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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]

Economists have long advocated road pricing as an efficiency-enhancing solution to traffic congestion, yet it has rarely been implemented because it is thought to create losers as well as winners. In theory, a judiciously designed toll applied to a portion of the lanes of a highway can generate a Pareto improvement, even before using the toll revenue. This paper explores the practical relevance of this theoretical possibility by using survey and travel time data, combined with a structural model of traffic congestion, to estimate the joint distribution of agent preferences over three dimensions—value of time, schedule inflexibility, and desired arrival time—and evaluate the effects of adding optimal time-varying tolls. I find that adding tolls on half of the lanes of a highway yields a Pareto improvement. Further, the social welfare gains from doing so are substantial—up to $1,740 per road user per year. [ABSTRACT FROM AUTHOR]

With the continuous growth of traffic demand and the mismatch of urban transportation facilities, urban traffic congestion has been caused, leading to various related problems, such as environmental pollution, traffic accidents, and slow economic development. Many cities have implemented relevant measures to improve traffic congestion, but fewer are ideal. This study used the hidden Markov model combined with the dissipative structure theory and entropy theory to predict the congestion more accurately. The temporal and spatial distributions of the online ride-hailing Didi data in Chengdu were analyzed. There are morning peaks, noon peaks, and evening peaks during workdays. During the noon peak and evening peak, travel demand in the city's central area is relatively stable. It is found that the prediction model has a higher accuracy after combining the dissipative structure theory and entropy theory, which could be used to propose methods to prevent congestion. [ABSTRACT FROM AUTHOR]

The paper proposes a conceptual model of an integrated solution for urban logistics and passenger flows to reduce the number of fossil combustion powered commercial vehicles traveling within city, solely for goods transportation, thus contributing to reduce negative effects of urban logistics activities, namely pollution, noise, traffic congestion and accidents. For this solution, a bus network is used to combine the cargo flow with the passenger flow within the cities. After a literature review, a user-centered approach supported by interviews with key stakeholders was followed to define the concept, followed by solution modelling using the Unified Modeling Language (UML) notation. Thus, the high-level requirements will be presented based on a use-cases diagram and the data model based on a class diagram. Also, the automatic order check-in and check-out process on the bus is designed, using Industry 4.0 and IoT principles. This process is relevant since it speeds up the handling of orders in transshipments, reduces the possibility of human error, while at the same time enhancing control and promoting real-time knowledge of the delivery status of orders. The results of this study expand the knowledge in an area with scarce literature. In practical terms this study presents valuable contributions, since it specifies a solution for urban logistics that can have environmental and social impact in the context of urban areas. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major problem in high populated cities, due to the frequent increase on travel demand and exponential vehicles growth. Resulting in increased vehicle queuing and slows road speed which can led to increase delays, air pollution, and fuel consumption. Poor traffic management in the congested spot result in elongated traffic jam. Therefore, this study aims to reduce traffic congestion by applying congestion charging system which is an effective method to regulate demand without increasing supply. Kuwait City network was selected to perform the system. Kuwait City is a vital city that has a lot of governmental and commercial facilities which make the city crowded especially during the peak hours. The current condition was analyzed in SYNCHRO simulation software in order to compare it with the improved condition to confirm the applicability. The genetic algorithm will be used as a practical method to apply congestion pricing in order to provide optimized solutions that will be analyzed in SYNCHRO software to examine the performance of the network. The impacts of applying congestion pricing system were remarkable. From traffic point of view, the demand was regulated, also total delays and fuel consumption were improved. The reduction of total delay is ranged between 24.4% to 40.58%, and the reduction of fuel used is ranged between 36.76% to 60.89%. From an economical point of view, the user cost decreased in a range between 27.77% and 43.75%. In addition, from an environmental aspect the emissions of HC, CO and NOx improved significantly which improved air quality. [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]

• Describes equilibrium of on-demand ride-pooling markets with traffic congestion. • Traffic congestion effects are characterized by a fundamental diagram. • Investigates joint effects of passenger demand and matching window. • A optimal matching window is found to minimize time cost of passengers. • A win-win situation can be found under certain conditions. Ridesourcing platforms, such as Uber, Lyft and Didi, are now launching commercial on-demand ride-pooling programs that enable their affiliated drivers to serve two or more passengers in one ride. It is generally expected that successful designs of ride-pooling programs can reduce the required vehicle fleet size, and achieve various societally beneficial objectives, such as alleviating traffic congestion. The reduction in traffic congestion can in turn save travel time for both ridesourcing passengers and normal private car users. However, it is still unclear to what extent the implementation of ride-pooling affects traffic congestion and riders' travel time. To this end, this paper establishes a model to describe the ridesourcing markets with congestion effects, which are explicitly characterized by a macroscopic fundamental diagram. We compare the time cost (sum of travel time and waiting time) of ridesourcing passengers and normal private car users (background traffic) in the ridesourcing markets without ride-pooling (each vehicle serves one passenger) and with ride-pooling (each vehicle serves one or more passengers). It is found that, a win-win situation can be achieved under some scenarios such that the implementation of on-demand ride-pooling reduces the time cost for both ridesourcing passengers and private car users. Furthermore, we find that the matching window is a key decision variable the platform leverages to affect the stationary equilibrium state. As the matching window increases, passengers are expected to wait for longer time, but the pool-matching probability (the proportion of passengers who are pool-matched) increases, which further alleviates traffic congestion and in turn reduces passengers' travel time. It is interesting to find that, there is a globally optimal matching window for achieving the minimum time cost for ridesourcing passengers in the normal flow regime. [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]

