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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]

Quantifying traffic congestion is a critical task for transportation planning and research. Numerous metrics have been developed, mainly focusing on changes in vehicle speeds, their extents, and travel time. In this study, new metrics are presented using the Hägerstrand's space-time cube that has been studied from time geography perspectives since the 1960s. Particularly, the product of distance and time, i.e., distanceTime, is proposed as a base metric to measure traffic congestion amounts. Using the base metric such as mileHours, metrics of weighted congestion and normalized congestion amounts were also developed. New metrics were applied to six metropolitan areas and their vicinities in the United States (Atlanta, Chicago, Washington, D.C. and Baltimore, Dallas and Fort Worth, Los Angeles, and New York), and congestion amounts were calculated and compared. The Google Traffic Layer API was used to obtain traffic congestion datasets for six months (April–September 2022), and GIS (geographic information systems) was used for delineating road features and traffic intensity levels. Among the six areas, New York and its vicinity showed the largest congestion when only heavy congestion was used. Los Angeles and its vicinity showed the largest congestion when all congestion levels were considered. This study shows that the proposed metrics are very effective in summarizing traffic amounts and broadly applicable for further analyses of traffic congestion phenomena by associating various other factors, such as weekdays, months, or gas prices. The new metrics developed in this research may help transportation researchers and practitioners by providing them with a set of metrics applicable to summarizing congestion amounts by synthesizing congestion intensity, extent, and duration. [ABSTRACT FROM AUTHOR]

Ever-growing mobility and traffic congestion within urban areas make the need for a sustainable form of transport inevitable. Traffic congestion has a significant effect on the amount of energy consumption of a vehicle and, as a result, on its associated environmental impacts. Any decision-making regarding structuring a fleet without taking into account the traffic congestion level (TCL) will lead to a less sustainable fleet with higher environmental and economic costs. To address this issue, this study examines the effects of the traffic congestion intensity level on the fleet structure of an urban car-sharing company over a certain planning period. We present a new optimization framework for finding an optimal vehicle composition of the fleet of an urban car-sharing company considering the energy consumption of vehicles at different traffic congestion levels. The results show that electric vehicles (EVs) are more competitive than diesel vehicles (DVs) in high-peak traffic congestion from the outset of the planning period. In addition, we perform a sensitivity analysis to take into account the effects of specific uncertain parameters such as the energy and purchasing costs of EVs on the total cost of ownership. As expected, the purchasing price of EVs, energy prices of DVs, and increase in diesel prices have the highest impact on the total cost. [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]

Global trends toward urbanization will exacerbate traffic congestion, delays in economic productivity, and air pollution issues for growing cities. Traffic congestion pricing is one method available to help ameliorate these concerns. New York City is on the verge of implementing a cordon-based traffic congestion pricing policy around its central business district. For budget-constrained municipalities, evaluating implementation of such policy could be costly. This article proposes a sketch-planning methodology, called Cordon Screen, for major U.S. cities to evaluate the net income, traffic mitigation, and avoided pollution emissions from cordon-based traffic congestion pricing. This method relies on national datasets and limited user-specific data inputs, along with a range of user-selectable assumptions informed by academic literature to deliver order-of-magnitude results. The numerous limitations of this method are acceptable for preliminary policy evaluation to determine if greater financial investment to obtain more accurate results is justified. The Denver metropolitan area is used to demonstrate Cordon Screen capabilities, with mid-range assumption results suggesting the policy is most effective at generating net income and increasing vehicle speeds on major interstates. For Denver, the policy is comparably less effective at reducing air pollution and increasing speeds on minor roadways. Validation against early implementation results from the London cordon are acceptable. However, users should discount revenue generation projections. Choice of cordon area may be the most difficult obstacle when using the Cordon Screen. With refinement, Cordon Screen could serve as a low-cost, open-source planning evaluation tool for growing and congested U.S. cities. Implications: As global urbanization trends continue, impacted local governments will be looking to explore policies to mitigate traffic congestion and reduce environmental emissions. Internationally, cordon-based traffic congestion pricing has been implemented in London, Singapore, and several other large cities. In America, New York City is implementing cordon-based congestion pricing around its central business district to reduce traffic and environmental emissions. Financial resource constraints, exacerbated by the COVID-19 pandemic, may limit the ability for local governments to invest in studying new policy options. The Cordon Screen method detailed in the manuscript presents a low-cost, open-source approach to assessing the potential benefits of cordon-based traffic congestion policy. The method utilizes national datasets to minimize user-specific data requirements and allows users to toggle between a range of values to test sensitivities to key assumptions. For example, emissions reductions are highly sensitive to how drivers respond to tolling. In this example, sensitivity testing enables users to understand how policy design can impact air quality goals. The Cordon Screen approach presented provides a strong platform for future stakeholder deliberation, refinement, and implementation. [ABSTRACT FROM AUTHOR]

