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Traffic congestion is a major problem in developing and developed countries vehicle traffic management systems. Traffic control system works based on the idea of removing instabilities and avoid accidents in order to minimize the traffic and maximize the vehicle flow. To control the congestion need to predict the upcoming traffic flow and it will be useful for Advanced Traffic Information Systems (ATIS), Advanced Traffic Management Systems (ATMS) and traffic analytics. Non–linear historical data and uncertain factors influence the vehicle congestion at peak hours which cannot be considered in existing algorithms. This study proposes hybrid neural network algorithms such as Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) network for short term traffic flow prediction based on multivariate analysis. Widely referred datasets Performance Measurement Systems (PEMS) and Mesowest have been used to evaluate this model. Experiment results shows that CNN-LSTM Hybrid prediction model achieves high accuracy compared with other models.

The major aspect of the paper is to understand the recurring traffic congestion and suggest a precautionary measure to the society. This implication of widening the existing roads or building new one by acquiring the space just results in increase of additional heavy traffic. Government also restricts the new building of roads or other transportation needs because already the total available space in cities is used for transportation needs. In this paper a new design implementation of automatic movable traffic road dividers is used for the congestion release strategy in traffic prone areas instead of thinking about the old mechanism of widening roads to reduce this congestion. Hence the paper aims to build a road divider which is automated road divider having the mechanism of moving or shifting the lane directing the rush of the traffic. It has the flexibility to add one or more lane based on the traffic in particular directions. This type of mechanisms in traffic systems not only save time and fuel but also create green benefits reducing pollution.

Using automatic identification system (AIS) data, this article first has extended the definition of three widely used roadway congestion indices to maritime transportation systems (MTS), traffic speed index (TSI), traffic rate index (TRI), and dwell time index (DTI). Next, a methodology is developed to measure the indices based on AIS data, considering various factors, including path geometry, time of day, and the type and size of vessels, and finally the method has been applied to the AIS data of the Houston Ship Channel (HSC) to evaluate the applicability in real cases. The results show that although average TSI and TRI cannot represent waterway congestion, the real-time values (rather than the average) at the micro level can help finding location, time, and severity of traffic congestion. Besides, while TSI and TRI have shortcomings, both average and real-time dwell time index (DTI) can quantify traffic congestion and highlight severity in different waterway segments for different types of vessels. When congestion happens at some narrow waterways, vessels need to wait at sea buoy or docks, thus dwell time index (DTI) can quantify traffic congestion better than in-transit indices such as travel speed, TSI. According to HSC DTI, most tankers experience long waiting times at the sea buoy and Galveston Bay, while cargo vessels experience delays at Bayport and Barbour's Cut terminals. This paper helps the decision-makers quantify congestion in different sections of a waterway and provides measures to compare congestion for national competing projects at different waterways.

Searching for an available parking space is a major cause of traffic congestion and pollution. Besides, getting a free spot constitutes a primary worry for drivers on the road, which results in their daily discomfort and stress. Therefore, well-designed parking management tools are needed to effectively allocate parking spaces to drivers, especially in dense urban areas. In this perspective, we propose two linear integer programming models for assigning parking spaces to all road users (private vehicles and carriers) while maximizing parking occupancy. The first combinatorial problem attributes to each driver only one parking space while the second one assigns two parking spaces if no space is suitable for that motorist. Feasible solutions are computed in an exact way by Improved primal simplex method and in an approximated manner by two metaheuristics (Genetic algorithm and Taboo search) for larger scale applications. However, parking problems still persist when parking requests are not properly balanced between a city's zones, a local gridlock problem arises where certain areas are over-used and others under-used. For this reason, we first redistribute parking demands over urban areas before dedicating one or two spaces to each driver. To do this, we consider both solicitation rates in these areas and coverage ratios of received parking demands. Our parking space allocation solution is just one component of a transportation system heavily backed by information and communication technologies. This system consists of effectively managing a city's parking spaces by predicting and understanding their occupancy patterns. The suggested approach is applied to Casablanca city with several parking scenarios in order to evaluate its effectiveness and test its ability to minimize total walking distance, parking costs as well as unmet demand percentages. Experimental results show that our mathematical models can effectively rule out parking problems. The related published methods satisfy at most 75% of received parking requests. However, with our proposal, it is possible to find parking spaces for 87.3% of drivers without any noticeable effort, even an increase of 16.4%. The ordinary walking distance is reduced by 40% if our solution is adopted. Yet, other research works report a decrease of this distance not exceeding 30%. The increase in parking demand satisfaction rates demonstrates the effectiveness of our system, especially in the eyes of urban stakeholders. The reduction in walking distance encourages drivers to use our solution and consequently increases their loyalty.

As an emerging computing paradigm, the Internet of Vehicles (IoV) brings a lot of benefits for drivers and consumers, such as route recommendation, reducing traffic congestion, and parking difficulty. However, participants in IoV are inevitably exposed to privacy threats because their data is outsourced to a third party. This paper presents a comprehensive analysis of the root causes of privacy risks in IoV and the potential solutions. We begin by introducing the concrete privacy challenges, which are tied to the structural properties of IoV. Then, we present a unified framework of data privacy preservation in IoV, which integrates multiple privacy techniques, including encryption, anonymity, and perturbation. Next, we investigate the possible research directions for route recommendation, charging deployment, traffic prediction under different privacy requirements in detail. Finally, we discuss emerging techniques and environments (e.g., blockchain, smart grid, 5G communication) to strengthen the ability and application of IoV under strong protection. With the fast development of communication and infrastructure technologies, the Internet of Vehicles (IoV) is attracting more and more attention from academia and industry [1]. The novel computing paradigm consists of hundreds of thousands of interacted and autonomous vehicles. Each is viewed as an intelligent object capable of sensing, computing, and data storage. Intelligent entities are able to undertake multiple different roles, which brings a series of functions, including autonomous driving and platooning, information sharing and learning, traffic control, and optimization.

