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One of the focal points in the field of intelligent transportation is the intelligent control of traffic signals (TS), aimed at enhancing the efficiency of urban road networks through specific algorithms. Deep Reinforcement Learning (DRL) algorithms have become mainstream, yet they suffer from inefficient training sample selection, leading to slow convergence. Additionally, enhancing model robustness is crucial for adapting to diverse traffic conditions. Hence, this paper proposes an enhanced method for traffic signal control (TSC) based on DRL. This approach utilizes dueling network and double q-learning to alleviate the overestimation issue of DRL. Additionally, it introduces a priority sampling mechanism to enhance the utilization efficiency of samples in memory. Moreover, noise parameters are integrated into the neural network model during training to bolster its robustness. By representing high-dimensional real-time traffic information as matrices, and employing a phase-cycled action space to guide the decision-making of intelligent agents. Additionally, utilizing a reward function that closely mirrors real-world scenarios to guide model training. Experimental results demonstrate faster convergence and optimal performance in metrics such as queue length and waiting time. Testing experiments further validate the method's robustness across different traffic flow scenarios. [ABSTRACT FROM AUTHOR]

Since its outbreak in December 2019, COVID-19 has spread rapidly across the world. To slow down the spread of the pandemic, various countries have implemented a series of policies and measures. The transportation system is not only an important carrier for COVID-19, but also a vital means for the prevention and control of the spread of the pandemic. Therefore, most anti-pandemic measures are based on travel restrictions, thereby slowing down the spread of the pandemic. As a result, because of the impact of the pandemic and corresponding control measures, the transportation system has undergone tremendous changes. By analyzing the evolution of the transportation system in response to the influence of COVID-19, it is possible to better understand socioeconomic changes and the changes in residents' daily life. Based on rich license plate recognition data, the characteristics of urban motorized travel under the influence of COVID-19 has been analyzed. According to the processes associated with the control of the pandemic and the resumption of work and production, the analysis period is divided into four stages. The changes in indicators of macroscopic traffic status are analyzed for each stage. The three types of typical motor vehicle groups (i.e., non-localized operating vehicles, taxis, and localized operating vehicles) are characterized by the traffic flow they contribute, the number of vehicles in transit, the average travel intensity, the average daily travel time of a vehicle, the average daily travel distance of a vehicle, and the spatiotemporal distributions of origins and destinations of trips. These data clarify the spatiotemporal evolution characteristics of peoples' travel behavior at different stages of the pandemic. The results of data analysis show that COVID-19 has deeply changed the motorized travel behavior of urban residents. In the initial stage of resumption of work and production, the willingness to engage in motorized travel had decreased significantly compared with that in the first stage. This willingness gradually resumed until the third and fourth stages, but still did not fully reach the level before the onset of the pandemic. Specifically, the traffic status during morning and evening peaks has basically recovered, and has even increased beyond the level before the pandemic; however, a certain gap was still found between off-peak hours. There were also significant differences in the extent to which different types of vehicles were affected by the pandemic. Among these, taxis were impacted the most by the pandemic. In the fourth stage (at the end of April), the average daily travel time of a vehicle and the average daily travel distance of a vehicle still decreased by 29.25% and 22.63% compared with the first stage, respectively. The operating time of many taxis was shortened from 22:00 PM to 19:00 PM. The spatiotemporal characteristics of vehicles show that the reduction of flexible travel demand (e.g., shopping, catering, and entertainment) is key to the reduction of the travel demand of the road network. This research provides data support for the implementation of traffic control measures under future grave public health events and enables the formulation of urban traffic policies in the post COVID-19 era. [ABSTRACT FROM AUTHOR]

