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In this paper we propose a novel approach for alleviating traffic congestion in freeways with multiple access locations through the use of dynamic toll pricing. The pricing strategy is determined using model-based feedback control, with the underlying model derived using a combination of both traffic flow modeling and driver behavior. The traffic segment we focus on is a suburban freeway with multiple access locations. A model derived from the cell transmission method was utilized to develop the traffic flow model, with past traffic information from on-road sensors utilized for determining the model parameters. The behavior of the driver with respect to the choice of whether or not to enter the freeway segment is modeled using utility theory and the Value of Time (VOT) relative to the toll value. The proposed toll-pricing scheme is tested with traffic data from Portuguese freeway A5 and with different hypothesis on the driver's VOT distribution, showing a significant improvement of the overall traffic conditions. The algorithm developed here provides an opportunity to improve on existing toll policy by guaranteeing more stable traffic conditions for the freeway users and optimizing the overall traffic throughput. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization in China has contributed to traffic events, such as traffic accidents and delays. It is difficult to detect and resolve highway traffic congestion in a timely manner using traditional methods because they are slow, require a large number of workers, and require the installation of a large amount of monitoring equipment. Therefore, it is imperative to introduce advanced technology to address these challenges. Recently, deep learning technology has made significant breakthroughs and has been widely applied to various fields with satisfactory results. Deep learning is among the most important technologies for the detection and evaluation of traffic congestion, enabling the accurate detection of the state of the expressway network's traffic congestion, the evaluation of traffic congestion, and the prediction of possible traffic congestion. This enables management to formulate traffic dredging strategies in advance to prevent the negative impact of traffic congestion on normal traffic flow. This paper proposes a framework based on deep learning for next-generation highway traffic management. This framework selects traffic congestion indicators to construct an index model, and then constructs a deep learning model based on self-coding. It predicts and classifies highway traffic environment data and excavates sample data based on the characteristics of traffic parameters. As soon as traffic data were classified, a prediction model based on SoftMax was established to detect and predict highway traffic congestion. We conducted a traffic congestion analysis of the Shanghai expressway network based on the speed performance data obtained from the China Traffic Management Bureau. As a result of their research, we developed an index to measure highway traffic congestion. For traffic control and management organizations to function effectively, it is crucial to have an accurate and clear picture of traffic network operations. We evaluated the proposed framework using data gathered from highway monitoring scenes, and the results indicated that 98.6% of the data could be correctly detected. Using the prediction model based on SoftMax for expressway vehicles during peak hours, the accuracy was 92%, and the misjudgment rate was 8%. This study demonstrates that detecting and evaluating the state of highway traffic environments using deep learning has high accuracy, can be applied to actual highway traffic systems, and is extremely useful for detecting highway congestion. This framework is a promising solution for next-generation highway traffic management and provides accurate and timely traffic congestion detection and evaluation. [ABSTRACT FROM AUTHOR]

Accurately predicting short-term congestions in ship traffic flow is important for water traffic safety and intelligent shipping. We propose a method for predicting the traffic flow of ships by applying the whale optimization algorithm to an extreme learning machine. The method considers external environmental uncertainty and complexity of ships navigating in traffic-intensive waters. First, the parameters of ship traffic flow are divided into multiple modal components using variational mode decomposition and extreme learning machine. The machine and the whale optimization algorithm constitute a hybrid modelling approach for predicting individual modal components and integrating the results of individual components. Considering a map between ship traffic flow parameters and congestion, fuzzy c-means clustering is used to predict the level of ship traffic congestion. To verify the effectiveness of the proposed method, ship traffic flow data of the Yangtze River estuary were selected for evaluation. Results from the proposed method for predicting ship traffic flow parameters are consistent with measurements. Specifically, the prediction accuracy of the ship traffic congestion reaches 76.04%, which is reasonable and practical for predicting ship traffic congestion. [ABSTRACT FROM AUTHOR]

To alleviate traffic congestion, an improved car-following model for connected vehicles is proposed in this paper by considering backward-looking effect and motion information of multiple vehicles. The backward-looking effect is common in traffic and motion information of multiple vehicles and is beneficial for alleviating traffic congestion. The linear and nonlinear stability of the proposed model is analyzed, and the stability condition and modified Korteweg-de Vries equations of the proposed model are derived. The theoretical analysis results prove the effectiveness of the proposed model in alleviating traffic congestion and improving the stability of the traffic system. Further, numerical simulation is designed to verify the promoting effect of the introduced parameters on the traffic stability and to test the effect of the improved model on large-scale car-following queues. Finally, the next-generation simulation (NGSIM) data sets are used to calibrate the parameters of the improved model. The results show that the improved model can effectively avoid traffic congestion and enhance the stability of traffic flow. The improved model can be used as active safety technology to prevent collision accidents or as a car-following strategy in driverless algorithms. [ABSTRACT FROM AUTHOR]

Maintaining roadway pavements and bridge decks is key to providing high levels of service for road users. However, improvement actions incur downtime. These actions are typically scheduled by asset class, yet implemented on any asset type, they have network-wide impacts on traffic performance. This paper presents a bilevel program wherein the upper level involves a Markov decision process (MDP) through which potential roadway improvement actions across asset classes are prioritized and scheduled. The MDP approach considers uncertainty in component deterioration effects, while incorporating the benefits of implemented improvement actions. The upper level takes as input traffic flow estimates obtained from a lower-level user equilibrium traffic formulation that recognizes changes in capacities determined by decisions taken in the upper level. Because an exact solution of this bilevel, stochastic, dynamic program is formidable, a deep reinforcement learning (DRL) method is developed. The model and solution methodology were tested on a hypothetical problem from the literature. The importance of obtaining optimal activity plans that account for downtime effects, traffic congestion impacts, uncertainty in deterioration processes, and multiasset classes is demonstrated. [ABSTRACT FROM AUTHOR]

