resolution approach at unsignalized intersections multi objective trajectory optimization in road segments and a composite strategy for route planning considering heterogeneous decision making behaviors of CAVs based on social and individual benefit respectively . Specifically we first identify a set of potential conflict points of different CAVs spatial trajectories at the intersection and then design different minimum safe time headways to resolve conflicts . Under the constraints of entry and exit conditions at adjacent intersections we propose a multi objective optimal control model by jointly considering vehicle safety energy conservation and ride comfort and then analytically derive a closed form solution for optimizing the CAVs trajectories . Furthermore with the purpose to adapt dynamic traffic demand we propose a composite strategy for route planning by coordinating heterogeneous decision making behaviors of CAVs in road networks . Finally extensive simulation experiments have been performed to validate our proposed method and to demonstrate its advantage over conventional baseline schemes in terms of global traffic efficiency . Additional numerical results are also provided to shed light on the impact of the proportion of CAVs with heterogeneous decision making behaviors on the global system performance . | An effective traffic organization method is proposed for CAVs in road networks. An effective iterative adjustment strategy is used to optimize trajectories. A novel composite road planning strategy is designed for different demands. The proposed method ensures safe efficient energy saving and comfortable trips. |
S0968090X19305868 | Resilience offers a broad social technical framework to deal with breakdown response and recovery of transportation networks adapting to various disruptions . Although current research works model and simulate transportation resilience from different perspectives the real world resilience of urban road network is still unclear . In this paper a novel end to end deep learning framework is proposed to estimate and predict the spatiotemporal patterns of transportation resilience under extreme weather events . Diffusion Graph Convolutional Recurrent Neural Network and a dynamic capturing algorithm of transportation resilience jointly form the backbone of this framework . The presented framework can capture the spatiotemporal dependencies of urban road network and evaluate transportation resilience based on real world big data including on demand ride services data provided by DiDi Chuxing and grid meteorological data . Results show that aggregate data of related precipitation events could be used for transportation resilience modeling under extreme weather events when facing sample imbalance problem due to limited historical disaster data . In terms of observed transportation resilience transportation network demonstrates different characteristics between sparse network and dense network as well as general precipitation events and extreme weather events . The response time is double or triple of the recovery time and an elastic limit exists in the recovery process of network resilience . In terms of resilience prediction the proposed model outperforms competitors by incorporating topological information and has better predictions of the system performance degradation than other resilience indices . The above results could assist researchers and policy makers clearly understand the real world resilience of urban road networks in both theory and practice and take effective responses under emergent disruptive events . | A deep learning framework is proposed to estimate and predict transportation resilience. The framework is built based on diffusion graph convolutional network and real world big data. Transportation resilience demonstrates different characteristics between general precipitation and disaster events. The proposed model outperforms other baseline methods. Directed graph and auxiliary features can further enhance prediction performance. |
S0968090X19306370 | Traffic status detection on arterial roads is challenging because of the complexity of urban traffic and the limited coverage and high deployment cost of traffic detectors . Ubiquitous mobile phones and data generated from the events of these mobile devices provide a promising approach for traffic status detection . This paper proposes a novel approach solely using cellular event data without the cellphone GPS information to detect traffic status on arterial roads . Different from the conventional methods the proposed approach uses features derived only from cellular data to estimate traffic status not requiring any cellphone location information . Both handoff and location update events generated at each cellular station were extracted from the original data to form a candidate feature set . A feature selection method based on joint mutual information was used to select features to cover the maximum information which can resolve issues such as loss of useful information caused by conventional feature selection techniques . A support vector machine algorithm was then employed to model the relationship between the selected features and traffic status . Finally the proposed method was validated by both a field experiment in Taicang China with 1 hour time interval samples and a simulation experiment on VISSIM with 5 minute time interval samples . This study provides a new perspective for traffic status detection which may help design strategies for traffic management and route navigation to improve traveling efficiency especially for the cities lack of traffic surveillance devices . | A feature extraction method from cellular events was proposed. A work to combine and use them for traffic status detection based on cellular events. We presented a traffic status detection method using cellular data. The classification accuracies for proposed model achieves good results. |
S0968090X19306436 | Cities around the world are turning to non motorized transport alternatives to help solve congestion and pollution issues . This paradigm shift demands on new infrastructure that serves and boosts local cycling rates . This creates the need for novel data sources tools and methods that allow us to identify and prioritize locations where to intervene via properly planned cycling infrastructure . Here we define potential demand as the total trips of the population that could be supported by bicycle paths . To that end we use information from a phone based travel demand and the trip distance distribution from bike apps . Next we use percolation theory to prioritize paths with high potential demand that benefit overall connectivity if a bike path would be added . We use Bogot as a case study to demonstrate our methods . The result is a data science framework that informs interventions and improvements to an urban cycling infrastructure . | Planning bike trips with novel data sources. Using percolation theory to optimize cost and maximize global connectivity. Network Science for detecting affinity of trips by income level and further inform local interventions. |
S0968090X19306448 | Network wide traffic forecasting is a critical component of modern intelligent transportation systems for urban traffic management and control . With the rise of artificial intelligence many recent studies attempted to use deep neural networks to extract comprehensive features from traffic networks to enhance prediction performance given the volume and variety of traffic data has been greatly increased . Considering that traffic status on a road segment is highly influenced by the upstream downstream segments and nearby bottlenecks in the traffic network extracting well localized features from these neighboring segments is essential for a traffic prediction model . Although the convolution neural network or graph convolution neural network has been adopted to learn localized features from the complex geometric or topological structure of traffic networks the lack of flexibility in the local feature extraction process is still a big issue . Classical wavelet transform can detect sudden changes and peaks in temporal signals . Analogously when extending to the graph spectral domain graph wavelet can concentrate more on key vertices in the graph and discriminatively extract localized features . In this study to capture the complex spatial temporal dependencies in network wide traffic data we learn the traffic network as a graph and propose a graph wavelet gated recurrent neural network . The graph wavelet is incorporated as a key component for extracting spatial features in the proposed model . A gated recurrent structure is employed to learn temporal dependencies in the sequence data . Comparing to baseline models the proposed model can achieve state of the art prediction performance and training efficiency on two real world datasets . In addition experiments show that the sparsity of graph wavelet weight matrices greatly increases the interpretability of GWGR . | A graph wavelet gated recurrent neural network GWGR is proposed. GWGR incorporating graph wavelet units as gates learns traffic networks as graphs. GWGR achieves better prediction performance with fewer weight parameters. The sparsity of learned weights in GWGR can enhance model interpretability. The learned weights in GWGR can help to identify the key roadway links. |
S0968090X19306461 | With the growing importance of bike sharing systems this paper designs a new framework to solve rebalancing problem . It contains two aspects dynamic rebalancing within each station and static rebalancing among stations . Firstly we give a new flow type task window by defining the consistency index of travelers . It is more suitable as a task window for rebalancing than time type task window based on three aspects analysis . Through three assumptions the temporal distribution learning model including task window and station storage configuration are built to realize new dynamic rebalancing . The spatial distribution learning method is introduced to divide management areas for static rebalancing . The empirical results show that F window can better match the strong time sensitive of demand fluctuation . Compared with traditional rebalancing needs hours each rebalancing within a station can be completed within average 4min . By setting the station storage configuration it makes rebalancing in this paper meets the demand of 28.3 times the hourly rebalancing within one week . And the number of vehicles visiting stations has dropped below 20 . | Introduce the spatial temporal distribution learning method for data cognition. Design a new type of rebalancing scheme with dynamic and static parts respectively. Introduce a new flow type window division which is better than time type window. Give the threshold of rebalancing start up through robustness of complex network. Divide effective management areas for large scale static rebalancing. |
S0968090X19306680 | Vehicular platooning requires an open access environment of vehicle communications to enable cooperating connected automated vehicles to share their travel information . However the open access environment makes the cooperating platoon CAVs vulnerable to cyberattacks causing concerns on traffic safety and mobility . So far due to the lack of analytical models integrating malicious effects of manipulated information into CAV dynamics we have limited knowledge on CAV platoon performance under cyberattack . It necessities a modeling framework to support a comprehensive analysis of the CAV platoon performance in a capricious travel environment . This study seeks to bridge this critical gap from the following three aspects . First we review potential safety related cyberattacks faced by CAVs related to manipulation of vehicle position speed and acceleration . Based on the review these cyberattacks are categorized into three types bogus messages replay delay and collusion attack . Second we develop a generalized vehicle dynamics model that accounts for the cyberattack effects on CAV dynamics . The proposed model labeled cooperative intelligent driver model integrates the dynamic communication topological structure to enable the analysis of effects of manipulated information on CAV dynamics . The dynamic communication structure is a time varying function which is determined by the communication range and the distance between two vehicles . Upon the proposed CIDM the third aspect focuses on simulation analysis of CAV platoon safety and efficiency under cyberattacks which demonstrate the cyberattack effects at the platoon level . This study contributes to the fundamental understanding of CAV platoon dynamics under cyberattacks and lays a foundation to enhance the cybersecurity of CAV systems . | Propose a cooperative platoon vehicles dynamics model to analyze cyberattack effects. Apply a bi layer structure to capture the spread of malicious messages. Incorporate a dynamic communication topology to factor cyberattack effects. Conduct simulation to illustrate the cyberattack effects on CAV platoon dynamics. |
S0968090X19306710 | The combination of self driving technology and taxis has a considerable potential in improving service quality and economic efficiency that can completely change traditional taxi operation . This study accordingly focuses on the autonomous taxi dispatching problem in hybrid request mode where travelers can either request immediate rides or reserve taxi services ahead of time . In this study a centralized dispatcher and decentralized autonomous dispatchers are designed to plan short term and long term routes for aTaxis in real time respectively . The centralized dispatcher integrates vehicle to passenger assignment with empty vehicle rebalance to guarantee solution quality . Decentralized autonomous dispatchers distribute a portion of the calculation to aTaxis thus reducing the centralized dispatchers workload . The dispatching strategy ensures that the response to traveler requests can be made immediately and if such requests are accepted the travelers will receive the expected services . Finally experiments are conducted based on the actual road network and the trip data of Manhattan to investigate the dispatching strategy . The results confirm the high performance of the proposed dispatching strategies in terms of service quality and economic efficiency . It is also found that the increase in reservation rates can enhance the robustness of the method with respect to errors in predicted requests . Moreover when the number of vehicles is adequate and reservations are made longer ahead of time the completion rate of requests and the revenue improve . | Propose a dispatching method to assign autonomous taxis to immediate requests and reservation requests simultaneously. Jointly optimize vehiclepassenger assignment and empty vehicle rebalance. Decentralize part of dispatching calculation to autonomous taxis to improve computational efficiency. Conduct various experiments to test the dispatching method using real world data from Manhattan New York. |
S0968090X19306874 | Drivers approaching lane closures due to roadworks tend to choose a target lane and seek suitable gaps to execute the plan . The plan is however latent or unobserved as the driver may or may not be able to move to the target lane due to the constraints imposed by the surrounding traffic . Hence only the actions of the driver are observed in the trajectory data . This paper analyses such mandatory lane changing behaviour in a roadworks environment in detail with data from a controlled driving simulator experiment and a simple stated preference survey with the same group of participants . While in the former drivers face similar constraints in implementing the plans as in the real world in the simple stated choice survey the same drivers elicit their preferred target lanes without a need to put the plan into action . We contrast the findings from the two sources and also show correlations between the latent plan and stated target components in a latent class model . The results provide new insights into lane changing behaviour that may be useful for example for traffic management purposes . Furthermore using stated choice data potentially reduces the cost of data collection for model development . | Combine driving simulator data with stated choice data. Propose framework for joint model. Separates intentions from actions. |
S0968090X19306898 | With the advent of seemingly unstructured big data and through seamless integration of computation and physical components cyber physical systems provide an innovative way to enhance safety and resiliency of transport infrastructure . This study focuses on real world microscopic driving behavior and its relevance to school zone safety expanding the capability usability and safety of dynamic physical systems through data analytics . Driving behavior and school zone safety is a public health concern . The sequence of instantaneous driving decisions and its variations prior to involvement in safety critical events defined as driving volatility can be a leading indicator of safety . By harnessing unique naturalistic data on more than 41 000 normal crash and near crash events featuring over 9.4 million temporal samples of real world driving a characterization of volatility in microscopic driving decisions is sought at school and non school zone locations . A big data analytic methodology is proposed for quantifying driving volatility in microscopic real world driving decisions . Eight different volatility measures are then linked with detailed event specific characteristics health history driving history experience and other factors to examine crash propensity at school zones . A comprehensive yet fully flexible state of the art generalized mixed logit framework is employed to fully account for distinct yet related methodological issues of scale and random heterogeneity containing multinomial logit random parameter logit scaled logit hierarchical scaled logit and hierarchical generalized mixed logit as special cases . The results reveal that both for school and non school locations drivers exhibited greater intentional volatility prior to safety critical events . Volatility in positive and negative vehicular jerk in longitudinal and lateral directions associates with increases the probability of unsafe outcomes at school zones . A one unit increase in intentional volatility measured by positive vehicular jerk in longitudinal direction associates with a 0.0528 increase in the probability of crash outcome . Importantly the effect of negative vehicular jerk in longitudinal direction on the likelihood of crash outcome is almost double . Methodologically Hierarchical Generalized Mixed Logit model resulted in best fit simultaneously accounting for scale and random heterogeneity . When accounted for separately more parsimonious models accounting for scale heterogeneity performed comparably to the less parsimonious counterparts accounting for random heterogeneity . Importantly even after accounting for random heterogeneity substantial heterogeneity due to a pure scale effect is still observed underscoring the importance of scale effects in influencing the overall contours of variations in the modeled relationships . The study demonstrates the value of observational study design and big data analytics for understanding extreme driving behaviors in safe vs. unsafe driving outcomes . Implications for designing personalized school zone behavioral countermeasures are discussed . | Microscopic driving volatility in school zones is characterized and examined. Correlations between driving volatility and unsafe events are quantified. Over 41 000 naturalistic events featuring over 9.4 million temporal samples are analyzed. Methodological issues of scale and random heterogeneity are captured. Greater volatility in school zones increases likelihood of near crashes crashes. Scale random heterogeneity should be simultaneously captured in naturalistic safety analysis. |