As a market-based instrument of transportation demand management, congestion charge can not only effectively reduce traffic congestion, but also improve air quality. However, due to its low public acceptability, this policy only has a few urban practices. As one of the fast-growing metropolises in emerging economies that are facing both traffic congestion and industrial pollution problems, Beijing is now considering the feasibility of implementing congestion charging. Some researchers address that though people with strong environmental concerns are more prone to support congestion charges, the associations between environmental concerns and support for congestion charges are context-dependent. A survey was conducted in Beijing in 2016 to understand how the pollution context in cities of emerging economies affects these associations. We find that the acceptability in Beijing is 33%, and expected policy effects and environmental concerns are the most important impact factors. Due to the influence of the regional industrial pollution context, most residents in Beijing do not consider congestion charge to be an effective way to tackle air pollution. Under these circumstances, even if the public environmental concerns, in general, are high and congestion charges are "marketed" as environmental policies, there is no guarantee that policy support will rise. [ABSTRACT FROM AUTHOR]

This paper is designed to evaluate and improve the effectiveness of transportation systems and reduce traffic congestion through the use of simulation models and scenario development. A system dynamics framework is used to test and evaluate the alternatives of future strategies for the city of Surabaya, Indonesia. Some factors affecting the effectiveness of transport systems include operational effectiveness and service effectiveness, as well as uncertainty. To improve the effectiveness of transportation systems, several strategies can be implemented, such as subsidizing public transportation, increasing the cost of private vehicle parking fees, raising taxes on private vehicles, and reducing delays in public transportation through scenario development. Scenario results show that, by pursuing these strategies, effectiveness could be improved by 80% as the impact of the increase in operational and service effectiveness, helping to mitigate traffic congestion. Congestion could be reduced to 70% (on average) due to the decrease in daily traffic. [ABSTRACT FROM AUTHOR]

Automatically identifying potential congestion spots in cities has significant practical implications for efficient urban development and management. It requires the ability to examine the relationships between urban built environment features and traffic congestion situations. This article presents a novel and effective approach for achieving the task based on a machine‐learning technique and publicly available street‐view imagery and point‐of‐interest (POI) data. The proposed multiple‐graph‐based convolutional network architecture can: (a) extract essential urban built environment features from street‐view imagery and neighboring POIs; (b) model the spatial dependencies between traffic congestion on road networks via graph convolution; and (c) evaluate the risk level of road intersections to emerging congestion situations based on local built environment features. We apply the model to Wuhan in China, and predict the potential congestion spots across the city. The results confirm that the model prediction is highly consistent (about 85.5%) when compared to the ground‐truth data based on traffic indices derived from a big taxi GPS trajectory dataset. This research enhances the understanding of traffic congestion situations under various geographic, societal, and economic contexts based on easily accessible road, street‐view, and POI datasets at large spatiotemporal scales. [ABSTRACT FROM AUTHOR]

To deal with the low accuracy of existing decision methods of traffic congestion under the condition of the uncertain and incomplete evidences, this paper proposed a weight-optimized neutral network. At first, this paper applied the quadraticform membership function to present the characteristics of evidences. It achieved the evidence contributions by using the information entropy, which could be considered as the weights on input layer in the neutral network. Moreover, the weights on the hidden and output layers were adaptively computed on the basis of initial weight and previous gradient sum. It reduced the accumulated error from the uncertain and incomplete evidences. Finally, the numerical example proved both accuracy and convergence of the proposed method in terms of actual traffic environment. The results indicate that the proposed method can make the stable decision on traffic congestion. [ABSTRACT FROM AUTHOR]

