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Automotive infotainment systems are expected to be first deployed on highways to service drivers travelling long distances, who are more likely to utilize the infotainment applications. In order to meet the stringent requirements of the infotainment systems, road side units (RSUs) are installed along the highway to facilitate a continuous vehicle-to-infrastructure (V2I) connectivity. Due to the long travelling distance and small coverage of the individual RSU, a more cost-effective solution would be to combine V2I with the vehicle-to-vehicle (V2V) communications to maintain the continuous connectivity. In this paper, we propose a new dynamic cooperation scheme that employs a dynamic forwarder selection strategy to generate an adaptive multi-hop V2V path for connectivity maintenance and throughput enhancement at a vehicle located outside of the RSU's coverage range. For the commonly assumed scenario that all vehicles travel in the same direction and at the same speed, we develop an analytical model and derive closed-formed expressions for the average out-of-range connection time, number of service resumptions and achieved throughput. The developed analytical model provides insights into the impacts of inter-RSU distance, vehicles' assistance willingness and the target vehicle's buffer size to the network performance. Simulation results with practical parameter settings show that our proposed scheme is effective in improving connectivity while offering a high throughput for the target vehicle. In particular, a high vehicle density, more assistance willingness by the forwarders and a large buffer size at the target vehicle are shown to be helpful in sparse RSU deployments.

A fully separated bicycle network from vehicular traffic is not realistic even for the most bicycle-friendly cities. Thus, all around the world urban cycling entails switching between streets of different safety, convenience, and comfort levels. As a consequence, the quality of bicycle networks should be evaluated not based on one but multiple factors and by considering the different user preferences regarding these factors. More comprehensive methodologies to assess urban bicycle networks are essential to the operation and planning of modern city transportation. This work proposes a multi-objective methodology to assess—what we refer to as—bikeability between origin–destination locations and over the entire network, useful for evaluation and planning of bicycle networks. We do so by introducing the concept of bikeability curves which allows us to assess the quality of cycling in a city network with respect to the heterogeneity of user preferences. The application of the proposed methodology is demonstrated on two cities with different bike cultures: Amsterdam and Melbourne. Our results suggest the effectiveness of bikeability curves in describing the characteristic features and differences in the two networks.

For station-based bike-sharing systems, the balance between user demand and bike allocation is critical for the operation. As a basic operational index, the short-term prediction of bike numbers (f...

Introduction: This research aims to investigate the perceptions and reactions of drivers regarding freeway merging situation, utilizing a new approach with the basis of a multilevel simulation platform which incorporates virtual reality (VR) technology. Methods: A VR driving environment integrated with traffic micro-simulation was developed to evaluate driving behaviors and the impact of merging decisions in terms of traffic conflicts. The driving experiments were conducted under a variety of circumstances, including varying traffic flows and the presence of ramp metering. The Surrogate Safety Assessment Model (SSAM) was utilized to extract the number of conflicts from the micro-simulation results. Results: The final results indicated that the probability of conflict has a positive correlation with traffic flow, while conflict frequency at freeway merges is affected by the presence of ramp metering due to its potentiality to enhance driver decisions and reduce the drivers’ pressure when they make maneuvers. Practical Applications: The findings reveal that the proposed VR simulation platform is a useful tool to improve the safety of freeway merging. It has the potential to enhance driver skills and can also be used in the study of human–machine interaction.

This paper develops a real-time agent-based simulation model to optimize the dynamic bus stop-skipping and holding schemes. The proposed agent-based simulation model is capable of modeling, schedul...

Perimeter control is an effective city-scale solution to tackle congestion problems in urban networks. To accommodate the unpredictable dynamics of congestion propagation, it is essential to incorporate real-time robustness against travel demand fluctuations into a pragmatic perimeter control strategy. This paper proposes robust perimeter control algorithms based on partial information feedback from the network. The network dynamics are modeled using the concept of the Macroscopic Fundamental Diagram (MFD), where a heterogeneously congested network is assumed to be partitioned into two homogeneously congested regions, and an outer region that acts as demand origin and destination. The desired operating condition of the network is obtained by solving an optimization program. Observer-based H∞ proportional (P) and proportional-integral (PI) controllers are designed based on Lyapunov theory, to robustly regulate the accumulation of each region and consequently to maximize the network outflow. The controller design algorithms further accommodate operational constraints by guarantying: (i) the boundedness of the perimeter control signals and (ii) a bounded offset between the perimeter control signals. Control parameters are designed off-line by solving a set of linear matrix inequalities (LMI), which can be solved efficiently. Comprehensive numerical studies conducted on the nonlinear model of the network highlight the effectiveness of the proposed robust control algorithms in improving the congestion in the presence of time-varying disturbance in travel demand.