S0968090X1930693X | Transportation systems are being reshaped by ride sourcing and shared mobility services in recent years . The transportation network companies have been collecting high granular ride sourcing vehicle trajectory data over the past decade while it is still unclear how the RV data can improve current dynamic network modeling for network traffic management . This paper proposes to statistically estimate network disequilibrium level namely to what extent the dynamic user equilibrium conditions are deviated in real world networks . Using the data based on RV trajectories we present a novel method to estimate the real world NDL measure . More importantly we present a method to compute zone to zone travel time data from trajectory level RV data . This would become a data sharing scheme for TNCs such that while being used to effectively estimate and reduce NDL the zone to zone data reveals neither personally identifiable information nor trip level business information if shared with the public . In addition we present an NDL based traffic management method to perform user optimal routing on a small fraction of vehicles in the network . The NDL measures and NDL based routing are examined on two real world large scale networks the City of Chengdu with trajectory level RV data and the City of Pittsburgh with zone to zone travel time data . We found that on weekdays in each city NDLs are likely high when travel demand is high . Generally a weekend midnight exhibits higher NDLs than a weekday midnight . Many NDL patterns are different between Chengdu and Pittsburgh which are attributed to unique characteristics of both demand and supply in each city . For instance NDL in Pittsburgh is much more stable from day to day and from hour to hour comparing to Chengdu . In addition we observe that origin destination pairs with high NDLs are spatially and temporally sparse for both cities . For the Pittsburgh network we evaluate the effectiveness of NDL based traffic routing which shows great potential to reduce total travel time with routing a small fraction of vehicles even using dated NDL that is estimated in the prior hour . | Use ride sharing vehicle trajectories data to statistically estimate real world NDL. Compute zone to zone travel time data from trajectory level RV data to estimate NDL. Provide a data sharing scheme that does not real individual or business information. DNL based user optimal traffic routing reduces traffic congestion. Though the NDL pattern varies from day to day its average is stable and interpretable. |
S0968090X19307004 | Departure metering is an airport surface management procedure that limits the number of aircraft in the runway queue by holding aircraft either at a predesigned metering area or at gates . Field tests of the procedure have shown significant fuel savings implying that the procedure can play an important role in the Next Generation Air Transportation System being implemented in the U.S . In this paper we study optimal departure operations at airports in the context of departure metering . More specifically we develop a stochastic dynamic programming framework for tactical management of pushback operations at gates and for determining the optimal number of aircraft to be directed to the runway queue from the metering areas . We introduce four easy to implement departure metering policies and perform comparative analyses between these practical policies and the numerical optimization based policies . In addition from a strategic perspective we identify optimal capacities for metering areas to be used as part of departure metering implementations . Overall we find that the annual fuel and operating savings for airlines could be around 1.7 million if our proposed policies are implemented at the Detroit Metropolitan Wayne County Airport . Such policies can also be adapted by other airports to improve the overall efficiency of surface traffic management and departure operations . | Develop a stochastic dynamic programming model for departure metering operations. Identify optimal departure policies and departure metering area capacity. Introduce easy to implement and near optimal heuristic policies. Propose surface management procedures that can be adapted by other airports. |
S0968090X19307053 | Origin Destination matrix is a tableau of travel demand distributed between different zonal pairs . Essentially OD matrix provides two types of information the individual cell value represents travel demand between a specific OD pair and group of OD pairs provides insights into structural information in terms of distribution pattern of OD flows . Comparison of OD matrices should account both types of information . Limited studies in the past developed structural similarity measures and most studies still depend on traditional measures for OD matrices comparison . Traditional performance measures are based on cell by cell comparison and often neglect OD matrix structural information within their formulations . | OD matrix structure is defined as its skeletal framework. The OD flows corresponding to the skeleton is termed as mass. A holistic comparison of OD matrices should include its structure and mass. Normalised Levenshtein distance for OD matrices NLOD is proposed for the structural comparison. NLOD satisfies the mathematical properties of a distance measure. |
S0968090X19307442 | The traffic control of an arbitrary network of signalized intersections is considered . This work presents a new version of the recently proposed max pressure controller also known as back pressure . The most remarkable features of the max pressure algorithm for traffic signal control are scalability stability and distribution . The modified version presented in this paper improves the practical applicability of the max pressure controller by considering as input travel times instead of queue lengths . The two main practical advantages of this new version are travel times are easier to estimate than queue lengths and max pressure controller based on travel times has an inherent capacity aware property i.e . it takes into account the finite capacity of each link . Travel time tends to diverge when the queue length is close to its capacity . It should be noted that previous max pressure algorithms rely exclusively on queue length measurements which may be difficult to accomplish in practice . Moreover these previous algorithms generally assume queues with unbounded capacity . This may be problematic because a model with unbounded capacity links is not able to reproduce spillbacks which are one of the most critical phenomena that a traffic signal controller should avoid . After presenting the new version of the max pressure controller it is analyzed and compared with existing control policies in a microscopic traffic simulator . Moreover results of a real implementation of the developed algorithm to a signalized intersection located at an urban arterial in Jerusalem are shown and analyzed . To the best of the authors knowledge this experiment is the first real implementation of a max pressure controller at a signalized intersection . | Developing a max pressure traffic controller based on travel times. Investigating work conservative property in the developed max pressure traffic controller. Comparison analysis with other traffic controllers via microsimulation test study. Real implementation of the travel time max pressure controller to a signalized intersection. |
S0968090X19307521 | The ability to obtain accurate estimates of city wide urban traffic patterns is essential for the development of effective intelligent transportation systems and the efficient operation of smart mobility platforms . This paper focuses on the network wide traffic speed estimation using trajectory data generated by a city wide fleet of ride sourcing vehicles equipped with GPS capable smartphones . A cell based map matching technique is proposed to link vehicle trajectories with road geometries and to produce network wide spatio temporal speed matrices . Data limitations are addressed using the Schatten | Address efficient map matching of large scale GPS traces massive ride sourcing GPS traces. Adopt Schatten p norm matrix completion algorithm to estimate missing traffic information at the urban network level. Estimate traffic speed based on recovered speed matrix on a real world large scale network. Monitor and visualize traffic dynamics via stochastic congestion maps. Has the potential to be applied in proactive congestion identification and mitigation strategies. |
S0968090X19307648 | Being one of the most promising applications enabled by connected and automated vehicles technology Cooperative Adaptive Cruise Control is expected to be deployed in the near term on public roads . Thus far the majority of the CACC studies have been focusing on the overall network performance with limited insights on the potential impacts of CAVs on human driven vehicles . This paper aims to quantify such impacts by studying the high resolution vehicle trajectory data that are obtained from microscopic simulation . Two platoon clustering strategies for CACC an ad hoc coordination strategy and a local coordination strategy are implemented . Results show that the local coordination outperforms the ad hoc coordination across all tested market penetration rates in terms of network throughput and productivity . According to the two sample Kolmogorov Smirnov test however the distributions of the hard braking events for HVs change significantly under local coordination strategy . For both of the clustering strategy CAVs increase the average lane change frequency for HVs . The break even point for average lane change frequency between the two strategies is observed at 30 MPR which decreases from 5.42 to 5.38 per vehicle . The average lane change frequency following a monotonically increasing pattern in response to MPR and it reaches the highest 5.48 per vehicle at 40 MPR . Lastly the interaction state of the car following model for HVs is analyzed . It is revealed that the composition of the interaction state could be influenced by CAVs as well . One of the apparent trends is that the time spent on approaching state declines with the increasing presence of CAVs . | Compare clustering strategies for CACC vehicles in mixed traffic conditions. Clustering strategy impacts on human driven vehicles at a vehicle trajectory level. Heterogeneous traffic flow characteristics with both CACC and human driven vehicles. |
S0968090X19307661 | The Macroscopic Fundamental Diagram describes the relation of average network flow density and speed in urban networks . It can be estimated based on empirical or simulation data or approximated analytically . Two main analytical approximation methods to derive the MFD for arterial roads and urban networks exist at the moment . These are the method of cuts and related approaches as well as the stochastic approximation . This paper systematically evaluates these methods including their most recent advancements for the case of an urban arterial MFD . Both approaches are evaluated based on a traffic data set for a segment of an arterial in the city of Munich Germany . This data set includes loop detector and signal data for a typical working day . It is found that the deterministic MoC finds a more accurate upper bound for the MFD for the studied case . The estimation error of the stochastic method is about three times higher than the one of the deterministic method . However the SA outperforms the MoC in approximating the free flow branch of the MFD . The analysis of the discrepancies between the empirical and the analytical MFDs includes an investigation of the measurement bias and an in depth sensitivity study of signal control and public transport operation related input parameters . This study is conducted as a Monte Carlo Simulation based on a Latin Hypercube sampling . Interestingly it is found that applying the MoC for a high number of feasible green to cycle ratios predicts the empirical MFD well . Overall it is concluded that the availability of signal data can improve the analytical approximation of the MFD even for a highly inhomogeneous arterial . | Compares two analytical MFD approximation methods to empirical data. Studies the impact of signal control and public transport related model parameters. The method of cuts estimates a more accurate arterial capacity. The stochastic approximation accurately predicts the free flow branch of the empirical MFD. |
S0968090X19307764 | In air traffic management research aircraft performance models are often used to generate and analyze aircraft trajectories . Although a crucial part of the aircraft performance model the aerodynamic property of aircraft is rarely available for public research purposes as it is protected by aircraft manufacturers for commercial reasons . In many studies a simplified quadratic drag polar model is assumed to compute the drag of an aircraft based on the required lift . In this paper using surveillance data we take on the challenge of estimating the drag polar coefficients based on a novel stochastic total energy model that employs Bayesian computing . The method is based on a stochastic hierarchical modeling approach which is made possible given accurate open aircraft surveillance data and additional analytical models from the literature . Using this proposed method the drag polar models for 20 of the most common aircraft types are estimated and summarized . By combining additional data from the literature we propose additional methods allowing aircraft total drag to be calculated under other configurations such as when flaps and landing gears are deployed . We also include additional models allowing the calculation of wave drag caused by compressibility at high Mach number . Though uncertainties exist it has been found that the estimated drag polars agree with existing models as well as CFD simulation results . The trajectory data performance models and results related to this study are shared publicly . | An estimation of aircraft drag polar using open aircraft surveillance data is presented. A hierarchical stochastic total energy model is proposed for aircraft performance state estimation. The Markov Chain Monte Carlo method is introduced to solve the estimation problem. Drag polar for 20 common aircraft types are obtained and publicly shared. |
S0968090X19307818 | The development of technologies related to connected and automated vehicles allows for a new approach to collect vehicle trajectory . However trajectory data collected in this way represent only sampled traffic flow owing to low penetration rates of CAVs and privacy concerns and fail to provide a comprehensive picture of traffic flow . This study proposes a method to reconstruct vehicle trajectories in fully sampled traffic flow on freeways that consists of human driven vehicles and CAVs by using the mobile sensing data acquired from CAVs . The expected behavior of vehicles within the detection range of CAVs is determined based on the driving state classified by the Wiedemann model i.e . free driving emergency closing and following . If the actual behavior is different from the expected it is deemed to be influenced by the undetected HVs . Then new HVs are inserted based on the estimated local traffic density and speed of the freeway . The trajectories of the inserted HVs are further reconstructed by using the established update rules of cellular automation i.e . uniform motion acceleration deceleration randomization and position update . Last the proposed method was examined by simulation experiments under different traffic densities and PRs of CAVs . The results show that the trajectories of fully sampled mixed traffic flow can be reconstructed reasonably well not only under traffic conditions without explicit congestion but in congested environments . | Reconstruct vehicle trajectories on freeways mixed with CAVs and HVs. Determine whether there are HVs in the undetected range of CAVs. Estimate the initial insertion position and speed of the inserted HVs. Estimate the trajectories of the inserted HVs using cellular automation. Examine the method under different traffic densities and PRs of CAVs. |
S0968090X19308009 | Recently deep learning models have shown promising performances in many research areas including traffic states prediction due to their ability to model complex nonlinear relationships . However deep learning models also have drawbacks that make them less preferable for certain short term traffic prediction applications . For example they require a large amount of data for model training which is also computationally expensive . Moreover deep learning models lack interpretability of the results . This paper develops a short term traffic states forecasting algorithm based on partial least square to help enhance real time decision making and build better insights into traffic data . The proposed model is capable of predicting short term traffic states accurately and efficiently by capturing dominant spatiotemporal features and day to day variations from collinear and correlated traffic data . Three case studies are developed to demonstrate the proposed model in short term traffic prediction applications . | A PLS based model is proposed for short term traffic state prediction. PLS can produce comparable results to deep learning models in a more efficient manner. PLS model can learn day to day variations and spatial dependencies of traffic states. PLS model is validated by real world traffic data. |
S0968090X19308010 | This paper presents scalable traffic stability analysis for both pure connected and autonomous vehicle traffic and mixed traffic based on continuum traffic flow models . Human drive vehicles are modeled by a non equilibrium traffic flow model i.e . Aw Rascle Zhang to capture HDV traffic s unstable nature . CAVs are modeled by a mean field game describing their non cooperative behaviors as rational utility optimizing agents . Working with continuum models helps avoiding scalability issues in microscopic multi class traffic models . We demonstrate from linear stability analysis that the mean field game traffic flow model behaves differently from traditional traffic flow models and stability can only be proved when the total density is in a certain regime . We also show from numerical experiments that CAVs help stabilize mixed traffic . Further we quantify the impact of CAV s penetration rate and controller design on traffic stability . The results may provide qualitative insights on traffic stability in mixed autonomy for human drivers and city planners . The results also provide suggestions on CAV controller design for CAV manufacturers . | Develops continuum traffic flow models for mixed autonomous traffic based on mean field games. Provides linear stability analysis to pure AV mean field game which behaves differently from traditional traffic flow model. Investigates mixed traffic stability from numerical experiments and quantifies AVs stabilizing effects with respect to its penetration rate and controller design. |
S0968090X19308095 | This paper proposes a stochastic model for mixed traffic flow with human driven vehicles and automated vehicles . The model is formulated in Lagrangian coordinates considering the heterogeneous behavior of human drivers . We further derive a first and second order approximation of the stochastic model describing the mean and the covariance dynamics respectively under different combinations of HVs and AVs in the traffic stream . The proposed model allows us to explicitly investigate the interaction between AVs and HVs considering the uncertainty of human driving behavior . Six performance metrics are proposed to measure the impact of AVs on the uncertainty of HVs behavior as well as on the stability of the system . The numerical experiment results show that AVs have significant impact on the uncertainty and stability of the mixed traffic flow system . Larger AV penetration rates can reduce the uncertainty inherent in HV behavior and improve the stability of the mixed flow substantially . Whereas AVs reaction time only has subtle impact on the uncertainty of the mixed stream as well as the position of AVs in the traffic stream has marginal influence in terms of reducing uncertainty and improving stability . | A stochastic Lagrangian model for the mixed traffic with Human driven vehicles and automated vehicles is proposed. Six performance metrics are proposed to measure uncertainty and instability of the mixed traffic. The AV penetration rate has significant impact on the uncertainty and stability of the mixed traffic. The performance metrics in terms of the speed provide more consistent results compared with the spacing. |