It is widely expected that automated vehicles (AVs) will revolutionise travel experience by better facilitating various on-board activities. While these activities could make travel more pleasant, as is often supposed, they could also affect daily schedules, the related travel choices, and finally, the aggregate travel patterns - possible influences that are still insufficiently studied. For example, a morning commuter deciding to perform some home or work activities during travel, instead of at home or work, could also reconsider his departure time to work. More such travellers together could reshape traffic congestion. This paper models exactly this scenario. It formulates new scheduling preferences, which account for home and/or work activities during morning commute, and uses these (1) to analyse the optimal departure times when there is no congestion, and (2) to obtain the equilibrium congestion patterns in a bottleneck setting. If there is no congestion, it is predicted that AV users would depart earlier (later), if the on-board environment supports their home (work) activities. If there is congestion, AV users that perform home (work) activities during travel skew the congestion to earlier (later) times, and AV users that perform both activities increase both early and late congestion. Engaging in any activity during travel worsens congestion, at least when assuming that AVs do not increase bottleneck capacity. If future AVs would be specialised to support only home, only work, or both home and work activities, and would do so to a similar extent, then 'Work AVs' would increase the congestion the least. [ABSTRACT FROM AUTHOR]

In autonomous vehicles networks, information is transferred through vehicle-to-vehicle and vehicle-to-infrastructure communication channels. We have added a new communication type V2IoT, which is used to communicate with internet of things devices (V2IoT) over a fifth-generation wireless system. For efficient communication, delays should be kept to a minimum and below the threshold. Increased delays may affect the performance metrics of a positioning terminal, which are characterized in terms of availability, accuracy, and integrity. Furthermore, simulation results show that scattering, reflection, and absorption weaken the signals received by vehicles or obliterate them entirely. Many untargeted transmitted signals can even interfere with other transmitted signals. This paper discusses the performance metrics for positioning terminals and proposes a new scheme based on an intelligent vehicle agent, which can reduce traffic congestion by performing seamless handoff between road sections. [ABSTRACT FROM AUTHOR]

Traffic congestion in urban areas is a challenging issue in transportation planning. Policy options have been proposed to evaluate the impacts of interventional action through change detection or before–after studies. In this research, low-cost traffic image data collected by smartphone-based application have been employed and the impact of new congestion charging scheme (CCS) upon congestion within congestion charging zone (CCZ) as well as the entire network in Tehran, the capital of Iran has been investigated. Applying statistical tests indicated the significance of change in congestion within CCZ by applying the new CCS. Differential Moran's I as spatial autocorrelation index specified the spatial patterns of congestion between the critical time of changing the scheme on weekdays (17:00–19:00) and weekend (6:00–13:00) after implementing the new CCS. The approach in this paper can be used with a low-cost appropriate instrument to monitor the probable change in traffic congestion by introducing any new scheme or sudden change. [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]

In developing countries, metropolitan cities, due to their economic activities, attract an increasing amount of commuters on a daily basis. This has led to major freeways and roads experiencing high levels of congestion and consequently high pollution levels. In 2020, due to a global pandemic of an outbreak of Corona Virus (COVID-19), the national government declared a national shutdown with only essential traffic being allowed to operate. Given the scenario of the national lock-down this allows for the statistical analysis of the impact of essential traffic on the overall transportation system. Consequently the aim of the paper was to assess the congestion and CO2 emission impact of essential traffic for the City of Johannesburg. Using an exploratory approach, we monitored and collected traffic congestion data from the Tomtom traffic index for the metropolitan city of Johannesburg, South Africa. We develop a relationship between congestion and pollution to visualise the daily variations in pollution and congestion levels. We demonstrate this by comparing variations in congestion levels in two epochs, viz the period without movement restrictions and the period whereby movement is restricted. The results reveal essential traffic on the congestion index to be below 22 percent for both weekends and weekdays. A scenario common only during weekends in 2019. Whilst for the emission index, CO2 levels are approximately less than 45 percent throughout the week. The paper concludes the investment into mining and analysing traffic data has a significantly role for future mobility planning in both the developed and developing world and, more generally, improving the quality of commuting trips in the city. [ABSTRACT FROM AUTHOR]

• Propose a bi-modal bottleneck model with time-varying scheduling preferences. • Reveal the relationship between departure intervals of auto and bus commuters. • Consider two kinds of congestion externalities: bottleneck congestion externality and in-bus passenger crowding externality. • Investigate time-varying and step congestion charging issues. • Identify a critical condition that congestion charging schemes drive passengers to shift from one mode to the other mode. This paper proposes a bi-modal bottleneck model with time-varying scheduling preferences for investigating the two-mode problem with bottleneck queuing and transit crowding. In the proposed model, the activity scheduling utilities of commuters at home and at work vary by the time of day, and two kinds of congestion externalities (i.e., traffic congestion among vehicles at the bottleneck and in-bus passenger crowding discomfort) are considered. Commuters choose their travel modes and departure times from home to work during the morning commute to minimize their own generalized travel cost. The properties of the proposed bi-modal bottleneck model are analytically explored. Particularly, the relationship between the departure intervals of auto and bus commuters is revealed. The time-varying charging schemes and step charging schemes for reducing the congestion externalities are also investigated. A critical condition for passenger shift from one mode to the other mode with congestion charging schemes is identified, and the effects of congestion charging schemes with and without internalizing the bus-related congestion externalities (including the bottleneck congestion externality induced by auto and bus interaction and the in-bus passenger crowding externality) on the bottleneck system are compared. [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]

