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Structural models of traffic congestion, such as the bottleneck model, are used to answer important, policy‐relevant questions. However, existing models typically assume that no travelers are inframarginal regarding when to travel; that is, given equilibrium travel times, no travelers strictly prefer their ex ante departure time to all others. In this article, I address this shortcoming by incorporating inframarginal travelers into these models. This change significantly improves these models' ability to fit the data and changes policy prescriptions. In the case of congestion pricing, it typically changes the optimal toll by at least 25% and significantly worsens the distributional impacts. [ABSTRACT FROM AUTHOR]

Developing country megacities suffer from severe road traffic congestion, yet the level of congestion is not a direct measure of equilibrium inefficiency. I study the peak‐hour traffic congestion equilibrium in Bangalore. To measure travel preferences, I use a model of departure time choice to design a field experiment with congestion pricing policies and implement it using precise GPS data. Commuter responses in the experiment reveal moderate schedule inflexibility and a high value of time. I then show that in Bangalore, traffic density has a moderate and linear impact on travel delay. My policy simulations with endogenous congestion indicate that optimal congestion charges would lead to a small reduction in travel times, and small commuter welfare gains. This result is driven primarily by the shape of the congestion externality. Overall, these results suggest limited commuter welfare benefits from peak‐spreading traffic policies in cities like Bangalore. [ABSTRACT FROM AUTHOR]

Dynamic toll pricing based on demand can increase transportation revenue while also incentivizing travelers to avoid peak traffic periods. However, given the unpredictable nature of traffic, travelers lack the information necessary to accurately predict congestion, so dynamic pricing has minimal effect on demand. Dynamic toll pricing also poses equity concerns for those who lack other travel options. This research explores a potential remedy to these concerns by using a simple “futures market” pricing mechanism in which travelers can lock in a toll price for expected trips by prepaying for future tolls, with the future price increasing as more travelers book an overlapping time slot. This approach encourages travelers to avoid driving during the peak periods when pricing increases toward capacity or to purchase trips in advance when the price remains low or discounted, thus using infrastructure capacity more efficiently. Travelers that do not prepurchase their trip are subject to the real-time market price, which is determined by dynamic congestion pricing. This futures-market mechanism can augment existing toll collection technologies and provide travelers with sufficient pricing information and purchasing options to preplan their travel and avoid excessive prices.

This paper evaluates a congestion management concept involving conversion of two existing general-purpose freeway lanes per direction on an 8-lane freeway (with 4 lanes per direction) to priced lanes offering free access for high-occupancy vehicles, in conjunction with cash payments to incentivize a sufficient number of commuters to travel as passengers in transit, carpools or vanpools such that congestion on the remaining free lanes would be no worse than it was before the conversion. Building on prior research, we use a refined modeling process to explore the viability of implementation of the concept on the eastern segment of the Capital Beltway in the Washington, DC metro area. The results of the analysis suggest that the strategy can benefit users of shared travel modes and toll-payers, improve travel speeds on toll-free general-purpose lanes, generate significant surplus revenues to support capital investments and operating subsidies for shared travel modes, and can absorb traffic growth through 2040. [ABSTRACT FROM AUTHOR]

Pricing and traffic rationing have emerged as effective and economically feasible strategies for mitigating traffic congestion in the central business districts of large cities. In Tehran, Iran's capital city, two separate surveys were conducted to evaluate the effects of time- and duration-based pricing strategies. The surveys included 1,388 participants from the congestion pricing zone (CBD) and 983 participants from the odd-even traffic rationing zone. The error component logit model was calibrated using stated preference (SP) scenarios for both the congestion pricing zone and the odd-even traffic zone; These models aimed to analyze modal shift, route choice, and time of travel in a day. Additionally, the mode choice behavior in the CBD was examined using the generalized mixed logit (GMXL) model, which was calibrated using both SPs and RPs. The findings indicate that implementing a duration-based scenario would result in a modal shift, changes in trip patterns, and even trip cancellations. On the other hand, the time-based scenario would primarily lead to changes in travel timing or destination choices. GMXL results show that duration-based pricing is more effective in shifting private vehicle trips to other modes. Furthermore, on-demand ride-hailing is less significant as a competitor mode within these zones. [ABSTRACT FROM AUTHOR]

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]