A novel approach is introduced for the detection of the location and direction of traffic congestion using GPS data from taxis. This approach uses a dynamic model that conceptualizes events, processes, and states. The states are the locations of the taxis, the processes are the motion of taxis, and the events are congestion. The model is implemented as a graph database, which represents the relationships between states, events, processes, and things (such as points of interest and road grid). Algorithms for constructing and updating the relationships and taxi behaviors dynamic retrieval method in Neo4j are presented and are used to demonstrate the capabilities in dynamic expression and reasoning. An implementation of Shanghai in 2015 finally demonstrated the ability of congestion direction detection and the cause searching of traffic congestion. [ABSTRACT FROM AUTHOR]

Static pricing is currently the most prevalent form of congestion pricing. Most existing methods for static pricing design use equilibrium-based indices as the optimization objective function. However, in reality, the equilibrium may not be reached within the toll planning horizon, and thus equilibrium-based pricing schemes might be ineffective in alleviating traffic congestion. To address this issue, this paper proposes a new method for the optimal static congestion pricing design. Specifically, we propose a new measure called cumulative network performance (CNP) as the optimization objective function. The CNP measure explicitly considers the fluctuation in network performance over the toll planning horizon, instead of only focusing on the performance at the equilibrium state. To capture the effect of static congestion pricing on CNP, we introduce a day-to-day dynamic model with the Weibit-based route flow adjustment, which can overcome the drawbacks of the user equilibrium or Logit-based route-choice criterion that are used in most current day-to-day dynamic models. Numerical experiments are conducted to show the effectiveness and advantages of the toll scheme resulting from the proposed method. [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]

This paper proposes a methodological framework that incorporates probabilistic facility disruption risks, last-mile customers travel path choices, and the induced traffic congestion near the facilities into the consideration of service facility location planning. The customers can be pedestrians, drones, or any autonomous vehicles that do not have to travel via fixed channels to access a service facility. Analytical models are developed to evaluate the expected performance of a facility location design across an exponential number of facility disruption scenarios. In each of these scenarios, customers travel to a functioning facility through a continuous space, and their destination and path choices under traffic equilibrium are described by a class of partial differential equation (PDE). A closed-form solution to the PDE is derived in an explicit matrix form, and this paper shows how the traffic equilibrium patterns across all facility disruption scenarios can be evaluated in a polynomial time. These new analytical results are then incorporated into continuous and discrete optimization frameworks for facility location design. Numerical experiments are conducted to test the computational performance of the proposed modeling framework. • Reliable facility location design under probabilistic facility disruption risks. • Congestion and continuous traffic equilibrium in a continuous space without guideways. • Analytical models to evaluate the expected performance of a facility location design. • System cost across an exponential number of disruption scenarios evaluated in a polynomial time. • Analytical results incorporated into continuous and discrete optimization frameworks. [ABSTRACT FROM AUTHOR]

• Employs quantitative research and analysis methods. • Explores traffic congestion in light of CSR ratings and its translation to real-world outcomes. • An innovative idea on sustainable transportation and policy in CSR. • Enhancing CSR policy in mitigating traffic congestion is an area where private entities can have a significant local impact on a social issue affecting people, plant, and profit. Traffic congestion (TC) is a complex issue that has an adverse impact on the environment, business operations, livability, and health of a community. Supply-side TC mitigation measures increase transportation capacities while demand-side measures attempt to modify travel behavior so that the travel demand is reduced. As part of the demand-side, some corporations are investing in TC reduction through actions that contribute to improved walkability, reduced peak travel demand, and active commuting. While beneficial for their local communities, TC reduction efforts are not specifically tagged as a part of corporate social responsibility (CSR) endeavors. CSR involves reporting of a company's stewardship towards its community and environmental impact. Research reflecting on the impact of CSR on TC has not been conducted. This study aims to fill this gap. Using corporations headquartered in the top traffic-congested cities in the United States, this study examines the relationship between TC and CSR. This research employed a hierarchical linear model with two datasets, Travel Time Index (TTI) and CSRHub ratings. Of the four CSR categories studied, community, employees, and environment ratings are significantly related to TTI, with employees and environment ratings having an inverse relationship to TTI. This shows that congestion has a strong impact on the environment and that companies, through their employee policies, can impact TC. The results also highlight the opportunities that companies have in potentially reducing their environmental impact by incorporating congestion reduction strategies as part of their CSR, either as a separate measure or as part of their environmental or employee CSR ratings. Our results are also a starting point for new tools/strategies that transportation policymakers and analysts can use to engage companies to help mitigate TC. A further study on other cities with major traffic problems may shed more light on CSR and TC. [ABSTRACT FROM AUTHOR]

