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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]

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 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]

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]

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]

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]

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]

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

This comparative study analyzes the impact of the COVID-19 pandemic on motorized mobility in eight large cities of five Latin American countries. Public institutions and private organizations have made public data available for a better understanding of the contagion process of the pandemic, its impact, and the effectiveness of the implemented health control measures. In this research, data from the IDB Invest Dashboard were used for traffic congestion as well as data from the Moovit© public transport platform. For the daily cases of COVID-19 contagion, those published by Johns Hopkins Hospital University were used. The analysis period corresponds from 9 March to 30 September 2020, approximately seven months. For each city, a descriptive statistical analysis of the loss and subsequent recovery of motorized mobility was carried out, evaluated in terms of traffic congestion and urban transport through the corresponding regression models. The recovery of traffic congestion occurs earlier and faster than that of urban transport since the latter depends on the control measures imposed in each city. Public transportation does not appear to have been a determining factor in the spread of the pandemic in Latin American cities. [ABSTRACT FROM AUTHOR]

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

Road traffic congestion has became a major problem in most countries because it affects sustainable mobility. Partitioning a transport network into homogeneous areas can be very useful for monitoring traffic as congestion is spatially correlated in adjacent roads, and it propagates at different speeds as a function of time. Spectral clustering has been successfully applied for the partitioning of transportation networks based on the spatial characteristics of congestion at a specific time. However, this type of classification is not suitable for data that change over time. Evolutionary spectral clustering represents a state-of-the-art algorithm for grouping objects evolving over time. However, the disadvantages of this algorithm are the cubic time complexity and the high memory demand, which make it insufficient to handle a large number of data sets. In this paper, we propose an efficient evolutionary spectral clustering algorithm that solves the drawbacks of evolutionary spectral clustering by reducing the size of the eigenvalue problem. This algorithm is applied in a dynamic environment to partition a transportation network into connected homogeneous regions that evolve with time. The number of clusters is selected automatically by using a density peak algorithm adopted for the classification of traffic congestion based on the sparse snake similarity matrix. Experiments on the real network of Amsterdam city demonstrate the superiority of the proposed algorithm in robustness and effectiveness. [ABSTRACT FROM AUTHOR]

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

Due to the limitations of data transfer technologies, existing studies on urban traffic control mainly focused on isolated dimension control such as traffic signal control or vehicle route guidance to alleviate traffic congestion. However, in real traffic, the distribution of traffic flow is the result of multiple dimensions whose future state is influenced by each dimension's decisions. Presently, the development of the Internet of Vehicles enables an integrated intelligent transportation system. This paper proposes an integrated intelligent transportation model that can optimize predictive traffic signal control and predictive vehicle route guidance simultaneously to alleviate traffic congestion based on their feedback regulation relationship. The challenges of this model lie in that the formulation of the nonlinear feedback relationship between various dimensions is hard to describe and the design of a corresponding solving algorithm that can obtain Pareto optimality for multi-dimension control is complex. In the integrated model, we introduce two medium variables—predictive traffic flow and the predictive waiting time—to two-way link the traffic signal control and vehicle route guidance. Inspired by game theory, an asymmetric information exchange framework-based updating distributed algorithm is designed to solve the integrated model. Finally, an experimental study in two typical traffic scenarios shows that more than 73.33% of the considered cases adopting the integrated model achieve Pareto optimality. [ABSTRACT FROM AUTHOR]