Despite the tremendous growth of the transportation sector, the availability of systems that ensure safe, efficient, sustainable transportation reduces traffic congestion, maintenance costs, the off-road time of the vehicle, enhances driver's experiences, and ensures a more reliable journey are very limited. The fast evolution of our economy, lack of driver training, and the grown affordability of our society are reasons for this mismatch in developing economies. We think that the inconsistency will increase and unfavorably affect our traffic structure unless intelligent algorithm-based solutions are developed and deployed. This article presents a system for providing safe, accurate, comfortable, reliable, fuel-efficient, and economical driving behavior using Machine Learning techniques like the hidden Markov model (HMM). Our proposed system recommends subsequent trips using a multi-objective optimization (MOO) technique for the driver. It provides suggestions concerning speed limits and alerts based on the driver's behavior score and fuel efficiency. We used a publicly available UAH-DriveSet dataset captured by the driving monitoring app DriveSafe for all of our experiments. The results reveal that the proposed model predicts behavior with 95% accuracy and calculates fuel efficiency to improve driving quality and experience. This system recommends safer, more comfortable, more reliable, more efficient, and economical rides, beneficial for everyone in our society.

To support safe driving and automatic operation, collecting and providing information are important in intelligent transport systems (ITSs). Information and communication technology utilization is needed even when disasters occur because it is possible to collect and analyze vehicle position information via roadside units and thus locate accidents and traffic jams. However, if the roadside units are damaged by disasters or power outages, these functions cannot be provided. Therefore, we consider ITS function assistance using unmanned aerial vehicles (UAVs), which can spread information without being affected by the availability of roads and traffic congestion. Simply flying UAVs without a plan will cause problems such as delays due to unnecessary movement and information transmission to areas where vehicles do not exist. Therefore, we propose a method for predicting information diffusion by vehicle-to-vehicle communication to control the flight trajectory of UAVs, with the aim of quickly spreading information to many vehicles. To predict information diffusion, we use a reaction-diffusion model, which is a partial differential equation, and construct a model that considers the effects of vehicle-to-vehicle communication. The effectiveness of the proposed method is evaluated through the simulation, and the results show that the method performs better than the comparison methods.

With the rapid growth of vehicles, the vehicular networks meet main challenges such as dynamic, heterogeneous, and large scaled. In addition, the cellular-based vehicular networks must satisfy further strict requirements including ultra low latency, high reliability, high spectrum efficiency and massive connections of the next generation (6G) network. Recently, by exploiting vehicle clustering utilized for reducing the complexity of vehicle-to-everything (V2X) systems, it could ultimately improve road traffic efficiency. In some specific scenarios related to Internet of Things (IoT), a group of vehicles can be served effectively in term of spectrum efficiency when two key techniques are enabled, i.e. non-orthogonal multiple access (NOMA) and cognitive radio (CR) schemes are joint deployed. These techniques certainly benefit to 6G V2X services to reduce specific challenges such as traffic congestion and massive connections. Different from existing works, we propose wireless power transfer (WPT) applied to roadside unit (RSU) to improve the situation that energy shortening in small devices deployed in V2X communications. In particular, we derive expressions of throughput to exhibit performance of the two grouped vehicles. To further indicate advantages of spectrum efficiency, we compare two schemes of V2X systems with and without CR schemes. Numerical results demonstrate that the non-CR NOMA-V2X scheme outperforms the CR-based NOMA-V2X scheme with fixed power allocation, but the non-CR NOMA-V2X scheme costs higher spectrum resource compared with the counterpart. Besides, by comparing with orthogonal multiple access (OMA)-assisted V2X, NOMA-V2X schemes demonstrate superiority in term of throughput performance whilst achieving the benefits of both NOMA and CR schemes.

Scheduling the recharging of electric vehicle fleets under different scenarios is an important but open problem. One important scenario is that vehicles travel at different speeds in different periods since traffic congestion is common in urban areas nowadays. Therefore, in this article, a novel time-dependent electric vehicle routing problem with congestion tolls is proposed. If a vehicle enters a peak period, a fixed congestion toll needs to be paid in this problem. A mixed integer linear programming model is established and an adaptive large neighborhood search (ALNS) heuristic is designed to solve the model. The model and solving method are validated and evaluated extensively with benchmark instances. Results indicate that a certain level of congestion tolls could prevent vehicles from entering peak periods and relieve road congestions significantly. Furthermore, the ALNS heuristic could provide much better solutions for the problem than typical optimization software, such as Gurobi, in much shorter running time.

With the deterioration of the transportation ecosystem and traffic congestion, traffic graph representations become more complex and lack intelligibility. The deep learning method provides a new way for traffic prediction by mining the spatiotemporal relations of historical states in traffic network. Recurrent neural networks based on gate control can avoid the gradient vanishing and graph convolutional networks provide theoretical support for spatial feature extraction of traffic graph. However, in the prediction of traffic evolution behaviors, the statuses of units and groups distributed in the traffic network have strong and continuous spatiotemporal correlations. Modeling only based on the temporal or spatial perspective usually lacks comprehensiveness and semantic relevance which results in poor prediction performance. In this paper, we propose a spatiotemporal hierarchical propagation graph convolutional network(SHPGCN) to get the whole spatiotemporal evolution features of traffic graph, which can be used to predict the propagation effects and state changes of traffic flow. Considered the inadequacy of graph convolution in the learning of hierarchical features, we construct a low-dimensional propagation graph representation that projects complex node relationships into first-order neighborhoods to capture dynamic changes at different spatial and temporal scales. The SHPGCN uses graph convolution to encode the traffic propagation features and embeds them into a bidirectional recurrent sequence for traffic prediction. The experiment results show that SHPGCN can provide good prediction accuracy and robustness for the traffic evolution behaviors.

Freeway control has been increasingly used as an innovative approach to ease traffic congestion, improve traffic safety and reduce exhaust emissions. As an important predictive model involved in freeway control, the predictive performance of METANET greatly influences the effect of freeway control. This paper focuses on modifying the METANET model by modeling the critical density. First, the critical density model is deduced based on the catastrophe theory. Then, the perturbation wave and traveling wave that are obtained using the macro and micro data, respectively, have been developed to modify the above proposed critical density model. Finally, the numerical simulation is established to evaluate the effectiveness of the modified METANET model based on the field data from the realistic motorway network. The results show that overall, the predicted data from the modified METANET model are closer to the field data than those obtained from the original model.