The cold chain logistics route of fresh product export is characterized by large quantity and complexity, which is prone to cause transportation risks of different degrees in the process of fresh product export transportation and affects the decision-making effect of the cold chain logistics route. Therefore, in order to improve the ability of cold chain logistics route planning and shorten the transportation time, an optimization method of fresh product export cold chain logistics route decision considering transportation risk was proposed. This paper analyzes the basic characteristics and classification of cold chain logistics by means of risk quantification and uses the K-nearest neighbor algorithm to predict the risk of traffic congestion, so as to shorten the transportation time. Ahp process is used to construct a risk factor judgment matrix and determine the index weight of risk factors, so as to reduce the error of path planning. A genetic algorithm is introduced to construct the optimal decision function of the cold chain logistics route of new product export and realize the optimization of cold chain logistics route decision of fresh product export. Experimental results show that the method presented in this paper can effectively improve the decision-making effect of cold chain logistics route and select the shortest and most smooth transportation path to complete logistics distribution. The decision-making accuracy of the route decision effect is 90%, and the transportation time is 31.45 min, which has certain feasibility and applicability. [ABSTRACT FROM AUTHOR]

One of the core reasons for urban traffic congestion is the mispricing of the traffic's main resource, the scarce road capacity. Recent proposals suggest a two-stage market for road capacity similar to other major utilities markets such as those for electricity. In such markets an Independent System Operator (ISO) provides road infrastructure which multiple Service Providers (SP) then resell to end consumers. The size of such markets in terms of the number of traded items, road segments per hour, is exceptional. A road network in a city consists of tens of thousands different road segments and it is unclear how a wholesale market for service providers should be designed to keep it tractable for buyers and sellers. Three problems arise in this context: First, how can a service provider express preferences for so many products. Second, the optimization problem is very large and it is far from obvious that problems of this size can be solved. Finally, the allocation problem is non-convex and finding competitive equilibrium prices on such markets is impossible in general. We suggest a parsimonious bid language based on origin–destination pairs and introduce a mixed-integer optimization problem to maximize welfare on such markets. Based on the MATSim traffic simulator, we show that one can solve realistic problems based on traffic data for the city of Berlin to optimality within 15 minutes. Importantly, we approximate competitive equilibrium prices which require only negligible side-payments as the allocation problem is "almost" convex. Overall, the paper provides a tangible proposal for wholesale markets for road capacity. [ABSTRACT FROM AUTHOR]

Cities need to develop, people's lives need to be improved, and the living environment needs to be enhanced. With the strengthening of the national policy of paying attention to people's livelihood, the speed of development, the increase of the number of cars, and the major demolition and construction of streets are in full swing everywhere. However, traffic congestion has become a serious social problem, which greatly affects economic development and people's daily life. Therefore, the study of signal coordination control of urban traffic arteries has become an urgent problem for all countries. In this paper, after a brief introduction of the basic concepts and related technologies of traffic control systems, we focus on the arterial coordinated control system. The adaptive control model studied in this paper reveals the vehicle arrival dynamics from a microscopic perspective, which helps to improve the adaptive control effect and alleviate urban intersection congestion. The use of traffic information acquisition and control systems can promote the application of vehicle networking technology, while moreover helping to provide important model simulations for future street simulation renovation. [ABSTRACT FROM AUTHOR]

The increasing of electric vehicles (EVs) alleviates the faced environmental problems but brings challenges to the optimal operation of transportation network (TN) and distribution network (DN). However, the most of existing research works consider EV charging station assignment and navigation services in the TN separately from charging station power scheduling services in the DN. To overcome this research gap, this paper proposes a collaborative optimal routing and scheduling (CORS) method, providing optimal route to charging stations and designing optimized charging scheduling schemes for each EV. In the order of reporting, whenever an EV reports its charging demand, a CORS optimization model is built and solved so that a specific charging scheme is designed for that EV. Then, the TN and DN status is updated to guide the subsequent EVs operating. The proposed CORS integrates the real-time state of the TN and DN, and effects positive benefits in helping EVs to avoid traffic congestion, improving the utilization level of charging facilities and enhancing charging economy. The combined distributed biased min consensus algorithm and generalized benders decomposition algorithm are adopted to solve the complex nonlinear optimization problem. Through comparing with the existing methods, better effectiveness is verified by simulation results. [ABSTRACT FROM AUTHOR]

With the ever-improving degree of car privatization, the number of car-owning residents has repeatedly reached new highs, but it has also caused traffic congestion, especially lane congestion. To reduce emergency lane congestion, and realize the intelligent detection and classification of emergency lanes, this paper introduces logistic regression theory and proposes a vehicle detection method based on logistic regression. Based on the powerful feature abstraction and automatic learning capabilities of convolutional networks, it studies vehicles based on convolutional networks. The recognition algorithm analyses the contribution of the convolution kernel in the convolutional network, and transforms the original emergency lane vehicle detection. The research results show that the emergency lane vehicle detection rate based on logistic regression and a deep convolutional network designed in this paper can reach approximately 98%, which is higher than the detection methods based on SVM and AdaBoost, and the detection time is not different from SVM and AdaBoost. After training, the convolution kernel of the deep convolutional network, the detection time and detection accuracy improved to a certain extent. This shows that the emergency lane vehicle detection and classification method based on logistic regression and a deep convolutional network, can play an important role in emergency lane intelligence. [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]

