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Recent advancements in connected automated vehicles (CAVs) and reinforcement learning (RL) hold significant promise for enhancing intelligent traffic control systems. This paper conducts a systematic review of studies on RL-based urban traffic control at signalised intersections, highlighting the significant impact of CAVs on traffic control performance improvement. We first review the fundamental concepts of RL algorithms, establishing a foundational understanding for subsequent RL-based traffic control methods. We then review recent progress in RL-based traffic signal control using CV/CAV trajectory data, RL-based CAV trajectory planning, and the cooperative control of both traffic signals and CAVs at signalised intersections. Our aim is to provide researchers with a comprehensive roadmap for future research in RL-based traffic control at signalised intersections. [ABSTRACT FROM AUTHOR]

The current traffic signal light timing scheme cannot effectively solve the problem of traffic congestion. This paper studied the intelligent control of signal lights based on infrared sensors. Firstly, the traffic flow was detected by infrared sensors; then, the future traffic flow was predicted using a back-propagation neural network; an intelligent control algorithm was designed based on the prediction results, and data were collected at the actual intersection for simulation experiments. It was found that the maximum and minimum root-mean-square error (RMSE) value of the BPNN prediction were 72.33 and 45.77, respectively, i.e., BPNN could predict the traffic flow accurately. Then, in the comparison of timing schemes, the maximum average queuing delay time of the BPNN was 0.55 s, and the maximum average queuing length was 92 m in the case of large traffic flow, both of which were better than the traditional fixed timing scheme. The results prove the reliability of the proposed signal light intelligent control algorithm. The algorithm can be further applied in practice. [ABSTRACT FROM AUTHOR]

Aiming at the problems of multi-AGV (automated guided vehicle) path planning in intelligent warehouse, such as poor real-time performance, weak obstacle recognition ability, multi-AGV collision, deadlock and congestion, a collaborative control method for intelligent warehouse traffic signals control and multi-AGV path planning is proposed. Traffic signals and multi-AGV path planning are regarded as a whole in this method. A collaborative control framework for traffic signals and multi-AGV path planning is designed. The LS-A3C (long short-asynchronous advantage actor-critic) algorithm and Bi-LSTM-CBAM (bi-long short-term memory-convolutional block attention module) algorithm are proposed as the core algorithm of the framework. The long-term and short-term information of traffic signals are encoded in LS-A3C algorithm, which uses a long short-term encoder and attention mechanism. They are represented by learning cell features. The A3C framework is used to calculate the Q value of the cell and the control strategy. The traffic signals time adapting to AGV flow is adjusted to solve the problems of multi-AGV collision, deadlock and congestion. The output result is spliced by calculating the state characteristics of the present moment and the leading moment in Bi-LSTM-CBAM algorithm to solve the problem of gradient disappearance and explosion in neural network effectively and improve real-time AGV path planning. The attention mechanism module CBAM is introduced. Weights are assigned based on how important the input is in order to strengthen the AGV's ability to identify obstacles. Finally, simulation experiments are carried out on Sumo and Gazebo joint simulation platform. The experimental results show that the collaborative control method significantly reduces the AGV collision, deadlock and congestion, significantly improves the obstacle recognition ability, and greatly enhances the real-time path planning. The purpose of improving the AGV operation efficiency is achieved. [ABSTRACT FROM AUTHOR]

In the process of collecting traffic information, traditional traffic flow control methods have some problems, such as high loss rate of sensing and detection information and long average queue length, which lead to the unsatisfactory effect of traffic control and evacuation in main roads. Therefore, based on multi-sensor information fusion technology, this study designs a new intelligent control method of traffic flow on main road. According to the multi-sensor information, the fusion algorithm and measurement equation are designed to obtain the main road traffic sensor position, vehicle linear velocity and other parameters, and update the fusion results in real time. Then, the short-term characteristics of traffic flow are used to control the appearance time of target section. Based on the ordinary differential equation, the main road traffic network is represented by intelligent directed graph, and the intelligent dredging model of urban main road traffic flow is constructed. The simulation results show that in five experimental roads, the average delay time of this method is less than 35 s, the maximum average number of stops is only 8.8, the average queue length is less than 30 m, the average travel speed is more than 25 km/h, the traffic flow per unit time is more than 250veh/10 min, and the traffic congestion index of main road is always more than 5.5. The above indexes of this method are better than those of traditional methods, which verifies that this method has better application performance. [ABSTRACT FROM AUTHOR]

Accurately predicting network traffic is helpful for improving a variety of spatial-temporal data mining applications, such as intelligent traffic control, network planning and anomaly detection. The mainstream graph-based methods are limited by the node-level message passing mechanism and require transforming dimensions to generate edge representations. Accordingly, some researchers propose using edge convolution to directly learn edge representations for network traffic prediction. However, node-level and edge-level information aggregation are two different perspectives, and their combination of them can achieve better performance. This paper proposes a novel model for network traffic prediction named the Enhanced Edge Convolution-based Spatial-Temporal Network (EESTN). Armed with a Graph Neural Network and Hypergraph Neural Network, EESTN employs the edge convolutions defined on the graph and hypergraph to effectively extract spatial features. EESTN further combines node convolution to capture the complex correlations among nodes and utilizes an attention mechanism to generate the edge convolution kernel for the decoder. Moreover, a 3D convolution-based multihead self-attention mechanism and a hierarchical loss function are proposed to capture the long-term temporal dependence and make full use of the model's represent ability. Finally, we conduct extensive experiments to validate the effectiveness of EESTN, and the related results demonstrate that EESTN outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Featured Application: It can help citizens bypass congested roads and avoid rush hours by predicting future traffic flows in advance, thereby reducing their travel time and costs and increasing the operational capacity and efficiency of the road network. Accurate traffic flow forecasting is pivotal for intelligent traffic control and guidance. Manually capturing the intricate dependencies between spatial and temporal dimensions in traffic data presents a significant challenge. Prior methods have primarily employed Recurrent Neural Networks or Graph Convolutional Networks, without fully accounting for the interdependency between spatial and temporal factors. To address this, we introduce a novel Hierarchical Spatial-Temporal Neural Networks with Attention Mechanism model (HSTAN). This model concurrently captures temporal correlations and spatial dependencies using a multi-headed self-attention mechanism in both temporal and spatial terms. It also integrates global spatial-temporal correlations through a hierarchical structure with residuals. Moreover, the analysis of attention weight matrices can depict complex spatial-temporal correlations, thereby enhancing our traffic forecasting capabilities. We conducted experiments on two publicly available traffic datasets, and the results demonstrated that the HSTAN model's prediction accuracy surpassed that of several benchmark methods. [ABSTRACT FROM AUTHOR]