Time-dependent origin–destination (OD) demand estimation using link traffic data in a large-scale network is a highly underdetermined problem. As a result, providing an accurate initial solution is crucial for obtaining a more reliable estimated demand. In this paper, we discuss the necessity of having a comprehensive demand profiling model that considers the spatial differences of OD pairs and we demonstrate its application in the calibration of large-scale traffic assignment models. First, we apply a departure choice model that adds a time dimension to the OD demand flows concerning their spatial differences. The time-profiled demand is then fed into the time-dependent OD demand estimation problem for further adjustment. Results show that in addition to reducing the error between simulation outputs and the observed link counts, the estimated demand profile more accurately reflects the spatial correlation of the OD pairs in the large-scale network being studied. Results provide practical insights into deployment and calibration of simulation-based dynamic traffic assignment models.

Over 30 years have passed since activity-based travel demand models (ABMs) emerged to overcome the limitations of the preceding models which have dominated the field for over 50 years. Activity-based models are valuable tools for transportation planning and analysis, detailing the tour and mode-restricted nature of the household and individual travel choices. Nevertheless, no single approach has emerged as a dominant method, and research continues to improve ABM features to make them more accurate, robust, and practical. This paper describes the state of art and practice, including the ongoing ABM research covering both demand and supply considerations. Despite the substantial developments, ABM’s abilities in reflecting behavioral realism are still limited. Possible solutions to address this issue include increasing the inaccuracy of the primary data, improved integrity of ABMs across days of the week, and tackling the uncertainty via integrating demand and supply. Opportunities exist to test, the feasibility of spatial transferability of ABMs to new geographical contexts along with expanding the applicability of ABMs in transportation policy-making.

There has been an increasing level of demand for faster, safer and greener transportation systems with higher levels of capacity and convenience, though the implementation of transportation systems overall is often restricted by geographical limitations, presenting a challenge to scientists and engineers in the field. However, we have been witnessing the evolution of the transportation systems over the last few decades, and at present we are facing a new era of intelligent transportation systems (ITS) empowered by artificial intelligence (AI) technologies. There have been classification, deep learning, and reinforcement learning techniques, to name a few, which collectively have enabled almost all technical elements of the ITS. For example, autonomous vehicle technologies are now mature enough to introduce self-driving cars, taxis, buses, and trucks on the roads and streets; traffic signals are controlled by AI-based systems for far more enhanced traffic efficiency; and machine learning based on big data is improving the operational performance of transportation systems to the next level of safety, efficiency, and sustainability.

The dynamic traffic assignment (DTA) problem has been studied intensively in the literature. However, there is no existing linear framework to solve the user equilibrium (UE) DTA problem. In this paper, we develop a novel linear programming framework to solve the UE-DTA problem in a dynamic capacity network that exploits the linkage between the UE and system optimal (SO) solutions underpinned by a first-in-first-out (FIFO) principle. This important property enables us to develop an incremental loading method to obtain the UE solutions efficiently by solving a sequence of linear programs. The proposed solution methodology possesses several nice properties such as a predictable number of iterations before reaching the UE solution, and a linear system of equations to be solved in each of the iterations. In contrast to the related iterative methods, such as Frank–Wolfe algorithm, successive average (MSA) or projection and their extensions where the purpose of iteration is to seek the solution convergence, whereas ours is to solve a linear problem over multiple iterations but only for a single unit of demand in each iteration. Furthermore, we provide a theoretical proof that in the limit, the SO objective can be used to obtain the UE solution as the system time step goes to zero given the satisfaction of the FIFO constraint. We show via numerical examples the significant improvements in the obtained UE solutions both in terms of accuracy and computational complexity.

Traffic state estimation plays an important role in Intelligent Transportation Systems (ITS). It provides the latest traffic information to travelers and feedback to signal control systems. The Interactive Multiple Model (IMM) filtering provides a powerful estimation method to deal with the non-differentiable nonlinearity caused by the phase transitions between the under-critical and above-critical traffic density regimes. The IMM filtering also accounts for the uncertainty in the current ‘mode of operation’. In this paper, we develop an enhanced IMM filtering approach to traffic state estimation, with an underlying Cell Transmission Model (CTM) for traffic flow propagation. We improve the IMM filtering with CTM in two ways: (1) We apply two simplifying assumptions that are highly likely to hold in urban roads in incident-free conditions, which makes the computational complexity to grow with the number of cells only polynomially , rather than exponentially as reported in prior work. (2) We apply a novel approach to noise modeling wherein the process noise is explicitly obtained in terms of the randomness in more fundamental quantities (e.g., free-flow speed, maximum flow capacity, etc.), which not only makes noise calibration using real data convenient but also makes the computation of the cross-correlation between the process and measurement noises transparent. However, it leads to ‘process dynamic’ and ‘measurement’ equations that involve multiplier matrices whose elements are random variables rather than deterministic scalars, and hence, standard filtering equations cannot be applied. We derive the appropriate filtering equations from first principles. We calibrate the traffic parameters and the total inflow and outflow on the links using the SCATS loop detector data collected in Melbourne and report significant improvements in accuracy, which is due to the accurate computation of the cross-covariance of process and measurement noises.