In this study, a traffic queue model is employed to simulate traffic parameters for optimization purposes. The primary objective is to enhance the average queue length, cumulative delay, and cumulative reward for varying numbers of vehicles generated using an innovative approach. The proposed methodology integrates deep Reinforcement Q-Learning techniques implemented through a realistic simulator, Simulation of Urban MObility, based on OpenStreetMap. This simulator is used to balance the flow of traffic by monitoring traffic conditions and adjusting signals (Red/Yellow/Green) accordingly. The research extensively evaluates simulation outcomes across different scenarios involving 50, 100, 500, and 1000 vehicles, with episodes 10, 50 and 100. The results provide valuable insights into training times, simulation durations, and total rewards obtained, indicating improvements in mitigating traffic congestion. The introduction of a traffic control strategy enhances model effectiveness through the utilization of four-layer deep neural network architectures with 400 neurons in hidden layers. However, the study also highlights persistent challenges related to real-time implementation and complexities in state-action-agent relationships. The progress highlights improvements in reducing congestion and enhancing efficiency at junctions by dynamically adjusting signal phases while leveraging historical data. The results highlight the potential of dynamic traffic management strategies in effectively addressing urban traffic challenges. [ABSTRACT FROM AUTHOR]

Traffic lights are crucial for urban traffic management, as they significantly impact congestion reduction and travel safety. Traditional methods relying on hand-crafted rules and operator experience are limited in their ability to adapt to changing traffic environments. To address this challenge, we have been exploring intelligent traffic light control using deep reinforcement learning techniques. However, current approaches often suffer from inadequate training data and unstable training processes, leading to suboptimal performance and real-world consequences. In this study, we propose RELight, a novel random ensemble reinforcement learning-based traffic light control framework. RELight effectively utilizes collected empirical data, ensuring a stable and efficient training process. To evaluate the performance of our proposed framework, we conducted a comprehensive set of experiments on a variety of datasets, including four synthetic datasets and a real traffic dataset collected from surveillance cameras at an intersection in Hangzhou, China. The results show that RELight outperforms existing approaches, demonstrating its superiority and potential for practical traffic light control applications. [ABSTRACT FROM AUTHOR]

Urban traffic congestion is of utmost importance for modern societies due to population and economic growth. Thus, it contributes to environmental problems like increasing greenhouse gas emissions and noise pollution. Improved traffic flow in urban networks relies heavily on traffic signal control. Hence, optimizing cycle timing at many intersections is paramount to reducing congestion and increasing sustainability. This paper introduces an alternative to conventional traffic signal control, EcoLight+, which incorporates future noise predictions with the deep dueling Q-network reinforcement Learning algorithm to reduce noise levels, CO 2 emissions, and fuel consumption. An innovative data fusion approach is also proposed to improve our LSTM-based noise prediction model by integrating heterogeneous data from different sources. Our proposed solution allows the system to achieve higher efficiency than its competitors based on real-world data from Tallinn, Estonia. [ABSTRACT FROM AUTHOR]

In this paper, first we introduce the notions of size, order, and degree of vertices and edges in a soft graph, and we give some examples for a better understanding of these concepts. Then, we define the fundamental concepts of the adjacency matrix of soft graphs, complement of soft graphs, and regularity based on the degree of vertices of soft graphs, and we investigate some related results. Moreover, we introduce the concept of soft multiset and soft multigraph and define the notions of union, intersection, complement, and sum, and then we investigate the relations among them. Finally, we give an application of soft graphs in controlling urban traffic flows. [ABSTRACT FROM AUTHOR]

Cooperation between intersections in large-scale road networks is critical in traffic congestion. Currently, most traffic signals cooperate via pre-defined timing phases, which is extremely inefficient in real-time traffic scenarios. Most existing studies on multi-agent reinforcement learning (MARL) traffic signal control have focused on designing efficient communication methods, but have ignored the importance of how agents interact in cooperative communication. To achieve more efficient cooperation among traffic signals and alleviate urban traffic congestion, this study constructs a Graph Cooperation Q-learning Network Traffic Signal Control (GCQN-TSC) model, which is a graph cooperation network with an embedded self-attention mechanism that enables agents to adjust their attention in real time according to the dynamic traffic flow information, perceive the traffic environment quickly and effectively in a larger range, and help agents achieve more effective collaboration. Moreover, the Deep Graph Q-learning (DGQ) algorithm is proposed in this model to optimize the traffic signal control strategy according to the spatio-temporal characteristics of different traffic scenes and provide the optimal signal phase for each intersection. This study also integrates the ecological traffic concept into MARL traffic signal control, which aims to reduce traffic exhaust emissions. Finally, the proposed GCQN-TSC is experimentally validated both in a synthetic traffic grid and a real-world traffic network using the SUMO simulator. The experimental results show that GCQN-TSC outperforms other traffic signal control methods in almost all performance metrics, including average queue length and waiting time, as it can aggregate information acquired from collaborative agents and make network-level signal optimization decisions. [ABSTRACT FROM AUTHOR]