Express lanes (ELs) implementation is a proven strategy to deal with freeway traffic congestion. Dynamic toll pricing schemes effectively achieve reliable travel time on ELs. The primary inputs for the typical dynamic pricing algorithms are vehicular volumes and speeds derived from the data collected by sensors installed along the ELs. Thus, the operation of dynamic pricing critically depends on the accuracy of data collected by such traffic sensors. However, no previous research has been conducted to explicitly investigate the impact of sensor failures and erroneous sensors' data on toll computations. This research fills this gap by examining the effects of sensor failure and faulty detection scenarios on ELs tolls calculated by a dynamic pricing algorithm. The paper's methodology relies on applying the dynamic toll pricing algorithm implemented in the field and utilizing the fundamental speed-volume relationship to 'simulate' the sensors' reported data. We implemented the methodology in a case study of ELs on Interstate-95 in Southeast Florida. The results have shown that the tolls increase when sensors erroneously report higher than actual traffic demand. Moreover, it has been found that the accuracy of individual sensors and the number of sensors utilized to estimate traffic conditions are critical for accurate toll calculations. [ABSTRACT FROM AUTHOR]

This paper deals with the HEV real-time energy management problem using deep reinforcement learning with connected technologies such as Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I). In the HEV energy management problem, it is important to run the engine efficiently in order to minimize its total energy cost. This research proposes a policy model that takes into account road congestion and aims to learn the optimal system mode selection and power distribution when considering the far future by policy-based reinforcement learning. In the simulation, a traffic environment is generated in a virtual space by IPG CarMaker and a HEV model is prepared in MATLAB/Simulink to calculate the energy cost while driving on the road environment. The simulation validation shows the versatility of the proposed method for the test data, and in addition, it shows that considering road congestion reduces the total cost and improves the learning speed. Furthermore, we compare the proposed method with model predictive control (MPC) under the same conditions and show that the proposed method obtains more global optimal solutions. [ABSTRACT FROM AUTHOR]

Traffic congestion experience in urban areas has negative impact on our daily lives by consuming our time and resources. Intelligent Transportation Systems can provide the necessary infrastructure to mitigate such challenges. In this paper, we propose a novel and scalable solution to model, store and control traffic data based on range query data structures (K-ary Interval Tree and K-ary Entry Point Tree) which allows data representation and handling in a way that better predicts and avoids traffic congestion in urban areas. Our experiments, validation scenarios, performance measurements and solution assessment were done on Brooklyn, New York traffic congestion simulation scenario and shown the validity, reliability, performance and scalability of the proposed solution in terms of time spent in traffic, run-time and memory usage. The experiments on the proposed data structures simulated up to 10,000 vehicles having microseconds time to access traffic information and below 1.5 s for congestion free route generation in complex scenarios. To the best of our knowledge, this is the first scalable approach that can be used to predict urban traffic and avoid congestion through range query data structure traffic modelling. [ABSTRACT FROM AUTHOR]

Traffic congestion in expressway networks has a strong negative influence on regional development. Understanding the spatiotemporal patterns of traffic congestion in expressway networks is critical for improving the exchange of products in regional production and promoting regional economic development. Nevertheless, existing studies pay less attention to these spatiotemporal patterns of traffic congestion. Considering that Origin–Destination (OD) data are available for the recorded spatial movements of vehicles in expressways, this study proposes a method with which to explore traffic congestion at the level of road segments in the regional expressway network, the mainstream of driving behaviors, and traffic regulations. Methods for analyzing spatial disparity and temporal changes in traffic congestion in expressway networks are also put forward in this paper. The empirical results show that the proposed methods could detect road segments where traffic congestion happens, and then uncover temporal patterns of several congested locations and spatial patterns of road segments with frequent congestion. These spatiotemporal patterns of traffic congestion could be in accord with the actual situation. This study provides a new approach to investigating traffic congestion in expressway networks based on low-cost data, which might be helpful for optimizing expressway network planning and promoting balanced regional development. [ABSTRACT FROM AUTHOR]