In order to solve the problem of traffic congestion in a certain area, this paper develops a set of traffic optimization decision system. For analyzing the actual traffic conditions and calculating the traffic volume, density and traffic speed, a traffic prediction model is established and updated iteratively to modify the prediction model parameters. Based on this model, the congestion degree is estimated at the current road section, thus, an intelligent decision-making and the coordinated optimization methods are proposed. Moreover, this paper implements some application experiments on the isometric road of a three-intersection and obtains better prediction results of traffic density and traffic speed on the three-section highway. At the same time, compared with other existing prediction methods, the prediction model presented in this paper not only has higher accuracy, shorter prediction time and stronger anti-interference ability, but also has better effect on vehicle diversion. In addition, it also greatly relieves the traffic pressure on the road, maximizes the complementary advantages between intersections, and balances the good cooperation between each intersection. [ABSTRACT FROM AUTHOR]

The problem of traffic congestion caused by the fast-growing travel demands has been getting serious in urban areas. Meanwhile, the future of urban mobility has been foreseen as being electric, shared, and autonomous. Accordingly, the routing and charging strategies for fleets of shared autonomous electric vehicles (SAEVs) need to be carefully addressed to cope with the characteristics of the rideshare service operation of the SAEV fleets. In the literature, much work has been done to develop various traffic control strategies for alleviating the problem in urban traffic congestion. However, little research has proposed effective solutions that integrate the route of charging strategies for SAEV fleets with the urban traffic congestion problem. In this regard, this work presents an integrated framework that tackles the route and charging of SAEV fleets as well as the urban traffic congestion prevention issues. Notably, our contribution in this work not only proposes a joint solution for the problems of the urban traffic congestion control and rideshare dispatch of SAEV fleets, but also fills the gap of the routing and charging strategies for mixed privately owned EVs (PEV) and SAEV fleets in the literature. A general optimization framework is formulated, and effective heuristics are proposed to tackle the above-mentioned problems in this work. The feasibility and effectiveness of the proposed algorithms were evaluated through four different scenarios in the simulation. After applying the proposed algorithms, the traffic volumes of the oversaturated main arterial road were diverted to other less busy road sections, and the traveling times of EV passengers were decreased by 28% during peak periods. The simulation results reveal that the proposed algorithms not only provide a practical solution to prevent the problem in urban traffic congestion during rush hours, but also shorten the travel times of EV passengers effectively. [ABSTRACT FROM AUTHOR]

Road traffic management has been a serious concern in the transportation sector for many years now. The explosion of the number of cars along with the inability of creating new highcapacity road infrastructures in big cities makes mitigating this danger a problem for the scientific research community. Traffic congestion contributes to increased pollution, economic loss, and a general deterioration in the quality of life. As a result, researchers are being asked to cope with the complexity of establishing effective and smooth traffic flow. However, as in traffic congestion control, real-world decision-making problems are always fraught with uncertainty and indeterminacy. The neutrosophic environment has been applied successfully to deal with these problems and recently, researchers tried to use various neutrosophic approaches to tackle the traffic congestion problem. This paper provides a brief overview of the most recently used neutrosophic techniques to handle traffic congestion and transportation problems in general. The aim of the investigation is to summarize the available neutrosophic traffic flow problems and their progress to enable future researchers to differentiate the major problems to be manipulated and identify conditions to be optimized. [ABSTRACT FROM AUTHOR]

Connected automated vehicles (CAVs) can significantly shorten the headway of car following, thereby effectively improving the traffic capacity and injecting new power to alleviate traffic congestion. To investigate the congestion characteristics of mixed traffic flow with CAVs and human-driven vehicles (HDVs), this paper proposes a cell transmission model to capture and simulate traffic congestion for mixed traffic flow. Firstly, the Newell, adaptive cruise control (ACC), and cooperative adaptive cruise control (CACC) models are adopted to capture the car-following behavior of different vehicles. Secondly, the fundamental diagram under different penetration rates of CAVs is derived based on car-following models. Then, the cell transmission model (CTM) of mixed traffic flow is developed based on the classical CTM and fundamental diagram of mixed traffic flow. Finally, two simulation methods, mixed traffic flow CTM and micro-simulation, are designed to verify the effectiveness of the proposed model. Moreover, taking the moving bottleneck on the expressway as an example, the congestion characteristics of mixed traffic flow are analyzed using multiple indexes, such as average travel speed, congestion delay, and congestion scale. The results show the following: (i) CAVs can significantly alleviate traffic congestion, (ii) the duration of the bottleneck is positively correlated with the degree of traffic congestion, and (iii) The traffic congestion assessment results under different model parameters slightly differ, but the impact is negligible. [ABSTRACT FROM AUTHOR]

The rapid growth in the number of vehicles has led to traffic congestion, pollution, and delays in logistic transportation in metropolitan areas. IoT has been an emerging innovation, moving the universe towards automated processes and intelligent management systems. This is a critical contribution to automation and smart civilizations. Effective and reliable congestion management and traffic control help save many precious resources. An IoT-based ITM system set of sensors is embedded in automatic vehicles and intelligent devices to recognize, obtain, and transmit data. Machine learning (ML) is another technique to improve the transport system. The existing transport-management solutions encounter several challenges resulting in traffic congestion, delay, and a high fatality rate. This research work presents the design and implementation of an Adaptive Traffic-management system (ATM) based on ML and IoT. The design of the proposed system is based on three essential entities: vehicle, infrastructure, and events. The design utilizes various scenarios to cover all the possible issues of the transport system. The proposed ATM system also utilizes the machine-learning-based DBSCAN clustering method to detect any accidental anomaly. The proposed ATM model constantly updates traffic signal schedules depending on traffic volume and estimated movements from nearby crossings. It significantly lowers traveling time by gradually moving automobiles across green signals and decreases traffic congestion by generating a better transition. The experiment outcomes reveal that the proposed ATM system significantly outperformed the conventional traffic-management strategy and will be a frontrunner for transportation planning in smart-city-based transport systems. The proposed ATM solution minimizes vehicle waiting times and congestion, reduces road accidents, and improves the overall journey experience. [ABSTRACT FROM AUTHOR]