In intelligent traffic control systems, the features extracted by loop detectors are insufficient to accurately impute missing data. Most of the existing imputation methods use only these extracted features, which leads to the construction of data models that cannot fulfill the required accuracy. This deficiency is the main motivation to propose an enrichment imputation method for loop detectors namely EIM-LD, in which the imputation accuracy is increased for different missing patterns and ratios by introducing a data enrichment technique using statistical multi-class labeling. It first enriches the clean data by adding a statistical multi-class label, including C1...Cn classes. Then, the class of samples in the missed-volume data is labeled using the best data model constructed from the labeled clean data by five different classifiers. Experts of the traffic control department in Isfahan city determined classes of the statistical multi-class label for n = 5 (class labels), and we also developed subclass labels (n = 20) since the number of samples in the subclass labels was sufficient. Next, the enriched data are divided into n datasets, each of them is imputed independently using various imputation methods, and their results are finally merged. To evaluate the impact of using the proposed method, the original data, including missing volumes, are first imputed without our enrichment method. Then, the proposed method's accuracy is evaluated by considering two class labels and subclass labels. The experimental and statistical results prove that the proposed EIM-LD method can enrich the real data collected by loop detectors, by which the comparative imputation methods construct a more accurate data model. In addition, using subclass labels further enhances the imputation method's accuracy. [ABSTRACT FROM AUTHOR]

To investigate the issue of multi-entry bus priority at intersections, an intelligent priority control method based on deep reinforcement learning was constructed in the bus network environment. Firstly, a dimension reduction method for the state vector based on the key lane was proposed, which contains characteristic parameters such as the bus states, the flow states, and the signal timing. Secondly, a control action method that can adjust phase sequence and phase green time at the same time was proposed under the constraints of maximum green and minimum green. Furthermore, a reward function, which can be uniformly converted into the number of standard cars, was established focusing on the indexes such as the busload and maximum waiting time. Finally, through building an experimental environment based on SUMO simulation, a real-time bus signal priority control method based on deep reinforcement learning was constructed. The results show that the algorithm can effectively reduce the waiting time of buses without affecting overall traffic efficiency. The findings can provide a theoretical basis for the signal control method considering bus priority and improve the operation efficiency of public transport. [ABSTRACT FROM AUTHOR]

Accurate forecasting of traffic flow is crucial for intelligent traffic control and guidance. It is very challenging to forecast the traffic flow due to the high non‐linearity, complexity and dynamicity of the data. Most existing forecasting methods focus on designing complicated graph neural network architectures to capture the spatio‐temporal features of traffic data with the help of predefined graphs. However, traffic data exhibit a strong spatial dependency, which means that there are often complex correlations between nodes in a road network topology graph. Moreover, the spatial correlations of the road network change over time. To capture the properties of the road network topology graph, a novel spatio‐temporal adaptive graph convolutional networks model (STAGCN) based on deep learning is proposed. Utilize adaptive graph generation block to capture the static and dynamic structures of the traffic road network respectively, and they are integrated to construct an adaptive road network topology graph. Then the spatio‐temporal features of the traffic data are captured using spatio‐temporal convolution blocks. Experiments were conducted on publicly available traffic datasets for freeway traffic in California, USA, and the results showed that the prediction accuracy of the STAGCN model outperformed multiple baseline methods. [ABSTRACT FROM AUTHOR]

Accurate and reliable traffic state identification is the prerequisite for developing intelligent traffic programs. With the improvement of intelligent traffic control measures, the traffic state of some highways has gradually stabilized. The current research on traffic state identification needs to fully meet the highly informative intelligent traffic system and traffic state subcategory analysis. To fill the gap above, we propose an improved support tensor machine (STM) method based on self-training and multiclassification for traffic state subcategory identification (ISTM) with ETC gantry data. This paper takes the excellent application of the support vector machine (SVM) in traffic state identification as the starting point of method design and extends to the STM. The ETC gantry data are represented as a third-order tensor model. This paper utilizes the similarity among tensor samples to construct the kernel function and recognize the traffic states. We simplify STM calculation with a one-against-one model and a self-training idea. An optimal fit of the characteristics is supplied by maximizing inter-subcategory tensor block distances and minimizing intra-subcategory tensor block distances throughout a joint utilization of the STM and multiscale training theories. The experiment in this paper uses ETC gantry data from the Jingtai highway in Shandong Province, and the findings reveal that the ISTM has optimum values of 0.2578 and 0.3254 for the SumD and 0.1718 and 0.1901 for the DBI as compared to K-mean clustering and the SVM. The ISTM trains the traffic state subcategory classifiers with high accuracy and strong generalization ability. [ABSTRACT FROM AUTHOR]

Actuated and pre-timed traffic signal controllers have been beneficial to the improvement of traffic flow in cities and dense urban environments around the world. While these methods have been effective in reducing traffic congestion, recent works have shown that incorporation of reinforcement learning (RL) or other artificial intelligence (AI) based optimization techniques may further improve the performance of traffic signal controllers. This work investigates a novel decentralized traffic signal control structure which encourages cooperative signal behavior via repeated negotiations between neighboring intelligent agents. This method capitalizes on emerging inter-infrastructure communications technologies to exercise 'system-level' control over a network of connected signalized intersections. The proposed method was tested in a simplified grid-network of 20 intersections. In this network, static arrivals of 1440 veh / l / h along east-west lanes and 360 veh / l / h along north-south lanes were supplied. In addition, a simulated shift to 720 veh / l / h along east-west lanes and 1080 veh / l / h along north-south lanes was analyzed to provide insights into the presented method's performance in response to any sudden shifts in traffic patterns. The findings indicated that, when compared to non-negotiating traffic signals, the presented method may improve the service rate of traffic networks under static conditions by 67 1 veh / h on average, reduce emissions by an average of 326 kg / h in addition to reducing travel time across a network of intersections. The performance characteristics were captured by a SUMO testbed, and computational efficiency was explored using a suite of simple testbeds developed in MATLAB. [ABSTRACT FROM AUTHOR]