• The effectiveness of existing traffic measures in Beijing is investigated. • A survey on drivers' opinions and attitudes towards the traffic situation is given. • Most respondents think congestion is still serious in Beijing. • The perceived impact of congestion on personal car use is relatively low. • The symbolic meaning of personal cars has large impact on willingness to change. Facing serious challenges of traffic congestion and air pollution, Beijing has implemented a series of traffic polices. In this paper, we first investigate the effectiveness of existing traffic measures in Beijing and analyse the underlying factors from the perspective of drivers, which provide insights for the traffic regulations in other cities worldwide. While the car ownership restriction has effectively limited the total number of vehicle in Beijing, the effect of the car use restriction only lasts for a short period. A survey on drivers' opinions and attitudes towards the traffic situation and a potential measure, i.e., tradable driving credit (TDC), is conducted. Although traffic data shows that congestion improved and traffic was slightly congested in recent years in Beijing, most respondents believe congestion is still serious in Beijing. However, they think the impact of traffic congestion on their personal car use is relatively low, which could partly explain why so many travellers still depend on driving given the relative convenient transit facilities in Beijing. In addition, a large proportion of respondents treat personal cars as a representation of social status and think driving bring them pleasures, which indicates that it may be hard to further decrease the share of car trips in Beijing. According to their attitudes and social-economical characters, respondents' willingness on their changes of travel behaviours under TDC is investigated. The results show that individuals' social-economic characters such as income level, education and family size and the symbolic meaning of personal cars have significant influence on respondents' willingness to switch to other transport modes. [ABSTRACT FROM AUTHOR]

• The time-varying effect of pricing on on-street parking behavior is investigated. • Field-collected panel data over four time periods is used. • Parking duration decreases as parking price increases. • The elasticity of parking turnover fluctuates during the study period. Parking pricing is considered a powerful tool for improving parking management and relieving traffic congestion in metropolitan areas. This study uses field-collected panel data over four time periods before and after the implementation of a new parking pricing policy in Nanning to investigate the time-varying effect of pricing on on-street parking characteristics, including parking duration and parking turnover. Results show that parking duration decreases as parking price increases, with the relationship presenting a growing elasticity. The elasticity of parking turnover fluctuates due perhaps to the influence of vehicle ownership. Kolmogorov–Smirnov (K-S) tests show that on-street parking characteristics differ significantly before and after the implementation of a new parking pricing structure. Strategies that can improve parking services are also investigated based on the empirical data and modeling results. [ABSTRACT FROM AUTHOR]

Massive traffic jam is the top concern of multiple disciplines (Civil Engineering, Intelligent Transportation Systems (ITS), and Government Policy) presently. Although literature constitutes several IoT-based congestion detection schemes, the existing schemes are costly (money and time) and, as well as challenging to deploy due to its complex structure. In the same context, this paper proposes a smart route Internet-of-Vehicles (IoV)-based congestion detection and avoidance (IoV-based CDA) scheme for a particular area of interest (AOI), i.e., road intersection point. The proposed scheme has two broad parts: (1) IoV-based congestion detection (IoV-based CD); and (2) IoV-based congestion avoidance (IoV-based CA). In the given area of interest, the congestion detection phase sets a parametric approach to calculate the capacity of each entry point for real-time traffic congestion detection. On each road segment, the installed roadside unit (RSU) assesses the traffic status concerning two factors: (a) occupancy rate and (b) occupancy time. If the values of these factors (either a or b) exceed the threshold limits, then congestion will be detected in real time. Next, IoV-based congestion avoidance triggers rerouting using modified Evolving Graph (EG)-Dijkstra, if the number of arriving vehicles or the occupancy time of an individual vehicle exceeds the thresholds. Moreover, the rerouting scheme in IoV-based congestion avoidance also considers the capacity of the alternate routes to avoid the possibility of moving congestion from one place to another. From the experimental results, we determine that proposed IoV-based congestion detection and avoidance significantly improves (i.e., 80%) the performance metrics (i.e., path cost, travel time, travelling speed) in low segment size scenarios than the previous microscopic congestion detection protocol (MCDP). Although in the case of simulation time, the performance increase depends on traffic congestion status (low, medium, high, massive), the performance increase varies from 0 to 100%. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AV) create new opportunities to traffic planners and policy-makers. In the case of shared autonomous vehicles (SAVs), dynamic pricing, vehicle routing and dispatch strategies may aim for the maximization of the overall system welfare instead of the operator's profit. In this study, an existing congestion pricing methodology is applied to the SAV transport mode. On the SAV operator's side, the routing- and dispatch-relevant cost are extended by the time and link-specific congestion charge. On the users' side, the congestion costs are added to the fare. Simulation experiments are carried out for Berlin, Germany in order to investigate the impact of SAVs and different pricing setups on the transport system. For the pricing setup, where SAV users only pay the base fare and there is no congestion charge added to the user costs, the model predicts an SAV share of 17.7% within the inner-city Berlin service area. The level of traffic congestion increases, air pollution levels decrease and noise levels slightly increase in the inner-city area. The SAV congestion charge pushes users from SAVs to the walk, bicycle and conventional (driver-controlled) private car (CC) mode. The latter effect is avoided by applying the same congestion charge also to CC users. Overall, this study highlights the importance to control both , the SAV and CC mode in order to improve a city's transport system. [ABSTRACT FROM AUTHOR]