S0968090X19308332 | With the rapid development of intelligent vehicles drivers are increasingly likely to share their control authorities with the intelligent control unit . For building an efficient Advanced Driver Assistance Systems and shared control systems the vehicle needs to understand the drivers intent and their activities to generate assistant and collaborative control strategies . In this study a driver intention inference system that focuses on the highway lane change maneuvers is proposed . First a high level driver intention mechanism and framework are introduced . Then a vision based intention inference system is proposed which captures the multi modal signals based on multiple low cost cameras and the VBOX vehicle data acquisition system . A novel ensemble bi directional recurrent neural network model with Long Short Term Memory units is proposed to deal with the time series driving sequence and the temporal behavioral patterns . Naturalistic highway driving data that consists of lane keeping left and right lane change maneuvers are collected and used for model construction and evaluation . Furthermore the driver s pre maneuver activities are statistically analyzed . It is found that for situation aware drivers usually check the mirrors for more than six seconds before they initiate the lane change maneuver and the time interval between steering the handwheel and crossing the lane is about 2s on average . Finally hypothesis testing is conducted to show the significant improvement of the proposed algorithm over existing ones . With five fold cross validation the EBiLSTM model achieves an average accuracy of 96.1 for the intention that is inferred 0.5s before the maneuver starts . | An ensemble Bi directional LSTM model is proposed for driver intention inference. Driver lane change intention can be precisely predicted before the acutal maneuver. Driver lane change behavior and critical moments are statistically analyzed. The proposed EBiRNN method show advantages over conventional methods. Different prediction horizons and real time inference results are analyzed. |
S0968090X19308356 | High fidelity infrastructure performance models are critical for transportation planning agencies to develop cost effective and sustainable resource allocation policies . This paper presents a new iterative methods approach to estimate infrastructure performance models based on sampling theory . The model addresses the issue around measurement uncertainty underlying infrastructure condition assessments for continuous distress indicators and its effect on the parametric models underlying decision support tools . Through a case study of pavement roughness data collected as part of FHWAs long term pavement performance program the new approach reduces the unexplained variance that would typically enter decision support tools by 14 . It also addresses concerns around heteroscedasticity surrounding conventional methods allowing modelers to recover efficiency in their statistical estimates . The proposed methodology is of particular significance for decision makers and stakeholders evaluating infrastructure distress data subject to considerable uncertainty . The contributions of this research will allow transportation agencies to integrate improved performance models within their asset management frameworks . | Novel iterative reweighted least squares approach to model infrastructure performance. Algorithm designed for continuous condition panel data with measurement uncertainty. Model reduces variance for probabilistic roughness model by 14 . Case study results closely align with sampling theory. |
S0968090X19308988 | Autonomous Vehicles are bringing challenges and opportunities to urban traffic systems . One of the crucial challenges for traffic managers and local authorities is to understand the nonlinear change in road capacity with increasing AV penetration rate and to efficiently reallocate the Right of Way for the mixed flow of AVs and Human Driven Vehicles . Most of the existing research suggests that road capacity will significantly increase at high AV penetration rates or an all AV scenario when AVs are able to drive with smaller headways to the leading vehicle . However this increase in road capacity might not be significant at a lower AV penetration rate due to the heterogeneity between AVs and HDVs . In order to investigate the impacts of mixed flow conditions this paper firstly proposes a theoretical model to demonstrate that road capacity can be increased with proper RoW reallocation . Secondly four different RoW reallocation strategies are compared using a SUMO simulation to cross validate the results in a numerical analysis . A range of scenarios with different AV penetration rates and traffic demands are used . The results show that road capacity on a two lane road can be significantly improved with appropriate RoW reallocation strategies at low or medium AV penetration rates compared with the do nothing RoW strategy . | Capacity of mixed flow has been proven to increase convexly with AV penetration rate. This property demonstrates road capacity can be increased with RoW reallocation. Crucial points of RoW strategies are identified quantitatively for policymakers. CCapacity can still be increased with RoW reallocation when AVs areworsethan HDVs. |
S0968090X19309015 | In the literature networks are usually assumed to operate at their steady states in static traffic assignment models . Given a set of demand management and supply regulation strategies whether the vehicular traffic on a network would approach or not to a steady state under certain assumptions of behavioral realism is of importance and great interest in practice . Moreover one primary reason for understanding the long term traffic evolution dynamics is to control or influence the system trajectory in an efficient manner . To address these problems we focus on a dynamic feedback control design and its subsequent steady state analysis for a class of day to day disequilibrium process so that the network traffic would evolve towards the desired stable traffic equilibrium . We propose an interconnected dynamic system framework for the stability and convergence analysis of DTD traffic adjustment process under the dynamic feedback control . Distinguished from the existing studies we introduce a notion of output stability as well as the Lyapunov like conditions to carry out the investigation noting that the steady states of the dynamic tolls that correspond to the Lagrange multipliers of the traffic equilibrium problem can be non unique . The proposed method gives rise to a new approach to selecting pricing strategy . The proposed method involves only two adjustment parameters with well identified physical meanings while the requirement on adjustment parameters for convergence and stability is mild . Finally several numerical examples are presented to illustrate the insightful findings . It is found that with dynamic feedback control the network traffic state can converge to the desired equilibrium in a much faster manner than the case without control . The dynamic feedback controller can be implemented as demand management and supply regulation strategies such as road pricing and signal control . | Dynamic feedback control for a class of day to day traffic adjustment processes. Demand management and supply regulations are considered. Only two adjustment parameters with well identified physical meanings are involved. The requirement on adjustment parameters for convergence and stability is mild. Novel output stability notion. |
S0968090X19309027 | Travel information has the potential to influence travellers choices in order to steer travellers to less congested routes and alleviate congestion . This paper investigates on the one hand how travel information affects route choice behaviour and on the other hand the impact of the travel time representation on the interpretation of parameter estimates and prediction accuracy . To this end we estimate recursive models using data from an innovative data collection effort consisting of route choice observation data from GPS trackers travel diaries and link travel times on the overall network . Though such combined data sets exist these have not yet been used to investigate route choice behaviour . A dynamic network in which travel times change over time has been used for the estimation of both recursive logit and nested models . Prediction and estimation results are compared to those obtained for a static network . The interpretation of parameter estimates and prediction accuracy differ substantially between dynamic and static networks as well as between models with correlated and uncorrelated utilities . Contrary to the static results for the dynamic where travel times are modelled more accurately travel information does not have a significant impact on route choice behaviour . However having travel information increases the travel comfort as interviews with participants have shown . | Combination of data on travel times information route choice and travel diaries. Analysis of travel time representation and perception on estimation and prediction. Interpretation of parameter estimates based on dynamic representation of travel time. Accurate predictions for both static and dynamic models using correlated utilities. Interpretation of parameter estimates differ for static and dynamic travel time representations. |
S0968090X19309155 | With the development of wireless charging technology charging while driving now becomes possible . In this paper we propose a model to optimize the location of the wireless charging lanes by taking into account their effects on road capacity and travelers route choice . The model is formulated as a nonlinear programming problem and solved by a linearization method . A new method is developed to reduce the maximum error in the linearization process . The model and solution method are tested on Nguyen Dupuis and Sioux Falls networks . The numerical results show that relatively high charging power and reasonable budget are necessary to recover the investment when charging lanes are deployed on road network . It is also demonstrated that the impacts of WCL on road capacity drop and travelers route choice are not negligible and should be considered in the determination of WCL location . | Propose a wireless charging lane WCL location model considering the WCL adverse effect. Linearize the model with minimum error. Test the model and solution method on two networks. Validate the necessity of considering the adverse effect of WCL. |
S0968090X19309350 | The approach taken by the second place winner of the TRANSFOR prediction challenge is presented . The challenge involves forecasting travel speeds on two arterial links in Xian City in China for two five hour periods on a single day . Travel speeds are measured from trajectory information on probe vehicles from a fleet of vehicles for a large sub area of the city . After experimenting with several deep learning methods we settle on a simple non parametric kernel regression approach . The method borrowed from previous work in fixed route transit predictions formalizes the intuition that in urban systems most failure patterns are recurrent . Our choice is supported by test results where the method outperformed all evaluated neural architectures . The results suggest simple methods are very competitive particularly considering the high lifecycle cost of deep learning models . | Travel speed forecasting using non parametric models. Outperforms standard deep learning architectures such as LSTMs stacked autoencoders MLP and other machine learning models. Accounting for lifecycle costs of models simpler approaches remain competitive. |
S0968090X19309362 | The distractive effects of mobile phones are well documented but the recent development of mobile phone apps that provide speed advisory warnings raises the possibility that this technology may be used to improve driver safety in older vehicles . We examined the effects of an intelligent speed advisory app on driving performance in a simulator . One hundred and four participants were allocated to complete the drive with the ISA app in one of five modes active audio visual active visual passive audio visual passive visual or control . Another 19 participants completed the study wearing eye tracking glasses . Participants drove a simulated 26.4km section of rural road which incorporated typical hazards and three speed compliance zones . The app led to good compliance with the posted speed limits particularly during the 60km h road segment where the control group drove at significantly higher speeds than the groups with the ISA app . No significant differences between the four versions of the ISA app were observed either for speed compliance or the number of speeding alerts received . Across the entire simulated drive there were relatively few glances at the app with an average glance duration of 190ms . | We examined the effects of an intelligent speed advisory app ISA on driving performance in a simulator. The ISA app led to good speed compliance particularly in lower speed zones. The ISA app did not lead to any negative effects on driving performance. When properly configured ISA apps have a safety benefit and do not distract drivers. |
S0968090X19309416 | Travel mode identification is among the key problems in transportation research . With the gradual and rapid adoption of GPS enabled smart devices in modern society this task greatly benefits from the massive volume of GPS trajectories generated . However existing identification approaches heavily rely on manual annotation of these trajectories with their accurate travel mode information which is both economically inefficient and error prone . In this work we propose a novel semi supervised deep ensemble learning approach for travel mode identification to use a minimal number of annotated data for the task . The proposed approach accepts GPS trajectories of arbitrary lengths and extracts their latent information with a tailor made feature engineering process . We devise a new deep neural network architecture to establish the mapping from this latent information domain to the final travel mode domain . An ensemble is accordingly constructed to develop proxy labels for unannotated data based on the rare annotated ones so that both types of data contribute to the learning process . Comprehensive case studies are conducted to assess the performance of the proposed approach which notably outperforms existing ones with partially labeled training data . Furthermore we investigate its robustness to noisy data and the effectiveness of its constituting components . | We propose a new DNN architecture for travel mode identification. We propose a proxy label based semi supervised learning algorithm. We conduct a series of comprehensive case studies to illustrate the performance. |
S0968090X19309568 | Recently there have been significant developments and applications in the field of unmanned aerial vehicles . In a few years these applications will be fully integrated into our lives . The practical application and use of UAVs presents several problems that are of a different nature to the specific technology of the components involved . Among them the most relevant problem deriving from the use of UAVs in logistics distribution tasks is the so called last mile delivery . | Truck drone delivery problem coded as a sequence. Mathematical modelling of a tandem formed by one truck and one drone. Battery capacity constraints and multiple customers visits per flight allowed. Design of a heuristic approach to solve the problem. Performance of the heuristic approach outperforms the exact resolution. |
S0968090X1930957X | Bridges are critical components of highways ensuring traffic can efficiently travel over obstructions such as bodies of water valleys and other roads . Ensuring bridges are in sound structural condition is essential for safe and efficient highway operations . Structural health monitoring systems designed to measure bridge responses have been developed to quantitatively track the health of bridges . More recently SHM systems have also begun to integrate measurement of vehicular loads that create the responses measured . However precise correlation of traffic loads to bridge responses remains a costly and technically difficult strategy . To address existing technical limitations a cyber physical system framework is proposed to track truck loads in a highway corridor to trigger SHM systems to record bridge responses and to automate the linking of bridge response data with truck weights collected by weigh in motion stations installed along the corridor but not collocated with the bridges . To link truck weights to bridge responses computer vision methods based on convolutional neural networks are used to automate the detection and reidentification of trucks using traffic cameras . The single stage CNN object detector YOLO is trained using a customized dataset to identify trucks from camera images at each instrumentation site high precision is obtained with the YOLO detector exceeding 95 average precision for an intersection over union threshold of 0.75 . To reidentify the same truck at different locations in the corridor this study adopts a CNN based encoder trained via a triplet network and a mutual nearest neighbor strategy using feature vectors extracted from images at each measurement location . The proposed reidentification method is implemented in the CPS cloud environment and obtains a F1 score of 0.97 . The study also explores the triggering of bridge monitoring systems based on visual detection of trucks by a traffic camera installed upstream to the bridges . The triggering strategy proves to be highly efficient with 99 of the triggered data collection cycles capturing truck events at each bridge . To validate the CPS architecture is implemented on a 20 mile highway corridor that has a WIM station already installed four traffic cameras and two bridge SHM systems are installed along the corridor and integrated with a CPS architecture hosted on the cloud . In total over 10 000 trucks are observed at all measurement locations over one year allowing peak bridge responses to be correlated to both measured truck weights and to one another . | Cyber physical system architecture created to link truck loads to bridge responses. Measured truck loads linked to bridge responses create input output data for SHM. Highway truck loads tracked using deep learning applied to traffic camera feeds. Bridge monitoring triggered by detecting trucks in real time using CNN algorithms. |
S0968090X19309611 | Efforts devoted to mitigate the effects of road traffic congestion have been conducted since 1970s . Nowadays there is a need for prominent solutions capable of mining information from messy and multidimensional road traffic data sets with few modeling constraints . In that sense we propose a unique and versatile model to address different major challenges of traffic forecasting in an unsupervised manner . We formulate the road traffic forecasting problem as a latent variable model assuming that traffic data is not generated randomly but from a latent space with fewer dimensions containing the underlying characteristics of traffic . We solve the problem by proposing a variational autoencoder model to learn how traffic data are generated and inferred while validating it against three different real world traffic data sets . Under this framework we propose an online unsupervised imputation method for unobserved traffic data with missing values . Additionally taking advantage of the low dimension latent space learned we compress the traffic data before applying a prediction model obtaining improvements in the forecasting accuracy . Finally given that the model not only learns useful forecasting features but also meaningful characteristics we explore the latent space as a tool for model and data selection and traffic anomaly detection from the point of view of traffic modelers . | We learn a subspace that contains the underlying characteristics of traffic data. A generative model imputes missing values online and unsupervised. It reduces data dimensions to improve the efficiency and accuracy of forecasting systems. Using the subspace we perform model and data selection and anomaly detection. |