Vehicle trajectory data play an important role in autonomous driving and intelligent traffic control. With the widespread deployment of roadside sensors, such as cameras and millimeter-wave radar, it is possible to obtain full-sample vehicle trajectories for a whole area. This paper proposes a general framework for reconstructing continuous vehicle trajectories using roadside visual sensing data. The framework includes three modules: single-region vehicle trajectory extraction, multi-camera cross-region vehicle trajectory splicing, and missing trajectory completion. Firstly, the vehicle trajectory is extracted from each video by YOLOv5 and DeepSORT multi-target tracking algorithms. The vehicle trajectories in different videos are then spliced by the vehicle re-identification algorithm fused with lane features. Finally, the bidirectional long-short-time memory model (LSTM) based on graph attention is applied to complete the missing trajectory to obtain the continuous vehicle trajectory. Measured data from Donghai Bridge in Shanghai are applied to verify the feasibility and effectiveness of the framework. The results indicate that the vehicle re-identification algorithm with the lane features outperforms the vehicle re-identification algorithm that only considers the visual feature by 1.5% in mAP (mean average precision). Additionally, the bidirectional LSTM based on graph attention performs better than the model that does not consider the interaction between vehicles. The experiment demonstrates that our framework can effectively reconstruct the continuous vehicle trajectories on the expressway. [ABSTRACT FROM AUTHOR]

The key to intelligent traffic control and guidance lies in accurate prediction of traffic flow. Since traffic flow data is nonlinear, complex, and dynamic, in order to overcome these issues, graph neural network techniques are employed to address these challenges. For this reason, we propose a deep-learning architecture called AMGC-AT and apply it to a real passenger flow dataset of the Hangzhou metro for evaluation. Based on a priori knowledge, we set up multi-view graphs to express the static feature similarity of each station in the metro network, such as geographic location and zone function, which are then input to the multi-graph neural network with the goal of extracting and aggregating features in order to realize the complex spatial dependence of each station's passenger flow. Furthermore, based on periodic features of historical traffic flows, we categorize the flow data into three time patterns. Specifically, we propose two different self-attention mechanisms to fuse high-order spatiotemporal features of traffic flow. The final step is to integrate the two modules and obtain the output results using a gated convolution and a fully connected neural network. The experimental results show that the proposed model has better performance than eight other baseline models at 10 min, 15 min and 30 min time intervals. [ABSTRACT FROM AUTHOR]

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GPS monitoring systems and the development of driverless vehicles are almost inseparable from camera images. The images taken by traffic cameras often contain certain sky areas and noise, the traditional dark channel prior (DCP) algorithm easily produces color distortion and halo effect, when processing the hazy traffic images with sky and high brightness areas. An optimized Retinex model and dark channel prior algorithm (ORDCP) is proposed in this paper. Firstly by adjusting the calculation method of dark channel image, the proportion of dark channel is improved; Then, the transmittance image is corrected and smoothed by guided filtering and mean filtering. Finally, the Retinex model is fused to save the details. ORDCP corrects the inaccurate calculation of scene transmittance value in DCP algorithm, and modifies some dehazing problems, such as the loss of details, halo effect, contrast and color distortion, etc. Using information entropy (IE) as the objective evaluation index, combined with the subjective evaluation, it is concluded that the algorithm proposed in this paper can effectively retain the detailed information of the image, and eliminate the halo effect. Meanwhile, it meets the visual characteristics of human eyes better, and has some practicality and applicability in traffic control and intelligent detection. [ABSTRACT FROM AUTHOR]

Summary: Miniaturization of Internet of Things (IoT) enabled devices has led to advancement in Intelligent Transportation System. Some of the challenges for efficient traffic management are connection of vehicles through an IP based infrastructure for transportation, reliable, and flexible traffic control management, maintaining quality of services for video streaming applications in Internet of Vehicles and many more. Today there is a need of an efficient transportation system with improved efficiency of traffic safety and lower traveling costs. An intelligent traffic control system using Ant Colony Optimization (ACO) algorithm has been proposed and analyzed in this paper. Proposed algorithm has been compared with existing state of art algorithms in terms of average waiting time and average traveling time. [ABSTRACT FROM AUTHOR]

With the development of Internet of Things infrastructures and intelligent traffic systems, the traffic congestion that results from the continuous complexity of urban road networks and traffic saturation has a new solution. In this research, we propose a traffic signal control scenario based on edge computing. We also propose a chemical reaction–cooperative particle swarm optimization (CRO-CPSO) algorithm so that flexible traffic control is sunk to the edge. To implement short-term real-time vehicle waiting time prediction as a collaborative judgment of CRO-CPSO, we suggest a traffic flow prediction system based on fuzzy logic. In addition, we introduce a co-factor (collaborative factor) set based on offline learning to take into account the experiential characteristics of intersections in urban road networks for the generation of strategies by the algorithm. Furthermore, the real case of Changsha County is simulated on the SUMO simulation platform. The issue of traffic flow saturation is improved by our method. Compared with other methods, our algorithm enhances the proportions of vehicles that reach their destinations on time by 13.03%, which maximizes the driving experience for drivers. Meanwhile, our algorithm reduces the driving times of vehicles by 25.34%, thus alleviating traffic jams. [ABSTRACT FROM AUTHOR]

Today's traffic control system is developing in a more intelligent direction. The progress of electronic technology has created a traffic control and management system using a variety of control methods. The development of computers creates a more intelligent traffic optimization model for optimizing network traffic. In the limited road space, traffic control research will focus on maximizing the use of existing traffic management equipment to improve vehicle circulation efficiency and avoid congestion. In this context, combined with the temporal and spatial characteristics of traffic congestion, this study investigates the storage capacity of existing road space and seeks the optimal control method for local regional road network congestion. For the technical problems of traffic diversion in the existing two-phase control method and the queuing order in the adjacent lanes in the four-phase control method, the optimal control method is proposed, which solves the technical problems of traffic control and the phenomenon of "queuing injustice" in the adjacent lanes in one direction. The safety degree and traffic efficiency of vehicle operation are improved. The traffic flow from the same entrance to the main intersection forms a signal phase while slowing down or blocking these traffic flows can effectively improve traffic safety and traffic efficiency. Combined with the development of traditional traffic control system, this study introduces the development status of Internet of things technology and puts forward an intelligent traffic control system based on Internet of things technology. Real-time vehicle traffic information is sent to the Lora gateway through Lora wireless network, which is responsible for data transmission and protocol conversion. [ABSTRACT FROM AUTHOR]