Classification of congestion patterns is important in many areas in traffic planning and management, ranging from policy appraisal, database design, to prediction and real-time control. One of the key constraints in applying machine learning techniques for classification is the availability of sufficient data (traffic patterns) with clear and undisputed labels, e.g. traffic pattern X or Y. The challenge is that labelling traffic patterns (e.g. combinations of congested and freely flow areas over time and space) is highly subjective. In our view this means that assessment of how well algorithms label the data should also include a qualitative component that focuses on what the found patterns really mean for traffic flow operations and applications. In this study, we investigate the application of clustering analysis to obtain labels automatically from the data, where we indeed first qualitatively assess how meaningful the found labels are, and subsequently test quantitatively how well the labels separate the resulting feature space. By transforming traffic measurements (speeds) into (colored) images, two different approaches are proposed to extract the features of a large number of traffic patterns for clustering: point-based and area-based. The point-based approach is widely applied in the image processing literature, and explores local interest points in images (i.e. where large changes occur in color intensity); whereas a new area-based approach combines domain knowledge with Watershed segmentation to partition the images into different spatial-temporal segments from which domain specific features, such as wide moving jam patterns, are extracted. The results show that the Watershed segmentation separates the traffic (congestion) patterns into more meaningful and separable classes, comparable to those that have been proposed in the literature. Since there is no ground-truth set of labels, the quantitative assessment tests how well both methods are able to separate the respective feature spaces they construct for the (large) database of traffic patterns. We argue that the more crisp this separation is; the better the labelling has turned out. For this quantitative comparison we train a multinomial classifier that maps unseen patterns to the labels discovered by each of the two labeling approaches. The most important result is that the classifier using the area-based feature vector achieves the highest average levels of confidence in its decisions to classify patterns, implying a highly separable feature vector space. We argue this is good news! Not only does the combination of image processing (Watershed) and domain knowledge (traffic flow characteristics) lead to meaningful labels that can be automatically retrieved from large databases of data; this method also leads to a more efficient separation of the resulting feature space. Our next endeavor is to further refine and use this method to develop a search engine for the (rapidly growing) 200 TB historical database of traffic data hosted by the Dutch National Datawarehouse (NDW).

Autonomous-vehicle (AV) technologies are rapidly advancing, but a great deal remains to be learned about their interaction and perception on public roads. Research in this area usually relies on AV trials using naturalistic driving which are expensive with various legal and ethical obstacles designed to keep the general public safe. The emerging concept of Wizard-of-Oz simulation is a promising solution to this problem wherein the driver of a standard vehicle is hidden from the passenger using a physical partition, providing the illusion of riding in an AV. Furthermore, head-mounted display (HMD) virtual reality (VR) has been proposed as a means of providing a Wizard-of-Oz protocol for on-road simulations of AVs. Such systems have potential to support a variety of study conditions at low cost, enabling simulation of a variety of vehicles, driving conditions, and circumstances. However, the feasibility of such systems has yet to be shown. This study makes use of a within-subjects factorial design for examining and evaluating a virtual reality autonomous vehicle (VRAV) system, with the aim of better understanding the differences between stationary and on-road simulations, both with and without HMD VR. More specifically, this study examines the effects on user experience of conditions including presence, arousal, simulator sickness and task workload. Participants indicated a realistic and immersive driving experience as part of subjective evaluation of the VRAV system, indicating the system is a promising tool for human-automation interaction and future AV technology developments. acceptedVersion

Dynamic traffic assignment (DTA) is an important method in the long term transportation planning and management processes. However, in most existing system optimum dynamic traffic assignment (SO-DTA), no side constraints are used to describe the dynamic link capacities in a network which is shared by multiple vehicle types. Our motivation is based on the possibility for dynamic system optimum (DSO) to have multiple solutions, which differ in where queues are formed and dissipated in the network. To this end, this paper proposes a novel DSO formulation for the multi-class DTA problem containing both human driven and automated vehicles in single origin-destination networks. The proposed method uses the concept of link based approach to develop a multi-class DTA model that equally distributes the total physical queues over the links while considering explicitly the variations in capacity and backward wave speeds due to class proportions. In the model, the DSO is formulated as an optimization problem considering linear vehicle composition constraints representing the dynamics of the link capacities. Numerical examples are set up to provide some insights into the effects of automated vehicles on the queue distribution as well as the total system travel times.

Disasters of different types are an increasing threat to modern societies. The speed at which people can evacuate a disaster may mean the difference between life or death. One way of modelling disaster evacuation is by using multi-agent simulation, in which each person in a population is simulated as an autonomous individual. This enables emergency management groups to analyse the results across a range of scenarios, under varying parameter settings, to better under-stand how to reduce evacuation times. Previous studies have used this approach to model disaster evacuations, but only considered evacuation on foot. This research studies the effect of public transport on the evacuation process using the Multi-Agent Transport Simulation (MATSim) platform. Experimental results show large reductions in the average evacuation time of agents can be achieved using public transport, compared with pedestrian traffic only. A sensitivity analysis illustrates the conditions under which additional buses can be added to further reduce evacuation time and the limit of this improvement.