The current research on the mixed traffic flow characteristics of human-driven vehicles (HDVs) and connected automated vehicles (CAVs) mainly focuses on the micro-level. To study the characteristics of the mixed traffic flow from the medium and macro level, this paper proposes a variable cell transmission model (VCTM). First, the fundamental diagram is introduced based on the phenomena of hysteresis of traffic flow. Second, the VCTM with different market penetration rates (MPR) of CAVs is proposed based on the classical cell transmission model (CTM). Then, the effectiveness of VCTM is verified by micro-simulation based on the intelligent driver model (IDM). Finally, some congestion indexes are selected to discuss the characteristics of mixed traffic flow based on the VCTM with an expressway simulation. The results show that the traffic capacity and congestion dissipation capacity gradually are increased with the increase of MPR of CAVs. The homogeneous CAVs traffic flow capacity can reach 1.41 times that of the homogeneous HDVs traffic flow, and the congestion dissipation time can be reduced by 25%. The larger MPR is, the greater the improvement effect is. In addition, compared with CTM, VCTM can reflect the delay, queuing, and dissipation of mixed traffic flow more accurately, which is helpful to capture the evolution mechanism of mixed traffic flow in the future. [ABSTRACT FROM AUTHOR]

Increased road traffic congestion is due to different factors, such as population and economic growth, in different cities globally. On the other hand, many households afford personal vehicles, contributing to the high volume of cars. The primary purpose of this study is to perform a comparative analysis of ensemble methods using road traffic congestion data. Ensemble methods are capable of enhancing the performance of weak classifiers. The comparative analysis was conducted using a real-world dataset and bagging, boosting, stacking and random forest ensemble models to compare the predictive performance of the methods. The ensemble prediction models are developed to predict road traffic congestion. The models are evaluated using the following performance metrics: accuracy, precision, recall, f1-score, and the misclassification cost viewed as a penalty for errors incurred during the classification process. The combination of AdaBoost with decision trees exhibited the best performance in terms of all performance metrics. Additionally, the results showed that the variables that included travel time, traffic volume, and average speed helped predict vehicle traffic flow on the roads. Thus, the model was developed to benefit transport planners, researchers, and transport stakeholders to allocate resources accordingly. Furthermore, adopting this model would benefit commuters and businesses in tandem with other interventions proffered by the transport authorities. [ABSTRACT FROM AUTHOR]

Congestion pricing is one effective demand management strategy to alleviate traffic congestion. This work investigates pricing schemes for mixed traffic flow systems where the human-driven vehicles (HVs) and autonomous vehicles (AVs) coexist. The emerging and integration of autonomous vehicles can help improve the overall transportation efficiency and safety. Given the coexistence of HVs and AVs in the near future, there is need to adjust the existing traffic management strategies to adapt to the mixed traffic conditions. In this study, congestion pricing is imposed on the HVs and the AVs differently, that is, a distance-based toll to the HVs while a delay-based toll to the AVs. We consider six user groups based on the value of time (VOT) and the vehicle types. Compared with the unified distance-based toll, the advantage of delay-based toll is demonstrated first. Then, a surrogate-based optimization framework, namely the regressing Kriging (RK) model, is formulated. Three pricing schemes are investigated and compared: equity-oriented (EQ), environment friendliness-oriented (EN), and revenue-oriented (RE) schemes. Results show that the RE scheme collects the highest revenues; however, its cost-efficiency is weakened. The EQ scheme reduces the variance in the average travel costs among user groups, thus solving the equity issue. [ABSTRACT FROM AUTHOR]

When multiple equilibria exist, the desired traffic equilibrium, may not be reached through a day-to-day dynamic adjustment process. Han et al. ["Discrete-Time Day-to-Day Dynamic Congestion Pricing Scheme Considering Multiple Equilibria." Transportation Research Part B: Methodological 104: 1–16.] proposed a day-to-day dynamic pricing scheme charged on all links to ensure the desired traffic equilibrium can be achieved even when multiple equilibria exist. However, due to many practical constraints, the second-best pricing scheme has more practical interests. This study first discussed the issues of existing congestion pricing schemes for second-best case. Then, this study developed a second-best dynamic road pricing scheme that is implemented on a dynamic subset of the road links in the network. Our rigorous theoretical proof and numerical tests prove that the dynamic pricing scheme can drive the traffic state evolution towards the desired second-best traffic user equilibrium state from any initial traffic state. [ABSTRACT FROM AUTHOR]

While bringing convenience to people, the growing number of vehicles already on roads causes inevitable traffic congestion. Traffic congestion can happen for both observable reasons, such as accidents, or occur naturally due to human driving style in high vehicles density scenarios. In the latter case, traffic congestion are referred to as phantom jams. To alleviate the traffic instability caused by phantom jams, bilateral control models have been proposed with the development of intelligent transportation system (ITS) in ideal situations. However, in road scenarios, some factors that severely affect the performance of existing models cannot be ignored, such as uncertainties of vehicle state measurements caused by on-board sensors, time delay caused by intervehicle communications and control systems of vehicles. In this paper, a novel predictable bilateral control model (PBCM) with local wireless communication, which consists of best estimation and state prediction, is proposed to accurately control the host vehicle in traffic flow to alleviate traffic instability. Compared with other models, theoretical analysis and simulation results show that more accurate control decisions could be obtained with the PBCM, which could distribute all vehicles more evenly, and our model could maintain higher velocities when disturbances occurred. The experimental results also reveal that our proposed model could more effectively suppress traffic instability (i.e., the improvement in control decision $a$ is more than 10%) and could restore traffic balance 20% faster than other models on average. [ABSTRACT FROM AUTHOR]