• Road pricing is a potential tool for controlling traffic congestion and pollution. • We study cordon tolls using the dynamic traffic network simulator METROPOLIS. • A Gaussian dispersion model is used to compute the distribution of pollution. • Step tolls have only a small advantage over flat tolls for pricing emissions. • Health effects vary with location relative to the cordon(s) and wind direction. Road traffic is a major contributor to air pollution which is a serious problem in many large cities. Experience in London, Milan, and Stockholm indicates that road pricing can be useful in reducing vehicle emissions as well as congestion. This study uses a dynamic traffic network simulator that models choices of mode, departure time, and route to investigate the effectiveness of tolls to target emissions and congestion externalities on a stylized urban road network during a morning commuting period. The spatial distribution of four pollutants is calculated using a Gaussian dispersion model that accounts for wind speed and direction. Single and double cordon tolls are evaluated, as well as flat tolls that do not change during the simulation period and step tolls that change at half-hourly intervals. The presence of emissions externalities raises optimal toll levels, and substantially increases the welfare gains from tolling, although the proportional advantage of step tolls over flat tolls is lower than if congestion is the only externality. The individual welfare-distributional effects of tolling vary strongly with residential and workplace locations relative to the cordon, and also differ for the upwind and downwind sides of the city. [ABSTRACT FROM AUTHOR]

To reduce the risk of traffic congestion to residents and the urban transportation system, this paper extracted frequently congested areas and major trunk roads based on the GPS (global positioning system) data of cabs and TPI (traffic performance index) data and identified traffic patterns and main trunk roads in the traffic grid, so as to analyze the evolution of traffic congestion and make effective suggestions. The results can not only enable travelers to effectively avoid peak periods and congested sections but also support the managers to optimize urban planning and implement efficient traffic management methods. The research process of this study is as follows: firstly, the research object area was divided into different grids based on one-week taxi GPS data and the distribution characteristics of taxi operations in Qingdao. Secondly, the two-dimensional grid traffic attribute information is constructed using the following two indicators: number of vehicles and the average speed of passenger trajectory. Then, the congestion discriminant model based on the three-dimensional traffic attribute information was established according to the variation rules of the number of position point in the grid. Finally, the TPI data was applied to compare and evaluate the identification results of the above two models to identify frequently congested grids and main trunk roads. The case analysis showed that the result of grid's congestion status identification considering three-dimensional traffic attribute information (25.198%) was better than that of grid congestion state considering two-dimensional traffic attribute information (23.997%). [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]

Motivated by the conflict between travelers' habitual choice behavior and traffic information search behavior, in this paper, a behavioral experiment under different types of traffic information (i.e., per-trip traffic information and en-route traffic information) was designed to obtain data regarding car commuters' daily route choices. Based on the observed data, participants' route choices, habit strength, response time, and information search behaviors were analyzed. It is concluded that, in the beginning, the traffic information had a great influence on the habit participants' route choices, let them think more, and made most of them switch from habit route to the best route (as recommended by traffic information); however, as time went on, the impact of traffic information declined, and several features of habits, such as automatically responding and repeated behavior, would reappear in some participants' decision-making. Meanwhile, the different way of traffic information search behaviors (i.e., in active performance or in passive reception) could cause different information compliance ratios. These results would help to understand the interrelationship between car commuters' daily route choice behaviors and traffic information search behaviors in short-term and in long-term, respectively, and provide an interesting starting point for the development of practical traffic information issuing strategies to enhance the impact of traffic information to alleviate traffic congestion during morning commuting. [ABSTRACT FROM AUTHOR]