With the development of the economy and the acceleration of urbanization, traffic congestion has become a worldwide problem. Advances in mobile Internet and sensor technologies have increased real-time data sharing, providing a new opportunity for urban route planning. However, due to the difficulty of handling complex global information, making correct decisions in large-scale and complex traffic environments is a problem that urgently needs to be solved. In this paper, a multiple route planning model (multi-route dynamic programming (DP) model) is proposed to solve the urban route planning problem with traffic flow information. In particular, we adopt the DP algorithm in this model, design a reward function suitable for urban path planning problems, and generate multiple routes based on the Q values. In addition, we design different scenarios using real-world road networks to test our model. Through the experiments, we demonstrate that our model has the potential to yield optimal results under large-scale scenarios with high efficiency. The advantages of integrating the distance contribution index (DCI) in the reward function are also elaborated. Moreover, our model can provide alternative routes to divert traffic from the optimal route, thus mitigating the congestion drift problem.

In recent years, developments in vehicle-to-everything communication (V2X) have steadily increased in applications such as platooning and collision avoidance. V2X provides vehicles with long-range information regarding traffic congestion and routing, but also short and mid-range information allowing cooperative adaptive cruise control, automatic collision warnings, and others. Despite being potentially beneficial in several aspects, such interdependence poses a set of specific challenges from safety and reliability standpoint due to the possibility of cyber-attacks aimed at influencing the behavior of the vehicles. In this paper, a Cloud-based approach, a sandboxing framework is presented to detect the false data injection attack on connected and automated vehicles (CAVs). The sandboxing concept comes from computer security and it is adopted in a control framework as a way to isolate and evaluate the data exchanged by the CAVs affecting the vehicle control system. Numerical experiments are conducted to show the effectiveness of the approach using microscopic traffic simulation. Simulation results show that our proposed methodology is able to detect the false data injection attack existing in the V2X communication and identify the attacker in real-time less than 0.2 s.

Container terminal capacity, as an important indicator of container terminal competitiveness, has become a primary concern of port managers, whereas container flows, which are supported by quay cranes, yard cranes, and automated guided vehicles, have a substantial influence on container terminal capacity. Container flows are analyzed in this article to estimate the performance of an automated container terminal considering traffic congestion, unbalanced task assignment, container batch arrival, and different berth and yard layouts. The operating system is formulated as a closed-queuing network with four stations. Theoretical analysis and the solution method are adopted to determine system capacity. The resource allocation and system design, specifically the number of vehicles, berth and yard layouts, and task assignment strategy, are optimized. Numerical experiments are conducted to provide management insights into port capacity planning, resource optimization, and layout design.

An efficient transportation system can substantially benefit our society, but road intersections have always been among the major traffic bottlenecks leading to traffic congestion. Appropriate traffic signal timing adapted to real-time traffic may help mitigate such traffic congestion. However, most existing traffic signal control methods require a huge amount of road information, such as vehicle positions. In this paper, we focus on a particular road intersection and aim to minimize the average waiting time. We propose a traffic signal control (TSC) system based on an end-to-end off-policy deep reinforcement learning (deep RL) agent with background removal residual networks. The agent takes real-time images at the road intersection as input. Upon sufficient training, the agent can perform (near-) optimal traffic signaling based on real-time traffic conditions. We conduct experiments on different intersection scenarios and compare various TSC methods. The experimental results show that our end-to-end deep RL approach can adapt to the dynamic traffic based on the traffic images and outperforms other TSC methods.

The purposes are to investigate the personalized situation adaptive Human-Computer Interaction (HCI) in the COVID-19 context, achieve accurate predictions for HCI in different urban transportation situations, and solve the urban intelligent transportation problems. Problems of Human-Vehicles-Interaction (HVI) in context awareness are analyzed. Historical traffic flow in three different situations, including novice user situation, mid user situation, and expert user situation, are taken as the data sources. The HVI data are preprocessed afterward. Next, Dilated Convolution (DC) and Long-Short Term Memory (LSTM) are integrated (DC-LSTM) to build an HVI model based on situation adaptive. The proposed model is simulated to analyze its performance. Simulation experiments suggest that the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of the proposed model are 4.64%, 5.34%, and 7.82%, respectively. Although these three metrics increase under the mid user and expert user situations, the proposed model can still provide a higher accuracy than LTSM, Convolutional Neural Network (CNN), Simple Recurrent Network (SRN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Besides, the prediction velocity can maintain about 60 Frame-Per-Second (FPS) under all three user situations. Regarding the path guidance performance, the proposed model can suppress the traffic congestion and dredge the congested sections effectively. Hence, the HVI model based on situational adaptation constructed has high prediction accuracy and traffic congestion evacuation performance, which can provide an experimental basis for the later intelligent transportation field and improving situational self-adaptability. IEEE

There is an increasing trend in the use of wireless communication along with new traffic signal control (TSC) algorithms to leverage and accommodate connected and autonomous vehicles. However, this development has increased the potential for cyber-attacks on TSC that can undermine the benefits of these new algorithms. An advanced persistent adversary can learn the behavior of TSC algorithms and launch attacks to preferentially get green time and/or to create traffic congestion in one intersection which can spread to the entire network. In this paper, we consider backpressure-based (BP-based) TSC algorithms and compare their performance under two misinformation attacks - 1) time spoofing attack in which vehicles alter their arrival times at the intersection and 2) ghost vehicle attack in which vehicles disconnect the wireless communication and thereby hide from the TSC. We show that these misinformation can influence the signal phases determined by BP-based TSC algorithms. We consider an adversary that determines a set of arriving vehicles to be attack vehicles from many candidate sets (attack strategies) in order to maximize the number of disrupted signal phases. We show that by formulating the problem as a 0/1 Knapsack problem, the adversary can explore the space of attack strategies and determine the optimal strategy that maximally compromises the performance in terms of average delay and fairness. We propose two protection algorithms, namely, auction-based (APA) and hybrid-based (HPA) algorithms and show that they are able to mitigate the impacts of the misinformation attacks.