Traffic volume data at fixed detectors are of great importance to track the time-varying states of urban traffic, and the volume of each turning movement (i.e., continuing straight, turning left, turning right) in an intersection plays a significant role in traffic control. It is a difficult issue to determine the complete road network volume due to the limited coverage of detector deployment. Existing traffic volume imputation methods seem to be hardly tractable to address this persistent missing data problem of fixed detectors because they mainly function by utilizing spatiotemporal patterns of historical data to address the problem of intermittent missing traffic data collected from probe vehicles. Considering the unattainable temporal information of the target movement, this paper aims to develop an end-to-end graph neural network-based imputation approach to impute traffic volume data with persistent missing patterns. We regard the road network as a directed graph with each turning movement as a node and employ a meta-learning-based graph attention mechanism with graph embedding in multiple spatial granularities to fully extract the relationship between the target node and its neighbours. In addition, a multitask learning mechanism is designed to improve the accuracy of the imputation results by setting auxiliary tasks according to the volume conservation constraints. The proposed approach has been validated with extensive simulation experiments and applied to field tests for Shenzhen and Shanghai, China, with considerable accuracy and reliability, satisfying the demand for traffic control. To the best of our knowledge, this is the first time that traffic volume has been imputed for persistent missing patterns with a graph neural network-based method. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN), a novel network paradigm, separates the control plane and data plane into different network equipment to realize the flexible control of network traffic. Its excellent programmability and global view present many new opportunities. DDoS detection under the SDN context is an important and challenging research field. Some previous works attempted to collect and analyze statistics related to flows, usually recorded in switches, to address DDoS threats. In contrast, other works applied machine learning-based solutions to identify DDoS and achieved promising results. Generally, most previous works need to periodically request flow rules or packets to obtain flow statistics or features to detect stealthy exceptions. Nevertheless, the request for flow rules is very time-consuming and CPU-consuming; moreover may congest the communication channel between the controller and the switches. Therefore, we present FORT, a lightweight DDoS detection scheme, which spreads the rule-based detection algorithm at edge switches and determines whether to start it by periodically retrieving the ports state. A time-series algorithm, ARIMA, is utilized to determine the port statistics adaptively, and an SVM algorithm is applied to detect whether a DDoS attack does occur. Representative experiments demonstrate that FORT can significantly reduce the controller load and provide a reliable detection accuracy. Referring to the false alarm rate of 1.24% in the comparison scheme, the false alarm rate of this scheme is only 0.039%, which significantly reduces the probability of false alarm. Besides, by introducing the alarm mechanism, this scheme can reduce the load of the southbound channel by more than 60% in the normal state. [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]

Traffic flow forecasting is of great significance to urban traffic control and public safety applications. The key challenge of traffic flow forecasting is how to capture the complex correlation of different time levels and learn time dependence. Some external information is closely related to traffic flow, such as accidental traffic accidents, weather, and Point of Interests (PoI) information. This paper proposes a deep learning-based model, called AttDeepSTN+, which is used to predict the inflow and outflow of each area of the entire city. Specifically, AttDeepSTN+ uses the structure of interactive attention and convolution to model the temporal closeness, trend, and periodicity of crowd flow, in the interactive attention layer, learn the importance of closeness to periodicity and trend respectively to model the long-term dependence of time, and then use feature fusion to capture complex correlations at different levels, thereby reducing model prediction accuracy. In addition, PoI information are combined with time factors to express the influence of location attributes on crowd flow, to learn prior knowledge of crowd flow. Finally, a new fusion mechanism is used to fuse the attention layer modules and PoI information and other information together into the module to capture the complex correlation between different levels of features, to predict the final crowd flow in each area, and further improve the prediction accuracy of the model. The New York City crowd flow experiment shows that the model is better than the current state-of-the-art baseline. [ABSTRACT FROM AUTHOR]