In this paper, a deep learning-based traffic state discrimination method is proposed to detect traffic congestion at urban intersections. The detection algorithm includes two parts, global speed detection and a traffic state discrimination algorithm. Firstly, the region of interest (ROI) is selected as the road intersection from the input image of the You Only Look Once (YOLO) v3 object detection algorithm for vehicle target detection. The Lucas-Kanade (LK) optical flow method is employed to calculate the vehicle speed. Then, the corresponding intersection state can be obtained based on the vehicle speed and the discrimination algorithm. The detection of the vehicle takes the position information obtained by YOLOv3 as the input of the LK optical flow algorithm and forms an optical flow vector to complete the vehicle speed detection. Experimental results show that the detection algorithm can detect the vehicle speed and traffic state discrimination method can judge the traffic state accurately, which has a strong anti-interference ability and meets the practical application requirements. [ABSTRACT FROM AUTHOR]

Static traffic assignment (STA) models have been widely utilized in the field of strategic transport planning. However, STA models cannot fully represent the dynamic road conditions and suffer from inaccurate assignment during traffic congestion. At the same time, an increasing number of installed sensors have become an important means of detecting dynamic road conditions. To address the shortcomings of STA models, we integrate multi-source traffic sensor datasets and propose a novel data-driven quasi-dynamic traffic assignment model, named DQ-DTA. In this model, records of toll stations are used for time-varying travel demand estimation. GPS trajectory datasets of vehicles are further used to calculate the dynamic link costs of the road network, replacing the imprecise Bureau of Public Roads (BPR) function. Moreover, license plate recognition (LPR) data are used to design a statistical probability-based multipath assignment method to capture travelers' route choices. The expressway network in the Hunan province is selected as the study area, and several classic STA models are also chosen for performance comparison. Experimental results demonstrate that the accuracy of the proposed DQ-DTA model is about 6% higher than that of the chosen STA models. [ABSTRACT FROM AUTHOR]

Although various unmanned aerial vehicle (UAV)-assisted routing protocols have been proposed for vehicular ad hoc networks, few studies have investigated load balancing algorithms to accommodate future traffic growth and deal with complex dynamic network environments simultaneously. In particular, owing to the extended coverage and clear line-of-sight relay link on a UAV relay node (URN), the possibility of a bottleneck link is high. To prevent problems caused by traffic congestion, we propose Q-learning based load balancing routing (Q-LBR) through a combination of three key techniques, namely, a low-overhead technique for estimating the network load through the queue status obtained from each ground vehicular node by the URN, a load balancing scheme based on Q-learning and a reward control function for rapid convergence of Q-learning. Through diverse simulations, we demonstrate that Q-LBR improves the packet delivery ratio, network utilization and latency by more than 8, 28 and 30%, respectively, compared to the existing protocol. [ABSTRACT FROM AUTHOR]

A rapidly growing number of vehicles in recent years cause long traffic jams and difficulty in the management of traffic in cities. One of the most significant reasons for increased traffic jams on the road is random parking in unauthorized and non-permitted places. In addition, managing of available parking places cannot achieve the expected reduction in traffic congestion related problems due to mismanagement, lack of real-time parking guidance to the drivers, and general ignorance. As the number of roads, highways and related resources has not increased significantly, a rising need for a smart, dynamic and effective parking solution is observed. Accordingly, with the use of multiple sensors, appropriate communication network and advanced processing capabilities of edge and cloud computing, a smart parking system can help manage parking effectively and make it easier for the vehicle owners. In this paper, we propose a multi-layer architecture for smart parking system consisting of multi-parametric parking slot sensor nodes, latest long-range low-power wireless communication technology and Edge-Cloud computation. The proposed system enables dynamic management of parking for large areas while providing useful information to the drivers about available parking locations and related services through near real-time monitoring of vehicles. Furthermore, we propose a dynamic pricing algorithm to yield maximum possible revenue for the parking authority and optimum parking slot availability for the drivers. [ABSTRACT FROM AUTHOR]