In this paper, the authors present their efforts in exploring a new type of traffic data, referred to as internet-connected vehicle (ICV) data, for traffic congestion management and operational planning. Most currently manufactured vehicles contain onboard GPS and cellular modules, and they constantly connect to automobile manufacturers' clouds via cellular networks and upload their status. Some automobile manufacturers have recently redistributed the nonpersonal part of such data, such as geolocation, to third-party organizations for innovative applications. Compared with the traditional vehicle GPS data, the ICV data contain high-resolution GPS waypoints accompanied with the vehicles' abnormal moving events (e.g., hard braking). The ICV data also have huge potential in congestion management and operational planning. They explore to identify and analyze traffic congestion on both freeways and arterials using the ICV data. The ICV data adopted for this research are redistributed by Wejo Data Service, representing 10%–15% of all moving vehicles in the Dallas–Fort Worth (DFW) area in Texas. Through one case study for a freeway segment and one for an arterial segment, new traffic performance metrics based on the characteristics of ICV data have been presented. The highlights of these efforts are as follows: (I) queue length and propagation at freeway bottlenecks can be directly measured based on where and when most internet-connected vehicles slow down and join the queue; (II) an internet-connected vehicle's actual delay time on arterials can be directly measured according to its slow movement percentage, without assuming the nondelay travel speed; and (III) the ICV data set are also combined with the high-resolution traffic signal events to generate a ground-truth time-space diagram (TSD) on arterials—a common visualization of arterial signal performance for transportation planning and operations. [ABSTRACT FROM AUTHOR]

Buses are the mainstay of public transport systems in many cities but are typically subject to significant delays due to traffic congestion. We examine the welfare effects of providing dedicated bus lanes in the city of Rome, Italy. We demonstrate that a dedicated bus lane reduces bus travel time by about 18 percent. Our welfare analysis focuses on the situation where mixed road lanes are turned into dedicated bus lanes. We find that bus lanes are undersupplied, despite the additional time costs due to reducing road capacity available to cars. [ABSTRACT FROM AUTHOR]

Traffic congestion is one of the biggest challenges around the world, resulting in multiple harmful consequences such as air pollution, road fatalities, and traffic jams. Thus, it is a vital need to develop preventive mechanisms that allow better traffic management and alleviate the burden put on the transport network. Vehicle-to-anything (V2X) communication is gaining massive research interest as a promising solution to mobility challenges. This technology will enable vehicles and the infrastructure to form a distributed network constantly exchanging traffic information in real-time. The availability of timely information can help road users make optimal choices and enable effective autonomous traffic control. Therefore, to avoid traffic congestion, mobility models need to be established to study vehicular dynamics and to forecast future traffic conditions. The main goal of this study is to develop a traffic model based on Markov chain to tackle the congestion issue in a highway environment. Based on traffic data collected from vehicles through V2X technology, the model studies the evolution of traffic flow along a multiple lane divided highway and locally calculates estimates of the expected number of vehicles traveling on a highway segment. Performance measures are then inferred to detect possible congestion and then prevent it from happening. The numerical results presented in this study validate the model accuracy and show its ability to reproduce the fundamental mobility aspects in a highway environment. [ABSTRACT FROM AUTHOR]

Objectives: Traffic congestion detection is one of the key points and difficulties of urban traffic management. The existing congestion detection methods are based on road sections, which is not conducive to the extraction of spatiotemporal evolution information of congestion. Moreover, most of the detection only involves the degree of congestion but lacks the congestion type identification. With the classification and regression tree (CART) algorithm, this paper proposes a method for the classification and detection of traffic congestion points, which takes the road section point as the detection unit. In the practical application of this method, congestion points and their categories can be detected in real time according to the average running speed on the road section. Methods: Firstly, the road section is divided at a specific interval and mapped to be road section points. According to the abnormal rules and patterns of spatiotemporal road conditions, the spatiotemporal evolution patterns of four congestion types are analyzed with road section points as units. Secondly, the spatial and temporal sequence of road conditions of road section points is extracted on the basis of the road condition detection of road sections, and the spatial and temporal sequence of road conditions is classified and labeled according to different congestion types. Thirdly, four speed indexes are selected to constitute the attribute set of samples, and the speed of each road section point at each period is extracted according to the attribute set, which forms the dataset of decision tree learning. Finally, with the CART algorithm, the optimal model is obtained by the training with cross-validation to achieve the best generalization ability. Results: This paper proposes a classification and detection method of traffic congestion points based on CART. On one hand, congestion point detection is added to refine the basic unit of congestion detection, and on the other hand, congestion point type detection is also involved. Classification and detection of congestion points is helpful to improve the efficiency of traffic management. Conclusions: The proposed method is compared with the support vector machine classification model, and the experimental results show that the method in this paper has higher accuracy, higher recall rate, and better classification and detection timeliness. [ABSTRACT FROM AUTHOR]