With the development of communication technology and artificial intelligence of things (AIoT), transportation systems have become much smarter than ever before. However, the volume of vehicles and traffic flows have rapidly increased. Optimizing and improving urban traffic signal control is a potential way to relieve traffic congestion. In general, traffic signal control is a sequential decision process that conforms to the characteristics of reinforcement learning, in which an agent constantly interacts with its environment, thus providing strategy for optimizing behavior in accordance with feedback in response. In this paper, we propose multiagent reinforcement learning for traffic signals (MARL4TS) to support the control and deployment of traffic signals. First, information on traffic flows and multiple intersections is formalized as input environments for performing reinforcement learning. Second, we design a new reward function to continuously select the most appropriate strategy as control during multiagent learning to track actions for traffic signals. Finally, we use a supporting tool, Simulation of Urban MObility (SUMO), to simulate the proposed traffic signal control process and compare it with other methods. The experimental results show that our proposed MARL4TS method is superior to the baselines. In particular, our method can reduce vehicle delay.

Network traffic prediction based on probe vehicle data is important for traffic management and route recommendation and has been intensively studied. Previous traffic prediction methods mainly focused on recurring traffic congestion. Predicting non-recurring traffic congestion, caused by events and accidents, is significantly more important; however, it has not been intensively studied. To predict non-recurring traffic congestion using probe data, we need to estimate the current traffic conditions based on sparse observations for large traffic networks to track traffic changes online. Conventional traffic forecasting methods have not been able to solve all of these problems. To address these problems, we propose a data assimilation method using a state space neural network (SSNN) with an incorporated topology of road networks. The SSNN model can easily model network traffic and can easily estimate its states and parameters by data assimilation using Bayesian filtering. In this study, we adopted a decoupled extended Kalman filter (DEKF) based data assimilation, which is scalable and applicable to large-scale network traffic, to estimate the states and parameters online. We evaluate the proposed method using an open dataset that includes a road network comprising over 30000 road segments. The results show that our method achieves higher prediction accuracy for predicting unknown traffic congestion and is more robust against data sparsity than conventional state estimation methods.

This paper introduces Sentinel, an onboard system for intelligent vehicles that guides their lane changing behavior during a freeway incident with the goal of reducing traffic congestion, capacity drop, and delay. When an incident blocking the lanes ahead is detected, Sentinel calculates the probability of leaving the blocked lane(s) before reaching the incident point at each time step. It advises the vehicle to leave the blocked lane(s) when that probability drops below a certain threshold, as the vehicle nears the congestion boundary. By doing this, Sentinel reduces the number of late-stage lane changes of vehicles in the blocked lane(s) trying to move to other lanes, and distributes those maneuvers upstream of the incident point. A simulation case study is conducted in which one lane of a four-lane section of the I-66 interstate highway in the U.S. is temporarily blocked due to an incident, to understand how Sentinel impacts traffic flow and how different parameters - traffic flow, system penetration rate, and incident duration - affect Sentinel's performance. The results show that Sentinel has a positive impact on traffic flow, reducing average delay by up to 37%, particularly when it has a considerable penetration rate. Working alongside Traffic Incident Management Systems (TIMS), Sentinel can be a valuable asset for reducing traffic delay and potentially saving billions of dollars annually in costs associated with congestion caused by freeway incidents., Comment: $\copyright$ 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

With the increase of private cars, traditional traffic signal control methods cannot alleviate the traffic congestion problem. Reinforcement learning (RL) is increasingly used in adaptive traffic light control. As urban traffic becomes more complex, reinforcement learning algorithms solely based on value or policy are not suitable for such scenarios. Moreover, the centralized method does not show a good effect on the multi-intersection, in particular, when the traffic flow is in the high-dimensional continuous state space. In this article, we propose a decentralized framework based on the advantage actor-critic (A2C) algorithm by assigning global control to each local RL agent or intersection. A2C algorithm involved in this article is a product of combining policy function and value function, which has good convergence ability and can be applied to continuous state space. The decentralized methods may put a new challenge: from the perspective of each local agent, the environment becomes partially observable. We overcome this problem by putting forward a region-aware cooperative strategy (RACS) based on graph attention network (GAT), which can incorporate the spatial information of the surrounding agents. We carry out experiments on synthetic traffic grid and real-world traffic network of Monaco city to compare with the existing A2C and Q-learning algorithms. Experimental results confirm that our RACS method has a shorter queue length and less waiting time than the two existing algorithms, and can reduce the total vehicle travel time.

The explosive growth of road vehicles especially the private cars has brought unprecedented pressure to a series of problems in urban transportation systems, such as traffic congestion and environmental pollution. Private cars trajectory data and perceiving their information provide a promising solution to these problems. However, the collection of large-scale trajectory data for private cars with high accuracy and reliability is still delicate tasks in urban environments. In this paper, we propose a low-cost and user-friendly implementation method for achieving large-scale private cars trajectory data acquisition via designing lightweight GPS module and On Board Diagnostics (OBD) reader. To ensure reliable trajectory data acquisition via GPS/OBD integration, we propose an ensemble learning based Gauss Process Regression (GPR) method so as to cope with the non-linearity, non-stationarity and incremental training problems during trajectory collection. We design a classification-type loss (CTL) function and build a regression to classification (R2C) method with Learn++ for realizing ensemble learning. The proposed approach implements incremental learning when new trajectory data arrives and is able to resolve the concept drifting problem. Experiments in real-world urban environment have demonstrated the effectiveness and reliability of the proposed method, it achieves better trajectory prediction performance than the comparative methods under various road conditions in GPS-denied areas.

Connected vehicles are potential solutions to address some of the existing challenges in transportation systems, such as emission, traffic congestion, and fuel consumption. However, vehicular communication networks endure from reliability and security issues. Cyber-attacks with performance-disrupting purposes can lead to catastrophic collisions in connected vehicles and increase traffic congestion. To ensure safety and reliability, a monitoring system with the capability of diagnosing cyber-attacks is necessary for connected vehicles. In this study, we consider a platoon of connected vehicles equipped with cooperative adaptive cruise control (CACC) that is subjected to a specific type of cyber-attack, namely ``False Data Injection'' (FDI) attack. A smart FDI attack is modeled with ghost vehicles injection into the connected vehicles network to disrupt the performance of the whole system. To ease the analysis, we develop a partial differential equation (PDE) model for the CACC vehicle dynamics using its ordinary differential equations (ODE). Furthermore, a PDE observer is designed to detect the FDI attack and locate the attack's injection point in the platoon. Lyapunov stability theory has been utilized to verify the observer's convergence under no-attack scenario and study residuals' behaviors in the presence of the attack. A non-zero constant threshold is considered to provide robustness in attack detection despite the measurement noises. Eventually, the effectiveness of the proposed algorithm is evaluated with simulation studies.