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 (for example, because of unclear boundaries between flow states). Smoothed Particle Hydrodynamics (SPH) 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 (a phenomenon observed in real crowds) 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 (also an observed phenomenon) using the proposed SPH framework, and this paper presents a solution to do so.

We develop in this paper a comprehensive linear mathematical framework to study the benefit of real-time information and the impact of resulting user adaptive route choice behaviours on network performance. The framework formulates the information-based stochastic user equilibrium (ISUE) dynamic traffic assignment (DTA) problem for a multiple origin-destination (OD) network. Using the framework, we prove the linkage between the user equilibrium (UE) and system optimal (SO) solutions underpinned by the first-in-first-out (FIFO) principle. This important property then enables us to develop an incremental loading method to obtain the ISUE solutions efficiently by solving a sequence of linear programs. Moreover, the proposed method is more scalable that avoids a huge enumeration of paths in large-scale networks as done in path-based methods of the existing literature on this topic. We show via numerical examples the impact of information on both route choices and network performance, and demonstrate the significant improvements in the obtained ISUE solution both in terms of accuracy and computational complexity.

Connected and autonomous (CA) vehicles have been verified to significantly improve traffic efficiency. However, there is still a long lifespan for heterogeneous traffic flow consisting of both human-driven and CA vehicles. Thus a deep understanding of the heterogeneous platoon dynamics is critical to the traffic stability issues for the deployment of CA vehicles in the near future. This paper aims to develop a multiclass microscopic model for a heterogeneous platoon which can explicitly demonstrate the interaction between human-driven and CA vehicles. Specifically, the consensus-based control algorithm is adopted to model the dynamics of CA vehicles and a typical car-following model is used to describe the dynamics of human-driven vehicles. We then theoretically and numerically study the linear stability condition of the heterogeneous platoon which takes into account the probabilistic delay in the communication, the penetration of the CA vehicles, and the relative order of the vehicle types in the platoon.

In this paper, we propose a novel mathematical framework to formulate a unified Bus Rapid Transit (BRT) with Transit Signal Priority (TSP) system for single destination networks, namely, Bus Priority System Optimal Dynamic Traffic Assignment with Signal Control (BP SODTA-SC). This framework considers dedicated bus lanes, bus routes, and priority for public bus transport in mixed bus-car scenarios. Furthermore, this approach assures fairness to all road users. It is linear and can be applied to analyze city-size BRT-TSP systems. Our numerical results show that bus priority significantly reduces Total System-wide Passenger Travel Time (TSPT).

Dynamic Traffic Assignment (DTA) is a mathematical framework that with a System Optimal (SO) objective is often used for long-term transport planning, design, and traffic management. However, the conventional SO-DTA formulation gives optimal solutions having an unrealistic vehicle Holding–Back (HB) property. Existing approaches in the literature aiming to resolve the HB problem are either computationally intractable or suffer from recursive parameter selection problem. In addition, most of the existing Signal Control (SC) models considered in the DTA formulation are mixed-integer or nonlinear in nature that are not scalable for large networks. With an exception, there exists a linear signal control model that can only set signal control cycle-length equal to DTA time-slot duration, and thus trades the accuracy of the SO-DTA solution for a more realistic cycle-length. In this article, we address the above issues by proposing a linear Non-Holding-Back SO-DTA with SC (NHB DTA-SC) formulation for sing...

Providing a railway transit system (RTS) in less populated areas is a challenging task for transportation agencies due to its high construction and operating costs. With the advent of automation, shared autonomous vehicles (SAVs) as an integral part of public transit services has the potential to enhance the design of transit systems. In this paper, we present a joint optimization framework of railway transit network design and SAV first-mile service that minimizes the total cost of the combined RTS-SAV services and commuters’ waiting time, while serving a dynamic travel demand in the network. The proposed model optimizes the SAV fleet size and the RTS alignment while enabling vehicle relocations to tackle the vehicle imbalance issue in the SAV service. Due to the non-linear and mixed-integer formulation, we develop a fixed-point algorithm for this joint RTS-SAV problem where we transform the original problem into a mixed-integer linear programming (MILP) formulation. Our results indicate that the joint RTS-SAV services can be constructed and operated at a lower cost than either of the RTS or SAV services alone. Furthermore, the resulting joint RTS-SAV services are underpinned by a shorter railway alignment and larger fleet size rather than a multi-link extension. Additionally, the joint RTS-SAV services is robust to the variation in total demand, with respect to the railway alignment, SAV utilization and commuters’ waiting time.