The traffic congestion has been reach to a critical level in the metropolitan cities, however; the local governments have developed various solution methods against this problem. The most effective solution among these methods is traffic congestion pricing. The local governments of the cities of London, Stockholm and Milan have successfully implemented the congestion pricing method. In this study, the solution of the congestion pricing solution in Erzurum Cumhuriyet Street, which is not applied before, has been examined for this problem. According to both the model created and the survey carried out, in the along the cordon route determined at Cumhuriyet Street, account of theoretical congestion pricing has been made. In the modeling study; car ownership, travel time, fuel consumption and emission costs have been used. As a result, it is foreseen that traffic congestion will decrease in the city center and a significant financial income will be obtained with the application of pricing. [ABSTRACT FROM AUTHOR]

In order to study the traffic jams and congestion, this paper investigated the stability of a car-following model on the basis of synchronisation theory of complex network. By using the Lyapunov stability theory and designing the appropriate controller, the car-following model is quickly stabilised and the stability condition of the model is obtained. In addition, the stability of car-following model is studied when the vehicle is subjected to external stochastic disturbance. Finally, the numerical simulation is carried out by using the MATLAB simulation technology, and the results show that the car-following model is rapidly stabilising, and congestion phenomenon is effectively alleviated under the controller designed. [ABSTRACT FROM AUTHOR]

ln order to study the traffic jams and congestion, the paper investigated the stability of a full velocity difference ear-following model on the basis of adaptive H∞ synchronization theory of complex networks. Through the controller designed in this paper, the full velocity difference car-following model is quickly stabilized. And the stability condition of the model is obtained by using the Lyapunov stability theory. In addition, the stability of the model is studied when the vehicle is subjected to external disturbance. Finally, the numerical simulations is carried out by using Matlab simulation technology, the results show that the full velocity difference car-following model is rapidly stabilizing and congestion phenomenon is effectively alleviated under the controller designed in this paper. [ABSTRACT FROM AUTHOR]

We investigates the performance of deep Convolutional Neural Network (CNN) for recognizing highway traffic congestion state in surveillance camera images. Different from the usual images in ImageNet, images generated by highway surveillance cameras usually have much more extensive range of perspective and thus larger area of background. Therefore the objective road and vehicles are not as prominent as target object in ImageNet images. And also these images from cameras across a large number of highway sites could show a very rich variance of scenes, road configurations. We are very interested to study whether convolutional networks are still reliably able to classify such images, without any special previous processing such as segmentation of objective roads. Two classic convolutional networks, AlexNet and GoogLeNet are employed to classify congestion state. We build a highway imagery dataset using real-life traffic videos to evaluate the CNNs recognition performance. These images cover a wide range of road configurations, times of the day, weather and lighting conditions, and have been labeled with one of the two states, congestion or non-congestion. The experimental results indicate that under the current strategy of feeding images directly into networks, both AlexNet and GoogLeNet can achieve an excellent recognition accuracy of 98% on held-out test samples. And many of the misclassified images turn out to be borderline cases. More results include that scale and perspective in photography could affect the recognition result. [ABSTRACT FROM AUTHOR]

Understanding factors that drive the parking choice of commercial vehicles at delivery stops in cities can enhance logistics operations and the management of freight parking infrastructure, mitigate illegal parking, and ultimately reduce traffic congestion. In this paper, we focus on this decision-making process at large urban freight traffic generators, such as retail malls and transit terminals, that attract a large share of urban commercial vehicle traffic. Existing literature on parking behavior modeling has focused on passenger vehicles. This paper presents a discrete choice model for commercial vehicle parking choice in urban areas. The model parameters were estimated by using detailed, real-world data on commercial vehicle parking choices collected in two commercial urban areas in Singapore. The model analyzes the effect of several variables on the parking behavior of commercial vehicle drivers, including the presence of congestion and queueing, attitudes toward illegal parking, and pricing (parking fees). The model was validated against real data and applied within a discrete-event simulation to test the economic and environmental impacts of several parking measures, including pricing strategies and parking enforcement. [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]