As one of the most widely used applications of the Internet of Things (IoT), intelligent transportation system (ITS) is of great significance for urban traffic planning, traffic control and traffic guidance. However, widespread traffic congestion occurs with the increased number of vehicles. Traffic flow prediction is a good idea for traffic congestion. Therefore, many schemes have been proposed for accurate and real-time traffic flow prediction, but there still exist many issues including low accuracy, weak adaptability and inferior real-time. Meanwhile, the complex spatial and temporal dependencies in traffic flow are still challenging. To address the above issues, we propose a novel spatial-temporal Chebyshev graph neural network model (ST-ChebNet) for traffic flow prediction to capture the spatial-temporal features, which can ensure accurate traffic flow prediction. Concretely, we firstly add a fully connected layer to fuse the features of traffic data into a new feature to generate a matrix, and then the long short-term memory (LSTM) model is adopted to learn traffic state changes for capturing the temporal dependencies. Then, we use the Chebyshev graph neural network (ChebNet) to learn the complex topological structures in the traffic network for capturing the spatial dependencies. Eventually, the spatial features and the temporal features are fused to guarantee the traffic flow prediction. The experiments show that ST-ChebNet can make accurate and real-time traffic flow prediction compared with other 8 baseline methods on real-world traffic datasets PeMS.

Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic prediction can assist route planing, guide vehicle dispatching, and mitigate traffic congestion. This problem is challenging due to the complicated and dynamic spatio-temporal dependencies between different regions in the road network. Recently, a significant amount of research efforts have been devoted to this area, especially deep learning method, greatly advancing traffic prediction abilities. The purpose of this paper is to provide a comprehensive survey on deep learning-based approaches in traffic prediction from multiple perspectives. Specifically, we first summarize the existing traffic prediction methods, and give a taxonomy. Second, we list the state-of-the-art approaches in different traffic prediction applications. Third, we comprehensively collect and organize widely used public datasets in the existing literature to facilitate other researchers. Furthermore, we give an evaluation and analysis by conducting extensive experiments to compare the performance of different methods on a real-world public dataset. Finally, we discuss open challenges in this field., to be published in IEEE Transactions on Intelligent Transportation Systems

This study proposes a short-term traffic flow prediction framework. The vector autoregression (VAR) model based on econometric theory and the CNN-LSTM hybrid neural network model based on deep learning are employed in the analysis. An intrinsic association among traffic variables is first evaluated using the VAR model, and the predictable relationship of these variables is determined. Then the multi-features speed prediction for one spatial location using the CNN-LSTM hybrid neural network model is conducted, the prediction results prove that prediction with multi-feature is better than that with a single feature. Subsequently, several popular deep learning models and other shallow predicted models are proposed to be compared with the constructed CNN-LSTM network model, and the comparison illustrates that the model performance of the developed CNN-LSTM network model is superior to other models in forecasting the short-term traffic flow. Then the multi-feature speed predictions for a group of spatial locations are further conducted using the CNN-LSTM model. The result demonstrates the predictive accuracies are associated with the spatial correlation of traffic flow. Finally, a heatmap is produced to visualize the predicted speed, from which the spatial-temporal traffic condition can be presented clearly. The research results have the potential to be applied to the travel information releasing and traffic congestion management.

Short-term traffic prediction is of great importance to the management of traffic congestion, a pervasive and difficult-to-solve problem in many metropolises all over the world. However, existing studies on traffic prediction contain rough traffic information at the carriageway level that ignore the distinction between different turns in one intersection. With the aim of predicting traffic at road intersections from big trace data on a finer scale, this study proposes a novel method, the fine-grained traffic prediction method (FTPG) with a graph attention network (GAT), which predicts traffic information, including traffic flow speeds, traffic states, and average queue lengths, at the turn level. In the FTPG, a method for estimation of the queue starting point is proposed to improve the accuracy of traffic information detection. Furthermore, the topology is constructed under turn-level conditions, and a GAT-based method, the spatio-temporal residual graph attention network (ST-RGAN), is proposed to improve the prediction accuracy. Experiments are performed using taxi GPS trace data collected in the city of Wuhan and show that the proposed FTPG method can make predictions with fine-grained traffic information for road intersections accurately and robustly.

It is indispensable for professional traffic signal engineers to perform manual operations of traffic signal control (TSC) to mitigate traffic congestion, especially with complicated scenarios. However, such a task is time-consuming, and the level of congestion mitigation heavily relies on individual expertise in engineering practice. Therefore, it is cost-effective to learn traffic engineers' knowledge to enhance the problem-solving skills for a large-scale urban traffic network. In this paper, a data augmented deep behavioral cloning (DADBC) method is proposed to imitate the problem-solving skills of traffic engineers. The method is under a conceptual framework, parallel learning (PL) framework, that incorporates machine learning techniques for solving decision-making problems in complex systems. The DADBC method enhances a hybrid demonstration by exploiting a generative adversarial network (GAN) and then uses the deep behavioral cloning (DBC) model to learn traffic engineers' control schemes. According to the validation results using the real manipulation data from Hangzhou, China, our method can imitate complex human behaviors in intervening traffic signal control operations to improve traffic efficiency in urban areas.

The technological advancements involving information and communication technologies (ICT), such as Connected and Automated Vehicles (CAVs) and the Intelligent Transport Systems (ITS), have enabled new efficient traffic control and management strategies to mitigate traffic congestion. Specifically, the combined traffic flow-speed advisory systems based on CAVs and ITS technologies could provide the individual vehicle with the optimal speed to reduce the fuel consumption, the number of stops, simultaneously reduce the network-wide traffic congestion and improve road safety. This article develops a novel bi-level control framework underpinned by the mutual interaction between a system optimal traffic flow control strategy at a network level and a speed control policy for an individual vehicle at a link level within a connected traffic environment. Our framework proposes the novel group-based method to guarantee the consistency and interaction between the macroscopic and microscopic models. To this end, it efficiently optimizes vehicular trajectories while meeting the network-wide objectives which have not been investigated previously in the literature. We propose an efficient algorithm for this problem that iteratively solves mixed-integer linear programming (MILP) models for each upper and lower level. Numerical results indicate the effectiveness of the proposed speed advisory method in vehicular emission reduction, favorable network queue formation, and its positive influence on traffic flow patterns over the network.