The massive increase in the number of vehicles has set a precedent in terms of congestion, being one of the important factors affecting the flow of traffic, but there are also effects on the world economy. The studies carried out so far try to highlight solutions that will streamline the traffic, as society revolves around transportation and its symmetry. Current research highlights that the increased density of vehicles could be remedied by dedicated short-range communications (DSRC) systems through communications of the type vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) or vehicle-to-everything (V2X). We can say that wireless communication technologies have the potential to significantly change the efficiency and road safety, thus improving the efficiency of transport systems. An important factor is to comply with the requirements imposed on the use of vehicle safety and transport applications. Therefore, this paper focuses on several simulations on the basis of symmetry models, implemented in practical cases in order to streamline vehicle density and reduce traffic congestion. The scenarios aim at both the communication of the vehicles with each other and their prioritization by the infrastructure, so we can have a report on the efficiency of the proposed models. [ABSTRACT FROM AUTHOR]

This study introduces a software-based traffic congestion monitoring system. The transportation system controls the traffic between cities all over the world. Traffic congestion happens not only in cities, but also on highways and other places. The current transportation system is not satisfactory in the area without monitoring. In order to improve the limitations of the current traffic system in obtaining road data and expand its visual range, the system uses remote sensing data as the data source for judging congestion. Since some remote sensing data needs to be kept confidential, this is a problem to be solved to effectively protect the safety of remote sensing data during the deep learning training process. Compared with the general deep learning training method, this study provides a federated learning method to identify vehicle targets in remote sensing images to solve the problem of data privacy in the training process of remote sensing data. The experiment takes the remote sensing image data sets of Los Angeles Road and Washington Road as samples for training, and the training results can achieve an accuracy of about 85%, and the estimated processing time of each image can be as low as 0.047 s. In the final experimental results, the system can automatically identify the vehicle targets in the remote sensing images to achieve the purpose of detecting congestion. [ABSTRACT FROM AUTHOR]

Traffic control is one of the most challenging issues in metropolitan cities with growing populations and increased travel demands. Poor traffic control can result in traffic congestion and air pollution that can lead to health issues such as respiratory problems, asthma, allergies, anxiety, and stress. The traffic congestion can also result in travel delays and potential obstruction of emergency services. One of the most well-known traffic control methods is to restrict and control the access of private vehicles in predetermined regions of the city. The aim is to control the traffic load in order to maximize the citizen satisfaction given limited resources. The selection of restricted traffic regions remains a challenge because a large restricted area can reduce traffic load but with reduced citizen satisfaction as their mobility will be limited. On the other hand, a small restricted area may improve citizen satisfaction but with a reduced impact on traffic congestion or air pollution. The optimization of the restricted zone is a dynamic multi-regression problem that may require an intelligent trade-off. This paper proposes Optimal Restricted Driving Zone (ORDZ) using the Genetic Algorithm to select appropriate restricted traffic zones that can optimally control the traffic congestion and air pollution that will result in improved citizen satisfaction. ORDZ uses an augmented genetic algorithm and determinant theory to randomly generate different foursquare zones. This fitness function considers a trade-off between traffic load and citizen satisfaction. Our simulation studies show that ORDZ outperforms the current well-known methods in terms of a combined metric that considers the least traffic load and the most enhanced citizen satisfaction with over 30.6% improvements to some of the comparable methods. [ABSTRACT FROM AUTHOR]

Alleviating public traffic congestion is an efficient and effective way to improve the travel time reliability and quality of public transport services. The existing public network optimization models usually ignored the essential impact of public traffic congestion on the performance of public transport service. To address this problem, this study proposes a data-based methodology to estimate the traffic congestion of road segments between bus stops (RSBs). The proposed methodology involves two steps: (1) Extracting three traffic indicators of the RSBs from smart card data and bus trajectory data; (2) The self-organizing map (SOM) is used to cluster and effectively recognize traffic patterns embedded in the RSBs. Furthermore, a congestion index for ranking the SOM clusters is developed to determine the congested RSBs. A case study using real-world datasets from a public transport system validates the proposed methodology. Based on the congested RSBs, an exploratory example of public transport network optimization is discussed and evaluated using a genetic algorithm. The clustering results showed that the SOM could suitably reflect the traffic characteristics and estimate traffic congestion of the RSBs. The results obtained in this study are expected to demonstrate the usefulness of the proposed methodology in sustainable public transport improvements. [ABSTRACT FROM AUTHOR]