To improve the operational efficiency of airport surfaces, this paper studies the air traffic congestion prediction of airport surfaces, demonstrates the limitations of traffic congestion prediction, and proposes a prediction method for airport surface traffic congestion based on decision tree. Firstly, the definition and measurement methods of traffic congestion in airport surfaces are promoted. Then, the key factors affecting traffic congestion are extracted, and a prediction model of traffic congestion is established. Finally, we verify the validity of the model based on actual operation data from Atlanta. The results show that the accuracy of the prediction is 70%. [ABSTRACT FROM AUTHOR]

Traffic congestion is among the main market failures in modern cities. Dynamic marginal external cost pricing is the textbook economic response to this externality. Recent empirical work has shown that there is an important distinction between short-run departure time choice versus long-run routine formation of commuters, also characterized by differences in values of time and schedule delays for the short-run versus the long-run problem. This paper investigates whether this affects optimal pricing of congested roads. Using a dynamic model of congestion, and integrating it with a dynamic model of routine formation, it is found that contrary to expectation, short-run optimal congestion pricing alone cannot optimally decentralize the optimal formation of long-run routines. A separate instrument is therefore needed to optimize routine formation. [ABSTRACT FROM AUTHOR]

Nowadays, both the modern and developing countries are planning to deploy Internet of Vehicles for smart transportation systems to face the traffic congestion problems. Shifting from current road conditions to smart ones is a real challenge where all the vehicles and road infrastructure are not equipped with wireless network interfaces. The urban environment is more congested than highway scenarios and has more issues of delay and travel time. Various types of route suggestion protocols have been proposed where most of them consider smart vehicles which are equipped with smart and intelligent communication devices. The most of existing schemes have not considered non-equipped vehicles which have a major cause of traffic congestion. This paper presents a route suggestion protocol to suggest an optimal congestion aware route in the network. Equipped and non-equipped vehicles are taking into account for congestion index formulation along with driving distraction factors. Simulation results indicated the greater performance of proposed protocol and reduce the travel time effectively when works with IoV as compared to traditional route suggestion protocols. [ABSTRACT FROM AUTHOR]

With the rapid development of society, urban traffic congestion has gradually become an important social problem that many cities need to solve. For Chongqing, traffic congestion not only affects residents' normal travel but also brings more serious environmental pollution. Aiming at the problem of urban traffic congestion and automobile exhaust pollution, this paper adopts the system dynamics method to establish a model for studying urban traffic congestion system from the perspectives of private cars, trucks, and public transportation. First, we determine city motor vehicle trips as an indicator of the degree of traffic congestion in this paper. Second, we analyze the causal relationship between the growth of private cars, the travel of trucks, public transportation, population, and other factors and then build a model and test the stability of the model. Then, we add some practical policies to the model for policy analysis. Finally, it is concluded that the private car restriction policy is effective in controlling the amount of private car travel, and the purchase restriction policy controls the growth of the number of private cars from the root cause, but the development of public transportation is the most effective treatment measure in the long run. [ABSTRACT FROM AUTHOR]

On the basis of analyzing connotation of congestion pricing, through the qualitative analysis of the main content and implementation effect of the congestion policy in cities such as Singapore, London and Stockholm, the basic conditions for urban congestion charges were summarized. Compared with Beijing's urban development, traffic development status, urban transport infrastructure and social factors, the current policy of traffic congestion charging is still not available from the perspective of the current status of car ownership in Beijing, residents' travel composition, and population density distribution. [ABSTRACT FROM AUTHOR]

Transportation agencies face escalating challenges in forecasting the traffic demand. Traditional prediction methods focused on individual transportation sectors and failed to study the inter-dependencies between the different transportation systems. Hence, there is a need for more advanced and holistic modeling techniques. To this end, this paper models and analyses an urban transportation system-of-systems incorporating seven various systems: population and GDP, CO2 emission, gasoline price and total vehicle trips, traffic demand, public and private transportation, transportation investment, and traffic congestion. Accordingly, this research simulates transportation networks as a collection of task-oriented systems that combine their resources to form a complex system with increased functionality. The goal of this paper is to understand the traffic complex behavior of urban transportation networks and to study the interdependencies between the different variables. The proposed framework could be implemented to any urban city, county, state, or country. The developed model incorporates a hybrid modeling approach that includes: logistic model, system dynamics, stochastic cellular automata, chaos theory, and Lotka-Volterra model. The final model is demonstrated using a case study. The contribution of this paper lies in modeling the transportation network as a dynamic system of systems rather than as static model as provided in previous studies. [ABSTRACT FROM AUTHOR]