Traffic congestion is becoming a serious problem with the large number of vehicle on the roads. In the traditional traffic control system, the timing of the green light is adjusted regardless of the average traffic rate at the junction. Many strategies have been introduced to solve and improve vehicle management. However, in order to handle road traffic issues, an intelligent traffic management solution is required. This article represents a self adaptive real-time traffic light control algorithm based on the traffic flow. We present a machine learning approach coupled with image processing to manage the traffic clearance at the signal junction. The proposed system utilizes single image processing via neural network and You Only Look Once (YOLOv3) framework to establish traffic clearance at the signal. We employed YOLO architectures because it is accurate in terms of mean average precision (mAP), interaction over union (IOU) values and fast in object detection tasks as well. It runs significantly faster than other detection methods with comparable performance. The average processing time of single image was estimated to be 1.3 s. Further based on the input from YOLO we estimated the 'on' time period green light for effective traffic clearance. Several real time parameters like number of vehicles (two wheelers, four wheelers), road width and junction crossing time are considered to estimate the 'on'time of green light. Moreover, we used the real traffic images to test the performance and trained the system with different dataset. Our experiments investigation reveals that the predicted vehicle counts were well matched with the actual vehicle count and proposed method apprehended an average accuracy of 81.1%. The reported strategy is self adaptive, highly accurate, fast and has the potential to be implemented in the traffic clearance at the junctions. [ABSTRACT FROM AUTHOR]

A vehicular Ad hoc NETwork -VANET has been emerged as a novel application for revolutionizing the driving experience and optimizing the traffic flow control networks. The development of high efficiency and optimized routing protocols for VANET remains a challenging task because of characteristics like rapid topology changes, large scale sizes and frequent link disconnections. Hence investigation of security and privacy problems is required to be emphasized. This paper presents a robust and novel method for improving the security in VANET environment and to evaluate the performance of the proposed ODMRP (On Demand Multi Cast Routing Protocol). Hence the study utilized RSA (Rivest-Shamir-Adleman) based key generation integrated with the asymmetric key generation with AES (Advanced Encryption Standard) algorithm.This proposed trust based integrated algorithm for key generation introduces a novel roadside unit message to the base station that enables Road side units- RSU responsible for the verification of the authentication of the encrypted messages received from the vehicles as well as for the notification of the results to the vehicles. The performance analysis represented that the proposed ODMRP with the use of RSA and AES algorithm possess a better adaptation capacity to VANET network in accordance to packet delivery ratio and throughput. The results obtained from the study have been compared with the existing system and proved to be more efficient. [ABSTRACT FROM AUTHOR]

Though effective in theoretical simulation, the established traffic control models and optimization algorithms will result in model mismatch or even control strategy failure in actual application. However, they are commonly adopted in traffic signal control research, resulting in the unavailability of many exceptional control algorithms in practice. Simulation should function as a bridge between theoretical research and actual application, allowing the gap between the two to be communicated and made up for. However, an effective connection between the two has yet to be established to enable simulation methods in existing traffic control research. To this end, we designed and developed a simulation platform for "Online Application—HILS (Hardware-in-the-Loop Simulation)—Practice" integration over traffic signal control. In this paper, the architecture and characteristics of the integrated simulation platform were described. Besides, the function of each module of the platform was detailed, followed by listing simulation examples for six complex scenarios, with the active control scenario being selected for simulation comparison analysis. The findings demonstrated extensive road network simulation with the integrated simulation platform, multidimensional control variables, control strategies with support, as well as stable and reliable operation. It can be used to verify several sorts of traffic control simulation with variable dimensions. [ABSTRACT FROM AUTHOR]