Alleviating traffic congestion is one of the main challenges for the Internet of Vehicles (IoV) in smart cities. Many congestion pricing systems have been proposed recently. However, most of them focus on punishing the vehicles that use certain roads during peak hours, neglecting the proven fact that rewards can encourage drivers to follow the rules. Therefore, in this paper, we propose a new congestion pricing system based on reward and punishment policies for the IoV in a smart city environment, where the vehicles are rewarded for voluntarily choosing to take an alternative path to alleviate traffic congestion. The proposed system is implemented using vehicular ad hoc networks, which eliminate the need for installing a costly electronic toll collection system. We propose a new virtual currency called T-Coin (traffic coin), that is used to reward the vehicles for their positive attitude. T-Coin is also used in the tender between vehicles to manage the road reservation process. The proposed system uses dynamic pricing to adapt to peak-hour traffic congestion. Using simulated traffic on a real map of Beijing city, we prove the usefulness of T-Coin as a traffic congestion pricing system. [ABSTRACT FROM AUTHOR]

Traffic congestion, especially during peak hours, has become a challenge for transportation systems in many metropolitan areas, and such congestion causes delays and negative effects for passengers. Many studies have examined the prediction of congestion; however, these studies focus mainly on road traffic, and subway transit, which is the main form of transportation in densely populated cities, such as Tokyo, Paris, and Beijing and Shenzhen in China, has seldom been examined. This study takes Shenzhen as a case study for predicting congestion in a subway system during peak hours and proposes a hybrid method that combines a static traffic assignment model with an agent-based dynamic traffic simulation model to estimate recurrent congestion in this subway system. The homes and work places of the residents in this city are collected and taken to represent the traffic demand for the subway system of Shenzhen. An origin-destination (OD) matrix derived from the data is used as an input in this method of predicting traffic, and the traffic congestion is presented in simulations. To evaluate the predictions, data on the congestion condition of subway segments that are released daily by the Shenzhen metro operation microblog are used as a reference, and a comparative analysis indicates the appropriateness of the proposed method. This study could be taken as an example for similar studies that model subway traffic in other cities. [ABSTRACT FROM AUTHOR]

Road traffic congestion has a large impact on travel. The accurate prediction of traffic congestion has become a hot topic in intelligent transportation systems (ITS). Recently, a variety of traffic congestion prediction methods have been proposed. However, most approaches focus on floating car data, and the prediction accuracy is often unstable due to large fluctuations in floating speed. Targeting these challenges, we propose a method of traffic congestion prediction based on bus driving time (TCP-DT) using long short-term memory (LSTM) technology. Firstly, we collected a total of 66,228 bus driving records from 50 buses for 66 working days in Guangzhou, China. Secondly, the actual and standard bus driving times were calculated by processing the buses' GPS trajectories and bus station data. Congestion time is defined as the interval between actual and standard driving time. Thirdly, congestion time prediction based on LSTM (T-LSTM) was adopted to predict future bus congestion times. Finally, the congestion index and classification (CI-C) model was used to calculate the congestion indices and classify the level of congestion into five categories according to three classification methods. Our experimental results show that the T-LSTM model can effectively predict the congestion time of six road sections at different time periods, and the average mean absolute percentage error ( M A P E ¯ ) and root mean square error ( R M S E ¯ ) of prediction are 11.25% and 14.91 in the morning peak, and 12.3% and 14.57 in the evening peak, respectively. The TCP-DT method can effectively predict traffic congestion status and provide a driving route with the least congestion time for vehicles. [ABSTRACT FROM AUTHOR]