Electric vehicles (EVs) not only serve as significant loads for the power grid but also play a crucial role in the operation of the traffic. Their travel and charging behaviors have an impact on both the power grid and the road network. In order to address the potential impacts of a large-scale deployment of EVs on the power grid and the exacerbation of traffic congestion, this paper first establishes a dynamic road network model based on graph theory and time-varying traffic data combined with a road impedance model. Then, the spatio-temporal distribution characteristics of EV travel are modeled. Furthermore, by incorporating real-time road network data, the traditional Dijkstra's algorithm for finding the optimal path is improved. At each node, the current real-time road impedance is used as the objective for EV path updates, thus accurately capturing the energy consumption of the EVs. Finally, using a standard testing problem on a typical working day based on data from a real case, the impacts of EV travel and charging behaviors on power distribution network operation and traffic congestion are analyzed under scenarios with no guidance and guidance for the shortest travel time. The results show that this method can significantly reduce the time cost by approximately 18% in travel time, which is of particular concern to users. This method balances the load of the charging stations, elevates the voltage level within the safety requirement of 7%, and simultaneously alleviates traffic congestion near the stations. [ABSTRACT FROM AUTHOR]

Traffic congestion has been considered one of the most serious global issues confronting all nations in recent years. Basrah City’s highways in general, and particularly the highways surrounding and leading to the University of Basrah, the campus of Bab Azzubair, and the college complex, have experienced traffic congestion and higher delays. The focus of this study is to propose some solutions to mitigate traffic congestion and higher delays on these highways. Field traffic data and questionnaires where SPSS was used to analyse the collected data, and Highway Capacity Software (HCS 2010) was used to assess the level of service (LOS) on the highways. The results showed that the LOS reaches level F on Baghdad Street, while other streets range from level B to level C. Two proposals have been suggested to improve the LOS on Baghdad Street: (1) applying a park-and-ride system; and (2) widening Baghdad Street by adding two lanes in each direction. The LOS for both proposals has been evaluated, and the results showed that the first proposal performed better. The analysis of the collected data also showed that public transportation services are very limited on the highways surrounding and leading to the Bab Azzubair campus. Therefore, public transportation services should be promoted and enhanced in Basrah City. [ABSTRACT FROM AUTHOR]

The use of multi-compartment vehicles (MCVs) in urban logistics distribution is increasing. However, urban traffic congestion causes high carbon emissions in the logistics distribution, resulting in unsustainable development in urban transportation. In addition, the application of the mixed carbon policy has gradually become the first choice for energy conservation and emission reduction in some countries and regions. The transportation industry is a major carbon-emitting industry, which needs to be constrained by carbon emission reduction policies. In this context, the research on the multi-compartment vehicle routing problem (MCVRP) considering traffic congestion under the mixed carbon policy is carried out. Firstly, a mathematical model of MCVRP considering traffic congestion under the mixed carbon policy is constructed. Secondly, a two-stage variable neighborhood threshold acceptance algorithm (VNS-TA) is proposed to solve the above mathematical model. Thirdly, 14 adapted standard examples of the MCVRP are used to verify the effectiveness and optimization ability of the two-stage VNS-TA algorithm. A simulation example of the MCVRP considering traffic congestion under the mixed carbon policy is used to conduct sensitivity analyses for different scenarios. Finally, the following conclusions are drawn: (1) the two-stage VNS-TA algorithm is effective and has strong optimization ability in solving the basic MCVRP, and (2) the two-stage VNS-TA algorithm can solve and optimize the MCVRP considering traffic congestion under the mixed carbon policy, which has the effects of cost saving and energy conservation and emission reduction. [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]

Traffic congestion detection method based on surveillance video is gradually widely used in intelligent transportation systems (ITS). Due to complex challenges such as weather change, vehicle occlusion, camera jitter, camera installation location, and so on, current methods are difficult to balance in real time and accuracy. Here, a new real‐time and robust traffic congestion detection framework and a vision‐based multi‐dimensional congestion detection model are proposed. Firstly, the framework introduces an object detector based on the lightweight convolutional neural network (CNN) and an efficient multi‐object IoU‐like tracker to obtain traffic dynamic information in real time. Then, traffic density, traffic velocity, and duration of instantaneous congestion are defined and a multi‐dimensional congestion detection model is established. Furthermore, an adaptive updating strategy of dynamic parameters is investigated. Finally, in multiple groups of comparative experiments, the framework is verified to be applicable to a variety of lightweight CNN detectors and IoU‐like trackers. The precision and recall of the multi‐dimensional congestion model can reach 95.1% and 92.1% respectively, with 43FPS. The comparative experimental results show that the proposed method is real‐time, more robust and accurate, and can be employed for online traffic congestion detection based on surveillance video. [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]