Noise and air pollution are among the most important problems and challenges in urban environments. Noise can cause several problems in functioning within urban settlements and may result in a variety of health problems. Continuous monitoring, prevention, and active work on solving these problems are in the focus of research communities, local and national agencies and organizations, and many international organizations. Traffic noise on city roads has been identified as one of the major problems in urban settlements. Fortunately, it can be monitored and controlled in a way that can reduce its level and negative effects. One of the most effective and cheapest ways to monitor and control traffic noise is to use wireless sensor networks, IoT technology, and cloud-based architectures. This article presents an approach in designing urban traffic noise monitoring system based on LoRaWAN and microservices oriented architecture supported with open-source hardware and software solutions. Design of the system includes planning LoRaWAN network and development of the IoT system layered architecture. Methods for traffic routing and rerouting in urban areas, based on experiments that uses Dijkstra and Floyd–Warshall algorithms are presented. The system implementation and evaluation of the proposed routing modifications are presented in the case of the city of Zrenjanin in Serbia. Discussion of extensions and improvements of the presented system, as well as future research directions, are also presented. [ABSTRACT FROM AUTHOR]

Traffic flow forecasting or prediction plays an important role in the traffic control and management of a city. Existing works mostly train a model using the traffic flow data of a city and then test the trained model using the data of the same city. It may not be truly intelligent as there are many cities around us and there should be some shared knowledge among different cities. The data of a city and its knowledge can be used to help improve the traffic flow forecasting of other cities. To address this motivation, we study building a universal deep learning model for multi-city traffic flow forecasting. In this paper, we exploit spatial-temporal correlations among different cities with multi-task learning to approach the traffic flow forecasting tasks of multiple cities. As a result, we propose a M ulti-city T raffic flow forecasting N etwork (MTN) via multi-task learning to extract the spatial dependency and temporal regularity among multiple cities later used to improve the performance of each individual city traffic flow forecasting collaboratively. In brief, the proposed model is a quartet of methods: (1) It integrates three temporal intervals and formulates a multi-interval component for each city to extract temporal features of each city; (2) A spatial-temporal attention layer with 3D Convolutional kernels is plugged into the neural networks to learn spatial-temporal relationship; (3) As traffic peak distributions of different cities are often similar, it proposes to use a peak zoom network to learn the peak effect of multiple cities and enhance the prediction performance on important time steps in different cities; (4) It uses a fusion layer to merge the outputs from distinct temporal intervals for the final forecasting results. Experimental results using real-world datasets from DIDI show the superior performance of the proposed model. [ABSTRACT FROM AUTHOR]

Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT-based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80,286 microprocessor for a smart city. The proposed system consists of '5' phases, namely IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data are collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80,286 microprocessor. An efficient OWENN algorithm for traffic prediction and traffic signal control using a Intel 80,286 microprocessor for a smart city. After extracting the features, the classification is performed in this step. Hereabout, the classification is done by using the optimized weight Elman neural network (OWENN) algorithm that classifies which places have more traffic. OWENN attains 98.23% accuracy than existing model also its achieved 96.69% F-score than existing model. The experimental results show that the proposed system outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Vehicle traffic in large cities is increasing every day. Despite the significant development of telecommunications, communication between such vehicles must still evolve to make mobility smarter. Many studies have been carried out to enable a network between vehicles and computational infrastructures, called Vehicular Ad-Hoc Networks (VANETs). Within the context of VANETs, traffic lights can play an important role in exchanging information with vehicles to reduce congestion. Intelligent traffic lights aim to manage intersections in urban traffic and avoid idle roads. Several research topics have already been explored related to smart traffic control such as network latency, people safety, driver assistance, among others. However, given the variety of problems and solutions, it is complex to identify research trends and gaps related to smart traffic field without a systematic review. This paper presents a systematic review of smart traffic in the context of VANETs. The paper selection was started with 1117 works followed with three processes of refinement wherein 115 papers were selected to thorough revision. The remaining 115 papers were cataloged and classified according to three criteria general focus, evaluation method, and communication type. The review revealed research gaps and trends in the area of smart traffic. This work provides a list of references to find the mapped papers, thus being able to guide more reliable research studies. Besides, we identify research challenges and directions based on the set of papers that were studied. The research challenges are presented according to the main topics explored in this systematic review: security, physical safety of people and protocols. [ABSTRACT FROM AUTHOR]