S0968090X19309623 | With the development of vehicle to infrastructure and vehicle to vehicle technologies vehicles will be able to communicate with the controller at the intersection . Autonomous driving technology enables vehicles to follow the instructions sent from the controller precisely . Autonomous intersection management considers each vehicle as an agent and coordinates vehicle trajectories to resolve vehicle conflicts inside an intersection . This study proposes an autonomous intersection management algorithm called AIM | Propose an autonomous intersection management algorithm with the max pressure control considering pedestrians. Prove the proposed algorithm can achieve the optimal throughput for the combined vehicle pedestrian flow. Show the efficiency of pedestrians and vehicles are negatively correlated at the intersection in simulations. |
S0968090X19309817 | Long term passenger subscription is vital to the survival and operation of the customized bus system which is a demand driven and user oriented transit service . A better understanding of passenger loyalty toward the CB service will help provide better operation . The urgent and outstanding issue is how to incorporate the unobserved heterogeneity in loyaltyin other words how to reflect the effects of the frailty to terminate subscription . This study fills the research gap through an empirical study in Dalian China . Three different survival models are developed to investigate the mechanism of subscription behaviors among which the shared frailty model considering the unobserved heterogeneity is demonstrated to be the most appropriate . The results indicate that the historical purchase characteristics are the most important to CB user loyalty modeling and forecasting . Males are more sensitive than females to the number of intermediate stations because of the potentially increased uncertainty in waiting time related to the intermediate stations . The heterogenous frailties resulting from the heterogeneity of the perceptible service quality in terms of convenience and efficiency in subscribing returning tickets and information availability in the progress of the CB system significantly contribute to user loyalty deviations . | The loyalty to customized bus is examined from the viewpoint of subscriber survival analysis. Mechanism of continuous subscription behaviors was investigated. Unobserved heterogeneity of service quality leads to user loyalty deviation. Rail transit availability affects user loyalty by changing the perceptions on travel speeds. |
S0968090X19309842 | Most perimeter control methods in literature are the model based schemes designing the controller based on the available accurate macroscopic fundamental diagram function with well known techniques of modern control methods . However accurate modeling of the traffic flow system is hard and time consuming . On the other hand macroscopic traffic flow patterns show heavily similarity between days and data from past days might enable improving the performance of the perimeter controller . Motivated by this observation a model free adaptive iterative learning perimeter control scheme is proposed in this paper . The three features of this method are No dynamical model is required in the controller design by virtue of dynamic linearization data modeling technique i.e . it is a data driven method the perimeter controller performance will improve iteratively with the help of the repetitive operation pattern of the traffic system the learning gain is tuned adaptively along the iterative axis . The effectiveness of the proposed scheme is tested comparing with various control methods for a multi region traffic network considering modeling errors measurement noise demand variations and time changing MFDs . Simulation results show that the proposed MFAILPC presents a great potential and is more resilient against errors than the standard perimeter control methods such as model predictive control proportional integral control etc . | An iterative form dynamic linearization description is extended. A model free adaptive iterative learning perimeter control strategy is proposed. A comprehensive comparison is conducted with typical perimeter control strategies. |
S0968090X19310022 | Although automatically collected human travel records can accurately capture the time and location of human movements they do not directly explain the hidden semantic structures behind the data e.g . activity types . This work proposes a probabilistic topic model adapted from Latent Dirichlet Allocation to discover representative and interpretable activity categorization from individual level spatiotemporal data in an unsupervised manner . Specifically the activity travel episodes of an individual user are treated as words in a document and each topic is a distribution over space and time that corresponds to certain type of activity . The model accounts for a mixture of discrete and continuous attributesthe location start time of day start day of week and duration of each activity episode . The proposed methodology is demonstrated using pseudonymized transit smart card data from London U.K . The results show that the model can successfully distinguish the three most basic types of activitieshome work and other . As the specified number of activity categories increases more specific subpatterns for home and work emerge and both the goodness of fit and predictive performance for travel behavior improve . This work makes it possible to enrich human mobility data with representative and interpretable activity patterns without relying on predefined activity categories or heuristic rules . | The paper develops a spatiotemporal topic model for human activity discovery. Each topic is a distribution over space and time that corresponds to an activity. The model accounts for a mixture of discrete and continuous travel attributes. The model fits the data significantly better than heuristic approaches. The number of topics controls the granularity of discovered activity patterns. |
S0968090X19310125 | Macroscopic pedestrian models are theoretically simpler than microscopic models and they can potentially be solved faster while producing reasonable predictions of crowd dynamics . Therefore they can be very useful for applications such as large scale simulation real time state estimation and crowd management . However the numerical methods presently used to solve macroscopic pedestrian models which are mostly grid based have some shortcomings that limit their applicability . More specifically they usually include complex procedures for grid generation and remeshing and they produce simulation results that may not be sufficiently accurate . Smoothed Particle Hydrodynamics constitutes an alternative numerical method that could potentially overcome these limitations . SPH is a meshfree method where a crowd is represented by a set of particles that possess material properties and move according to macroscopic laws . Relevant state variables at each particle are approximated using information about the material properties of the neighboring particles and a smoothing function . This paper puts forward for the first time a generic SPH framework for solving macroscopic pedestrian models in addition it demonstrates that an SPH based simulation model can produce meaningful and accurate results by means of three case studies . The first case study shows that the proposed numerical method can approximate well the analytical solution of a simple macroscopic model applied to a queue discharge scenario . The second case study demonstrates that the proposed numerical method can potentially reproduce density dispersion more accurately than grid based methods due to its meshfree Lagrangian and particle based nature . The third case study highlights the need to reformulate the acceleration equation of the basic macroscopic model in order to reproduce lane formation in bi directional flows using the proposed SPH framework and this paper presents a solution to do so . | A generic meshfree SPH framework is proposed for macroscopic crowd simulation. This framework is applied to solve a specific macroscopic pedestrian flow model. The SPH method is numerically accurate a sensitivity analysis elicits its mathematical insight. A comparison study demonstrates potential advantages of SPH over grid based methods. The method can reproduce key phenomena observed in pedestrian flows. |
S0968090X19310241 | Electric propulsion for commuter air transportation is a promising technology because of significant strides in battery specific energy and motor specific power . Energy storage and rapid battery recharge remain nonetheless challenging owing to the significant energy and power requirements of even small aircraft . By modifying algorithms developed in the field of scheduling theory we propose | Sizing of battery swap and recharge infrastructure for electric aircraft operations. Machine scheduling implemented using network flow model to schedule recharges. Power optimized strategy to minimize peak power draw and electricity costs. Power investment optimized strategy to minimize capital and operating expenditures. Significant reductions in peak power and electricity prices are obtained. |
S0968090X19310320 | The new era of sharing information and big data has raised our expectations to make mobility more predictable and controllable through a better utilization of data and existing resources . The realization of these opportunities requires going beyond the existing traditional ways of collecting traffic data that are based either on fixed location sensors or GPS devices with low spatial coverage or penetration rates and significant measurement errors especially in congested urban areas . Unmanned Aerial Systems or simply drones have been proposed as a pioneering tool of the Intelligent Transportation Systems infrastructure due to their unique characteristics but various challenges have kept these efforts only at a small size . This paper describes the system architecture and preliminary results of a first of its kind experiment nicknamed | Designing one of a kind experiment to monitor urban congestion with a swarm of drones. Creating the most complete urban multimodal dataset nicknamed. to study congestion. Investigating traffic phenomena at different scales of modeling. Developing an open science initiative with almost half a million trajectories for transportation oriented research. |
S0968090X19310332 | With growing consumer demand and expectations companies are attempting to achieve cost efficient and faster delivery operations . The integration of autonomous vehicles such as drones in the last mile network design can curtail many operational challenges and provide a competitive advantage . This paper deals with the problem of delivering orders to a set of customer locations using multiple drones that operate in conjunction with a single truck . To take advantage of the drone fleet the delivery tasks are parallelized by concurrently dispatching the drones from a truck parked at a focal point to the nearby customer locations . Hence the key decisions to be optimized are the partitioning of delivery locations into small clusters identifying a focal point per cluster and routing the truck through all focal points such that the customer orders in each cluster are fulfilled either by a drone or truck . In contrast to prior studies that tackle this problem using multi phase sequential procedures this paper presents mathematical programming models to jointly optimize all the decisions involved . We also consider two polices for choosing a cluster focal point restricting it to one of the customer locations and allowing it to be anywhere in the delivery area . Since the models considering unrestricted focal points are computationally expensive an unsupervised machine learning based heuristic algorithm is proposed to accelerate the solution time . Initially we treat the problem as a single objective by independently minimizing either the total cost or delivery completion time . Subsequently the two conflicting objectives are considered together for obtaining the set of best trade off solutions . An extensive computational study is conducted to investigate the impacts of restricting the focal points and the influence of adopting a joint optimization method instead of a sequential approach . Finally several key insights are obtained to aid the logistics practitioners in decision making . | New optimization problem for last mile delivery using multiple drones and a truck. Integrated approach for customer location clustering and truck drone routing. Mathematical models for minimizing cost and delivery completion time are provided. Truck stops for drone dispatch can be a customer or non customer location. Efficient machine learning based heuristic proposed to accelerate solution time. |
S0968090X19310381 | Whereas deep neural network is increasingly applied to choice analysis it is challenging to reconcile domain specific behavioral knowledge with generic purpose DNN to improve DNNs interpretability and predictive power and to identify effective regularization methods for specific tasks . To address these challenges this study demonstrates the use of behavioral knowledge for designing a particular DNN architecture with alternative specific utility functions and thereby improving both the predictive power and interpretability . Unlike a fully connected DNN which computes the utility value of an alternative | Use behavioral knowledge to design a new DNN architecture with alternative specific utility ASU DNN . ASU DNN provides a more regular substitution pattern of travel mode choices. ASU DNN improves both the predictive power and interpretability. Behavioral knowledge can function as an effective domain knowledge based regularization. |
S0968090X19310630 | This manuscript is focused on transit smart card data and finds that the release of such trajectory information after simple anonymization creates high concern about breaching privacy . Trajectory data is large scale high dimensional and sparse in nature and thus requires an efficient privacy preserving data publishing algorithm with high data utility . This paper describes the investigation of the publication of non interactive sanitized trajectory data under a Differential Privacy definition . To this end a new prefix tree structure an incremental privacy budget allocation model and a spatial temporal dimensionality reduction model are proposed to enhance the sanitized data utility as well as to improve runtime efficiency . The developed algorithm is implemented and applied to real life metro smart card data from Shenzhen China which includes a total of 2.8 million individual travelers and over 220 million records . Numerical analysis finds that compared with previous work the proposed model outputs sanitized dataset with higher utilities and the algorithm is more efficient and scalable . | Quantitatively measure the privacy breach risks of transit smart card data. A privacy preserving data publishing PPDP algorithm is proposed. The proposed algorithm outperforms two previous models on data utility and runtime efficiency. |
S0968090X19310642 | In this study we examine a class of firstbest congestion pricing schemes that employ various strategies to differentiate price spatially . Since spatial price differentiation raises the issue of privacy infringement two hybrid pricing schemes are proposed to internalize travelers privacy cost . Under these schemes a traveler is given the options to maintain her anonymity and pay a regular toll or compromise her privacy and receive a discount . One of the hybrid schemes allows the travelers privacy cost to be dependent on the link composition of the travelers path . In other words she can choose to disclose none part or all of her path information . We prove that the minimum toll burdenwith or without the privacy costrequired to decentralize a system optimum gradually decreases as the toll becomes more spatially differentiated . We also show that the new hybrid scheme demonstrates some interesting analytical properties compared to existing schemes . | A class of first best congestion pricing schemes to differentiate price spatially are examined. Two hybrid pricing schemes are proposed to internalize privacy cost. Minimum toll burden to decentralize SO decreases as toll becomes more differentiated. With link additive privacy cost new hybrid scheme demonstrates interesting properties. |
S0968090X19311118 | Based on the Bayesian network paradigm we propose a hybrid model to predict the three main consequences of disruptions and disturbances during train operations namely the primary delay | Three factors are ascertained to measure the effects of disruptions. Real time prediction requirements are particularly considered in the model. The model shows high accuracy in predicting the effects of disruptions. The model shows strong generalizability on two different high speed railway lines. |
S0968090X19311295 | This paper introduces a novel approach for synchronised demand capacity balancing within a proposed Collaborative Air Traffic Flow Management framework . The approach is aimed to realise optimising traffic flow and scheduling airspace configuration in a more harmonised manner . Options such as delay assignment and alternative trajectories are intended for regulating the traffic flow . Airspace reconfiguration involves on the other side adjusting the opening schemes of predefined configurations or creating new ones through dynamic sectorisation . Results suggest that using the proposed approach the required system delay can be reduced remarkably whereas the number of opened sectors and the total capacity provision decrease at the same time due to the increased capacity utilisation per operating sector . | An approach for synchronised DCB is introduced within a collaborative ATFM framework. An interactive trajectory design and airspace configuration process is proposed. An integrated model is presented with all options from the interactive process. Real world data are collected and used to provide an assessment of the approach. |
S0968090X19311404 | Safer and more efficient airport ground movements can be planned by routing and scheduling systems based on the 4 dimensional trajectory . In order to achieve the benefits envisioned in the planning stage an effective taxiing guidance system is indispensable . The Follow the Greens guidance concept provides an augmented means for 4DT based taxiing with pilot in the loop which is expected to guide the piloted aircraft by dynamically adjusting the lit position of green ground navigation lamps according to the assigned 4DTs . This paper presents a simulation study to investigate the feasibility of FtG based on a control theoretic modeling of the taxiing system . The 4DT conformance errors with different navigation lamp control strategies are investigated . The key performance indices including temporal constraint violation and fuel consumption are analysed . The results demonstrate that it is feasible to follow conflict free 4DTs through FtG if an appropriate lamp controller is available . The results also highlight the need to proactively handle the potential conformance errors in the routing and scheduling stage . | The feasibility of FtG taxiing guidance with conflict free 4DTs is demonstrated. A PID ground lamp controller is devised to improve 4DT conformance. 4DT conformance error is quantified based on a large set of pre planned 4DTs. 4DT conformance error has a clear impact on temporal constraints and fuel consumption. Minimal time separation in 4DT planning helps in absorbing 4DT conformance error. |