Shared parking, complementary to public parking, effectively alleviates the problem of parking difficulties in city. Shared parking spaces could reduce the difficulty of parking, which will potentially trigger the increasing of vehicles traveling, resulting in congestion, pollution and other negative externalities. In the view of environmental performance, exhaust emissions from road traffic, mainly cars, are the main reason of pollution. The rising prices of parking space, which may decrease vehicle travelling, could be an effective policy to reduce automobile travel, encourage public transport then reduce the impact of traffic on urban environment. Different from the discussion of shared parking itself, this study constructs an idealized model by discrete choice to find the relationship between the social benefits of city and public parking fees. Through the instance analysis, this paper analyzes the impact of government pricing on public parking spaces on a certain city problem including giving priority to alleviating traffic congestion, increasing fiscal revenue, reducing exhaust emissions and mitigate air pollution or the goal of comprehensive development. Increasing parking fees will result in an increase in overall income including parking fees and bus fares. And with the increasing of parking fees, more people will transfer to public transport, which will ease congestion and reduce traffic emission. To achieve the maximum social benefits, the urban public agents should develop an optimal public parking prices within a certain range. [ABSTRACT FROM AUTHOR]

We consider a class of variational inequality problems with linear constraints, where the mapping is unknown and the system is an oracle. The capacitated traffic congestion pricing problem of transportation is such an application, and many classical methods cannot deal with this class of problems. Note that the cost of the observation (observe the exact solution of the subproblem) is very expensive. It is important to get an inexact solution instead of an exact solution, especially when the iteration is far from the solution set. In this paper, we propose a modified inexact prediction–correction method. Under the mild condition that the underlying mapping is strongly monotone, we prove the global convergence. Some numerical examples are presented to illustrate the efficiency of the inexact strategy. [ABSTRACT FROM AUTHOR]

High‐occupancy vehicle (HOV) lanes have been promoted to encourage carpools, reduce traffic congestion, and improve air quality. At the partial equilibrium level, commuting with three workers per automobile clearly reduces highway congestion, lowers carbon emissions, and saves energy compared with three single drivers. This paper develops a numerical urban simulation model to generate the general equilibrium effects of HOV lanes on urban spatial structure, energy use, and greenhouse gas emissions. The major findings are that while HOV lanes reduce traffic congestion and improve welfare, the fall in transportation cost leads to urban sprawl, which results in higher dwelling energy use and a larger carbon footprint. Overall, the HOV lane policy has little effect on total energy consumption and carbon emissions. This is another classic case of general equilibrium effects reversing the partial equilibrium effects of an urban policy. In contrast, a gasoline tax policy leads to less urban sprawl but is less effective at lowering energy consumption and carbon emissions. Imposing congestion tolls is a more effective tool at reducing traffic congestion, saving energy, and lowering carbon emissions. [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]

The vehicular networks heavily rely on beaconing for traffic safety and congestion awareness. These periodic broadcasted safety messages may lead to heavy data traffic, resulting in excessive inaccuracy and failure of applications based on vehicular communication environments. With the help of data congestion control schemes,Vehicular networks can be made more reliable and effective. However, these approaches use time-average metrics while not consideringbeacon receptions temporal behavior. In order to resolve the congestion problem, we present a Beacon Inter-Reception time based Adaptive beaconing (BIRA) in this article. The comparative simulation results show that the proposed scheme BIRA is effective in congestion control in comparison with the similar class scheme. [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]

Urban public transport is a very essential mode for urban residents' commute travel; however, the unbalanced spatial and temporal distribution of travel demand usually leads to passenger flow congestion risk at certain section and time. Meanwhile, the risk is short of quantified description. Based on the Pressure-State-Response framework, the study puts forward three bus passenger flow congestion risk evaluation indexes including the alternative pressure, the congestion intensity, and the transport efficiency. Then, the evaluation model is proposed based on the entropy method, and the risk is divided into four levels by K -means clustering. The article considers the 3rd Ring Road corridor in Beijing as a case to identify the risk level. The results show that the risk in the peak hours of weekdays is generally about 1.5 times higher than the risk in the weekends. The congestion risk is stable in level 3 during the majority time of morning peak hours. The duration intensity of level 4 risk is less than 0.1 during weekdays, indicating that the highest flow congestion can be quickly evacuated in a short time. The integrated passenger risk identification and evaluation model was proposed to identify the passenger flow risk level and induce the network flow distribution more reasonable. The study also provides technical support for ensuring the public transit system safety. [ABSTRACT FROM AUTHOR]