Benefiting from dense connections in data center networks (DCNs), load balancing algorithms are capable of steering traffic into multiple paths for the sake of preventing traffic congestion. However, given each path’s time-varying and asymmetrical traffic state, this may also lead to worse congestion when some paths are overutilised. Especially in the two-tier hybrid optical/electrical DCNs (Hoe-DCNs), the port contentions and large-grained optical packets of the fast optical switch (FOS) require the top-of-rack (TOR) switch to have microsecond-level load balancing capability for microburst traffic. This paper establishes a leaf-spine Hoe-DCN model to illustrate the principal characteristic of dynamic load balancing in TOR switches for the first time. Moreover, we propose the dynamic distributed multi-path (DDMP) load balancing algorithm that relies on dynamic hashing computing for network flow distribution in DCNs, which dynamically adjusts traffic flow distribution at microsecond level according to the inverse ratio of the buffer occupancy. The simulation results show that our proposed algorithm reduces the TOR-to-TOR latency by 15.88% and decreases the packet loss by 22.06% compared to conventional algorithms under regular load conditions, which effectively improves the overall performance of the Hoe-DCNs. Moreover, our proposed algorithm prevents more than 90% packet loss under low load conditions.

The increased market penetration of route guidance tools-relaying real-time traffic information to drivers-can have damaging effects on transportation networks, including traffic congestion oscillation resulting from the overreaction phenomenon and the inability to control system performance. To address these issues, this study leverages V2I communication capabilities to integrate strategic real-time traffic information perturbation into an online, in-vehicle coordinated routing mechanism for connected vehicles using a mixed-strategy congestion game (CRM-M-IP). Under the CRM-M-IP, the routing decisions of all vehicles are coordinated to prevent overreaction. Additionally, the routing decisions for all vehicles are based on strategically perturbed traffic information (a convex combination between average and marginal link travel times), to ensure that the selfish route choices made by users also help improve system performance. We prove that low information perturbation levels can lead to high system performance gains with correspondingly low individual user optimality losses. From numerical experiments conducted on the Sioux Falls network, we observe that the CRM-M-IP leads to a system performance improvement greater than 3%, and average individual travel time reduction up to 3.5% as compared to the case with no perturbation. Moreover, we find that the average individual user optimality loss resulting from information perturbation is less than 2%. However, we find that when perturbation is high, some users can experience losses approaching 30%--illustrating the need to not over-perturb to ensure compliance of drivers.

Current traffic congestion and the resulting carbon emissions are two of the main problems threatening the sustainability of modern cities. The challenges facing today's cities focus primarily on the optimization of traffic ow and the transition to electric vehicles. The latter aspect implies the need for an adequate deployment of the infrastructure of charging stations. The inherent complexity in today's cities and the difficulty in implementing new policies whose benefits are difficult to measure and predict has led in recent years to consider the enormous potential of simulation tools and in particular of the agent-based simulation (ABS). ABS allows the speciffication of complex models that reect the complexity and dynamism of urban mobility. Current technology in ABS has evolved and matured sufficiently to provide very sophisticated tools but lacking facilities for a exible and realistic generation of input data in the execution of the experiments. In line with this, this paper introduces two configurable generators that automatize the creation of experiments in agent-based simulations. The generators have been developed with the SimFleet simulation tool enhancing the simulation of realistic movements and location of vehicles, passengers and other users of the urban traffic system within a city. The generators proved to be useful for comparing different distributions of locations as well as different agent movement behaviors based on real city data.

The great increase in car ownership has led to the daily recurrence of traffic congestion. Thus, traffic mobility, safety and emission concerns have become the most serious challenges for transportation researchers. To mitigate traffic congestion, a variety of proactive traffic-control strategies, such as ramp metering (RM), have been intensively investigated and deployed. With the aim of improving freeway traffic conditions, RM regulates the on-ramp flows dynamically in response to dynamic road conditions. However, most early RM strategies focus on optimising the traffic from one single aspect. This paper presents an RM control algorithm that predicts and evaluates the RM-controlled future traffic states. The impact of RM control was evaluated using a macroscopic traffic-flow model. The designed RM control algorithm possesses a multi-objective optimisation module, which improves the traffic network from the aspects of mobility, safety and emissions. The designed algorithm is evaluated through simulation and calibrated using field data collected over an 11 km major freeway stretch in Edmonton, Alberta, Canada. The comparison of the proposed algorithm-controlled scenario and the uncontrolled scenario shows that the proposed RM control algorithm can effectively relieve traffic congestion, improve safety and reduce carbon emissions concurrently.

Traffic congestion is a significant issue in urban areas and can cause adverse effects. In this paper, our proposal categorizes the traffic activity from video through three steps: vehicle monitoring, feature extraction, and classification. The vehicle monitoring step comprises an object detector based on a convolutional neural network and multi-object tracker. The feature extraction step uses information related to each detected vehicle, in various points of the road, to represent the traffic condition through three features: density, flow, and velocity. We tested on the UCSD dataset and achieved the best performance with 98.82% of accuracy, which outperformed the state-of-the-art methods.

The function of the network is mainly related to its composition and structure. With the arrival of the big data era, the network congestion has become more and more serious. Many networks are proved to either have multilayer nature in themselves or interdepend with other networks. We focus on the multilayer network model formed by different speeds layers and study traffic dynamics on four different structure multilayer network models based on random and scale-free networks. It is found that the low-speed layer has a greater impact on the network traffic, and this layer with homogeneous network structure can better avoid congestion. Moreover, simulation results show that high-speed and low-speed layers with heterogeneity structure is easy to make the multilayer networks occur traffic congestion while the homogeneity structure is beneficial to obtain large traffic capacity.