S0968090X19311623 | With the introduction of connected and autonomous trucks truck platooning is expected to be more feasible and prevalent . The reported benefits of the truck platooning include regularizing traffic reducing congestion increasing highway safety and decreasing fuel consumption and emission . Truck platooning may however decrease pavement longevity because it would cause channelized load application and hinder the healing properties of asphalt concrete . This study proposes a centralized control strategy that converts the pavement related challenges of truck platooning into opportunities . This strategy leverages the auto pilot technologies in CATs by optimizing the lateral position of each platoon or group of platoons . The efficiency of the proposed control strategy was demonstrated in a case study . Results showed that pavement life cycle costs could be reduced up to 50 by controlling the lateral position of the platoons for each day . | The pavement related challenges of truck platooning are highlighted. V2I based optimization framework for truck platooning is proposed. The results showed that pavement life cycle cost can be reduced up to50 . |
S0968090X1931174X | The virtual and physical worlds are increasingly inter connected . Although there is considerable research into the effects of information and communication technologies on activity travel choices there is little understanding of the inter relationships between online and in person activity participation and the extent to which the two worlds complement one another or substitute for one another . Shopping is one of the activity realms in which the virtual and physical spaces are increasingly interacting . This paper aims to unravel the relationships between online and in person activity engagement in the shopping domain while explicitly distinguishing between shopping for non grocery goods grocery products and ready to eat meals . Data from the 2017 Puget Sound household travel survey is used to estimate a multivariate ordered probit model of the number of days in a week that a sample of households engages in in person activity engagement and online activity engagement for each of these shopping activity types leading to a model of six endogenous outcomes . Model results show that there are intricate complementary and substitution effects between in person and online shopping activities that these activities are considered as a single packaged bundle and that the frequencies of these activities are significantly affected by income built environment attributes and household structure . The findings suggest that travel forecasting models should incorporate model components that capture the interplay between in person and online shopping engagement and explicitly distinguish between non grocery and grocery shopping activities . Policies that help bridge the digital divide so that households of all socio economic strata can access goods and services in the virtual world would help improve quality of life for all . Finally the paper highlights the need to bring passenger and freight demand modeling at least within urban contexts into a single integrated structure . | This paper sheds light on online and in person shopping eat out activity engagement. A multivariate ordered probit model is estimated. Online and in person activities of multiple purposes are decided as a package bundle. The online in person relationship is different between grocery and other shopping. There is a need for an integrated passenger and freight demand modeling structure. |
S0968090X19311829 | To improve the accessibility of the metro network during night operations this study aims to investigate a collaborative optimization for the last train timetable in an urban rail transit network . By using a space time network framework all the involved transportation activities are well characterized in an extended space time network in which the train space time travel arcs passenger travel arcs transfer arcs etc . are all taken into account . Two performance measures are proposed to evaluate the network based timetable of the last trains . Through considering the route choice behaviors the problem of interest is formulated as 01 linear programming models from the perspective of a space time network design . To effectively solve the proposed models we dualize the hard constraints into the objective function to produce the relaxed models by introducing a set of Lagrangian multipliers . Then the sub gradient algorithm is proposed to iteratively minimize the gap of the lower and upper bounds of the primal models . Finally two sets of numerical experiments are implemented in an illustrative network and the Beijing metro network respectively and experimental results demonstrate the efficiency and performance of the proposed methods . | Consider the accessibility based last train timetable problem from the perspective of DNDP. Formulate two 01 linear programming models under the space time network. Decompose the original models by Lagrangian relaxation. Design a sub gradient based algorithm to iteratively minimize the gap. |
S0968090X19311854 | In the foreseeable future the traffic stream will be likely mixed with connected automated vehicles and regular vehicles . In the mixed traffic environment when following a RV due to the lack of vehicle to vehicle communications it may take longer time for a CAV to sense and react than a human driver which results in longer time headway and the loss of highway throughput . To address such a connectivity gap this paper investigates an infrastructure based solution i.e . the deployment of roadside units to help CAVs in the heterogeneous traffic stream . Specifically it is envisioned that these roadside units can sense vehicles in their coverage areas and provide the beyond line of sight motion information to CAVs to empower them to react proactively as they would do when following other CAVs . This paper is devoted to the analysis of the impacts of this type of roadside units at a strategic planning stage . In doing so we first derive an analytical link performance function to capture their impact on the link capacity and travel time and then develop a network equilibrium model to gauge their effect on travelers route choices and thus the flow distribution of both RVs and CAVs across the whole network . This modeling development will allow us to conduct a cost benefit analysis for a given deployment plan of roadside units . For fair analyses we further develop an optimization model to determine the optimal deployment plan for a given budget while focusing on the worst case of its impact because the flow distribution resulting from our network equilibrium model is not unique . Such a model provides a conservative estimate of the benefit brought by roadside units . Lastly we offer case studies to demonstrate the models and unveil the potential of such an infrastructure based solution . | Deployment of roadside units to close connectivity gap in mixed traffic with CAVs. Roadside units provide the beyond line of sight motion information to CAVs. Capturing the benefit of roadside units on network performance for strategic planning. A deployment model developed to provide a conservative estimate of the benefit. Cost and benefit analysis unveils the potential of such an infrastructure based solution. |
S0968090X19311969 | The macroscopic fundamental diagram can effectively reduce the spatial dimension involved in dynamic optimization of traffic performance for large scale networks . Solving the Hamilton Jacobi Bellman equation takes center stage in yielding solutions to the optimal control problem . At the core of solving the HJB equation is the value function that represents choosing a sequence of actions to optimize the system performance . However this problem generally becomes intractable for possible discontinuities in the solution and the curse of dimensionality for systems with all but modest dimension . To address these challenges a neural network is used to approximate the value function to obtain the optimal controls through policy iteration . Furthermore a saturated operator is embedded in the neural network approximator to handle the difficulty caused by the control and state constraints . This policy iteration can be implemented as an iterative data driven technique that integrates with the model based optimal design based on real time observations . Numerical experiments are conducted to show that the neuro dynamic programming approach can achieve optimization goals while stabilizing the system by regulating the traffic state to the desired uncongested equilibrium . | Optimal feedback perimeter control of macroscopic fundamental diagram systems. A neuro dynamic programming framework for dealing with the curse of dimensionality. Convergence to optimality and stability of the closed loop system are guaranteed. State and input constraints of the MFD dynamics are addressed. No local system linearization is required. |
S0968090X19312252 | Modern traffic control and management systems in urban networks require real time estimation of the traffic states . In this paper a novel approach for modeling traffic flow in urban networks that is especially suitable for state estimation is proposed . The complexity of the urban traffic model is reduced by assuming availability of connected vehicle data . We first investigate the observability issue in urban traffic networks using a graphical approach . Then the proposed model for the evolution of the traffic flow in urban traffic networks is developed and used in two layers i.e . link layer and network layer to estimate in high resolution the traffic states in the whole network . Traffic states in the link layer include queue tail location and the number of vehicles in the queue while in the network layer estimation of the total number of vehicles per link and turning rates at the intersections is carried out . In a first step it is shown that the estimation approach only requires the detectors at the borders of the network . We further demonstrate that in the proposed scheme one may reduce or drop the need for spot detectors for the price of reduced but still reasonable estimation accuracy . The validation of the approach has been undertaken by comparing the produced estimates with realistic micro simulation results as ground truth and the achieved simulation results are promising . | A methodology developed for traffic states estimation in urban traffic network. Observability analysis of the two layer network. Estimation of vehicle accumulation and turning ratios. Accurate second by second estimation even with the presence of measurement noise. Application of the algorithm in networks even without and spot measuring tools. |
S0968090X19312288 | In the future when traffic streams comprise a mix of conventional and automated vehicles AVs may be employed as mobile actuators to regulate or manage traffic flow across an urban road network to enhance its performance . This paper develops a path control scheme to achieve the system optimum of the network by controlling a portion of cooperative AVs as per the SO routing principle . A linear program is formulated to delineate the scheme and determine the minimum control ratio of CAVs to achieve SO . The properties of the MCR are mathematically and numerically investigated . Numerical examples based on real world networks reveal that the SO of most of the tested networks can be achieved with an MCR below 23 . Considering the low market penetration of AVs at early stages of their deployment we further investigate a joint path based control and pricing scheme to replicate SO . Numerical examples demonstrate the remarkable synergy of these combined instruments on reducing the MCR with little collected tolling revenue . | Controlling a portion of cooperative AVs to achieve system optimum. A linear program is formulated to find the minimum control ratio. Numerical examples show the ratio is typically below 23 . Combining path based pricing significantly reduces the ratio without collecting much toll. |
S0968090X19312409 | Traffic state estimation is a crucial elemental function in Intelligent Transportation Systems . However the collected traffic state data are often incomplete in the real world . In this paper a novel deep learning framework is proposed to use information from adjacent links to estimate road traffic states . First the representation of the road network is realized based on graph embedding . Second with this representation information the generative adversarial network is applied to generate the road traffic state information in real time . Finally two typical road networks in Caltrans District 7 and Seattle area are adopted as cases study . Experimental results indicate that the estimated road traffic state data of the detectors have higher accuracy than the data estimated by other models . | DeepWalk is used for graph embedding of the road network. Based on the results of DeepWalk GAN is applied to generate road traffic states. The road traffic state estimation results based on GE GAN have higher accuracy. |
S0968090X19312434 | The traffic state in an urban transportation network is determined via spatio temporal traffic propagation . In early traffic forecasting studies time series models were adopted to accommodate autocorrelations between traffic states . The incorporation of spatial correlations into the forecasting of traffic states however involved a computational burden . Deep learning technologies were recently introduced to traffic forecasting in order to accommodate the spatio temporal dependencies among traffic states . In the present study we devised a novel graph based neural network that expanded the existing graph convolutional neural network . The proposed model allowed us to differentiate the intensity of connecting to neighbor roads unlike existing GCNs that give equal weight to each neighbor road . A plausible model architecture that mimicked real traffic propagation was established based on the graph convolution . The domain knowledge was efficiently incorporated into a neural network architecture . The present study also employed a generative adversarial framework to ensure that a forecasted traffic state could be as realistic as possible considering the joint probabilistic density of real traffic states . The forecasting performance of the proposed model surpassed that of the original GCN model and the estimated adjacency matrices revealed the hidden nature of real traffic propagation . | The present study proposed a novel graph convolution model to forecast future traffic speeds. The proposed model differentiated the intensity of connecting to neighbor roads unlike existing GCNs. The present study was focused on devising a GCN model that mimic true propagation patterns of traffic. The proposed model shows promise for application to a real time traffic forecasting system. |
S0968090X19312537 | Although automated vehicles could offer a potentially effective solution to improving road safety the benefit associated with AVs can be realized only when the public intend to use them . While some efforts have been made to understand why people would use AVs few of them have investigated the role of social and personal factors in AV acceptance . The present study aimed to fill in this research gap . An AV acceptance model was proposed by extending the Technology Acceptance Model with social and personal factors i.e . initial trust social influence and the Big Five personality and sensation seeking traits . The validity of the proposed model was confirmed with a questionnaire survey administrated to 647 drivers in China . Results revealed that at the very beginning of AV commercialization perception factors from the original TAM showed significant influence on users intention to use AVs . But more importantly it was social influence and initial trust that contributed most to explain whether users would accept AVs or not . Some personality traits also played certain roles in AV usage intention . In particular sensation seekers and those with a higher openness to experience were more likely to trust AVs and had a higher intention to adopt them . In contrast neurotic people showed a lower level of trust and were less likely to accept AVs . Practically these findings suggest that promotion of AVs to influential individuals that could help form good social opinions would have significant downstream effects on AV acceptance at the early state of its marketization . | Social influence and initial trust played the most important roles in AV acceptance. Some personality traits contributed to AV usage intention. Sensation seekers and those with a higher openness to experience had a higher intention to adopt AVs. Neurotic users were less likely to accept AVs. |
S0968090X19312574 | This paper studies the purchase subsidy design problem for human driven electric vehicles and autonomous electric vehicles . The proposed range and mode specific purchase subsidy aims to maximize the social benefits from vehicle electrification and automation . In this study we first classify electric vehicles into several classes based on electric driving ranges . Each EV class contains two driving modes i.e . human driving and automated driving . We provide a simplified model to estimate the greenhouse gas emission and the inconvenience costs of vehicle charging . The nested logit model is used to characterize users vehicle choice behaviors . A mixed integer nonlinear programming model is formulated for the purchase subsidy design problem . A customized branch and bound method is developed to seek a globally optimal solution to the formulated MINLP model . The numerical examples show that the developed solution method can effectively solve the proposed problem in a reasonable time . The local search strategy embedded in the customized B B method helps reduce 7 computation time on average . Some managerial insights obtained from the numerical experiments are discussed which can help the government agency to achieve a reasonable budget allocation between HDEVs and AEVs with different electric driving ranges . | A novel purchase subsidy policy for AEVs and HDEVs. The nested logit model is applied to characterize users vehicle choice behavior. The problem is formulated as a mixed integer nonlinear programming model. A customized branch and bound method is developed to obtain the optimal solution. |
S0968090X1931263X | Quay crane scheduling is considered one of the most complex seaside operations in container terminals and is directly correlated with vessel service and waiting times . Traditionally quay cranes can handle one container at a time . However this is expected to change with the recently patented next generation quay crane The Ship to Shore Multi trolley Portal Gantry Container Crane . These next generation cranes can access two bays simultaneously and can operate on four containers at a time . In this work we introduce a mixed integer programming formulation and an exact solution approach to solve the next generation quay crane scheduling problem . The solution technique breaks the main problem into two sequential stages . The first stage uses a fast set partitioning formulation to solve the general case and a closed form analytic approach to solve specific cases while the second stage uses a partitioning heuristic combined with a branch and price algorithm . A real workload case study and simulated workload case studies are used to assess the performance of the next generation cranes versus traditional ones . Results show that the use of two to three next generation cranes can generally reduce the service time beyond the best possible service time achieved by traditional cranes . Moreover average service times can be reduced by up to 65 . Finally results of a computational study and sensitivity analyses show that the proposed solution approach has low sensitivity to the different parameters and clearly outperforms CPLEX in that it can solve real sized cases rapidly in the computational study all cases were solved in less than 20s . | This paper models the scheduling of a recently patented container quay crane design. A mixed integer program and a two stage exact solution methodology are developed to solve the problem. The technique solves the first stage using a set partitioning formulation or a closed form expression and solves the second stage using a partitioning heuristic and a branch and price algorithm. Computational studies and sensitivity analyses are used to evaluate the efficiency of the proposed method. Case studies are used to assess the expected benefits of the next generation cranes. |