• This paper proposes a network-wide method to identify intersection traffic congestion in a road network. • The proposed method is solely based on low-frequency probe vehicle data, i.e., map-independent. • It is able to rapidly and approximately detect intersection congestion along all turning directions. • It is a computer-aided method in the era of big data can greatly decrease traffic engineers' workload. Locating the bottlenecks in cities where traffic congestion usually occurs is essential prior to solving congestion problems. Therefore, this paper proposes a low-frequency probe vehicle data (PVD)-based method to identify turn-level intersection traffic congestion in an urban road network. This method initially divides an urban area into meter-scale square cells and maps PVD into those cells and then identifies the cells that correspond to road intersections by taking advantage of the fixed-location stop-and-go characteristics of traffic passing through intersections. With those rasterized road intersections, the proposed method recognizes probe vehicles' turning directions and provides preliminary analysis of traffic conditions at all turning directions. The proposed method is map-independent (i.e., no digital map is needed) and computationally efficient and is able to rapidly screen most of the intersections for turn-level congestion in a road network. Thereby, this method is expected to greatly decrease traffic engineers' workloads by providing information regarding where and when to investigate and solve traffic congestion problems. [ABSTRACT FROM AUTHOR]

With the increase in travel demand and car ownership, traffic congestion is becoming more and more serious. To alleviate traffic congestion, based on the user heterogeneity, this paper proposes to implement both toll and tradable credit scheme for the public-private partnership transportation network, i.e., tolling and charging tradable credits for the users who use the government-built public roads and BOT private roads, respectively. Based on the travelers' routing behavior under this mixed strategy, we build the variational inequality model of user equilibrium and market equilibrium. Then, according to the system optimal model, the mixed strategy set can be obtained that promotes the user equilibrium flow pattern to the system optimal flow pattern, and the upper bound of system efficiency loss under mixed strategy is also derived. Furthermore, we propose two bi-level programming models covering other objectives and obtain the optimal mixed strategy. Finally, a numerical experiment on a specific road network is conducted, which can obtain the specific amount of tradable credits and charging value for each public road and BOT private road respectively, and the total number of windows for that need to be set in the road network. [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]

• Average speed emission model prediction refinements by using a micro-simulator. • Coupled traffic and vehicle emission modelling utilized in a micro and macro level. • 4 mean speed-fuel consumption functions developed for free and congested traffic. • From normal to congested traffic the fuel consumption increases more than 18%. • COPERT could estimate fuel consumption under congested traffic conditions. This study aims to demonstrate a new approach for estimating fuel consumption from vehicles circulating on urban roads with different levels of congestion. A microscopic dynamic traffic model (AIMSUN) was coupled with an instantaneous emissions model (CRUISE) and their output was used to refine the average speed model (COPERT) functions. The integrated methodology was applied in an urban corridor in Turin using two validated vehicle models of Euro 5 passenger cars. Three traffic conditions were examined, corresponding to free-flow, normal and congested traffic. New average speed – fuel consumption functions were developed for free and congested traffic, which successfully estimated the differences in fuel consumption when moving from normal to other conditions. The results showed that under congested conditions the fuel consumption can increase by more than 18% in some cases, indicating the significance of incorporating similar congestion algorithms in macro emission models. Moreover, if a more comprehensive method does not exist, the standard COPERT functions could be used to estimate fuel consumption under congested traffic conditions. [ABSTRACT FROM AUTHOR]

Understanding congestion in space-time domain using performance measures is essential prior to suggesting improvement schemes to reduce congestion. With technological advances like Global Positioning System (GPS), many metropolitan planning organizations give more emphasis on travel time based performance measures to quantify congestion, than on traditional way of using volume-to-capacity (V/C) ratios. In India, often it may not be possible to use personal vehicles as probes for travel time data collection. However, the public transit buses fitted with GPS devices could be used as cheap and effective probes to estimate the congestion status of other types of vehicles in the stream. The present study is an attempt in this direction. Two bus transit routes in Chennai, India were considered as case studies in order to cover the wide range of geometric and traffic conditions on urban arterials. GPS-fitted buses on these routes were used as probes in congestion quantification. As the dwell time at bus stops is a unique characteristic of transit buses when compared to other vehicles in the stream, a methodology has been proposed to find the dwell times including acceleration and deceleration times based on the approaching and departing speeds at bus stops. Regression models were then developed to predict the Congestion Index (CI) for various types of vehicles using bus CI, weighted carriageway width and the presence or absence of signalized intersection as independent variables. The results are promising and could be considered for real-time display of congestion levels for Advanced Traveler Information System (ATIS) applications. [ABSTRACT FROM AUTHOR]