This paper analyzes and evaluates several policies aiming to mitigate the congestion effect a Transportation Network Company (TNC) brings to bear on an idealized city that contains a dense central core surrounded by a larger periphery. The TNC offers both solo and pooling e-hail services to the users of public transport. We develop a spatial market equilibrium model over two building blocks: an aggregate congestion model that describes the traffic impact of TNC operations on all travelers in the city, including private motorists, and a matching model that estimates the TNC's level of service based on the interactions between riders and TNC drivers. Based on the equilibrium model, we formulate and solve the optimal pricing problem, in which the TNC seeks to optimize its profit or social welfare subject to regulatory costs and/or constraints. Three congestion mitigation policies are implemented in this study: (i) a trip-based policy that charges a congestion fee on each solo trip starting or ending in the city center; (ii) a cordon-based policy that charges TNC vehicles entering the city center with zero or one passenger; and (iii) a cruising cap policy that requires the TNC to maintain the fleet utilization ratio in the city center above a threshold. Based on a case study of Chicago, we find TNC operations may have a significant congestion effect. Failing to anticipate this effect in the pricing problem leads to sub-optimal decisions that worsen traffic congestion and hurt the TNC's profitability. Of the three policies, the trip-based policy delivers the best performance. It reduces traffic congestion modestly, keeps the TNC's level of service almost intact, and improves overall social welfare substantially. The cruising cap policy benefits private motorists, thanks to the extra congestion relief it brings about. However, because other stakeholders together suffer a much greater loss, its net impact on social welfare is negative. Paradoxically, the policy could worsen the very traffic conditions in the city center that it is designed to improve. [ABSTRACT FROM AUTHOR]

In order to study multimedia urban road path optimization based on genetic algorithm, a dynamic path optimization based on genetic algorithm is proposed. Firstly, for the current situation of traffic congestion, time constraints are strictly considered based on the traditional hard time window logistics distribution vehicle scheduling problem model. Then, the mathematical model is established, and the optimal solution is solved by the combination of decomposition coordination algorithm and genetic algorithm. We divide multiple customers into different customer groups and determine the service object order of each express car in each customer group, so as to obtain the most valuable scheduling scheme. Finally, in the process of solving the model, the relevant and reliable distribution basis for enterprise distribution is collected, including customer geographical coordinates, demand, delivery time window, unit cost required for loading and unloading, loading and unloading time, and penalty cost to be borne by distribution enterprises after early arrival and late arrival. Using the improved genetic algorithm, the optimal solution of each objective function is actually obtained in about 140 generations, which is faster than that before the improvement. Using the genetic algorithm based on sequence coding, a hybrid genetic algorithm is constructed to solve the model problem. Through the comparative analysis of experimental data, it is known that the algorithm has good performance, is a feasible algorithm to solve the VSP problem with time window, and can quickly obtain the vehicle routing scheduling scheme with reference value. [ABSTRACT FROM AUTHOR]

To make full use of road resources, improve the operation efficiency of the road network system, and alleviate the coexistence between traffic congestion and road resources idle caused by the traffic tidal phenomenon, the impact of the number of lanes on traffic capacity is examined, and the mixed-integer bilevel programming model for reversible lane optimization is established with the aim to minimalize the total travel time of the system. Taking a test road network as an example, the influence of the reversible lane optimization on characteristic values of sections, the route travel time between OD pairs, and the total time of the system are analyzed. The results indicate that the reversible lane optimization can make full use of the idle road resources and make the road network structure match the travel demands better, and the system index after the reversible lane optimization is obviously better than the original system index. [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]

This paper evaluated the congestion reduction benefits over a 6-year period from the implementation of an adaptive traffic control system (ATCS) in a congested area of Manhattan, New York, bounded by 2nd and 6th Avenues and 42nd and 57th Streets—known as the Midtown-in-Motion (MIM) area. A methodology using taxi Global Positioning System (GPS) sensors was used to measure traffic speed. Traffic speeds were calculated for May weekdays before the ATCS was installed (May 2011) and for each subsequent year (May 2012 to May 2016). Within 1 to 2 years after the implementation of the ATCS, the area’s traffic speed increased on avenues and cross streets. This gain, however, could not be sustained in subsequent years because of intervening changes in key factors impacting traffic congestion. These factors included reduction in roadway space/capacity for motor vehicles, lack of effective traffic enforcement to maintain/protect available roadway capacity, increased vehicle miles traveled (VMT) from transportation network company vehicles, and increasing volume of bicycle trips sharing street space with vehicles/pedestrians. This paper’s two key findings are, first, traffic speed gains initially seen in the MIM area after 1 year of ATCS implementation could not be sustained because intervening external factors reduced capacity and increased VMT. However, during the same analysis period, the rest of the Midtown Core area (without ATCS deployment) experienced a greater speed loss than the MIM area, indicating the effectiveness of ATCS deployment in minimizing losses in traffic speed. Second, to protect street network capacity and to minimize VMT growth, midtown Manhattan requires adopting proactive, collaborative, and coordinated strategies by the three key agencies involved with traffic management in New York City (NYC): the NYC Department of Transportation (traffic control system technology), the NYC Police Department (traffic enforcement), and the Mayor’s office (traffic demand mitigation policies). [ABSTRACT FROM AUTHOR]