Route choice behavior has been recognized as an important factor in the traffic management and control strategy design. State-of-the-art research in this field (e.g. anticipatory traffic control approaches) often formulates this as a bi-level optimization problem where users’ reactions to changes in signal control are taken into account in the design of the control policies. Solving such a combined optimization problem is possible but difficult and complex which often involves the determination of the dynamic user equilibrium (UE). Nevertheless, anticipatory control inherently aims to optimize the signal control that anticipates or reacts to the users’ route choice and thus is a reactive control. Furthermore, the approaches used in anticipatory control are based on the coupling between control and traffic assignment where the route choice behavior of road users is unrealistically assumed to be in equilibrium.This paper proposes a novel utility based optimization framework to design a distributed control scheme based on a so-called BackPressure principle that is scalable and proactively influences the drivers’ route choice behavior. In particular, as opposed to the well-studied anticipatory control and bi-level optimization, the proposed utility optimization formulation solves for a system optimal problem where the optimal turning fraction (as an output decision variable) is enforced as a desirable outcome. The optimal turning fractions would then be achieved in a practical deployment either by directly asking the users to follow the systems routing advice or by influencing, i.e. manipulating, the route choice decisions made by them so that the resulting turning fraction is (or is close to) the optimal value. Although drivers may not follow the computed optimal fractions as desire, there exist several ways, e.g. see Groot et al. (2015), to design appropriate incentives for drivers to follow the route guidance, and thus improve the compliance rate and overall journey experience for everyone inside the network. To this end, we demonstrate by numerical results that better network performance is achieved using the proposed framework even with a limited compliance from the users. Furthermore, we have formally proved that our proposed utility optimization based BackPressure scheme stabilizes the network for any feasible traffic demands thus maximizing network throughput. In addition, we show through simulation and numerical results that the new policy outperforms the original BackPressure-based distributed control scheme both in terms of network throughput and other congestion measures such as travel time.

Conceptually, a Green Light Optimal Speed Advisory (GLOSA) system suggests speeds to vehicles, allowing them to pass through an intersection during the green interval. In previous papers, a single speed is computed for each vehicle in a range between acceptable minimum and maximum values (for example between standstill and the speed limit). This speed is assumed to be constant until the beginning of the green interval, and sent as advice to the vehicle. The goal is to optimise for a particular objective, whether it be minimisation of emissions (for environmental reasons), fuel usage or delay. This paper generalises the advice given to a vehicle, by optimising for delay over the entire trajectory instead of suggesting an individual speed, regardless of initial conditions – time until green, distance to intersection and initial speed. This may require multiple acceleration manoeuvres, so the advice is sent as a suggested acceleration at each time step. Such advice also takes into account a suitable safety constraint, ensuring that vehicles are always able to stop before the intersection during a red interval, thus safeguarding against last-minute signal control schedule changes. While the algorithms developed primarily minimise delay, they also help to reduce fuel usage and emissions by conserving kinetic energy. Since vehicles travel in platoons, the effectiveness of a GLOSA system is heavily reliant on correctly identifying the leading vehicle that is the first to be given trajectory advice for each cycle. Vehicles naturally form a platoon behind this leading vehicle. A time loop technique is proposed which allows accurate identification of the leader even when there are complex interactions between preceding vehicles. The developed algorithms are ideal for connected autonomous vehicle environments, because computer control allows vehicles’ trajectories to be managed with greater accuracy and ease. However, the advice algorithms can also be used in conjunction with manual control provided Vehicle-to-Infrastructure (V2I) communication is available.

We develop a comprehensive analytical model for multiple long-lived multipath Transmission Control Protocol (TCP) connections downloading content from a remote server in the Internet using parallel paths with Wi-Fi and cellular last-miles. This is the first analytical model developed in the literature that captures the coupling between the paths through heterogeneous wireless networks where the coupling arises due to the multipath TCP coupled congestion control protocol. The model also takes into account the impact of the shared nature of the wireless medium and the finite access point (AP) buffer in the Wi-Fi last-mile. The accuracy of the proposed model is demonstrated via extensive ns-2 simulations. Furthermore, we discover a new type of throughput unfairness among the competing regular and multipath TCP connections going through the same AP with a droptail buffer; the regular TCP connections essentially steal almost all the Wi-Fi bandwidth away from the multipath TCP connections. To tackle this problem, we present two simple solutions utilizing our analytical model and achieve fairness.

In vehicular networks (VNs), due to the high deployment cost of infrastructure, namely road side unit (RSU), RSUs can be deployed sparsely. The sparse RSU placement, coupled with the RSUs’ limited coverage range, has led to the existence of uncovered areas between two consecutive RSUs. When vehicles transition within these areas, the problem is that they must frequently experience service disruptions. This paper proposes a cooperation scheme that combines vehicle-to-vehicle (V2V) communication with the existing vehicle-to-infrastructure (V2I) communication to address the above problem. Specifically, a multi-hop forwarding scheme among vehicles is proposed. When a target vehicle has just left a RSU’s coverage range, it searches the farthest forwarder from its neighboring vehicles behind it. If the forwarder also enters the uncovered area, it will generate a multi-hop path back to that RSU’s coverage range using the same strategy of the target vehicle. Thus, there is always a multi-hop forwarding path to the target vehicle through V2V communications in the uncovered area. The analytical expressions are derived for the average achieved throughput and the average number of service disruptions at the target vehicle. Through a close agreement between analytical and simulation results, the advantages of our proposed scheme, such as high average throughput and service continuity, are verified. Additionally, the results indicate the improvement of our scheme compared to the sole V2I network.