The dense population and the large amount of domestic waste generated make it difficult to determine the best route and departure time for waste removal trucks in a city. Aiming at the problems of municipal solid waste (MSW) removal and transportation not in time, high collection and transportation costs and high carbon emissions, this paper studies the vehicle routing problem of municipal solid waste removal under the influence of time-dependent travel time, traffic congestion and carbon emissions. In this paper, a dual objective model with the lowest total economic cost and the highest garbage removal efficiency is established, and a DCD-DE-NSGAII algorithm based on Dynamic Crowding Distance and Differential Evolution is designed to improve the search ability, improve the convergence speed and increase the diversity of the optimal solution set. The results show that: according to the actual situation of garbage collection and transportation, the method can scientifically plan the garbage collection and transportation route, give a reasonable garbage collection scheme and departure time, and effectively avoid traffic congestion time; Through algorithm comparison, the algorithm and model proposed in this paper can reduce collection and transportation costs, improve transportation efficiency and reduce environmental pollution. [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]

The advent of autonomous vehicles (AVs) is likely to introduce new mobility experiences for travelers. In particular, AVs would allow travelers to get off at the destinations and then drive themselves elsewhere to park rather than cruise for parking or park at a location with a high parking fee. The self-parking capability is likely to increase the utility of private-owned AVs (PAVs) and make this mobility option more attractive than human-driven vehicles. The present study investigates the dynamics of travelers shifting to PAVs from other transport modes and its negative impact on road traffic congestion. To this end, we propose an agent-based demand model which considers different travel cost components depending on crucial travel attributes such as trip purpose and activity duration. The estimated demand is then fed into a mesoscopic traffic simulation model to examine the resulting road traffic conditions. As charging private vehicles for the congestion they cause is an effective tool for demand management and congestion alleviation, we also integrate a distance-based pricing scheme into the overall modeling framework to investigate its impact on mode choice and transport network performance. A case study is conducted in Melbourne, Australia to demonstrate the proposed methodology. The results indicate that the distance-based pricing scheme can effectively limit the usage of PAVs and reduce traffic congestion, especially in the city center and peripheral suburbs. [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]

This study investigates the morning commute problem in a single corridor with a bottleneck under a fully automated vehicle environment. By extending Vickrey's bottleneck model, we formulate the joint decisions on departure time and parking choices of morning commuters who make trade-offs among travel cost, the cost associated with parking, and the cost of tradable credits. To alleviate traffic congestion and improve social welfare, we propose a time-varying tradable credit scheme and integrate it with the morning commute problem. We explore the travel patterns and the optimal design of tradable credit schemes for the morning commute problem with homogeneous and heterogeneous commuters, respectively. For the homogeneous case, we derive the conditions on the tradable credit scheme to ensure the existence of equilibrium. The system-wide travel cost decreases with parking density after tradable credits are incorporated. Additionally, the efficiency of the tradable credits scheme can be improved by increasing the rate of credit charge rate. For the heterogeneous case, we propose an initial distribution strategy and combine it with the optimal tradable credit in order to guarantee social equity. The commuters with a low value of time (VOT) should be allocated more credits and the commuters with high VOT should be charged more credits. For both cases at system optimum, we find that the equilibrium price of tradable credits increases with parking density and decreases with the total amount of tradable credits. [ABSTRACT FROM AUTHOR]

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

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

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]

With ongoing urbanization, traffic congestion and the air pollution it induces are worsening. Using a system dynamics (SD) approach, this study constructed a driving-restriction policy model to explore the effects of different stages of policy implementation on variables such as traffic congestion, emissions, and parking demand. Medium- and long-term dynamic simulation showed that the effect of the policy was obvious in the initial stage but gradually weakened in the medium term, leading to a "fading" effect on traffic-congestion alleviation; a "rebound" effect was even observed at the end of the simulation. Thus, the policy will not effectively reduce traffic congestion in the long term and will induce a new demand for car purchases, resulting in paradoxical effects, which will aggravate parking demand, congestion, and pollution. Yet, it was also found that introducing penalty policies and an air pollution charging fee could weaken the paradoxical effects and compensate for some defects of the policy. Such strategies could help reduce emissions, traffic congestion, parking demand, the number of illegal trips, and the overall number of vehicle trips. These findings can provide not only a theoretical basis for further research but also practical guidance for policy improvement. [ABSTRACT FROM AUTHOR]