Traffic status recognition and classification is an important prerequisite for traffic management and control. Based on the idea of weight optimal, a weighted fuzzy c-means clustering method for improving the accuracy of traffic classification is proposed in this study to ease traffic congestion. First, since there are many indexes that affect the traffic flow state classification, three commonly used indexes namely, volume, speed and occupancy are chosen as the main parameters for the traffic flow state classification in this paper. Second, in order to quantitatively analyze the influence degree of different traffic flow parameters on traffic flow state division, based on the principle of weight optimization, the objective function of weight optimization is established. Then the weight of each attribute index is obtained by using the branch and bound algorithm. Finally, since the traditional fuzzy c-means clustering method will not consider the influence of different traffic flow parameter weights on the traffic flow state classification results, the classification effect needs to be further improved. A fuzzy weighted c-means classification method which uses weighted Euclidean distance instead of Euclidean distance is proposed to classify the traffic flow states. Based on the same traffic flow data sample on the same road section, the traffic state classification results with different methods show that it is helpful to improve the traffic flow state classification accuracy by weighting the clustering index. Because the influence of different parameters on the traffic flow state classification is considered in the process of clustering, it is more conducive to improve the classification accuracy. Moreover, it can provide more accurate classification information for traffic control and decision making. [ABSTRACT FROM AUTHOR]

Traffic congestion detection poses challenges in spatiotemporal data mining and intelligent transportation research. Existing studies primarily detect traffic congestion based on the speed estimation of traffic flows. Such detection techniques may overlook the formation of traffic congestion in space and time. This research proposes a density-based approach to moving object clustering that extracts the spatiotemporal extents of traffic congestion in three steps. The first step applies a map-matching strategy to project original trajectory points in a planar space onto a road network space and segments the trajectories into consecutive time windows. In the second step, we statistically detect moving clusters with significantly high-density subject to network constrained clustering. The final third step determines moving clusters indicative of traffic congestion through the analysis of both vehicle speed and time spans. Comparative experiments on both simulated trajectories and the real-life taxi trajectories in Wuchang demonstrate that the proposed method outperforms other methods through quantitative evaluations using three indicators, i.e. the precision, recall and F1 value. The proposed approach can illustrate the spatiotemporal regularities of traffic congestion, which can inform dynamic route planning and network design optimization. [ABSTRACT FROM AUTHOR]

In order to overcome the problems of long prediction time and low prediction accuracy existing in traditional congestion prediction methods, this paper designs a traffic congestion prediction method based on genetic algorithm. The congestion information of trafficjam section is sampled and combined with the optimal grid parameter distribution model of traffic flow distribution, the output characteristics of congestion are extracted. On this basis, V2X communication technology is used to obtain the real-time traffic flow data in the road section, and the congestion volume is predicted according to the coding, cross mutation and selection process of genetic algorithm. The experimental results show that the maximum time of generating congestion prediction results is only 4.76s, and the maximum fitting degree between the predicted results and the actual results can reach 98%, which proves that the proposed method can effectively achieve the design expectation. [ABSTRACT FROM AUTHOR]

Traffic congestion induces significant economic loss each year, and identifying traffic congestion patterns is necessary for better traffic control and management. Floating car data (FCD) provides a cost-effective alternative for assessing traffic status and detecting congestion on a large scale. Recently, a new speed performance index (SPI) has been proposed to evaluate traffic status considering both traffic flow speeds and road speed limits. This research proposes a new categorization criterion to define traffic conditions as five levels based on SPI values, and applies the proposed criterion in a case study to investigate traffic status and detect traffic congestion patterns on urban freeways based on FCD analysis. The research results reveal a clear understanding regarding urban freeway traffic status and congestion spatial and temporal distribution. With more comprehensive FCD from larger spatial and temporal domains, continued research could focus on travel speed prediction, travel time estimation, prediction, and reliability analysis at both the road segment and network levels. [ABSTRACT FROM AUTHOR]

Planned special events (PSEs) attract more people than usual to specific areas, which leads to increased traffic flows and congestions on the roads. Roadwork zones are among the most vulnerable areas on the roads, where increased traffic can lead to congestion. In roadwork zones, the vehicle flow capacity is already lower than in the conventional situations without roadworks, but at the time of PSEs, these zones become difficult to pass if no attention is paid to the change of the traffic management scheme. This kind of events poses many threats for road authorities, thus, new traffic management systems should be considered. This paper analyzes 2 PSEs and one national celebration in Lithuania and a significant impact they have on the regular traffic flow. PSEs are taken into consideration as they attract traffic to a known place; however, national celebrations distort traffic along all roads and it is not known exactly, which roads will be congested the most. Since roadwork zones cause congestion problems even in conventional situations, this paper presents traffic capacity calculations at these road stretches during PSEs and considers how they change depending on the traffic management scheme. [ABSTRACT FROM AUTHOR]

Urban traffic congestion has become an important issue not only affecting our daily lives, but also limiting economic development. The primary cause of urban traffic congestion is that the number of vehicles is higher than the permissible limit of the road. Previous studies have focused on dispersing traffic volume by detecting urban traffic congestion zones and predicting future trends. However, to solve the fundamental problem, it is necessary to discover the cause of traffic congestion. Nevertheless, it is difficult to find a research which presents an approach to identify the causes of traffic congestion. In this paper, we propose a technique to analyze the cause of traffic congestion based on the traffic flow theory. We extract vehicle flows from traffic data, such as GPS trajectory and Vehicle Detector data. We detect vehicle flow changes utilizing the entropy from the information theory. Then, we build cumulative vehicle count curves (N-curve) that can quantify the flow of the vehicles in the traffic congestion area. The N-curves are classified into four different traffic congestion patterns by a convolutional neural network. Analyzing the causes and influence of traffic congestion is difficult and requires considerable experience and knowledge. Therefore, we present a visual analytics system that can efficiently perform a series of processes to analyze the cause and influence of traffic congestion. Through case studies, we have evaluated that our system can classify the causes of traffic congestion and can be used efficiently in road planning. [ABSTRACT FROM AUTHOR]

If not restricted by tolls, private decisions to drive on a highway result in inefficiently high usage which leads to traffic jams. When traffic demand is high, traffic jams can occur simply because of the interaction of vehicle drivers on the road, a phenomenon called phantom jam. The probability of phantom jams occurring increases with traffic flow. Unpriced externalities lead to inefficiently high road usage. We offer a method for quantifying traffic jam externalities and identifying and isolating the phantom jam externality. We examine the method by applying it to a specific highway section in Germany. The maximal congestion externality for the analyzed highway section is about 38 cents per vehicle and kilometer. Congestion charges that are calculated ignoring phantom jam externalities, can only internalize two-thirds of the true externality. [ABSTRACT FROM AUTHOR]