S0968090X19312987 | In this paper we review trajectory data based traffic flow studies that have been conducted over the last 15years . Our purpose is to provide a roadmap for readers who have an interest in the latest developments of traffic flow theory that have been stimulated by the availability of trajectory data . We first highlight the critical role of trajectory data trajectory dataset in the recent history of traffic flow studies . Then we summarize new traffic phenomena models at the microscopic mesoscopic macroscopic levels and provide a unified view of these achievements perceived from different directions of traffic flow studies . Finally we discuss some future research directions . | Provide a comprehensive review of trajectory based traffic flow studies. Make a survey focusing on the new phenomena and the new models in the last 15years. Highlight the future research directions. |
S0968090X19313099 | In conditionally automated driving drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving . Factors influencing takeover performance such as takeover lead time and the engagement of non driving related tasks have been studied in the past . However despite the important role emotions play in human machine interaction and in manual driving little is known about how emotions influence drivers takeover performance . This study therefore examined the effects of emotional valence and arousal on drivers takeover timeliness and quality in conditionally automated driving . We conducted a driving simulation experiment with 32 participants . Movie clips were played for emotion induction . Participants with different levels of emotional valence and arousal were required to take over control from automated driving and their takeover time and quality were analyzed . Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk . However high arousal did not yield an advantage in takeover time . This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance . The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not . | Positive valence leads to better takeover quality. High arousal does not result in faster takeover time. We cannot simply apply the findings in manual driving to automated driving. |
S0968090X19313208 | This study addresses the location problem of electric vehicle charging stations considering drivers range anxiety and path deviation . The problem is to determine the optimal locations of EV charging stations in a network under a limited budget that minimize the accumulated range anxiety of concerned travelers over the entire trips . A compact mixed integer nonlinear programming model is first developed for the problem without resorting to the path and detailed charging pattern pre generation . After examining the convexity of the model we propose an efficient outer approximation method to obtain the optimal solution to the model . The model is then extended to incorporate the charging impedance e.g . the charging time and cost . Numerical experiments in a 25 node benchmark network and a real life Texas highway network demonstrate the efficacy of the proposed models and solution method and analyze the impact of the battery capacity path deviation tolerance budget and the subset of OD pairs on the optimal solution and the performance of the system . | A compact mixed integer programming model is developed. Path deviation and drivers range anxiety are considered. An outer approximation method is proposed to solve the model. Numerical experiments in a real life Texas highway network are conducted. |
S0968090X19313270 | This study presents the findings of an evacuation experiment involving a mixed population of able bodied walking pedestrians and wheelchair users . A simulation model capable of reproducing the results is also introduced . The experiment was designed as a square room having four hidden exits that were different in nature . Participants selectively received information allowing them to know the nature of each exit . The conditions under which nobody everybody knew a priori the configuration of the room were tested alongside the condition where only wheelchair users had access to this information . The results show that evacuation time was greatly reduced when the three wheelchair users were informed of exit location and nature . A further but more limited improvement was seen when all participants received the same information . A more detailed analysis indicates that the smooth motion of wheelchair users has benefits in relation to the surrounding crowd dynamics highlighting the importance of improving accessibility for disabled people and making available to them information on exit route characteristics . The same results were obtained using the simulation model developed to account for the presence of wheelchair users and employed to investigate more detailed scenarios . More generally this study suggests that information provision to specific groups of pedestrians could be more efficient and achieve similar results in comparison with trying to reach the largest possible number of pedestrians . The results and the methods presented in this study are particularly relevant to the development of automatic information systems which are becoming the backbone of large pedestrian facilities . | Evacuation experiments in the presence of wheelchair users are analyzed. Egress time is reduced if wheelchair users are informed on exit type and location. A method to estimate decision time is presented. A simulation model for crowds including wheelchair users is presented and validated |
S0968090X19313488 | Traffic congestion is forecast for neighborhoods within a region using a deep learning model . The model is based on Long Short Term Memory neural network architecture . It forecasts a congestion score defined as the ratio of the vehicle accumulation inside a neighborhood to its trip completion rate . Inputs include congestion scores measured at earlier times in neighborhoods within a region and three other real time measures of regional traffic . | We define a neighborhood wide congestion score for congestion management and four input signals necessary for predicting the congestion score. We define a deep learning model based on LSTM architecture for predicting the congestion score. We demonstrate how the model can be made robust even to adverse settings. We further improve the model by representing the inputs through weighted undirected graphs that incorporate the route choice of individuals and learning features through graph convolutions. We demonstrate of the models usefulness in designing traffic control schemes. |
S0968090X19313531 | The efficiency of automated container terminals primarily depends on the synchronization of automated guided vehicles and automated cranes . Accordingly we study the integrated rail mounted yard crane and AGV scheduling problem as a multi robot coordination and scheduling problem in this paper . Based on a discretized virtualized network we propose a multi commodity network flow model with two sets of flow balance constraints for cranes and AGVs . In addition two side constraints are introduced to deal with inter robot constraints to reflect the complex interactions among terminal agents accurately . The Alternating Direction Method of Multipliers method is adopted in this study as a market driven approach to dualize the hard side constraints therefore the original problem is decomposed into a set of crane specific and vehicle specific subtasks . The cost effective solutions can be obtained by iteratively adjusting both the primal and dual costs of each subtask . We also compare the computational performance of the proposed solution framework with that of the resource constrained project scheduling problem model using commercial solvers . Comparison results indicate that our proposed approach could efficiently find solutions within 2 optimality gaps . Illustrative and real world instances show that the proposed approach effectively serves the accurate coordination of AGVs and cranes in automated terminals . | Construct a multi robot coordinating scheduling model for automated contrainer hubs. Decompose multi robot tasks with inter robot constraints using ADMM based method. Propose a real time scheduling framework based on rolling horizon method. Verify feasibility and efficiency in Putian container terminal in China. |
S0968090X19313543 | Traffic flow fundamental diagram is viewed as the basis of traffic flow theory and has various applications in transportation . However the fundamental diagram of mixed human driven vehicles and connected automated vehicles traffic has not been well studied . This paper derives the FD for mixed HV and CAV traffic considering the stochastic headway . Firstly the deterministic FD of pure CAV traffic and pure HV traffic are built . Then the FD of mixed HV and CAV traffic is developed with CAV penetration and platooning intensity taken into consideration . A Gaussian mixture model is applied to model the stochastic headway based on which the stochastic FD is derived . Impact of CAV penetration and platooning intensity on the stochasticity of FD is studied . Results from theoretical analysis and case study show that increasing CAV penetration can reduce the scattering of FD while higher platooning intensity may result in more scattering of FD . | First the deterministic FDs of pure CAVs traffic and pure HVs traffic are built. Then the FD of mixed HVs and CAVs traffic is developed. A Gaussian mixture model GMM is applied to model the stochastic headway. Results show that increasing CAVs penetration can reduce the scattering of FD. Higher platooning intensity may result in more scattering of FD. |
S0968090X19313865 | Lanes used by taxis and other shared ride vehicles at airports and rail terminals are often congested . The present paper examines congestion mitigating strategies for a special type of lane inside of which taxis are prohibited from overtaking each other while dropping off patrons . Taxis must therefore often wait in first in first out queues that form in the lane during busy periods . Patrons may be discharged from taxis upon reaching a desired area near the terminal entrance . When wait times grow long however some taxis discharge their patrons in advance of that desired area . | Taxi outflows from FIFO drop off lanes are examined via simulation calibrated to real data. Taxis decisions on the location and time for drop offs are explicitly modeled. Taxi outflow would increase significantly if the lanes present batching strategy is rescinded. Even greater outflow gains can be achieved by regulating taxis drop off behavior. Roles for technology in implementing proposed strategies are discussed. |
S0968090X19314573 | Ride hailing services are shaping travel behaviours and emergent urban mobility patterns . From their initial diffusion centres in North America and Europe these on demand mobility services are increasingly becoming available in developing countries . Yet empirical research from these contexts on the impact of ride hailing services is lacking . To address this gap this paper examines the factors driving the adoption of ride hailing and the associated travel characteristics and mode substitution effects in Ghana Sub Saharan Africa . Using data from a large sample survey of commuters in a multi variable structural equation model the paper shows that socio demographic factors perceived benefits and ease of use of ride hailing perceived safety risks and car dependent lifestyles influence adoption and use of ride hailing services . Similar to other contexts individuals reference ride hailing trips were mainly for special occasion purposes but work and school journeys were also high . Shorter travel times and single passenger journeys within inner suburban and outer suburban localities typify ride hailing trips . This contrasts with other contexts where ride hailing is used frequently by urban dwellers and less so by those in the suburbs . Ride hailing use replaced conventional taxis public transport private car and walking suggesting mode substitution effects for individuals reference trips . Further exploration of a full days travel mode choices also revealed that individuals use other available modes of transport in addition to ride hailing services . However multi modal integration is weak suggesting that ride hailing tends to be used alone for full door to door journeys instead of complementing other existing modes in serving first last mile access for example . The implications of the findings for sustainable mobility are discussed . | Ride hailing adoption factors travel characteristics and mode substitution effects are examined. Socio demographic factors perceived benefits and safety risks and car dependency influence ride hailing services use. Ride hailing replaced conventional taxis private car and walking51 36 10 and 1 of reference trips respectively. Ride hailing is mostly used for shorter and single passenger trips in suburban localities. Ride hailing offers standalone services as they are not well integrated into existing travel modes. |
S0968090X19315414 | The understanding of charging behavior has been recognized as a crucial element in optimizing roll out of charging infrastructure . While current literature provides charging choices and categorizations of charging behavior these seem oversimplified and limitedly based on charging data . | We provide an unsupervised method to derive rules for behavior based on data. We develop a typology of charging behavior in terms of session types and user types. Our typology can be used to improve realistic charging behavior in simulation models. We found both typical residential and commuting as well as non typical user types. We see a change in EV user population composition over time due large Battery Electric Vehicle uptake and Charging point scarcity. |
S0968090X1931589X | The uptake of on demand services is increasing rapidly all over the world . However the market share of their pooled version is still low despite its potential in addressing the mobility challenges that dense urban cities are facing . In this research we analyse user preferences towards pooled on demand services regarding their time reliability cost trade offs . We study via stated preference experiments the value of time and value of reliability of the different trip stages . We target urban Dutch individuals and address commuting and leisure trips . Results show in vehicle VOT for pooled on demand services to amount to 7.8810.80 h. These values are somewhat higher than known values of traditional public transport . We also find waiting VOT to be lower than values previously reported in literature . In general we find VOR to be lower than VOT the reliability ratio for both the waiting stage and the in vehicle stage being around 0.5 . In order to understand different preferences we also estimate latent class choice models . The analysis shows that the main difference between classes pertains to the overall time cost and reliability cost trade offs rather than in different valuations of the reliability ratio . In addition to serving as input for demand forecasting models such as macroscopic static assignment and agent based simulation models our findings can support service providers in developing their strategy when designing pooled on demand services . | Design of stated preference experiments for pooled on demand services. Analysis of VOT and VOR for the waiting in vehicle and transfer trip stages. Latent class choice models reveal different VOT and VOR segments. Findings support providers in developing the offered service portfolio. |
S0968090X19317486 | This paper presents the Plugin Hybrid Electric Vehicle routing problem that finds the optimal set and sequence of customers visited by PHEVs to minimize total energy consumption . PHEVs use electricity and gasoline as their two energy sources . A power management model finds the optimal draw of power from the two sources along the vehicles path . To solve the PHEVRP we present an exact branch and price and a heuristic algorithm . We derive the complexity order of the algorithms and show that the heuristic becomes faster at larger battery capacities . We present a case study situated in the City of Toronto and show that the PHEVs use electricity in congested downtown regions and gasoline in free flow conditions of highways . | We present the Plugin Hybrid Electric Vehicle PHEV routing problem. We model the impact of power management strategies in the optimal routes. We offer an exact four index formulation and a decomposition based heuristic solution. PHEVs use battery power in congested links but use gasoline in free flow highways. |
S0968090X20301479 | In todays era of mega ships rail is increasingly being recognized as an important and sustainable mode to transport containers in and out of congested marine terminals . However rails potential impact on terminal and road congestion is highly dependent on the ability to fully utilize the available train space . This paper contributes to the literature by presenting an optimization model that can create high utilization loading plans for double stack trains at marine terminals . To facilitate the solution of the model it will be shown how the initial binary nonlinear model can be linearized . Model properties will also be derived and discussed . To illustrate the usefulness of the proposed model in a practical setting a realistic case study is conducted . The results demonstrate that the model is able to find optimal loading plans for double stack trains in realistic settings . | Rail is critical in reducing marine terminal congestion and emission reduction. Optimization model proposed for loading double stack trains. Model based on real world setting at U.S. marine terminal. Model properties are rigorously analyzed. |
S0968090X20305490 | While a number of studies have investigated driving behaviors detailed microscopic driving data has only recently become available for analysis . Through Basic Safety Message data from the Michigan Safety Pilot Program this study applies a Markov Decision Process framework to understand driving behavior in terms of acceleration deceleration and maintaining speed decisions . Personally Revealed Choices that maximize the expected sum of rewards for individual drivers are obtained by analyzing detailed data from 120 trips and the application of MDP . Specifically this paper defines states based on the number of objects around the host vehicle and the distance to the front object . Given the states individual drivers reward functions are estimated using the multinomial logit model and used in the MDP framework . Optimal policies are obtained through a value iteration algorithm . The results show that as the number of objects increases around a host vehicle the driver prefer to accelerate in order to escape the crowdedness around them . In addition when trips are segmented based on the level of crowdedness increased levels of trip crowdedness results in a fewer number of drivers accelerating because the traffic conditions constrain them to maintaining constant speed or deceleration . One potential application of this study is to generate short term predictive driver decision information through historical driving performance which can be used to warn a host vehicle driver when the person substantially deviates from their own historical PRC . This information could also be disseminated to surrounding vehicles as well enabling them to foresee the states and actions of other drivers and potentially avoid collisions . | Microscopic driving decisions are analyzed using Connected Vehicle Data and Markov Decision Process. Speed choices were extracted from Basic Safety Messages to understand drivers behavior. States were defined based on crowdedness surrounding the host vehicle and distance to the front object. Perceived individual rewards from state transitions were estimated from revealed choices. A method for learning personalized driving preferences was developed which can be used to provide warnings. |