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This research presents a vehicle ID-based congestion aware message (CAM) for beacon signals on the vehicle environment. At the MAC protocol of the vehicle environment, enhanced vehicle ID-based analysis model is given first. With the automobile ID embedded in their separate CAMs, the model weights the randomized back-off numbers chosen by cars engaging in the back-off procedure. This leads to identifying a car ID-based randomized back-off code, which reduces the likelihood of a collision due to the identical back-off number. A traffic density based-congestion control algorithm (TDCCA) is suggested in this research. The revised mathematical approach surpasses previous work's overall packet latency because just one-fourth of the congestion window is employed during the experiment. The research includes a congestion management method that adjusts the rate of CAM transmitted over the host controller to improve the efficiency of the model parameters. The method considers various circumstances, from nonsaturated to substantially saturated networks (in terms of congestion probability) and sparsely dispersed and teemed networks (in the form of vehicular intensity). The technique is run across various automobile ID-based back-off values for high-standard results analysis. The simulation outcomes in terms of packet delivery ratio, energy consumption, delay, success rate, and collision ensure the effectiveness of the TDCCA method. Even at high traffic densities, the automobile ID-based CAM following information method outperforms the typical fixed CAM frequency IEEE 802.11p, according to simulation findings for all back-off figures. [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]

The government sector and the private firm are assumed to build a new transport project through the Build--Operate--Transfer (BOT) mode. We develop a multi-stage game-theoretical model to investigate the transport BOT project contract design problem containing four critical decision variables: the length of the concession period, service charge, construction quality, and project capacity. Firstly, we obtain the optimal solutions for BOT project contract design through the backward induction method. Secondly, we analyze the influence of the length of the concession period on the optimal charge of both the government sector and the private enterprise and the optimal construction quality of transport projects. Thirdly, we derive the optimal length of the concession period from the perspective of the social welfare and the private firm's maximization, respectively. Finally, we extend the basic model to examine the impact of traffic congestion in the transport project on the transport BOT project contract design. [ABSTRACT FROM AUTHOR]

The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various systems. Such as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects worldwide. Traffic congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the road. To address this overwhelming problem, in this article, a cloudbased intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy approach. The aim of the study is to reduce the delay in the queues, the vehicles experience at different road junctions across the city. The proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained fromvarious implanted Internet of Things (IoT) sensors across the road. After due preprocessing over the cloud server, the proposed approach makes use of this data by incorporating the neuro-fuzzy engine. Consequently, it possesses a high level of accuracy by means of intelligent decision making with minimum error rate. Simulation results reveal the accuracy of the proposed model as 98.72% during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%, 95.84%, 97.56% and 98.03%, respectively. As far as the training phase analysis is concerned, the proposed scheme exhibits 99.214% accuracy. The proposed prediction model is a potential contribution towards smart cities environment. [ABSTRACT FROM AUTHOR]

When imposing traffic congestion pricing around downtown commercial centers, there is a concern that commercial activities will have to consider relocating due to reduced demand, at a cost to merchants. Concerns like these were important in the debates before the introductions of congestion charges in both London and Stockholm and influenced the final policy design choices. This study introduces a sequential experimental game to study reactions to congestion pricing in the commercial sector. In the game, merchants first make location choices. Consumers, who drive to do their shopping, subsequently choose where to shop. Initial responses to the introduction of congestion pricing and equilibrium selection adjustments over time are observed. These observations are compared to responses and adjustments in a condition where congestion pricing is reduced from an initially high level. Payoffs are non-linear and non-transparent, making it less than obvious that the efficient equilibrium will be selected, and introducing possibilities that participants need to discover their preferences and anchor on past experiences. We find that initial responses reflect standard inverse price–demand relations, and that adjustments over time rely on signaling by consumers leading to the efficient equilibrium. There is also evidence that priming from initial experiences influence play somewhat. We confirm that commercial activities relocate following the introduction of congestion pricing and that the adjustment process is costly to merchants. [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]

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]