In recent years, the sharp increase in the number of residential motor vehicles and non-motorized vehicles has led to a huge challenge in the intelligent control of urban road traffic operation status. To address the problems of low accuracy of small target detection in complex traffic scenes and the difficulty of balancing the accuracy and detection speed of multiple targets, we propose a deep learning-based Semantic interpolation target detection network SI-SSD based on feature pyramid network FPN and feature graph fusion method, and transfer it to target detection in complex traffic scenes. Shallow feature maps have multi-edge information and deep feature maps have high semantic information. With this idea in mind, the high-resolution shallow feature layer Conv4_3 for the detection of small targets and the deep feature layer Conv7_2 with rich semantic information in SSD network are fused, so as to enhance the sensitivity of the network to the feature information of small targets and improve the network's ability to detect small targets in complex traffic scenarios. The experimental results show that the improved SI-SSD network model has good robustness in the detection of relatively small targets, which can ensure the detection speed and improve the accuracy of detection and recognition significantly. Tests of the data sets UA-DETRAC and VOC2007 show that in the detection of relatively small targets such as Motorbike and Pedestrian, the accuracy is 89.3% and 86.7%, respectively, and the mean average precesion (mAP) is 89.05%. Compared with other target detection networks based on deep learning, the average accuracy of all types of target detection is improved by 4%, and the detection speed is maintained at about 32 fps. [ABSTRACT FROM AUTHOR]

Intelligent traffic controller leads to manage traffic at intersection in order to minimize traffic congestion and has been intensively researched for a several decades. Multi-intersection cooperative traffic signal control (CTSC) is an efficient system that has received a great deal of attention and development in recent years. One problem with multi-intersection CTSC is that controller's actions are based only on the traffic state or on the decisions taken at previous time step (t-1) at adjacent intersection. To address this problem, in this work a Double Deep Q Network Cooperative Traffic Signal Controller (CTSC-DDQN) is proposed. The CTSC-DDQN algorithm is a reinforcement learning agent that, depending on current traffic conditions, changes the traffic phase distribution order for nintersection simultaneously. Experimental results under real scenarios show that the proposed approach outperforms other static approach which fixe the time length and the phase order despite the traffic situation and actuated controllers which change the traffic light properties based on the queue length in term of average Q-length and average waiting which eventually leads to mitigate traffic congestion. The results show that our could minimize the average waiting time by up to 79% and the average queue length by up to 80%. [ABSTRACT FROM AUTHOR]

This article proposes a novel ontology design for intelligent controlling of traffic signals, considering the investigated factors, crowded factors, road factors, visibility conditions, and emergency situations. Essentially, the proposed method uses video-based knowledge and key feature from a monocular video camera only, capturing footage from either a traffic signals perspective or the top of the road lane. The key factors and entities in the traffic scene are formed into an ontology, which has been evaluated using synthetic datasets to interpret challenging cases. Semantic features related to the key factors in the scene are obtained and fed to the ontology. The experimental results indicate that the proposed method is capable of controlling traffic signals more efficiently than the fixed intervals protocol. [ABSTRACT FROM AUTHOR]

The traffic in urban areas is primarily controlled by traffic lights, contributing to the excessive, if not properly installed, long waiting times for vehicles. The condition is compounded by the increasing number of road accidents involving pedestrians in cities across the world. Thus, this work presents an integrated tripartite module for an intelligent traffic light system. This system has enough ingredients for success that can solve the above challenges. The proposed system has three modules: the intelligent visual monitoring module, intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is a visual module capable of identifying the conditions of traffic in the streets. The intelligent traffic light control module configures many intersections in a city to improve the flow of vehicles. Finally, the intelligent recommendation module for emergency vehicles offers an optimal path for emergency vehicles. The evaluation of the proposed system has been carried out in Al-Sader city/Bagdad/Iraq. The intelligent recommendation module for the emergency vehicles module shows that the optimization rate average for the optimal path was in range 67.13% to 92%, where the intelligent traffic light control module shows that the optimization ratio was in range 86% to 91.8%. [ABSTRACT FROM AUTHOR]

The stable and fast service of a bus network is one of the important indicators of the service quality and management level of urban public transport. With the continuous expansion of cities, the bus network complexity has been increasing accordingly. The application of new technologies such as self-driving buses has made the bus network more complex and its vulnerability more obvious. Therefore, how to collect information on passenger flow, traffic flow, and transport distribution using intelligent means, and how to establish an effective intelligent bus scheduling control method have been important questions surrounding the improvement of the level of urban bus operation. To address this challenge, this paper proposes the design method of a bus controller based on data collection and the edge computing requirements of autonomous driving buses; and installs them widely on buses. In addition, an intelligent bus control scheduling method based on the simulated annealing genetic algorithm was developed according to the current scheduling requirements. The proposed method combines the strong local search ability of the simulated annealing algorithm, which prevents the search process from falling into a local optimum, and the strong search ability of the genetic algorithm in the overall search process, leading an intelligent bus control scheduling method based on the simulated annealing genetic algorithm. The proposed method was verified by experiments on the optimal scheduling of multi-destination public transport as an example, we verified the research method, and finally, simulated it using historical data. There is good model prediction of the experimental results. Therefore, the intelligent traffic control can be realized through efficient bus scheduling, thus improving the robustness of the bus network operation. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization and the development of the automobile industry, the contradiction between the traffic capacity of existing urban roads and the growing traffic demand has become increasingly acute. Traffic congestion is becoming increasingly prominent. The purpose of this article is to consider the characteristics of traffic risk factors and to study the method of establishing a flexible traffic system. It can relieve traffic congestion and provide a smooth and orderly traffic environment using intelligent transportation systems to control and direct traffic flow. Based on the large urban road network, this research uses the theory of coordinated control and learning mechanism, fuzzy control, dynamic reprogramming, and other theories to study phase sequence, balance peripheral load, and overall traffic flow. It also uses on-board sensors to optimize the collection and processing of network information, decompose traffic guidance work, select the optimal route, and autonomously guide the intelligent transportation system. Under the flexible demand scheme, the average load of the trunk road is reduced by a larger degree, which is 4% lower than that of the fixed demand scheme. At the same time, the average load of the branch has increased more, which is 4% higher than that of the fixed demand scheme. It can be seen that under the elastic demand scheme, the distribution of traffic flow in the road network is more balanced and the optimization effect of relieving traffic pressure on trunk roads and improving the utilization rate of branch roads is more significant. [ABSTRACT FROM AUTHOR]