S0968090X20305593 | Transportation agencies are starting to leverage increasingly available GPS trajectory data to support their analyses and decision making . While this type of mobility data adds significant value to various analyses one challenge that persists is lack of information about the types of vehicles that performed the recorded trips which clearly limits the value of trajectory data in transportation system analysis . To overcome this limitation of trajectory data a deep | A CNN approach for classifying vehicles based on GPS data is proposed. A novel representation of a GPS trajectory suitable for deep learning is proposed. The approach outperforms traditional machine learning methods. The approach increases usability of GPS trajectory data. |
S0968090X2030560X | The aim of this work is to investigate the coordinated control of urban expressway integrating adjacent signalized intersections based on pinning synchronization of complex networks . An expressway network integrating adjacent signalized intersections was used as the studied object where no signal light is set on ramps and ramp metering is achieved only through the use of signal lights at adjacent intersections . An improved cell transmission model for each segment of the studied object comprising a mainline an on ramp an off ramp side roads and adjacent intersections was established . Each node of the system was defined and a node coupling model integrating adjacent signalized intersections was also established . The coordinated controller was designed with the signal timings of adjacent intersections used as decision variables . Using the stability theory of complex networks the concrete pinning nodes corresponding to the subsystems of regulating the inflow from on ramps adjacent intersections to mainline could be obtained and the signal timing schemes at intersections could be optimized . The outflow from the mainline to off ramps adjacent intersections could be appropriately regulated in order to mitigate off ramp congestion . The proposed method was validated through simulation experiments . The results indicate that the traffic jam phenomenon can be suppressed utmost off ramp congestion can be mitigated and the operational efficiency can be enhanced at minimal control cost . | A coupling model of expressway integrating adjacent intersections was established. The coordinated controller was designed by pinning synchronization method. The adjusted signal timing schemes of adjacent intersections can be obtained. |
S0968090X20305611 | Vehicles risky lane changing maneuver has significant impact on road traffic safety . As an innovation compared with the posterior LC risk prediction methods proposed in previous studies this study develops a pre emptive LC risk level prediction method which is able to estimate the crash risk level of an LC event in advance before the LC car completes the LC maneuver . The basic concept of this method is to apply a machine learning classifier to predict the LC risk level based on cars key space series features at the beginning of the LC event . To boost the prediction performance an innovative resampling method namely ENN SMOTE Tomek Link and an advanced machine learning classifier namely LightGBM are proposed and employed in the development of the P LRLP method . Meanwhile an algorithm which can measure the stability of the selected key features in terms of the randomness and size of training samples is developed to evaluate the feature selection methods . A digitalized vehicles trajectory dataset the Next Generation Simulation is used for method validation . The validation results manifest that the EST can achieve satisfactory resampling performance while Random Forest as an embedded FS method achieves remarkable performance on both stability of selected features and prediction of risk level . The results also show that the LC risk level can be most accurately predicted when the LC car moves to the position where the distance between the longitudinal center line of the LC car and the marking line separating the two lanes equals 1.5ft . As an innovative LC risk level prediction technique the P LRLP method could be integrated with advanced driver assistance system and vehicle to vehicle communication to remedy potential risky LC maneuver in the future . | A pre emptive lane changing risk level prediction method is developed and validated. The proposed innovative resampling method shows satisfactory resampling performance. An algorithm which can comprehensively evaluate the stability of selected features is proposed. Random forest feature selection method shows high performance on stability and prediction. |
S0968090X20305623 | The paper proposes a methodology for providing personalized predictive in vehicle crowding information to public transport travellers via mobile applications or at stop displays . Three crowding metrics are considered the probability of getting a seat on boarding the expected travel time standing and the excess perceived travel time compared to uncrowded conditions . The methodology combines prediction models of passenger loads and alighting counts based on lasso regularized regression and multivariate PLS regression a probabilistic seat allocation model and a bias correction step in order to predict the crowding metrics . Depending on data availability the prediction method can use a combination of historical passenger counts real time vehicle locations and real time passenger counts . We evaluate the prediction methodology in a real world case study for a bus line in Stockholm Sweden . The results indicate that personalized predictive crowding information that is robust to varying data availability can be provided sufficiently early to be useful to travellers . The methodology is of value for agencies and operators in order to increase the attractiveness and capacity utilization of public transport . | Personalized crowding metrics show different spatial patterns from passenger loads. Predictive crowding information is robust to varying data availability. Systematic historical load variations are useful to provide baseline predictions. When available real time APC predictors always improve prediction performance. Real time AVL can substantially improve predictions even without real time APC. |
S0968090X20305635 | This paper reviews and systematically classifies the existing literature of bicycle sharing service planning problems at strategic tactical and operational decision levels with the reference to the novel bicycle sharing service planning process introduced herein . The current research gaps are identified and discussed . The future research directions of the three decision level problems are proposed according to four main categories namely new diversity realism integrality and technology . This review also points out important future research directions for multi level BSPPs and the integration of bicycle sharing systems with existing multi modal transportation systems . | We provide a comprehensive survey of bicycle sharing service planning problem studies. We offer a systematic classification of the problems. We introduce a novel planning process for bicycle sharing services. We identify potential research gaps. We provide future research directions. |
S0968090X20305647 | Variable speed limit control is a flexible way to improve traffic conditions increase safety and reduce emissions . There is an emerging trend of using reinforcement learning methods for VSL control . Currently deep learning is enabling reinforcement learning to develop autonomous control agents for problems that were previously intractable . In this paper a more effective deep reinforcement learning model is developed for differential variable speed limit control in which dynamic and distinct speed limits among lanes can be imposed . The proposed DRL model uses a novel actor critic architecture to learn a large number of discrete speed limits in a continuous action space . Different reward signals such as total travel time bottleneck speed emergency braking and vehicular emissions are used to train the DVSL controller and a comparison between these reward signals is conducted . The proposed DRL based DVSL controllers are tested on a freeway with a simulated recurrent bottleneck . The simulation results show that the DRL based DVSL control strategy is able to improve the safety efficiency and environment friendliness of the freeway . In order to verify whether the controller generalizes to real world implementation we also evaluate the generalization of the controllers on environments with different driving behavior attributes . and the robustness of the DRL agent is observed from the results . | A deep reinforcement learning method for differential variable speed limit control. The reward engineering issue. Improve freeway throughput reduce emission and enhance safety. The generalization capability of deep reinforcement learning. |
S0968090X20305659 | This study addresses the problem of calibrating utility maximizing nested logit activity based travel demand model systems . After estimation it is common practice to use aggregate measurements to calibrate the estimated model systems parameters prior to their application in transportation planning policy making and operations . However calibration of activity based model systems has received much less attention . Existing calibration approaches are myopic heuristics in the sense that they do not consider the fundamental inter dependencies among choice models and do not have a systematic way to adjust model parameters . Also other purely simulation based approaches do not perform well in large scale applications . In this study we focus on utility maximizing nested logit activity based model systems and calibrating aggregate statistics such as activity shares mode shares time dependent mode specific OD flows and time dependent mode specific sensor counts . We formulate the calibration problem as a simulation based optimization problem and propose a stochastic gradient based solution procedure to solve it . | We formulate the ABM calibration problem as a simulation based optimization problem. We calculate objective function and its gradient using approximate analytical expressions. We solve the formulated problem using stochastic gradient based algorithms. We demonstrate the correctness efficacy and convergence of the procedure. |
S0968090X20305660 | This study develops a new map matching algorithm targeting off line applications . The algorithm takes a holistic view of the entire GPS trajectory and finds its match by first dividing it into several segments . This segmentation is made possible through creating a multi layer road index system for the original road network . For each segment a global map matching strategy is employed to identify the best match . The algorithm is compared against three state of the art map matching algorithms from the literature . To get ground truth data we design and perform numerous test drives with predefined paths that have a total length of 234km . GPS trajectories recorded during the test drives are used to evaluate the algorithms . Our numerical experiments show the proposed algorithm improves match efficiency by up to two order of magnitude compared to the benchmark algorithms . Importantly it achieves this remarkable speedup with negligible losses in matching accuracy . | A method EICN is designed to build a semantic multi layer road index MRI system. A map matching algorithm SMRI is proposed based on a well established MRI system. TA real world experiment shows the reliability of EICN and the efficiency of SMRI. The MRI system can support urban analysis at directional road segment DRS level. |
S0968090X20305672 | Traffic signals while serving an important function to coordinate vehicle movements through intersections also cause frequent stops and delays particularly when they are not properly timed . Such stops and delays contribute to significant amount of fuel consumption and greenhouse gas emissions . The recent development of connected and automated vehicle technology provides new opportunities to enable better control of vehicles and intersections that in turn reduces fuel consumption and emissions . In this paper we propose a trajectory optimization method PTO GFC to reduce the total fuel consumption of a CAV platoon through a signalized intersection . In this method we first apply platoon trajectory optimization to obtain the optimal trajectories of the platoon vehicles . In PTO all CAVs in one platoon are considered as a whole that is all other CAVs follow the trajectory of the leading one with a time delay and minimum safety gap which is enabled by vehicle to vehicle communication . Then we apply gap feedback control to control the vehicles with different speeds and headways merging into the optimal trajectories . We compare the PTO GFC method with the other two methods in which the leading vehicle adopts the optimal trajectory or drive with maximum speed respectively and the other vehicles follow the leading vehicle with a simplified Gipps car following model . Furthermore we extend the controls into multiple platoons by considering the interactions between the two platoons . The numerical results demonstrate that PTO GFC has better performance than LTO and AT particularly when CAVs have enough space and time to smooth their trajectories . The reduction of travel time and fuel consumption shows the great potential of CAV technology in reducing congestion and negative environmental impact of automobile transportation . | A platoon trajectory optimization PTO method is proposed to reduce fuel use. The PTO method also reduced platoon travel time. The PTO method is extended to multiple platoons with a boundary constraint. The extended PTO allows platoons to move through multiple intersections efficiently. |
S0968090X2030574X | This study develops a novel mixed integer non linear program to control the trajectory of mixed connected automated vehicles and connected human driven vehicles through signalized intersections . The trajectory of CAVs is continuously optimized via a central methodology while a new white phase is introduced to enforce CHVs to follow their immediate front vehicle . The movement of CHVs is incorporated in the optimization framework utilizing a customized linear car following model . During the white phase CAVs lead groups of CHVs through an intersection . The proposed formulation determines the optimal signal indication for each lane group in each time step . We have developed a receding horizon control framework to solve the problem . The case study results indicate that the proposed methodology successfully controls the mixed CAV CHV traffic under various CAV market penetration rates and different demand levels . The results reveal that a higher CAV market penetration rate induces more frequent white phase indication compared to green red signals . The proposed program reduces the total delay by 19.6 96.2 compared to a fully actuated signal control optimized by a state of practice traffic signal timing optimization software . | Jointly controlling vehicle trajectory and signal timing in mixed human driven and automated vehicle flows. Introducing a new white phase to enforce following immediate front vehicle. Using traffic lights as stationary and connected automated vehicles as moving controllers. Customizing a car following model to incorporate signal control variables into the formulation. Utilizing white phase yields significant improvements in traffic operations. |
S0968090X20305751 | License plate recognition data are emerging data sources in urban transportation systems which contain rich information . Large scale LPR systems have seen rapid development in many parts of the world . However limited by privacy considerations LPR data are seldom available to the research community which lead to huge research gap in data driven applications . In this study we propose a complete solution using LPR data for link based traffic state estimation and prediction for arterial networks . The proposed integrative data driven framework provides the inference of both cycle maximum queue length states and average travel times of links using LPR data from a subset of intersections in an arterial network . The framework contains three novel data driven sub components that are highly customized based on the characteristics of LPR data including a traffic signal timing inference model to find signal timing information from the LPR timestamp sequences a light weighted queue length approximation model to estimate lane based cycle maximum queue lengths and a network wide traffic state inference model to perform network level estimation and prediction using partially observed data . This study exploits and utilizes the unique features of LPR data and other similar vehicle re identification data for urban network wide link based traffic state estimation and prediction . A six days LPR dataset from a small road network in the city of Langfang in China and a more comprehensive link level field experiment dataset are used to validate the model . Numerical results show that the framework provides good estimation and prediction accuracy . The proposed framework is efficient and calibration free which can be easily implemented in urban networks for various real time traffic monitoring and control applications . | Urban network level traffic states inference model using partially available data. Complete solution for link queue lengths and travel times inference using LPR data. New framework combines traffic flow theory and customized machine learning models. Applicable to LPR data and other similar vehicle re identification data. The framework is efficient calibration free and easily deployable in real world. |
S0968090X20305763 | Bus bunching has been a long standing problem that undermines the efficiency and reliability of public transport services . The most popular countermeasure in practice is to introduce static and dynamic holding control . However most previous holding control strategies mainly consider local information with a pre specified headway schedule while the global coordination of the whole bus fleet and its long term effect are often overlooked . To efficiently incorporate global coordination and long term operation in bus holding in this paper we propose a multi agent deep reinforcement learning framework to develop dynamic and flexible holding control strategies for a bus route . Specifically we model each bus as an agent that interacts with not only its leader follower but also all other vehicles in the fleet . To better explore potential strategies we develop an effective headway based reward function in the proposed framework . In the learning framework we model fleet coordination by using a basic actor critic scheme along with a joint action tracker to better characterize the complex interactions among agents in policy learning and we apply proximal policy optimization to improve learning performance . We conduct extensive numerical experiments to evaluate the proposed MDRL framework against multiple baseline models that only rely on local information . Our results demonstrate the superiority of the proposed framework and show the promise of applying MDRL in the coordinative control of public transport vehicle fleets in real world operations . | A multi agent deep reinforcement learning framework is proposed for bus holding control. A reward function is defined to achieve headway self equalization. The action of each agent is considered by introducing a joint action tracker. A scheme based on proximal policy optimization is designed to train the agents. The framework outperforms other baselines in simulation studies. |