Time-dependent origin-destination (TDOD) demand estimation is often formulated as a bi-level quadratic optimization in which the estimated demand in the upper-level problem is evaluated iteratively through a dynamic traffic assignment (DTA) model in the lower level. When congestion forms and propagates in the network, traditional solutions assuming a linear relation between demand flow and link flow become inaccurate and yield biased solutions. In this study, we study a sensitivity-based method taking into account the impact of other OD flows on the links’ traffic volumes and densities. Thereafter, we compare the performance of the proposed method with several well-established solution methods for TDOD demand estimation problem. The methods are applied to a benchmark study urban network and a major freeway corridor in Melbourne, Australia. We show that the incorporation of traffic density into flow-based models improves the accuracy of the estimated OD flows and assist solution algorithm in avoiding converging to a sub-optimal result. Moreover, the final results obtained from the proposed sensitivity-based method contains less amount of error while the method exceeds the problem’s computational intensity compared to the traditional linear method.

In car-following models, the driver reacts according to his physical and psychological abilities which may change over time. However, most car-following models are deterministic and do not capture the stochastic nature of human perception. It is expected that purely deterministic traffic models may produce unrealistic results due to the stochastic driving behaviors of drivers. This paper is devoted to the development of a distinct car-following model where a stochastic process is adopted to describe the time-varying random acceleration which essentially reflects the random individual perception of driver behavior with respect to the leading vehicle over time. In particular, we apply coupled Langevin equations to model complex human driver behavior. In the proposed model, an extended Cox-Ingersoll-Ross (CIR) stochastic process will be used to describe the stochastic speed of the follower in response to the stimulus of the leader. An important property of the extended CIR process is to enhance the non-negative properties of the stochastic traffic variables (e.g. non-negative speed) for any arbitrary model parameters. Based on stochastic process theories, we derive stochastic linear stability conditions which, for the first time, theoretically capture the effect of the random parameter on traffic instabilities. Our stability results conform to the empirical results that the traffic instability is related to the stochastic nature of traffic flow at the low speed conditions, even when traffic is deemed to be stable from deterministic models.

As a way of obtaining a visual expression of knowledge, mapping knowledge domain (MKD) provides a vision-based analytic approach to scientometric analysis which can be used to reveal an academic community, the structure of its networks, and the dynamic development of a discipline. This study, based on the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) articles on road safety, employs the bibliometric tools VOSviewer and CitNetExplorer to create maps of author co-citation, document co-citation, citation networks, analyze the core authors and classic documents supporting road safety studies and show the citation context and development of such studies. It shows that road safety studies clustered mainly into four groups, whose we will refer to as "effects of driving psychology and behavior on road safety", "causation, frequency and injury severity analysis of road crashes", "epidemiology, assessment and prevention of road traffic injury", and "effects of driver risk factors on driver performance and road safety", respectively. Through our analysis, the core publications and their citation relationships were quickly located and explored, and "crash frequency modeling analysis" has been identified to be the core research topic in road safety studies, with spatial statistical analysis technique emerging as a frontier of this topic.

This paper proposes a novel congestion pricing scheme for the metropolitan urban networks where a cumulative prospect theory (CPT) is adopted to quantify the travelers’ degree of satisfaction during their trips. Using the concept of the cumulative prospect values (CPV) in CPT, an area-based congestion price (or toll) is designed to reduce the peak-hour travel demand while taking into account the level of service perceived by the travelers. We demonstrate the effectiveness and efficiency of the proposed pricing scheme via an agent-based simulation for the Melbourne network. Results of the proposed CPV-based distance toll is compared with the flat distance-based toll and bi-direction cordon toll in terms of the network performance, behavioral changes, and social welfare improvement. Promisingly, the proposed CPV-based pricing scheme can reduce the long-distance trips (greater than10 km) inside the congested central business district by over 30% while discourage non-commuting trips during peak hours, making more road resources available to commuting trips. Furthermore, it is found that the proposed CPV-based pricing scheme yields positive savings, where the total time savings outweigh the revenues collected, thus proving its high efficiency and desirability among all the comparative pricing schemes.