The London congestion charging (LCC) scheme was first introduced in 2003. It did not only help alleviate traffic congestion and reduce vehicle emissions, but also had favorable safety effect. Western Extension of LCC was applied in 2007, but then removed in 2011. It was suggested that adjacent areas of the congestion charging zone could also be benefited. As well, the benefits would not disappear immediately after the removal of congestion charging scheme, which is known as residual effect. This paper attempts to examine the affected area and residual period of the safety benefits by the LCC scheme using the traffic and crash data from 352 Middle Super Output Areas (MSOAs) of London, with which the original LCC scheme was imposed in 24 MSOAs ('treatment' units for Analysis I), the Western Extension scheme was imposed in 27 MSOAs ('treatment' units for Analysis II), and no congestion charging was implemented at all in 301 MSOAs ('control' units for both Analysis I and II). Factors including traffic flow, land use, built environment and population demographics are considered. To eliminate the bias by the selection of treatment and control groups, Propensity Score matching (PSM) method is applied. Results indicate that favorable effect on safety is prevalent in the 1.5 km buffer area of LCC zone. On the other hand, for the residual effect, considerable crash reduction could be found in the first year after the removal of Western Extension of LCC. However, no evidence could be established for significant crash reduction in the second and third years after the removal. Findings should be indicative to the transport management policy that could improve the road safety in the Greater London in the long run. • This study examined the safety effects of congestion charging using Propensity Score Matching approach. • Affected area and residual period of the safety benefits are examined. • Safety benefit is revealed in the 1.5 km buffer area of congestion charging zone. • Crash reduction is observed one year after the policy removal. [ABSTRACT FROM AUTHOR]

• Established the relationship between car following instability and traffic oscillation. • Categorized oscillation into four types. • Developed oscillation criteria by modifying the stability criteria. • Demonstrated the impact of the stability profile of connected and automated vehicles (CAVs) on oscillation. • Found promising CAV parameters for improving stability, and smoothing oscillation. This paper focuses on two fundamental issues in traffic flow modelling: string stability of car following (CF), and oscillation of traffic flow. Its aim is to explore the complementary use of CF instability analysis and oscillation analysis to compress and ease traffic congestion in a connected environment. Each of these topics has been extensively investigated in the literature. However, each topic has been investigated separately and, despite the inherent conceptual and empirical closeness of the two concepts, little effort has been devoted to untangling their relationship. To address this failing, we first define four types of oscillation – amplitude-decay oscillation, amplitude-ceiling oscillation, speed-deviation ceiling oscillation, and speed-deviation growth oscillation – and reveal their similarities and dissimilarities to CF instability. Based on the stability criterion, we then develop oscillation criteria to identify different types of oscillation by relaxing two unrealistic assumptions used in CF stability analysis (i.e., infinitely-long platoon and long-wavelength perturbation). Finally, to demonstrate how CF instability analysis and oscillation analysis can be combined to influence individual vehicle stability and improve traffic oscillations in a connected environment, a platoon of vehicles that experience an oscillation in the NGSIM data is used in a case study. In this case study, different control factors are used for different vehicle types: connected (but human-driven) vehicles, and automated vehicles. Our analysis shows that a higher stability of some individual vehicles can alleviate the oscillation severity for the platoon. It also shows that desired time gap and maximum acceleration are two promising parameters that can be used to both improve individual vehicle stability and significantly smooth the oscillation of the platoon in a connected and/or automated environment. Of particular note, when considering all of the factors explored in our analysis, adjustment of the desired time gap is the most effective factor in smoothing traffic oscillations. [ABSTRACT FROM AUTHOR]

Traffic congestion is a common phenomenon in road transportation networks, especially during peak hours. More accurate prediction of dynamic traffic flows is very important for traffic control and management. However, disturbances caused by the time-varying origin-destination matrix, dynamic route choices, and disruptions make the modelling of traffic flows difficult. Therefore, this study focuses on modelling the dynamic evolution processes of traffic flows under disturbances and estimating dynamic travel times for arbitrary moment. A revised Lighthill–Whitham–Richards (RLWR) model with non-equilibrium states is presented to describe the dynamic traffic states on individual roads, and the ripple-spreading model (RSM) is integrated to investigate the interactions among several shockwaves from multiple roads. We propose a hybrid RLWR–RSM to model the congestion and congestion-recovery propagations in an entire transportation network. After predicting the dynamic traffic flows by the RLWR–RSM, the road travel times for arbitrary moment were estimated. Theoretical analyses indicated that (1) the RLWR–RSM inherits the advantages of macroscopic traffic flow models and integrates the characteristics of both low- and high-order continuum models, and (2) the RLWR–RSM considers multiple disturbances. From numerical experiments with various inputs, the variation in travel times under disturbances was investigated, and this further demonstrated that (1) the modelled dynamic traffic flows have four basic properties, and (2) the experimental results validate the theoretical analyses. In addition, the RLWR–RSM can explain several distinct traffic phenomena. Finally, the estimated travel times can provide decision supports for vehicle navigation. [ABSTRACT FROM AUTHOR]

In this paper, an extended car-following model with consideration of the driver's desire for smooth driving and the self-stabilizing control in historical velocity data is constructed. Moreover, for better reflecting the reality, we also integrate the velocity uncertainty into the new model to analyze the internal characteristics of traffic flow in situation where the historical velocity data are uncertain. Then, the model's linear stability condition is inferred by utilizing linear stability analysis, and the modified Korteweg-de Vries (mKdV) equation is also obtained to depict the evolution properties of traffic congestion. According to the theoretical analysis, we observe that the degree of traffic congestion is alleviated when the control signal is considered, and the historical time gap and the velocity uncertainty also play a role in affecting the stability of traffic flow. Finally, some numerical simulation experiments are implemented and the experiments' results demonstrate that the control signals including the self-stabilizing control, the driver's desire for smooth driving, the historical time gap, and the velocity uncertainty are of avail to improve the traffic jam, which are consistent with the theoretical analytical results. [ABSTRACT FROM AUTHOR]