Recently, the intelligent traffic control system and its uncertainty analysis are considered one of the hot spots for utilizing the available techniques. It became more essential when the automatic car, electric vehicle, and other smart cars have introduced the transportation. To control the traffic accident and smooth road services an intelligent traffic control system required. It will be also useful in decreasing the time, reaction time, and efficiency of traffic. However, the problem arises while characterization of true, false or uncertain regions of traffic flow and its future approximation. To deal with this issue some available mathematical technique for traffic flow using rough set, fuzzy rough set, and its extension with the neutrosophic set is discussed in this paper. Some of the papers related to graphical visualization of traffic flow is also discussed for further improvement. The rough set theory can be useful for dealing the uncertain, incomplete, and indeterminate data set. Hence, the hybridization of the neutrosophic set and rough can be considered one of the efficient tools for intelligent traffic control and its approximation via automatic red, green and yellow lights. This paper tried to provide an overview of each available technique to solve the traffic problem. It is hoped that the proposed study will be helpful for several researchers working on traffic flow, traffic accident diagnosis, and its hybridization as future research. [ABSTRACT FROM AUTHOR]

The Software Defined Networking (SDN) promises exciting new networking functionality. However, there is always remains a chance of programming errors that result in unreliable data communication. The centralized programming model helps decrease bugs' probability where a single controller manages the whole network. Yet, many real-time events occur at switches and end hosts, which often affect and add a delay in the communication process. One of those events includes unannounced destination host migration after installing flow rules during receiving of data packets. Such destination host movement results in the loss of packets because controller is not aware of this recent event. Therefore, we need an efficient approach to transmit packets without any packet loss despite destination host migration. This paper proposed a design to achieve the objective as mentioned above by defining a layer named Intelligent Transmission Control Layer (ITCL). It monitors all the end hosts' connections at their specific locations and performs necessary actions whenever the connection state changes for one or multiple hosts. The controller collects information of end nodes and state change through ITCL using A star search algorithm. After that, it updates flow tables accordingly to accommodate a location-change scenario with a route-change policy. ICTL is developed on prototype-based implementation using a popular POX controller platform. By comparing ITCL with the existing solution, we conclude that our proposed approach exhibits efficient performance in terms of Packet loss, Bandwidth usage, and Network Throughput. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Software-defined wireless sensor networks (SDWSN), where the data and control planes are decoupled, are more suited to handling big sensor data and effectively monitoring dynamic environments and events. To overcome the limitations of using static routing tables under high traffic intensity, such as network congestion, high packet loss rate, low throughput, etc., it is critical to design intelligent traffic routing control for the SDWSNs. In this paper we propose a deep graph reinforcement learning (DGRL) model-based intelligent traffic control scheme for SDWSNs, which combines graph convolution with deterministic policy gradient. The model fits well for the task of intelligent routing control for the SDWSN, as the process of data forwarding can be regarded as the sampling of continuous action space and the traffic data has strong graph features. The intelligent control policies are made by the SDWSN controller and implemented at the sensor nodes to optimize the data forwarding process. Simulation experiments performed on the Omnet++ platform show that, compared with the existing traffic routing algorithms for SDWSNs, the proposed intelligent routing control method can effectively reduce packet transmission delay, increase packet delivery ratio, and reduce the probability of network congestion. [ABSTRACT FROM AUTHOR]

Accurate vehicle speed prediction is of great significance to the urban traffic intelligent control system. However, in terms of traffic speed prediction, the modules that integrate temporal and spatial features in the existing traffic speed prediction methods are effective in short-term prediction, but the medium-term or long-term prediction errors are relatively large. In order to reduce the errors of existing methods in short-term prediction and predict the medium-term and long-term traffic speed, this paper proposes a traffic speed prediction method that combines attention and Spatial–temporal features, referred to as ASTCN. Specifically, unlike previous methods, ASTCN can use the temporal attention convolutional network (ATCN) to separately extract temporal features from the traffic speed features collected by each sensor, and use the spatial attention mechanism to extract spatial features and then perform spatial–temporal feature fusion. Experiments on three real-world datasets show that the proposed ASTCN model outperforms the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Urban traffic control has become a big issue to help traffic management in recent years. With data explosion, Intelligent Transportation System (ITS) is developing rapidly. ITS is an advanced data-based method for traffic control, which requires timely and effective information supply. This research aims at providing real-time and accurate traffic flow data by intelligent prediction method. Applying multiple road traffic flow data of the Caltrans Performance Measurement System (PeMS) and separating the time series, the mechanism of spatial-temporal differences was taken into consideration. Based on the basic Long Short-Term Memory (LSTM) model, an improved LSTM model with Dropout and Bi-structure (Bi-LSTM) for traffic flow prediction was presented. In the prediction process, we applied three models including the improved Bi-LSTM model, Gated Recurrent Unit (GRU) model and Linear Regression in the experiment, and made a comparison from aspects of model structure complexity, operating efficiency and prediction accuracy. To validate the portability of the prediction model, the features of traffic flow from different datasets were further analyzed. The experimental results show that the improved Bi-LSTM model performs best in traffic flow prediction with comprehensive rationality, which reaches an accuracy of about 92% when considering temporal differences. Particularly, the specific factors of traffic situations and locations which is more applicable to be predicted by the improved Bi-LSTM model are summarized considering spatial differences. This research proposes an advanced and accurate model to provide real-time and short-term traffic flow prediction data, which is of great help to intelligent traffic control. Considering the mechanism between model and road traffic properties, the results suggest that it is more applicable in urban commercial area. [ABSTRACT FROM AUTHOR]

In the past few decades, intelligent traffic controllers have been developed to responsively cope with the increasing traffic demands and congestions in urban traffic networks. Various studies to compare and evaluate the performance of traffic controllers have been conducted to investigate its effect on traffic performances such as its ability to reduce delay time, stops, throughputs and queues within a traffic network. In this paper, the authors aim to present another comparative study on heuristics versus meta-heuristics traffic control methods. To our knowledge, such comparison has not been conducted and could provide insights into a purely heuristic controller compared to meta-heuristics. The study aims to answer the research question “Can heuristics traffic control strategies outperformed meta-heuristics in terms of performance and computational costs?” For this purpose, a heuristics model-based control strategy (MCS) which was previously developed by the authors is compared to genetic algorithms (GA) and evolution strategy (ES) respectively on a nine intersections symmetric network. These control strategies were implemented via simulations on a traffic simulator called UTNSim for three different types of traffic scenarios. Performance indices such as average delays, vehicle throughputs and the computational time of these controllers were evaluated. The results revealed that the heuristic MCS outperformed GA and ES with superior performance in average delays whereas vehicle throughputs were in close agreement. The computation time of the MCS is also feasible for real-time application compared to GA and ES that has longer convergent time. [ABSTRACT FROM AUTHOR]