Growing car ownership and congestion pose a threat to urban transportation sustainability in most urban areas globally. Harare Metropolitan Region has not been immune to traffic congestion due the influx of vehicles and the subsequent growth in private vehicle ownership. The study sought to determine the effect of vehicle miles travelled (VMT) on traffic congestion. A cross-section survey and a pragmatism research philosophy were utilized. The research focused on Harare Metropolitan Region households, urban planners, transportation planners, and motorists. The study's sample size was 384 people, chosen using convenience and purposive sampling techniques. Data was gathered from respondents using structured questionnaires and semi-structured interviews. To examine the validity of all the items included in the study, exploratory factor analysis (EFA) was utilized. Structural Equation Modeling (SEM) in Amos version 21 was used to test the study hypothesis. According to the study, VMT has a positive influence on traffic congestion. The study suggests that there is a need for actor-factor analysis, in which policies are developed by realistically connecting social actors from the top to the bottom. Furthermore, policymakers should enact policies and make appropriate investments that encourage people to drive less. [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]

Carpooling is an efficient measure to fight car ownership and reduce vehicle kilometres travelled. By individuals sharing their commutes, vehicle occupancy increases and congestion is reduced. We develop a dynamic ADL (Arnott, de Palma, Lindsey)–Vickrey approach for a corridor monocentric city à la Hotelling. First, we formulate the matching problem of heterogeneous users in carpooling as an MILP problem and we discuss its analytical properties when there is no congestion. Next, we construct a bi-level optimization problem involving matching (first stage) and dynamic traffic congestion with scheduling preferences (second stage) when congestion is endogenous. We provide a heuristic to attain an optimal matching for a dynamic traffic equilibrium with congestion. Such a template allows studying the two-way causality between dynamic congestion and carpooling matching. • Describe the matching of drivers and passengers in a carpooling framework through an MILP and fundamental theoretical properties. • Design an integrated framework of carpooling matching and dynamic bottleneck congestion. • Propose an iterative matching algorithm that incorporates dynamic bottleneck congestion. • Provide insight in the effect of dynamic bottleneck congestion on the optimal carpooling matching solution. [ABSTRACT FROM AUTHOR]

Congestion pricing policies (CPPs) are a common strategy for addressing urban traffic congestion. Research has explored several impacts of these policies (e.g., air quality, equity, congestion relief). The purpose of this review was to synthesize findings from publications examining CPP impacts on road user safety outcomes. We conducted a systematic search of relevant literature in four large research databases (Transport Research International Documentation, Web of Science, PubMed, and Scopus), searching from database inception through January 2021. We identified 18 eligible publications. Safety-related outcomes included overall crashes and injury crashes with stratification by injury severity and road user type (e.g., bicyclist, pedestrian). A majority of the publications examined zone-based CPPs (n = 13) and used observed data involving real policies (n = 10), as compared to a predicted or simulated analysis. Decreases in overall crashes and injuries for some road users were observed (e.g., car occupants). While some studies estimated short-term increases in injuries and crashes for bicyclists and motorcyclists (likely due to shifts from personal vehicle use to other transportation modes and increased exposure), most analyses focused on longer-term impacts and generally found a reversal and eventual decrease in injuries and crashes after a few years. The relative scarcity of safety outcomes in published literature, along with the wide breadth of CPP types, implementation contexts, and outcomes measured, demonstrates that more research on safety outcomes is needed. Cities and regions planning to implement CPPs should consider potential mode shifts and safety supports for all road users (e.g., bicycle and pedestrian infrastructure). [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]

Recently, many parents drive their children to and from schools, leading to serious road congestion around the school gate. The school-related congestion is a special type of congestion caused by periodic impulsive aggregation of specific travellers for certain events. In this study, the individual long short-term traffic behaviours were reconstructed based on automatic vehicle identification (AVI) technologies. The cause and countermeasure of congestion around the service centers were identified through the individual behavioural properties. The vehicles that were primarily responsible for periodic impulsive aggregation congestion (PIAC) around the school gate were precisely targeted via a proposed vehicle grading clustering framework. The road management objectives were updated in the AVI data environment and it was found that only 3%–5% of the total number of vehicles passing by the school gate require specific management such as traffic enforcement activities. A series of traffic measures were formulated based on the results of vehicle grading clustering and achieved positive effects in a periodic impulsive aggregation area. It is an effective way to solve the PIAC by formulating management with different activity levels and resolutions for specific travellers. The methodologies and experience presented in this study may provide a useful tool for relieving such special type of congestion around other service centers faced with similar scenarios. [ABSTRACT FROM AUTHOR]

In order to solve the problems of high packet loss rate, low perception accuracy and long perception time existing in traditional urban road congestion real-time sensing methods, this paper proposes a new urban road congestion real-time sensing method based on Internet of Vehicles technology. The Internet of Vehicles technology is used to collect urban road data, and the collected data is cleaned and repaired. On this basis, the k-Nearest Neighbor algorithm is used to estimate the travel time of vehicles on the current road, and the grade of urban road congestion state is designed. Combined with the travel time estimation results, the real-time perception of urban road congestion state is realized. The experimental results show that the packet loss rate of this method is always below 5%, the perceptual accuracy is above 96%, and the average time is 0.61s, the practical application effect is good. [ABSTRACT FROM AUTHOR]