Traffic jams occur even without bottlenecks, simply because of interaction of vehicles on the road. From a driver's point of view, the instability of the traffic flow arises stochastically. Because the probability of a traffic jam increases with the number of cars on the road, there is a traffic flow breakdown externality. This paper offers a method to calculate this externality for traffic on a circuit. Ignoring the stochastic nature of traffic flow breakdowns results in congestion charges that are too small. [ABSTRACT FROM AUTHOR]

High-occupancy vehicle (HOV) lanes have been widely deployed in freeways with high demands and traffic congestion. This paper discusses how an alternative design with the HOV lane on the right side of the freeway could effectively mitigate congestion problems at freeway merge, diverge, and weaving areas as compared with the benchmark design of HOV lanes on the left. Simulation experiments show that the alternative design will result in a reduction in average delay by 5% to 10% and increase in throughput by 10% to 30% for all traffic. While the alternative design generally improves performance for the general purpose (GP) lanes, it will degrade HOV lane performance. Nonetheless, HOV lane vehicles enjoy a higher level of service than GP lane vehicles under the same background settings. The study suggests that it is practical to implement the alternative design on freeways, especially for areas with low HOV traffic volumes. [ABSTRACT FROM AUTHOR]

One of the major tasks within the concept of an intelligent transportation system is the immediate indication of traffic breakdowns. A conventional approach evaluates a traffic condition by classifying (1) traffic volume and (2) vehicles average speed. This mathematical approach is acceptable and leads to good results as long as the analyzed data correctly represents the observed situation. However, both traffic situations and behaviour of individual drivers cannot be foreseen. In such circumstances, 'crisp' computational models cannot deal effectively with accompanied ambiguities and uncertainties. An alternative approach is to apply fuzzy logic systems, which enable knowledge-based analysis for effective and efficient traffic congestion detection. In this paper, traffic flow and density are inputs for the proposed fuzzy inference model and the output comes in form of detected levels of congestion (ranging from 'congestion free' to 'extreme congestions' conditions). The results show that fuzzy logic inference model for congestion detection might be highly suitable for transportation planning, management and security assessment. [ABSTRACT FROM AUTHOR]

In view of the different impacts of different traffic periods on traffic congestion and single factor fail to accurately characterize the traffic congestion state, this paper proposed a multi-index fuzzy comprehensive evaluation method for traffic congestion state evaluation. The method used the particle swarm optimization algorithm to optimize the support vector regression to predict the average road speed and traffic flow, obtained the predicted values of the average speed v, traffic flow density D, and road saturation S. It input the three factor indexes to multi-index fuzzy comprehensive evaluation model to establish the set of factors and evaluation (level) for traffic congestion. It determined the weight coefficients of three factors under the morning peak, the evening peak and other periods by the entropy method, then determined the degree of membership of each index in each period by the trapezoidal membership function. Finally, it divided the traffic congestion state into six levels. The results of predictive evaluation at the traffic data of the 1405 highway in PeMS show that the traffic congestion state evaluated by the proposed method is basically consistent with the actual state, the proposed method has a high prediction accuracy, the correct rate can reach 94.79 % . [ABSTRACT FROM AUTHOR]

Previous urban traffic network-based studies have been based mostly on single-layer networks. Based on their shortcomings, starting from the perspective of a multilayer urban traffic network, this paper takes the different anticongestion abilities and network characteristics of various network structures under the condition of traffic congestion as the research object. Then, a comparative experiment is performed via simulation, and the optimal multilayer urban traffic network topology is obtained under different conditions. It is found that these scale-free related multilayer networks have relatively strong ability to support more traffic flows and have higher anticongestion abilities, regardless of whether it is a lower-layer or upper-layer network. The research results are helpful to deepen our understanding of the characteristics of traffic network structures, help scholars further cognize the structural properties of multilayer urban traffic networks, practically help urban traffic network planners to further optimize the urban traffic network, and broaden the study of multilayer traffic networks. [ABSTRACT FROM AUTHOR]

Most of the urban roadways do experience traffic flow congestion at various road sections called critical traffic points, which is partly contributed by the presence of various factors on the roadways. A number of studies have used travel time indices to determine congested links of the road networks. However, the travel time-delay based indices have found less application in the identification and quantification of congestion levels in the road networks. As a result, a limited number of studies have examined the factors contributing to the propagation of congestions at various road sections using the travel time-delay indices. This paper aims to identify factors contributing to propagation of traffic congestions at frequently congested traffic critical points and to estimate their influence on the entire road network using travel time-delay data. Travel time-delay data were collected by using test moving cars. The results indicate that low travel-delay transition index below 0.70 signify the jam and crowded traffic flow condition, while higher values greater than 0.70 indicate free flow phenomena. On the other hand, high congestion index values indicate jam and crowded flow conditions whereas the low values below 0.5 signify free flow conditions. Further, the results showed that low transition index and high congestion index values were associated with roadway design factors such as T-joints, cross joints, bus stops, humps and traffic lights, which are considered to be static bottlenecks which impedes the vehicle flow. [ABSTRACT FROM AUTHOR]

This paper considered the congestion pricing to be the effective way to solve the traffic congestion, the main idea of road traffic congestion was to charge for some easy to cause road congestion, and appropriated compensation for other underutilized roads. It presented a subgradient heuristic traffic delay compensation strategy based on function. Firstly, it used a nonlinear programming model in the road toll/subsidies, Beckmann was the main realization based on minimizing the objective function, and used the Kuhn Hills-Lagrange multiplier model to construct the constraint condition. Secondly, the algorithm used the pricing compensation strategy to establish the road traffic model based on heuristic algorithm, and also used the marginal cost model to establish the delay function analysis model. Finally, the simulation experiments on real road networks show that the proposed algorithm has better performance in terms of travel time, traffic flow, convergence and so on, and the effectiveness of the proposed algorithm is verified. [ABSTRACT FROM AUTHOR]