Vehicle platoon, a novel vehicle driving paradigm that organizes a group of vehicles in the nose-to-tail structure, has been considered as a potential solution to reduce traffic congestion and increase travel comfort. In such a platoon system, head vehicles' performances are usually evaluated by user vehicles' feedbacks. Selection of an appropriate and reliable head vehicle while not disclosing user vehicles' privacy has become an interesting problem. In this article, we present a trust-based and privacy-preserving platoon recommendation scheme, called TPPR, to enable potential user vehicles avoid selecting the malicious head vehicles. The basic concept of TPPR is that each user vehicle holds a trust value, and the reputation score of the head vehicle is calculated via a truth discovery process. To preserve vehicles' privacy, pseudonyms and Paillier cryptosystem are applied. In addition, novel authentication protocols are designed to ensure that only the valid vehicles (i.e., the vehicles holding the truthful trust values and joining the vehicle platoon) can pass the authentication. A comprehensive security analysis is conducted to prove that the proposed TPPR scheme is secure against several sophisticated attacks in vehicular ad hoc networks. Moreover, extensive simulations are conducted to demonstrate the correctness and effectiveness of the proposed scheme.

Smart cities are part of the ongoing advances in technology to provide a better life quality to its inhabitants. Urban mobility is one of the most important components of smart cities. Due to the growing number of vehicles in these cities, urban traffic congestion is becoming more common. In addition, finding places to park even in car parks is not easy for drivers who run in circles. Studies have shown that drivers looking for parking spaces contribute up to 30% to traffic congestion. In this context, it is necessary to predict the spaces available to drivers in parking lots where they want to park. We propose in this paper a new system that integrates the IoT and a predictive model based on ensemble methods to optimize the prediction of the availability of parking spaces in smart parking. The tests that we carried out on the Birmingham parking data set allowed to reach a Mean Absolute Error (MAE) of 0.06% on average with the algorithm of Bagging Regression (BR). This results have thus improved the best existing performance by over 6.6% while dramatically reducing system complexity.

The intelligent transportation has been extensively investigated as an enabling technology for ubiquitous data processing and content sharing among vehicles and terrestrial infrastructures. In intelligent transportation systems, numerous vehicles and infrastructures are connected for information and data sharing to enable different operations. Since there are some urban areas that face the traffic congestion or cannot be well served, space-air-ground integrated networks (SAGIN) can be carried out to provide continuous network connectivity for vehicles. In particular, unmanned aerial vehicles (UAVs) are deployed as data collectors to receive data packets from vehicles due to the advantages of high mobility and low operating cost. It is noteworthy that the information freshness is critical to enable services for timely decision, e.g., autonomous driving and accident prevention. In this paper, we develop UAV-aided intelligent transportation systems to enhance the usage of vehicular networks and support low latency vehicular services, where the concept of age-of-information (AoI) is adopted to measure the freshness of data packets of vehicles. Then, the performance of UAV-aided intelligent transportation systems is analyzed in terms of the average AoI. In addition, the deployment of multiple UAVs is optimized to minimize the average peak AoI according to the traffic intensity of vehicles under seamless coverage, finite queue, and coverage probability constraints. To this end, the deployment optimization problem is formulated as a multi-constrained non-convex optimization problem and solved by considering each soft constraint separately. Simulation results show that our proposed system can provide timely data transmission.

The continuous urbanization with extensive dynamic situations on evolving cities, urban, and suburban areas, it is not feasible to categorize the navigation as fastest route, toll-free, and other variants. Metropolitan areas are more prone to traffic congestion, lane blocking, accidents, etc., due to the overcrowding and dynamic change of commuters’ arrival rates. In the metropolitan areas, most of the commuters’ use Google map to reach their desired destinations. It is quite often that route specified by navigation will not be reliable because sometimes due to the inability to update the sudden occurrence of incidents on the routes. Currently, Google map and GPS provide the time required to cover the distance and shortest route to reach the destination. The main issues with the existing Google map are it does not considers the impact of sudden occurrence of incidents, does not show the type of incidents that occurred, clearance time, and optimal routes. These issues are solved by designing an efficient context-aware vehicle incidents route service management for an intelligent transport system. The proposed system takes the context information of incidents, vehicles, weather conditions, roadside units, roads, and so on. This context information will be collected and shared with the nearby vehicles and roadside units using both mobile agents and dedicated short-range communication protocols. The proposed system suggests alternative routes with minimal delay and traffic clearance time and severity of incidents to the commuters. Also, it provides the incident information to the neighborhood vehicles, roadside units, nearby hospitals, ambulance, and members of the victims. The proposed system is exhaustively simulated in objective modular network testbed in C++, simulation of urban mobility, and Veins with different simulation parameters. The proposed system’s simulation results reduce the travel time (7 min) compared to the without the context information system (25 min), least collision rate (0.785%) compared to the existing system, minimizes the traffic clearance time in the incident zone, and uniform distribution of vehicle traffic on the estimated routes.

This paper investigates the spatiotemporal characteristics and predictability of the emerging modern traffic behavior, ridesourcing. We collect a comprehensive data set of Didi ridesourcing cars on a large geographical scale of a capital city in China, including both the temporal order information and the GPS-recorded spatial trajectories. To extract the features of this kind of traffic behavior, we construct a large-scale network by considering every traffic flow of the orders. Therein, a driver consecutively visiting different regions of the city connects the relationship of these sites. The weighted ridesourcing network shows a consistency of the distribution of trip orders and the Clark model for population distribution. The network also has spatial and temporal features with power laws, sometimes with exponential truncations and log-normal distributions. Furthermore, we propose a general analytical method to quantify the predictability of this kind of behavior by calculating the entropy at a collective level, which can be extended to quantify other traffic behaviors. Finally, by considering the traffic congestion factor, we propose a better neural network based model for predicting dwelling time of the ridesourcing behavior. We suggest that the traffic behavior of ridesourcing cars indicates specific non-Markovian characteristics, which can be systematically analyzed from the viewpoint of network sciences.

Improving the efficiency of traffic signal control is an effective way to alleviate traffic congestion at signalized intersections. To achieve effective management of the system-wide traffic flows, current researches tend to focus on applying reinforcement learning (RL) techniques for collaborative traffic signal control in a traffic road network. However, the existing collaboration-based methods often ignore the impact of transmission delay for exchanging traffic flow information on the system. Most of the studies assume that the signal controllers can collect all instantaneous vehicular features without delay. To fill the gap, we propose an RL-based cooperative traffic signal control scheme considering the data transmission delay issue in a traffic road network. In this paper, we (1) design our new RL agents to cooperative control the traffic signals by improving the reward and state representation based on the state-of-the-art max-pressure control theory; (2) propose a traffic state prediction method to address the data transmission delay issue by decreasing the discrepancy between the real-time and delayed traffic conditions; (3) evaluated the performance of our proposed work on both synthetic and real-world scenarios with different range of data transmission delays. The results demonstrate that our method surpassed the performance of the previous max-pressure-based traffic signal control methods and well addressed the data transmission delay issue.