S0968090X20305775 | A model used for velocity control during car following is proposed based on reinforcement learning . To optimize driving performance a reward function is developed by referencing human driving data and combining driving features related to safety efficiency and comfort . With the developed reward function the RL agent learns to control vehicle speed in a fashion that maximizes cumulative rewards through trials and errors in the simulation environment . To avoid potential unsafe actions the proposed RL model is incorporated with a collision avoidance strategy for safety checks . The safety check strategy is used during both model training and testing phases which results in faster convergence and zero collisions . A total of 1 341 car following events extracted from the Next Generation Simulation dataset are used to train and test the proposed model . The performance of the proposed model is evaluated by the comparison with empirical NGSIM data and with adaptive cruise control algorithm implemented through model predictive control . The experimental results show that the proposed model demonstrates the capability of safe efficient and comfortable velocity control and outperforms human drivers in that it 1 has larger TTC values than those of human drivers 2 can maintain efficient and safe headways around 1.2s and 3 can follow the lead vehicle comfortably with smooth acceleration . Compared with the MPC based ACC algorithm the proposed model has better performance in terms of safety comfort and especially running speed during testing . The results indicate that the proposed approach could contribute to the development of better autonomous driving systems . Source code of this paper can be found at | Reinforcement learning for safe efficient comfortable vehicle velocity control. A reward function is developed by combining driving features. Collision avoidance strategy is incorporated for safety and faster convergence. The model outperforms human drivers and has faster running speed than MPC. |
S0968090X20305787 | A major challenging issue related to the emerging mixed traffic vehicular system composed of connected and automated vehicles together with human driven vehicles is the lack of adequate modeling and control framework especially at traffic bottlenecks such as highway merging areas . A hierarchical control framework for merging areas is first outlined where we assume that the merging sequence is decided by a higher control level . The focus of this paper is the lower level of the control framework that establishes a set of control algorithms for cooperative CAV trajectory optimization defined for different merging scenarios in the presence of mixed traffic . To exploit complete cooperation flexibility of the vehicles we identify six scenarios consisting of triplets of vehicles defined based on the different combinations of CAVs and conventional vehicles . For each triplet different consecutive movement phases along with corresponding desired distance and speed set points are designed . Through the movement phases the CAVs engaged in the triplet cooperate to determine their optimal trajectories aiming at facilitating an efficient merging maneuver while complying with realistic constraints related to safety and comfort of vehicle occupants . Distinct models are considered for each triplet and a Model Predictive Control scheme is employed to compute the cooperative optimal control inputs in terms of acceleration of CAVs accounting also for human driven vehicles uncertainties such as drivers reaction time and desired speed tracing error . Simulation investigations demonstrate that the proposed cooperative merging algorithms ensure efficient and smooth merging maneuvers while satisfying all the prescribed constraints . | Introducing triplets of vehicles to utilize the full flexibility of CAVs in the mixed traffic. Presenting cooperative optimal merging maneuvers satisfying safety and comfort constraints. Providing smooth merging maneuvers avoiding the merging vehicle to stop. The controllers do not need to be readjusted. |
S0968090X20305799 | Dedicated Lanes have been proposed as a potential scenario for the deployment of Automated and or Connected Vehicles on the road network . However evidence based knowledge regarding the impacts of different design configurations utilization policies and the design of their access egress on traffic safety and efficiency is limited . In order to develop an adequate design for DLs first a conceptual framework describing the relations and interrelations between these factors and traffic safety and efficiency is needed . Therefore the main aim of this paper is to develop a conceptual framework accounting for the factors that could affect the safety and efficiency of DLs . This conceptual framework is underpinned based on relevant literature on how the deployment of C AVs driver behaviour and DL design and operation affect traffic safety and efficiency . Based on the conceptual framework the knowledge gaps on DL design for C AVs were identified and a research agenda including prioritization of the research needs is proposed . | A conceptual framework for dedicated lane design for automated vehicles is presented. The conceptual framework is underpinned based on examples of relevant literature. The knowledge gaps were identified and a research agenda is proposed. Future research should take into account the driver behaviour in designing DLs. |
S0968090X20305805 | Urban ride hailing demand prediction is a long term but challenging task for online car hailing system decision taxi scheduling and intelligent transportation construction . Accurate urban ride hailing demand prediction can improve vehicle utilization and scheduling reduce waiting time and traffic congestion . Existing traffic flow prediction approaches mainly utilize region based situation awareness image or station based graph representation to capture traffic spatial dynamic while we observe that combination of situation awareness image and graph representation are also critical for accurate forecasting . In this paper we propose the Multiple Spatio Temporal Information Fusion Networks a novel deep learning approach to better fuse multiple situation awareness information and graphs representation . MSTIF Net model integrates structures of Graph Convolutional Neural Networks Variational Auto Encoders and Sequence to Sequence Learning model to obtain the joint latent representation of urban ride hailing situation that contain both Euclidean spatial features and non Euclidean structural features and capture the spatio temporal dynamics . We evaluate the proposed model on two real world large scale urban traffic datasets and the experimental studies demonstrate MSTIF Net has achieved superior performance of urban ride Hailing demand prediction compared with some traditional state of art baseline models . | To the best of our knowledge it is the first exploration to fuse graph level representation and pixel level representation to obtain superior joint representation in ride hailing demand prediction. We transfer hybrid GCN model from station based scenes to grid based scenes by modeling adjacency matrices without any additional data. We conduct extensive experiments on two real world datasets and our proposed approach has achieved superior performance compared with traditional methods. |
S0968090X20305817 | The recent years have witnessed a greater demand for understanding how people move in urban environments . Due to the widespread usage of mobile phones there have been several trajectory based studies focusing on extracting the characteristics of human mobility from georeferenced mobile phone data . Mobile positioning data is generally generated as scattered points in CDRs . Even though CDR data can be regarded as an inexpensive scalable source of information on human mobility mobility studies in urban settings based on such data sources still prove to be a research challenge due to the coarseness of CDR spatial granularity . Motivated by the need for transforming large scale CDRs to movement trajectories the present study offers a new solution which is made of two principal building blocks Developing a Bayesian based induction method through adopting a GIS based wave propagation model to solve the GSM based localization problem when methods such as triangulation are not applicable due to the lack of measurements from more than one base station Reconstruction of movement trajectories from cellular location information using overlapping relations existing between observed cells as well as detection of ping pong phenomena as auxiliary information . A case study employing CDR and GPS records obtained from an experimental survey on one of the central urban zones of Tehran was conducted which showed the effectiveness of the proposed methodology in comparison to current approaches with respect to three perspectives including movement path exploration individual oriented movement features extraction and crowd movement modelling . | An approach to improve the accuracy of reconstructed CDR based trajectory is proposed. Radio wave propagation modelling can improve CDR based localization accuracy. GSM based probability localization reduces the uncertainty of Cell ID positioning. The proposed approach is effective in individual movement exploration. The proposed approach can be employed to accurately map the cellular trips to the desired urban zones of an OD matrix. |
S0968090X20305829 | The use of smartphone applications to acquire real time and readily available journey planning information is becoming instinctive behavior by public transport users . Through the apps a passenger not only seeks a path from origin to destination but a satisfactory path that caters to the passengers preferences at the desired time of travel . Essentially apps attempt to provide a means of personalized PT service . As the implications of the Covid 19 pandemic take form and infiltrate human and environmental interactions passenger preference personalization will likely include avoiding risks of infection or contagious contact . The personal preferences are enabled by multiple attributes associated with alternative PT routes . For instance preferences can be connected to attributes of time cost and convenience . | Establish an adjusted design framework for optimal paths for public transport users considering their preferences at the requested time of travel. Devise a novel lexicographical comparison methodology with a just noticeable difference JND consideration that captures human perception elements combined with preferences over different PT attributes. Establish the theorem for the comparison method to satisfy the axiom of transitivity and develop a sorting algorithm and prove its correctness. Case study using simulation on the Copenhagen PT network and the results of the case study imply a favorable potential for real life applications. |
S0968090X20305830 | Modality style defined as a set of frequent travel modes characterizing the travelers habits routines and predispositions is a key player in forming dynamics of travelers mode choice behavior . This study aims to uncover the dynamics of modal preferences while the Mobility on Demand services operate in the market . Using the 2017 National Household Travel Survey data a Multiple Discrete Continuous Extreme Value model is developed to analyze the dynamics of individuals modality style . This model enables us to take into consideration marginal rates of substitutions between different transportation modes . Variables of interest in this analysis include the frequency of use of mobility on demand services as well as the frequency of walking biking transit and auto trips over the course of a month . The results of this study offer city planners and policymakers an opportunity to better understand the factors underlying modality styles and which priorities to focus on when designing a sustainable development plan for resident centric Smart Cities . According to the results age work status education auto availability and the built environments are among the significant contributors to the modality styles . The results also indicate that the extent of the substitution relationship between transit and MoD services is highly context dependent . | We investigate the dynamics of modal preferences while the MoD services operate. This study uses the 2017 National Household Travel Survey data. We utilizedthe MDCEV formulation to consider marginal rates of substitution. The substitution relationship between transit and MoD is highly context dependent. A pro active approach to planning towards Smart Cities will be vital. |
S0968090X20305842 | A control framework is developed that regulates a Metro type rail line in the presence of disruptions while taking into account the tradeoff between user and operator costs . Unlike approaches based solely on computational intensive optimization models the proposed methodology is based on three eminently solvable analytical models . These formulations sequentially apply train holding and speed control strategies with the objective of reducing social cost . In the initial phase immediately following a disruption one of the holding models determines the magnitude of the holds to be imposed on each train on the line . Once the trains are moving again and the recovery period begins the other holding model and the speed control model take over defining respectively the train holdings at each of the stations and the train speeds between them . Simulation results demonstrate that the frameworks performance is similar to that of an optimization model applying overall control to the entire system . The methodology can also be used to reveal the effects of different relevant parameters on the control decisions to be taken . | A control framework is developed that regulates a Metro line under disruptions. Introduce a user travel time cost formulation that is sensitive to crowdedness. Control strategies including a combination of speed control and holding strategies. Analytical models result closely approaches the performance of an optimization tool. |
S0968090X20305854 | This study models a multi modal network with ridesharing services . The developed model reproduces the scenario where travelers with their own cars may choose to be a solo driver a ridesharing driver a ridesharing rider or a public transit passenger while travelers without their own cars can only choose to be either a ridesharing rider or a public transit passenger . The developed model can capture the time dependent choices of travelers and the evolution of traffic conditions i.e . the within day traffic dynamics . In particular the within day traffic dynamics in a city region is modeled through an aggregate traffic representation i.e . the Macroscopic Fundamental Diagram . This paper further develops a doubly dynamical system that examines how the within day time dependent travelers choices and traffic conditions will evolve from day to day i.e . the day to day dynamics . Based on the doubly dynamical framework this paper proposes two different congestion pricing schemes that aim to reduce network congestion and improve traffic efficiency . One scheme is to price all vehicles including both solo driving and ridesharing vehicles while the other scheme prices the solo driving vehicles only in order to encourage ridesharing . The pricing levels can be determined either through an adaptive adjustment mechanism from period to period driven by observed traffic conditions or through solving a bi level optimization problem . Numerical studies are conducted to illustrate the models and effectiveness of the pricing schemes . The results indicate that the emerging ridesharing platform may not necessarily reduce traffic congestion but the proposed congestion pricing schemes can effectively reduce congestion and improve system performance . While pricing solo driving vehicles only may encourage ridesharing it can be less effective in reducing the overall congestion when compared to pricing both solo driving and ridesharing vehicles . | This study models the ridesharing program in a doubly dynamical system. This study adopts a trip based MFD approach to model within day traffic dynamics. This study finds that the emerging ridesharing program may not necessarily reduce traffic congestion. This study develops and compares different pricing strategies to improve system performance. |
S0968090X20305866 | Traffic forecasting is a classical task for traffic management and it plays an important role in intelligent transportation systems . However since traffic data are mostly collected by traffic sensors or probe vehicles sensor failures and the lack of probe vehicles will inevitably result in missing values in the collected raw data for some specific links in the traffic network . Although missing values can be imputed existing data imputation methods normally need long term historical traffic state data . As for short term traffic forecasting especially under edge computing and online prediction scenarios traffic forecasting models with the capability of handling missing values are needed . In this study we consider the traffic network as a graph and define the transition between network wide traffic states at consecutive time steps as a graph Markov process . In this way missing traffic states can be inferred step by step and the spatialtemporal relationships among the roadway links can be incorporated . Based on the graph Markov process we propose a new neural network architecture for spatialtemporal data forecasting i.e . the graph Markov network . By incorporating the spectral graph convolution operation we also propose a spectral graph Markov network . The proposed models are compared with baseline models and tested on three real world traffic state datasets with various missing rates . Experimental results show that the proposed GMN and SGMN can achieve superior prediction performance in terms of both accuracy and efficiency . Besides the proposed models parameters weights and predicted results are comprehensively analyzed and visualized . | Defining the transition between traffic states as a graph Markov process. Proposing a graph Markov network GMN for spatialtemporal data forecasting. Graph Markov network can predict traffic states and infer missing data simultaneously. |
S0968090X20305878 | High quality reliable data and robust models are central to the development and appraisal of transportation planning and policy . Although conventional data may offer good content it is widely observed that it lacks context i.e . who and why people are travelling . Transportation modelling has developed within these boundaries with implications for the planning design and management of transportation systems and policy making . This paper establishes the potential of passively collected GPS based Track Trace datasets of individual mobility profiles towards enhancing transportation modelling and policy making . T T is a type of New and Emerging Data Form lying within the broader Big Data paradigm and is typically collected using mobile phone sensors and related technologies . These capture highly grained | First definition of mobile phone Track Trace T T personal mobility data. Limitations of conventional data and models within transportation are summarised. Mobility content and individual context ceonceptualise New and Emerging Data Forms. The unique potential and challenges of GPS based T T data are characterised. We propose that T T data can be integrated into existing planning and policy methods. |
S0968090X2030588X | Sparsity and missing data problems are very common in spatiotemporal traffic data collected from various sensing systems . Making accurate imputation is critical to many applications in intelligent transportation systems . In this paper we formulate the missing data imputation problem in spatiotemporal traffic data in a low rank tensor completion framework and define a novel truncated nuclear norm on traffic tensors of location | A low rank tensor completion framework is developed for spatiotemporal traffic. We use a truncated nuclear norm TNN in tensor rank approximation. The TNN based model shows superior performance on various traffic data sets. |
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