Abstract We study different mixes of private and public supply of roads in a network with bottleneck congestion and heterogeneous users. There are two parallel links for one origin and destination pair and two groups of travellers, where the group with the higher value of time also has higher schedule delay values. Previous scholars argued that as users become more heterogeneous, they benefit more from product differentiation, making private supply of roads more efficient. However, we find that local monopoly power might also increase if there is a 'separating equilibrium', which is an equilibrium where at least one group only uses one private road due to the different combinations of toll and congestion of the two roads. The private road can thus increase its toll without worrying about the competition from the other road for this group: it has a local monopoly over them. This lowers the efficiency of private supply. The problem is especially severe with flat tolls, which are constant over the peak. With fine tolls – which vary continuously over the day – there tends to be a pooling equilibrium – where both types use both roads – and competition remains intense. Flat tolling is also worse for users than fine tolling, as it has higher generalized prices. [ABSTRACT FROM AUTHOR]

Abstract In US urban areas, traffic congestion remains a growing and costly problem. The Texas Transportation Institute estimated that in 2014 delays due to congestion caused an extra 7 billion extra hours in delay, leading to wasted fuel, increased environmental degradation, and high user costs. Previous studies have focused on the impacts to congestion via highway toll lanes and the income distributional effects of tolling. While tolling can be effective in reducing congestion levels, these tolls have been found to be at least as regressive as fuel taxes. Further, these studies do not investigate the impacts tolling has on rural households compared to urban households. This study presents a per-mile congestion tax as an alternative policy tool to congestion tolling. Using a Suits Index with the Oregon Households Activity Survey, we find a per-mile congestion charge to be progressive on income. This suggests that the administratively challenging toll revenue redistribution payments that would be necessary to make congestion tolling progressive would not be needed. At the same time, however, the Suits Index suggests that a per-mile congestion charge would be regressive with regards to rural households. Econometric models are estimated to further support and explain these outcomes. While rural households, in general, drive more during peak hours compared to urban households, we find that low-income rural households are less likely to drive during these periods than wealthier rural households. Highlights • Income and spatial distributional effects of a per-mile congestion charge is evaluated using upper and lower bound scenarios. • Per-mile congestion charge is found to be vertically progressive on income distributions. • Per-mile congestion charge is found to be regressive for rural households compared to urban households. • This type of congestion charging is a more fair alternative than congestion tolling. [ABSTRACT FROM AUTHOR]

The second-best congestion pricing schemes including common optimum, one cordon, and multiple cordons schemes are compared with the first-best optimum pricing scheme. A cross-subsidy effect exists in these second-best pricing models. However, the scheme with more cordons will diminish the cross-subsidy and approach an efficient and equitable outcome. The relative efficiency of a cordon pricing scheme for the case of Taipei metropolis is very high. One single cordon yields excellent performance of 93% relative efficiency. There might be some factors causing the good results: the uncongested traffic condition, the linear unit distance cost in traffic flow forming a nonlinear cost function, and the trip demands with continuous space and the same destination (the central business district) in the network. [ABSTRACT FROM AUTHOR]

This paper estimates the traffic volume and travel time effects of the road congestion pricing implemented on the San Francisco-Oakland Bay Bridge. I employ both difference-in-differences and regression discontinuity approaches to analyze previously unexploited data for the two years spanning the price change and obtain causal estimates of the hourly average treatment effects of the policy. I find evidence of peak spreading in traffic volume and decreases in travel time during peak hours. I also find suggestive evidence of substitution to a nearby bridge and decreases in travel time variability. In addition, I calculate own- and cross-price elasticities. [ABSTRACT FROM AUTHOR]

Traffic and transportation are a major source of CO 2 emissions. As the volume of heavy goods vehicle (HGV) transportation is difficult to reduce, many governments target private traffic. Next to the direct effect of fewer private vehicles on the roads, an indirect effect may be very important: The less congestion, the more fuel-efficient the remaining drivers may be able to drive because there would be less need to speed up, brake, and maneuver, for example while overtaking. On the other hand, clear roads may be tempting for drivers to speed and thus result in a negative impact of less private traffic. The direction and size of this indirect effect is difficult to measure for two reasons: First, we usually have no real-time driving data from heavy trucks. Second, traffic density usually does not vary to a large extent. We set out to assess the margins of this indirect effect using a unique data set from a large German logistics fleet. We measured truck drivers' driving behavior as well as emissions from their trucks between January and May 2020. During that time, the COVID-19-related policy measures led to a significant decline in private traffic on highways. We find that less private traffic volume results in improved fuel-efficient driving behavior, resulting in reduced overall CO 2 emissions. The effect is u-shaped; too little private traffic density leads to less fuel-efficient driving by the remaining heavy goods vehicle drivers. Moreover, removing the worst drivers from the roads has the same effect in terms of magnitude as reducing private traffic congestion to the medium-density optimum. • Policies targeting car traffic may backfire depending on truck drivers' responses. • Low traffic may result in fuel-efficient driving or speeding of truck drivers. • We use unique data including the actual behavior of about 700 German truck drivers. • We find a u-shaped relation between traffic density and truck emissions. • Optimal traffic density reduces emissions like removing the worst 10% of drivers. [ABSTRACT FROM AUTHOR]

To enhance traffic flow stability, the intricacy dynamic of road geometry characteristics on traffic safety is explored in this paper. And an extended macro traffic flow model is proposed coupled with electronic throttle (ET) dynamics effect for a single lane system. Based on the linear stability analysis, we can distinctly find that the stable region will varies due to the change of slope angle when the electronic throttle opening angle is fixed. Characteristics of density wave propagation near the neutral stability lines are described by means of the KdV equation, which is obtain from nonlinear analysis method. The new model also captures the evolution characteristics of shock and rarefaction waves. The simulation example verifies that the road geometric characteristics have a marked impact on the total energy consumption of the transportation system. The traffic congestion can be restrained efficiently by introducing the ET dynamics effect. The numerical simulation shows a good agreement with the analytical result. • An extended macro model of single lane system considering the dynamic effect of electronic throttle is established. • The complex dynamic influence of road geometric characteristics on traffic flow stability is analyzed. • Shock and rarefaction waves are performed and the influence of slope on total traffic energy consumption is simulated. [ABSTRACT FROM AUTHOR]