This paper proposes a bottleneck model for managing the ship lock congestion in an inland waterway caused by the uneven temporal distribution of ship traffic demand. Due to safety checks and regular maintenance, the lock only works within a specified time window during a day. A bottleneck model with a service time window is presented to describe the dynamic scheduling process of the ships passing through the lock. The properties of the proposed model are investigated. To alleviate ships' delays at the lock area, two congestion tolling schemes (i.e., first-best and second-best) and a novel administrative scheme of moving the service time window (MST) are explored together with a combined scheme of tolling and MST. The results show that the ship's preferred departure time and the value of time for schedule delay matter in the choice of the counter-measures against ship traffic congestion. MST alone can be an effective substitute for the tolling schemes in most cases and it even outperforms the first-best tolling under certain conditions. This finding is appealing given the academic debate and public controversy raised by congestion tolling. Besides, combining MST with either tolling scheme can always yield higher efficiency. However, the benefits of MST can be marginal or even zero when the difference between the ship's preferred departure time and lock's initial service start time is around a certain value. In such cases, the authority should be cautious about combining any tolling scheme with MST since the implementation costs might outweigh the additional benefits brought by MST. • Extend the classical Vickrey's bottleneck model by introducing a service time window constraint based on the operating feature of ship locks. • Illustrate the dynamic scheduling process of the ships at a congested ship lock, which is different from the results of the Vickrey's model. • A novel administrative scheme of adjusting the lock service start time is proposed and it performs even better than the traditional congestion tolling schemes in some cases. • Combining the administrative scheme and the tolling scheme can yield higher efficiency but it is not always recommended due to the implementation cost. [ABSTRACT FROM AUTHOR]

This paper studies the optimal spatial pricing for a ride-sourcing platform subject to a congestion charge. The platform determines the ride prices over the transportation network to maximize its profit, while the regulatory agency imposes the congestion charge to reduce traffic congestion in the urban core. A network economic equilibrium model is proposed to capture the intimate interactions among passenger demand, driver supply, passenger and driver waiting times, platform pricing, vehicle repositioning and flow balance over the transportation network. The overall optimal pricing problem is cast as a non-convex program. An algorithm is proposed to approximately compute its optimal solution, and a tight upper bound is established to evaluate its performance loss with respect to the globally optimal solution. Using the proposed model, we compare the impacts of three forms of congestion charge: (a) a one-directional cordon charge on ride-sourcing vehicles that enter the congestion area; (b) a bi-directional cordon charge on ride-sourcing vehicles that enter or exit the congestion area; (c) a trip-based congestion charge on all ride-sourcing trips. We show that the one-directional congestion charge not only reduces the ride-sourcing traffic in the congestion area, but also reduces the travel cost outside the congestion zone and benefits passengers in these underserved areas. We establish that in all congestion charge schemes the largest share of the tax burden is carried by the platforms, as opposed to passengers and drivers. We further show that compared to other congestion charges, the one-directional cordon charge is more effective in congestion mitigation: to achieve the same congestion-mitigation target, it imposes a smaller cost on passengers, drivers, and the platform. On the other hand, compared with the other charges, the trip-based congestion charge is more effective in revenue-raising: to raise the same tax revenue, it leads to a smaller loss to passengers, drivers, and the platform. We validate these results through realistic numerical studies for San Francisco. • The optimal spatial pricing strategies are derived for the ride-sourcing platforms. • Empty vehicle relocation and interceptions during relocation are considered. • One-directional cordon price limits congestion in the urban core and reduces travel costs in the suburb. • One-directional cordon price is more effective in congestion mitigation. • Trip-based congestion charge is more effective in revenue-raising. [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]

An agent-based, multichannel simulation of a downtown area reveals the impacts of both time-shifting traffic demand with congestion pricing, and supplying extra capacity by banning left turns. The downtown street network was idealized, and loosely resembles central Los Angeles. On the demand-side, prices were set based on time-of-day and distance traveled. On the supply side, left-turn maneuvers were prohibited at all intersections on the network. Although both traffic management measures reduced travel costs when used alone, the left-turn ban was much less effective than pricing. When combined with pricing under congested conditions, however, the left-turn ban's effectiveness increased considerably—it more than doubled in some cases. Furthermore, the two measures combined reduced travel costs in synergistic fashion. In some cases, this synergistic effect was responsible for 30% of the cost reduction. This strong synergy suggests that turning bans should be considered as an added option when contemplating congestion pricing. [ABSTRACT FROM AUTHOR]