Road pricing is an urban traffic management mechanism to reduce traffic congestion. Currently, most of the road pricing systems based on predefined charging tolls fail to consider the dynamics of urban traffic flows and travelers' demands on the arrival time. In this paper, we propose a method to dynamically adjust online road toll based on traffic conditions and travelers' demands to resolve the above-mentioned problems. The method, based on deep reinforcement learning, automatically allocates the optimal toll for each road during peak hours and guides vehicles to roads with lower toll charges. Moreover, it further considers travelers' demands to ensure that more vehicles arrive at their destinations before their estimated arrival time. Our method can increase the traffic volume effectively, as compared to the existing static mechanisms.

We study a novel chemical tanker port call optimization framework to improve the efficiency of waterway traffic control, in which chemical tankers load and unload cargoes while visiting multiple terminals. We formulate this problem as a mixed-integer program to minimize the total cost of vessel operations while compressing the schedule of cargo operations. The port call optimization framework integrates decentralized scheduling efforts by different stakeholders to minimize the traffic congestion satisfying policy, operational, and technical requirements, while embracing the current competitive scheduling discipline. Solving real-world problems can be computationally challenging. Hence we propose efficient solution techniques to promote fast convergence to optimum solutions. As we compare the vessel operational schedule from the proposed port call optimization model with monthly transit data in the past, terminal utilization is improved by more than 250% and the number of individual tanker’s transit is reduced by 28%. Suggested computational techniques ensured the solving of real-world problems in approximately 10 minutes.

Perimeter control (PC) and route guidance (RG) have become two powerful traffic control methods to address the urban traffic congestion, especially for multi-region urban traffic systems (MRUTS). Accurate traffic system model is necessary in majority of the existing PC and RG methods. If the traffic model is inaccurate or unavailable, the aforementioned PC and RG strategies may not work well or cannot be applied. In this paper, a novel data driven scheme called constrained model free adaptive predictive control (cMFAPC) is provided for PC and RG of MRUTS. Two outstanding advantages of this method are that it can only use the input and output (I/O) data of the controlled MRUTS to design the PC and RG strategies, instead of utilizing the traffic dynamics model, and the merits of model free adaptive control (MFAC) method and model predictive control (MPC) approach are combined in the proposed cMFAPC strategy. The effectiveness of cMFAPC strategy and its superiority over other commonly used PC and RG methods are verified via simulation.

Edge computing in vehicles is emerging as an essential candidate for the Internet of Vehicles (IoV) to improve traffic efficiency. The proliferation of IoV pushes the horizon of edge computing. The social features and connections among vehicles are significant for traffic efficiency solutions. However, it is quite challenging to perform collaborative edge computing (CEC) for social IoV systems because of network heterogeneity, vehicle mobility, user selfishness, privacy, and so on. This article focuses on the CEC, in the social IoV system to alleviate urban traffic congestion. Recent research reveals that intelligent traffic lights control through city-wide mobile edge computing (MEC) servers can reduce vehicles' average waiting time at signal intersections. This article has focused on a CEC-based traffic management system (CEC-TMS) to reduce the average waiting time. It utilizes multiagent-based deep reinforcement learning (DRL) for the MEC servers that interact with IoV and traffic lights to generate dynamic green waves at congested intersections. Results demonstrate the effectiveness of the proposed system under the paradigm of multiagent DRL.

Sharing private parking spaces during their idle time periods has shown great potential for addressing urban traffic congestion and illegitimate parking problems in smart cities. In this article, aiming to address the online parking-space sharing issue while ensuring the privacy of customer parking destination locations, we propose a novel destination privacy-preserving online parking sharing (DPOPS) incentive scheme. In particular, the online parking-space sharing problem is formalized as a social welfare maximization problem in a two-sided market, where parking-space providers (PSPs) and customers are regarded as sellers and buyers. Then, novel threshold value-based rules are designed to determine winners, payments, and reimbursement. Finally, winners are matched by solving a mixed-integer nonlinear programming problem, aiming to minimize the distance between customer's destination and allocated parking space. In addition, the location privacy of the customers' destinations is protected by the Laplace mechanism. We prove that DPOPS achieves several economically effective properties and approximate differential privacy. We analyze the upper bound of the efficiency loss of our scheme. Extensive evaluation results demonstrate that our scheme can not only achieve good performance regarding social welfare, PSP satisfaction ratio, privacy preservation, and computation overhead but also leads to shorter travel distances for customers comparing to the baseline scheme.

The important reasons for traffic congestion are classified into some categories. Some of the main reasons for traffic congestion are work zones, weather, special events, traffic incidents. This bad condition of weather makes changes the behavior in the driver and the traffic flow is affected. Accurate traffic prediction is important for the road user's traffic system administrators. Communication technology is influenced by various domains. Due to the advancement of technology machine learning concepts are used in traffic forecasting. This proposed model uses four types of machine learning concepts like DAN(Deep Autoencoder), DBN(Deep Belief Network), RF(Random Forest), and LSTM (Long Short Term Memory). The performance of the proposed model can be measured by using the accuracy, precision, recall, and error value metrics of machine learning approaches. From the four approaches, LSTM generates 95.2% accuracy.

The study carried out an analysis of the vehicle traffic parameters on a national road in 2011-2016. The variability and uncertainty of results were evaluated. An analysis of traffic data recorded on the city's entry and exit lanes was carried out. The variations in traffic volume are of interest e.g. in dynamic traffic management systems and navigation services, examining the benefits of flexible work time and places and assessing the environmental effects of traffic congestion. Research has shown that the assumption that lanes perform equally is not always true. Traffic volume models should be periodically calibrated taking into account the shape of the daily profile, which may, for example, allow public transport timetables to be more responsive to the needs of travelers.