This paper is about a theoretical investigation of a Congestion pricing system in the municipality of Budakeszi. Due to its special geographical background the city is heavily affected by congestion problems, produced by the high number of commuters passing through. This paper illustrates the usage of transport planning models as the Four Step Model to picture this situation and how this model can be used in the simulation of a theoretical congestion pricing system. Finally the utilities of different systems are investigated for the Budakeszi area. [ABSTRACT FROM AUTHOR]

Multi-lane roads are typical scenarios in traffic systems. Vehicles usually have preference on lanes according to their routes and destinations. Few of the existing studies looks into the problem of controlling vehicles to drive on their desired lanes. This paper proposes a formation control method that considers vehicles' preference on different lanes. A bi-level formation control framework is utilized to plan collision-free motion for vehicles, where relative target assignment and path planning are performed in the upper level, and trajectory planning and tracking are performed in the lower level. The collision-free multi-vehicle path planning problem considering lane preference is decoupled into two sub problems: calculating assignment list with non-decreasing cost and planning collision-free paths according to given assignment result. The Conflict-based Searching (CBS) method is utilized to plan collision-free paths for vehicles based on given assignment results. Case study is conducted and simulations are carried out in a three-lane road scenario. The results indicate that the proposed formation control method significantly reduces congestion and improves traffic efficiency at high traffic volumes, compared to the rule-based method. • The problem of collision-free formation control problem of CAVs is modeled as the collision-free multi-agent cooperative path planning problem in RCS. The positions of vehicles are projected from GCS to RCS, where collision-free relative motion of vehicles is calculated. • The collision-free multi-vehicle path planning problem considering lane preference is decoupled into two sub problems: calculating assignment list with non-decreasing cost and planning collision-free paths according to given assignment result. • The proposed method is verified in case study and the efficiency improvement is validated in simulation at different input traffic volumes. The results indicate that the proposed formation control method can significantly reduce congestion and improve traffic efficiency. [ABSTRACT FROM AUTHOR]

In recent years, the route-planning problem has gained increased interest due to the development of intelligent transportation systems (ITSs) and increasing traffic congestion especially in urban areas. An independent route-planning strategy for each in-vehicle terminal improves its individual travel efficiency. However, individual optimal routes pursue the maximization of individual benefit and may contradict the global benefit, thereby reducing the overall transport efficiency of the road network. To improve traffic efficiency while considering the travel time of individual vehicles, we propose a new dynamic route-planning method by innovatively introducing a bidding mechanism in the connected vehicle scenario for the first time. First, a novel bidding-based dynamic route planning is proposed to formulate vehicle routing schemes for vehicles affected by congestion via the bidding process. Correspondingly, a bidding price incorporating individual and global travel times was designed to balance the travel benefits of both objectives. Then, in the bidding process, a new local search algorithm was designed to select the winning routing scheme set with the minimum bidding price. Finally, the proposed method was tested and validated through case studies of simulated and actual driving scenarios to demonstrate that the bidding mechanism would be conducive to improving the transport efficiency of road networks in large-scale traffic flow scenarios. This study positively contributes to the research and development of traffic management in ITSs. [ABSTRACT FROM AUTHOR]

In this paper, we develop models for adjusting or setting fares on a transit system to encourage passengers to choose travel strategies that lead to the least travel delay for the entire system. In our problem setting, these fares vary with time of day. Similar to the one used to reduce congestion on vehicular traffic networks, our goal is to adjust or set fares so that a user equilibrium solution under the new fares yields the least delay or is system optimal. On the other hand, pricing frameworks for traffic networks such as marginal cost pricing do not readily apply because the travel delay in transit systems involves factors different from those in vehicular traffic and cannot be expressed in closed functional forms. The models herein are schedule based and account for loading priorities and individual vehicle capacities explicitly. Differences among the proposed models are illustrated with a small network. [ABSTRACT FROM AUTHOR]

The article focuses on addressing urban congestion. Quantity instruments, such as tradable permits of road usage and real estate development, are found to have the capability to better internalize externalities congestion produces. It is stated that policies that address urban congestion should be based on fostering as much collective mobility as possible. It has been proven that pricing schemes need a constant increase in the congestion charge to keep the traffic level reduction effects.

The development of autonomous driving technology and communication technology has made it possible for the realization of vehicle platoon. In the future, for a long time, there will be a mixture of platoons and normal vehicles on the road. The platoon will have a greater impact on traffic flow. Therefore, it is necessary to study the characteristics of the traffic mixed by platoons and normal vehicles. In this paper, we propose a traffic flow model for the traffic mixed by platoons and normal vehicles based on cellular automata and analyze the influence of platoon market penetration and platoon size on the road capacity, traffic congestion, and lane change frequency. The paper draws the following main conclusions. Under a given traffic environment, there is an optimal platoon penetration and platoon size that can maximize the capacity. Platoons can effectively alleviate traffic congestion, while the congestion ratio approaches zero when the proportion and size of the platoon reach a certain threshold. When the platoon market penetration is below 40%, the lane change frequency of the platoon gradually increases with the increment of the platoon penetration and size, while the platoon penetration is above 60%, the lane change frequency of the platoon decreases with that. When the platoon size is large enough, the lane change frequency will reduce to zero. • Established a traffic flow models for the traffic mixed by platoons and normal vehicles. • Investigated the impact of platoon market penetration and platoon size on traffic flow. • There is an optimal platoon penetration and platoon size that can maximize the capacity. • Considering the congestion information will reduce the lane-changing frequency. [ABSTRACT FROM AUTHOR]