The Dynamic System Optimum (DSO) traffic assignment problem aims to determine a time-dependent routing pattern of travellers in a network such that the given time-dependent origin-destination demands are satisfied and the total travel time is at a minimum, assuming some model for dynamic network loading. The network kinematic wave model is now widely accepted as such a model, given its realism in reproducing phenomena such as transient queues and spillback to upstream links. An attractive solution strategy for DSO based on such a model is to reformulate as a set of side constraints apply a standard solver, and to this end two methods have been previously proposed, one based on the discretisation scheme known as the Cell Transmission Model (CTM), and the other based on the Link Transmission Model (LTM) derived from variational theory. In the present paper we aim to combine the advantages of CTM (in tracking time-dependent congestion formation within a link) with those of LTM (avoiding cell discretisation, providing a more computationally attractive with much fewer constraints). The motivation for our work is the previously-reported possibility for DSO to have multiple solutions, which differ in where queues are formed and dissipated in the network. Our aim is to find DSO solutions that optimally distribute the congestion over links inside the network which essentially eliminate avoidable queue spillbacks. In order to do so, we require more information than the LTM can offer, but wish to avoid the computational burden of CTM for DSO. We thus adopt an extension of the LTM called the Two-regime Transmission Model (TTM), which is consistent with LTM at link entries and exits but which is additionally able to accurately track the spatial and temporal formation of the congestion boundary within a link (which we later show to be a critical element, relative to LTM). We set out the theoretical background necessary for the formulation of the network-level TTM as a set of linear side constraints. Numerical experiments are used to illustrate the application of the method to determine DSO solutions avoiding spillbacks, reduce/eliminate the congestion and to show the distinctive elements of adopting TTM over LTM. Furthermore, in comparison to a fine-level CTM-based DSO method, our formulation is seen to significantly reduce the number of linear constraints while maintaining a reasonable accuracy.

With urban population rapidly growing, traffic congestion is becoming a major problem. In this paper, a framework is proposed for identifying the spatial congested partitions in a dynamic urban road network and for monitoring the temporal changes in their locations and structure. To that end, a given road network is transformed into a suitable graph representation, an initial partitioning based on a spectral clustering approach is performed, and then the partitions continue to be updated incrementally on the basis of the newly obtained traffic data at each new time point. The congested partitions are then identified on the basis of traffic measures (e.g., volume and green time utilization) available from the traffic signal control system. Experiments with the proposed method are conducted with real historical traffic data collected from the 493 signalized traffic sites in Melbourne, Victoria, Australia, with a total of 1,444 road segments and 581 intersection points. Experimental results show that large-scale urban traffic networks undergo many rapid but regular and frequent traffic patterns, which often go unnoticed by the traffic network operators. Tracking these kinds of changes in real time by means of the proposed framework can improve the reaction time of the traffic management team and result in less congestion.

Accident Analysis & Prevention (AA&P) is a leading academic journal established in 1969 that serves as an important scientific communication platform for road safety studies. To celebrate its 50th anniversary of publishing outstanding and insightful studies, a multi-dimensional statistical and visualized analysis of the AA&P publications between 1969 and 2018 was performed using the Web of Science (WoS) Core Collection database, bibliometrics and mapping-knowledge-domain (MKD) analytical methods, and scientometric tools. It was shown that the annual number of AA&P's publications has grown exponentially and that over the course of its development, AA&P has been a leader in the field of road safety, both in terms of innovation and dissemination. By determining its key source countries and organizations, core authors, highly co-cited published documents, and high burst-strength publications, we showed that AA&P's areas of focus include the "effects of hazard and risk perception on driving behavior", "crash frequency modeling analysis", "intentional driving violations and aberrant driving behavior", "epidemiology, assessment and prevention of road traffic injuries", and "crash-injury severity modeling analysis". Furthermore, the key burst papers that have played an important role in advancing research and guiding AA&P in new directions - particularly those in the fields of crash frequency and crash-injury severity modeling analyses were identified. Finally, a modified Haddon matrix in the era of intelligent, connected and autonomous transportation systems is proposed to provide new insights into the emerging generation of road safety studies.

In this paper, we develop a hierarchical approach to optimize the signal timings in an urban traffic network taking into account the different dynamics in all traffic regimes. The proposed hierarchical control framework consists of two layers. The first layer--the network coordination layer--uses a model predictive control strategy based on a simplified traffic flow model to provide reference outflow trajectories. These reference outflow trajectories represent average desired link outflows over time. These are then mapped to green-red switching signals which can be applied to traffic lights. To this end, the second layer--the individual intersection control layer--then selects at every intersection the signal timing stage that realizes an outflow which has the smallest error with respect to the reference outflow trajectory. The proposed framework is tested using both macroscopic and microscopic simulations. It is shown that the control framework can outperform a greedy control policy that maximizes the individual intersection outflows, and the control framework can distribute the queues over the network in a way that the network outflow is improved. Simulations using a macroscopic model allow the direct application of the reference outflows computed by the network coordination layer, and the results indicate that the mapping of the reference outflows to the detailed signal timings by the individual intersection control layer only introduces a small performance loss.