In this paper, an asymmetrical congestion game is used to build a fairness concern‐based coordinated route guidance model for alleviating traffic congestion. Coordinated vehicle route guidance is commonly recognized as an effective way to alleviate the "route flapping" phenomenon, where a new congestion appears since numerous vehicles obey the same guidance in selfish route diversion. The existing studies mainly use the classic (symmetrical) congestion games, whose payoff depends only on the number of chosen players, to calculate the flow proportion for the alternative roads. This mechanism may make some front vehicles receive the worse payoffs than the rear ones, which further results in the front vehicles being unwilling to execute the recommended paths due to their unfair psychology. Players' fairness concern in game theory has been noted in some fields (such as supply chain) but not traffic route guidance. In this work, to fill the above gap, the concept of vehicular fairness concern is introduced and the corresponding mathematical expression is formulated. An asymmetrical congestion game‐based coordination route model is built, which can improve the fairness coefficient of vehicles. Moreover, a reinforcement learning‐based approximated solving algorithm is designed since the existing solving theorems are not necessarily valid for the asymmetrical congestion game. An experimental study shows that (1) the Nash equilibrium coefficients generated by the approximated solving algorithm all converge to 1.00, and (2) the averaged fairness coefficient is increased from 0.92 to 0.96 on the basis of equal travel time due to the improved payoff functions. [ABSTRACT FROM AUTHOR]

Using data from the 545 largest European cities, we study whether the expansion of their highway capacity provides a solution to the problem of traffic congestion. Our results confirm that in the long run, and in line with the 'fundamental law of highway congestion', the expansion in cities of lane kilometres causes an increase in vehicle traffic that does not solve urban congestion. We disentangle the increase in traffic due to the increases in coverage and in capacity. We further introduce road pricing and public transit policies in order to test whether they moderate congestion. Our findings confirm that the induced demand is considerably smaller in cities with road pricing schemes, and that congestion decreases with the expansion of public transportation. [ABSTRACT FROM AUTHOR]

Traffic congestion is the major problem due to rapid urbanization and exponential growth of private vehicles, a decrease in the use of active transport, which results in problems of transport sustainability and public health issues. Generally, commuters often find it challenging to travel along the congested routes in urban areas due to increased travel time or air pollution. The traffic congestion of those congested routes may be minimized using strategies like congestion pricing, but public acceptability is one of the main hurdles in establishing a congestion pricing scheme. The present study attempted to understand the public acceptability of the congestion pricing system in India. To fulfil the objective of the study, a questionnaire survey was conducted to ascertain the public's perception of the congestion pricing system. In order to achieve this objective, a multinomial logistic regression (MNL) model was developed by considering public opinion on support to implement congestion pricing as a dependent variable, and other variables were considered as independent variables. From the model results, it is understood that the travel frequency (viz., 1-2 and 3-4 times) and number of times struck in congestion of commuters are most likely to remain neutral. The revenue generated by congestion pricing is allocated to road infrastructure, car tax reductions, public transportation, and parking areas, which are more likely to remain in neutral and under review after implementation. The study results are more useful to policy makers in urban areas while they relook at congestion pricing strategies in developing countries. [ABSTRACT FROM AUTHOR]

Urban areas are more prone to accidents and traffic congestions due to ever-increasing vehicles and poor traffic management. The increase in the emission of harmful gases is another important issue associated with vehicular traffic. Attaining a level of QOS is often challenging as it has to meet the eco-friendly factors along with reliable and safe transportation. Smart and accurate congestion management systems in VANET can significantly reduce the risk of accidents and health issues. To fulfil the requirements of QOS the congestion control methods should consider the properties such as fairness, decentralization, network characteristics, and application demands in VANET. We proposed an Adaptive Congestion Aware Routing Protocol (ACARP) for VANET using the dynamic artificial intelligence (AI) technique. The ACARP presents the adaptive congestion detection algorithm using the type-2 fuzzy logic AI technique. The fuzzy model detects the congestion around each vehicle using three fuzzy inputs viz. bandwidth occupancy, link quality, and moving speed. This is followed by inference model to estimate congestion probability for each vehicle. Finally, defuzzification determines status of congestion detection using the pre-defined threshold value for each vehicle. The status of congestion and its probability values were utilized to establish safe and reliable routes for data transmission. It also saves significant communication overhead and hence congestions in the network. The simulation results provide the evidence that the proposed protocol improves the QOS and assist in reduction of traffic congestions significantly. [ABSTRACT FROM AUTHOR]

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

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]