The rapidly increasing traffic congestion is affecting the population of the world in a variety of ways including personal (physical and mental health), family, social, environmental, accidents and passage of emergency and law and order vehicles. Out of the many varieties of approaches tested for solutions, the current research used roadside sensors to collect data and use them for simulations using PTV Vissim simulator with MS Excel algorithmic optimisation. The location of the test was which St Marks Road in the Merton region of the UK. This is indeed an inlet into a major road (A217). A subset of data from the Department of Transport (2019) [1] was chosen which had an AADT of less than 2,000. In 2018, it was an AADT of 1,086 for this location. Simulation results showed the superiority of adaptive green signal timing over fixed timing signals. The simulation results were applied to optimisation to specify the green light timing requirements. Accordingly, congestion can be prevented using adaptive green signal timings. The results were discussed within the scope of many approaches used by different researchers. [ABSTRACT FROM AUTHOR]

As the speed of high-speed trains continues to increase, the intelligent monitoring of high-speed trains has become a concern of people. This research mainly discusses the application of multimode intelligent control of multidata fusion filtering in high-speed train traffic signal and control. In multimodal intelligent control, BangBang, PI control, adaptive fuzzy PID control, and expert monitoring control under special circumstances can be used, respectively, according to the error and the rate of change of the error, which can achieve the best control effect under safe conditions. Take the allowable speed of ATP as the target speed of the control system, and combine the operation process, operation requirements, traction characteristics, braking characteristics of high-speed trains, and meet the two conditions for improving the operating efficiency of high-speed trains. According to the dynamic expected speed value of high-speed trains, dynamically adjust the switching threshold. This study uses a pulse signal generator to simulate the speed data of the vehicle speed sensor (all pulse data), and then read the speed (pulse) signal data through the pulse signal acquisition card, and display the simulated speed data under the Kingview software. The monitoring computer is used to collect train speed information, display speed information, manage speed information, and output speed information. Then, through OPC technology, the simulation speed data is transmitted to MATLAB software for multidata fusion filtering processing and multimodal control simulation. In the simulation process, the train adopts a multimodal intelligent control response scheme, with a total time of 2183.7 s, which is shortened by 214.5 s and improved by nearly 10%. The multimode intelligent control scheme of multidata fusion filtering proposed in this study can better meet the control of high-speed train traffic signals. [ABSTRACT FROM AUTHOR]

As the global economy develops rapidly, traffic congestion has become a major problem for first-tier cities in various countries. In order to address the problem of failed real-time control of the traffic flow data by the traditional traffic light control as well as malicious attack and other security problems faced by the intelligent traffic light (ITL) control system, a multi-agent distributed ITL control method was proposed based on the fog computing platform and the Q learning algorithm used for the reinforcement learning in this study, and the simulation comparison was conducted by using the simulation platform jointly constructed based on the VISSIM-Excel VBA-MATLAB software. Subsequently, on the basis of puzzle difficulty of the computational Diffie–Helleman (CDH) and Hash Collision, the applicable security control scheme of ITL under the fog computing was proposed. The results reveal that the proposed intelligent control system prolongs the time of green light properly when the number of vehicles increases, thereby reducing the delay time and retention rate of vehicles; the security control scheme of ITL based on the puzzle of CDH is less efficient when the vehicle density increases, while that based on the puzzle of Hash collision is very friendly to the fog equipment. In conclusion, the proposed control method of ITL based on the fog computing and Q learning algorithm can alleviate the traffic congestion effectively, so the proposed method has high security. [ABSTRACT FROM AUTHOR]

The high growth of vehicular travel in urban areas, in particular, requires a traffic control system that optimizes traffic flow efficiency. Traffic congestion can also occur by large de-lays in Red Light etc. The delay in lighting is difficult to code and does not rely on real traffic density. It follows that traffic controls are simulated and configured to better meet this rising demand. So, in order to avoid the traffic control problem, the Adaptive Intelligent Traffic Light control system (AITLCS) has been proposed based on OpenCV and Image processing technique. The system proposed is designed to ensure smooth and efficient traffic flow for daily life as well as emergency and public transportation safety. Based on the road density instead of the levels set the proposed system provides the timing for the traffic light signal so that a highly loaded side switched on over long periods compared with the other lanes. It can also be used at an intersection with traffic signs, which controls the traffic light signal at the intersection. If timers are smart to predict the exact time, the system is more efficient because it reduces the time spent on unintended green signal significantly. With the help of OpenCV software, this paper aims to have a signal management SMART solution that will be cost-effective at the end. The system consists of a camera facing a lane taking pictures of the route we want to travel and then the density of the pedestrian and vehicle is taken and compared with each image employing image processing. Such images are processed effectively to learn the density of traffic. [ABSTRACT FROM AUTHOR]

Smart transportation relies on data collection, transmission, processing, and release, involving various terminal devices, control systems, central platforms, and communication links, so its control process is more complicated. In order to improve the operation efficiency of the intelligent traffic control system, based on the open Internet of Things and machine learning, this paper builds an intelligent three-way intelligent traffic control system, sets various parameters, and builds a simulation model using cellular automata as a platform. Moreover, in order to study the performance of the model, the model constructed in this paper is compared with the model of the traditional road traffic control system. In addition, this paper analyzes the model constructed in this paper through the statistics of the highest vehicle flow on the road and the relationship between road occupancy and vehicle speed. The research results show that the model constructed in this paper has good performance and can be applied to intelligent traffic control. [ABSTRACT FROM AUTHOR]

Advanced information technologies, such as artificial intelligence (AI) and big data analytics, along with global navigation systems and the Internet of Things (IoT), are increasingly finding application in the transportation industry. With this sector, pavement construction holds a prominent position, and its technologies are swiftly transitioning toward digitalization, automation, intelligence, and informationization. This paper elaborates on the key principles of intelligent technologies utilized in paving and paving compaction, discussing high-precision intelligent control technology, intelligent driving system control technology, compaction detection technology, intelligent compaction positioning technology, field coordination technology, traffic control technology, intelligent monitoring technology, and intelligent management systems. The paper highlights the research progress and development trends of each technology, while also addressing their respective shortcomings and current research limitations. Ultimately, future development directions for each intelligent technology are provided, including insights to advance the field of intelligent paving and paving compaction. [Display omitted] • Literatures are reviewed on intelligent paving and compaction technologies for asphalt pavement. • Current status of the existing technologies is elaborated and summarized. • Gaps and prospects for the existing technologies with the practical requirements are identified. • It provides a reference for promoting and applying intelligent paving and compaction technologies. [ABSTRACT FROM AUTHOR]