Traffic congestion is a major concern in metropolitan areas and a quick congestion assessment of large-scale network is required for modern traffic management referred to as Intelligent Transport Systems (ITS). However, ITSs are facing the challenge of real-time storage, retrieval and processing of a vast amount of collected data over a large-scale network. Compressed sensing (CS) is an efficient technique of sampling high-dimensional data and is successfully used to reconstruct signals via a small set of non-adaptive, linear measurements. Whilst CS has mainly been applied for data reconstruction, we propose in this paper the use of CS for classification and estimation of some meaningful parameters in the traffic problem. To this end, we develop a novel sensing matrix for congestion analysis and show that for traffic data collected in Melbourne urban network, our CS provides an opportunity to extract the desired information from a small number of random projections. In addition, our method could improve the efficiency of database query and has the ability to deal with missing or defective data.

This paper develops a new signal control model in an optimal dynamic traffic assignment (DTA) framework to study the optimal signal settings and route choices, taking into account the transition between phases. The proposed model is convex and has a capability of predicting on-and-off traffic dynamic at intersections accurately. To the best of our knowledge, there have not been any previous work in the literature treating this feature of the traffic signal in a continuous-and-convex formulation. The DTA framework together with the new signal control model enables us to study the interaction between the dynamic route choices and signal settings (i.e., phases and green time) while minimizing the total system cost. Via a numerical example, we show the impact of the dynamic traffic demand on the phase ordering and routing, and the interdependency between them.

Searching through and selecting data sets from large traffic databases with sensor information is often a cumbersome manual process. In this paper we present an idea that may dramatically fasten and streamline this process. The idea is to build a fast search index (COSI: COngestion Search engIne) based on meta data in combination with features from the traffic patterns along routes. Instead of ploughing through the raw detector data, COSI makes it possible to search through higher level traffic (congestion) patterns. This paper explores a first step in developing COSI: a method to classify traffic congestion patterns reconstructed with the well known adaptive smoothing method. We demonstrate the method using a preliminary set of loop detector data in the Netherlands. We extract and store individual traffic congestion patterns of these regions as images. We then use SURF (Speeded Up Robust Features) to identify the descriptors vector of each image, and apply the bag-of-features technique to generate low-dimensional representation vectors for them. Finally, we use a multiclass extended SVM algorithm to classify these patterns. The results of the method are presented and a synthesis of the findings is given. We close with some preliminary conclusions and an outlook to the further development of COSI.

The congestion scenario on a road network is often represented by a set of differently congested partitions having homogeneous level of congestion inside. Due to the changing traffic, these partitions evolve with time. In this paper, we propose a two-layer method to incrementally update the differently congested partitions from those at the previous time point in an efficient manner, and thus track their evolution. The physical layer performs low-level computations to incrementally update a set of small-sized road network building blocks, and the logical layer provides an interface to query the physical layer about the congested partitions. At each time point, the unstable road segments are identified and moved to their most suitable building blocks. Our experimental results on different datasets show that the proposed method is much efficient than the existing re-partitioning methods without significant sacrifice in accuracy.

We consider a discrete population of users with homogeneous service demand who need to decide when to arrive to a system in which the service rate deteriorates linearly with the number of users in the system. The users have heterogeneous desired departure times from the system, and their goal is to minimise a weighted sum of the travel time and square deviation from the desired departure times. Users join the system sequentially, according to the order of their desired departure times. We model this scenario as a non-cooperative game in which each user selects his actual arrival time. We present explicit equilibria solutions for a two-user example, namely the Subgame Perfect and Cournot Nash equilibria and show that multiple equilibria may exist. We further explain why a general solution for any number of users is computationally challenging. The difficulty lies in the fact that the objective functions are piecewise-convex, i.e., non-smooth and non-convex. As a result, the minimisation of the costs relies on checking all arrival and departure order permutations, which is exponentially large with respect to the population size. Instead we propose an iterated best-response algorithm which can be efficiently studied numerically. Finally, we compare the equilibrium arrival profiles to a socially optimal solution and discuss the implications.

Identifying and classifying traffic and congestion patterns are essential parts of modern traffic management underpinned by the emerging intelligent transport systems. This paper explores the potential of using a combination of image processing methods to identify and classify regions of congestion within spatiotemporal traffic (speed, flow) contour maps. The underlying idea is to use these regions as (archetype) shapes that in many combinations can make up a wide variety of larger-scale traffic patterns. In this paper, use of a so-called statistical shape model is proposed as a low-dimensional representation of the archetype shape, and an active shape model algorithm coupled with linear classification is developed to classify the patterns of interest. Application of the proposed method is demonstrated with a preliminary set of speed contour maps reconstructed from loop detector data in the Netherlands. The results show that the extended active shape model can be used as a multiclass classifier. In particular, 70% of the traffic patterns in the test data were correctly classified with use of only two archetype shapes and simple logistic classifiers. The results point to the importance of use of expert knowledge by means of (a priori) manual classification of the training examples. This work opens many research directions, including semiautomated searches through traffic databases, automatic detection, and classification of new traffic patterns.