A systematic review was undertaken to examine the solutions available for traffic congestion and associated problems in smart cities. Google Scholar and Google were used as search engines, leading to the final selection of 35 eligible papers for inclusion in this review, after a serious of screening based on definite criteria. Intelligent transport systems were found to be the most suitable solution to traffic congestion and associated problems in smart cities. Certain models and frameworks of smart cities include smart mobility and transport management systems. These can be approximated to intelligent transport systems. True intelligent transport systems are infrastructure-based or intelligent vehicle based or more preferably, a combination of both. The Internet of Things and cloud computing should be built into the system as they enable the operation of smart transport networks. Some methods of designing traffic control systems combining both Eulerian and Lagrangian approaches have been discussed for the possibility of using any of them to design a new automatic traffic monitoring and control system for smart cities. The practical implication of this research is that it can improve quality of life of people by minimizing traffic congestion. Limitations of this paper include this being a systematic review, availability of very few papers and not considering adaptive intelligent traffic control systems. Explanations for these limitations have been provided. [ABSTRACT FROM AUTHOR]

In this paper, we state a combining programming approach to optimize traffic signal control problem. The objective of the model is to minimize the total queue length with weight factors at the end of each phase. Then, modified Twin Gaussian Process (MTGP) is employed to predict the arrival rates for the traffic signal control problem. For achieving automatic control of the traffic signal, an intelligent control method of the traffic signal is proposed in view of the combining method, that is to say, the combining method of MTGP and LP, called MTGPLP, is embraced in the intelligent control system. Furthermore, some numerical experiments are proposed to test the validity of the model and the MTGPLP approach. In particular, the results of numerical experiments show that the model is effective with different arrival rates, departure rates, and weight factors and the combining method is successful. [ABSTRACT FROM AUTHOR]

The intelligent traffic dispatch delay control system is an application system that combines system integration and command dispatch, and system integration serves command dispatch. This system has relatively independent system functions and various research results obtained from multi-data source analysis. Therefore, it is advisable to adopt a technical route of functional modularization and multi-layered architecture. Each functional module relatively completes the required functions independently, and provides a hierarchical service relationship between the modules. Maximizing the efficiency of road use is an important part of urban road traffic control. Urban road traffic control is mainly the control of traffic signals. Simulation results show that this system can effectively improve the efficiency of traffic dispatching, and at the same time has a real-time feedback function. The shortest path can be calculated through vehicle monitoring and combined with the GIS module. The vehicle position information and the shortest path between each vehicle and the accident point are displayed in the map panel, and the operator selects a vehicle to be dispatched according to a vehicle selected on the map and associated with a row in the vehicle information panel table. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Network (VANET) simulators have been gaining widespread attention in recent years and are crucial for conducting vehicular research. The fact that VANET simulators can simulate near real life scenarios to evaluate driver-safety applications without endangering human lives vastly contributes to their popularity. In this paper, we present the VEhicular NeTwork Open Simulator (VENTOS), a closed-loop VANET simulator that combines the capabilities of both communication network and vehicular traffic simulators. More specifically, VENTOS comprises of Simulation of Urban Mobility (SUMO) and Objective Modular Network Testbed in C++ (OMNET++). As an alternative to current proprietary Vehicular Ad-hoc Network (VANET) simulators, VENTOS is a free, open-source simulator that is designed for vehicular traffic flow analysis and can incorporate new control logic, such as intelligent traffic controller, collaborative driving, dynamic routing, and self-driving capability. VENTOS also allows for V2X (Vehicle-to-Vehicle or Vehicle-to-Infrastructure) communication through dedicated short-range communication (DSRC) integration. We discuss about the VENTOS architecture as well as various example applications that use VENTOS. [ABSTRACT FROM AUTHOR]

For intersection traffic congestion phenomenon, combined fog computing and reinforcement learning theory, this paper proposed a traffic light control model FRTL. The model performed intelligent coordinated control of traffic lights based on real-time traffic flow information. The fog node uploaded the collected real-time traffic flow information to the fog server, and the fog server realized information sharing on the fog platform, and the fog platform combined the processed shared data and Q learning to formulate a traffic light control algorithm. The algorithm used the detected real-time traffic data to calculate a suitable traffic light timing scheme, which was finally applied to the traffic light. The simulation results show that compared with the traditional time-phase control method and the main road control method (ATL), the FRTL control method improves the throughput of intersections, reduces the average waiting time of vehicles, and achieves the goal of properly regulating traffic lights and alleviating traffic congestion. [ABSTRACT FROM AUTHOR]

• Representation of moving objects in the form of temporal clusters. • A faster inference scheme for temporal clustering. • Occlusion handling incorporated in the inference scheme. • Traffic phase duration prediction considering spatial occupancy of vehicles on road. • Intelligent traffic signaling that optimize throughput and average waiting time. Computer vision-guided traffic management is an emerging area of research. Intelligent traffic signal control using computer vision is a less explored area of research. In this paper, we propose a new approach of traffic flow-based intelligent signal timing by temporally clustering optical flow features of moving vehicles using Temporal Unknown Incremental Clustering (TUIC) model. First, we propose a new inference scheme that works approximately 5-times faster as compared to the one originally proposed in TUIC in a dense traffic intersection. The new inference scheme can trace clusters representing moving objects that may be occluded while being tracked. Cluster counts of approach roads have been used for signal timing for traffic intersections. It is done by detecting cluster motion inside the regions-of-interest (ROI) marked at the entry and exit locations of intersection approaches. Departure rates are learned using Gaussian regression to parameterize traffic variations. Using the learned parameters as a function of cluster count, an adaptive signal timing algorithm, namely Throughput and Average Waiting Time Optimization (TAWTO) has been proposed. Experimental results reveal that the proposed method can achieve better average waiting time and throughput as compared to the state-of-the-art signal timing algorithms. We intend to publish two datasets as part of this work for enabling the research community to explore computer vision aided solutions for typical problems such as intelligent traffic controlling, violation detection in chaotic road intersections, etc. [ABSTRACT FROM AUTHOR]

Wiley and Hindawi regrets that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity. We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. This article has been retracted by Hindawi following an investigation undertaken by the publisher [[1]]. [Extracted from the article]

Vehicular Ad-hoc Network (VANET) simulators have been gaining widespread attention in recent years and are crucial for conducting vehicular research. The fact that VANET simulators can simulate near real life scenarios to evaluate driver-safety applications without endangering human lives vastly contributes to their popularity. In this paper, we present the VEhicular NeTwork Open Simulator (VENTOS), a closed-loop VANET simulator that combines the capabilities of both communication network and vehicular traffic simulators. More specifically, VENTOS comprises of Simulation of Urban Mobility (SUMO) and Objective Modular Network Testbed in C++ (OMNET++). As an alternative to current proprietary Vehicular Ad-hoc Network (VANET) simulators, VENTOS is a free, open-source simulator that is designed for vehicular traffic flow analysis and can incorporate new control logic, such as intelligent traffic controller, collaborative driving, dynamic routing, and self-driving capability. VENTOS also allows for V2X (Vehicle-to-Vehicle or Vehicle-to-Infrastructure) communication through dedicated short-range communication (DSRC) integration. We discuss about the VENTOS architecture as well as various example applications that use VENTOS. [ABSTRACT FROM AUTHOR]

In an advanced metering infrastructure (AMI), data acquisition points (DAPs) are responsible for collecting traffic from several smart meters and automated devices and transmitting them to the utility control center. Although the problem of optimized data collector placement has already been addressed for wireless broadband and sensor networks, the DAP placement is quite a new research area for AMIs. In this paper, we investigate the minimum required number of DAPs and their optimized locations on top of the existing utility poles in a distribution grid, such that the smart grid quality of service requirements can best be provided. In order to solve the problem for large-scale AMIs, we devise a novel heuristic algorithm using a greedy approach for identifying potential pole locations for the DAP placement and the Dijkstra’s shortest path algorithm for constructing reliable routes. We employ the characteristics of medium access schemes from the IEEE 802.15.4g smart utility network (SUN) standard and consider mission-critical and non-critical smart grid traffic. The performance and the time complexity of our algorithm are compared with those obtained by the IBM CPLEX software for small scenarios. Finally, we apply our devised DAP placement algorithm to examples of realistic smart grid AMI topologies. [ABSTRACT FROM AUTHOR]

Group-based control is an advanced traffic signal strategy capable of dynamically generating phase sequences at intersections. Combined with the phasing scheme, vehicle actuated timing is often adopted to respond to the detected traffic. However, the parameters of a signal controller are often predetermined in practice, and the control performance may suffer from deterioration when dealing with highly fluctuating traffic demand. This study proposes a group-based signal control approach capable of making decisions based on its understanding of traffic conditions at the intersection level. In particular, the control problem is formulated using a framework of stochastic optimal control for multi-agent system in which each signal group is modeled as an intelligent agent. The agents learn how to react to traffic environment and make optimal timing decisions according to the perceived system states. Reinforcement learning, enhanced by multiple-step backups, is employed as the kernel of the intelligent control algorithm, where each agent updates its knowledge on-line based on a sequence of states during the process. In addition, the proposed system is designated to be compatible with the prevailing signal system. A case study was carried out in a simulation environment to compare the proposed control approach with a benchmark controller used in practice, group-based vehicle actuated (GBVA) controller, whose parameters were off-line optimized using a genetic algorithm. Simulation results show that the proposed adaptive group-based control system outperforms the optimized GBVA control system mainly because of its real-time adaptive learning capacity in response to the changes in traffic demand. [ABSTRACT FROM AUTHOR]

• The state-of-the-art technologies and research status of different ALRM are reviewed. • The development history and status of FRMs are summarized. • PPRMs are the leading contender in all ALEMs due to an excellent afterglow intensity and duration. • The application and progresses of ELRMs in intelligent traffic control are introduced. Road marking is a core part of traffic safety facilities that plays an irreplaceable role in traffic control. With the increasing requirement for road marking, active luminous road markings (ALRMs) have been proposed and have progressed over the years owing to their compelling features of autoluminescence and good recognition. However, there are still some limitations to be further studied, including the luminescence performance, long-term durability, and economic cost. In this study, a comprehensive analysis was conducted to understand the state-of-the-art progress and required future work on ALRMs. Different types of ALRMs were classified, including fluorescent road markings (FRMs), persistent phosphorescent road markings (PPRMs), and electric luminous road markings (ELRMs). Moreover, this paper elaborates on the technical principle, service performance, applications, and existing problems associated with various ALRMs technologies in detail. Furthermore, the challenges and future prospects involved in the development of ALRMs are emphasized to provide avenues and opportunities for future research. It is anticipated that PPRMs and ELRMs, as next-generation road markings, will exhibit the unique advantages of traffic safety improvement, smart traffic control, and an intelligent transportation system. [ABSTRACT FROM AUTHOR]

• Generation of upper and lower bound series of traffic speed. • An improved grey autoregressive model is constructed. • Interval prediction of short-term traffic speed. Short-term traffic speed prediction, including level and interval prediction, is a key component of proactive traffic control in the intelligent transportation systems (ITS). In particular, predicting intervals may provide traffic managers with more useful information for making reasonable decisions than predicting traffic levels. In this study, a combined model (FIG-GARM) of fuzzy information granulation (FIG) and grey autoregressive model (GARM) is proposed for the prediction interval (PI) of traffic speed. In order to investigate the performance of the FIG-GARM model, using real-world traffic speed data collected from an urban freeway in Edmonton, Canada, and the proposed FIG-GARM model is compared with the interval-grey model first order single variable (GM (1,1)), FIG-GM (1,1), and interval-GARM for PI of traffic speed. The results show that the FIG-GARM model can generate workable PI of the traffic speed, proving the validity of the proposed model. In addition, the PI of traffic speed obtained by FIG-GARM model has higher prediction interval coverage probability (PICP), narrower width interval (WI), and higher index P, which can provide decision support for the robust and accurate prediction of intelligent transportation systems. [ABSTRACT FROM AUTHOR]

Freeway traffic state estimation is useful for intelligent traffic guidance, control, and management. The freeway traffic state is featured with rapid and dramatic fluctuations, which presents a strong nonlinear feature. In theory, a particle filter has good performance in solving nonlinear problems. This paper proposes a particle filter based approach to estimate freeway traffic state. The freeway link between the west of Peace Bridge and the west of San Yuan Bridge of the third ring in Beijing is used as the experimental object. According to the traffic characteristics and measurement mode of the link, the second-order validated macroscopic traffic flow model is adopted to set up the link model. The implementation steps of the particle filter for freeway traffic state estimation are described in detail. The estimation error analysis for the experiments proves that the proposed approach has an encouraging estimation performance. [ABSTRACT FROM AUTHOR]