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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]

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]

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]

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]

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]

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]

Traffic is a major challenge for electronic cities and coping with it requires analyzing traffic congestion in the city road network. In this paper, the performance index of vehicle speed was estimated to evaluate the conditions of road networks. This study analyzes the traffic density for the main network of Hamedan communication routes based on the collected data of Speed performance of Hamedan traffic control system. According to this analysis, the congestion index and traffic peak hours were determined. Also the relationship between vehicle speed and traffic congestion was predictably predictable by neural network and the genetic algorithm. In this study areas of traffic were identified using Hamedan traffic control center due to the speed of vehicles. [ABSTRACT FROM AUTHOR]

The urban green wave traffic control system is one of the effective means to solve the problems of urban traffic jams and reduce vehicle delay. Aiming at the problems of traditional green wave traffic control in optimization computation, this paper introduces the adaptive mechanism and crossover, mutation operators to artificial fish swarm algorithm (AFSA) in order to adjust the evolution group. Through setting bulletin board measure and retention strategy, the individual status of the optimal artificial fish is recorded. In that way, the adaptive genetic AFSA is established. Finally, the paper applies this algorithm to green wave traffic control in five continuous intersections on Jianning Road, Lanzhou, China, gains one traffic control program better than traditional method through calculation, and verifies the feasibility and effectiveness of adaptive genetic-AFSA to optimize green wave traffic control system. [ABSTRACT FROM AUTHOR]

Urban traffic congestion is a common problem that affects many cities around the world. In this paper, an actual urban traffic simulation model (AUTM) for traffic congestion predicting and avoiding is proposed, which includes three key components: the map and transfer (MT) conversion method, the optimized spatial evolution rules, and a congestion-avoidance routing algorithm. Three key techniques are combined in our proposed model: (1) The MT conversion method is proposed to get actual urban cellular spaces, which apply the optimized spatial evolution rules to simulate the vehicular dynamics better. (2) AUTM is proposed for simulating traffic congestion and predicting the effect of adding overpasses and roadblocks. (3) The congestion-avoidance routing algorithm is proposed for vehicles to dynamically update their routes toward their destinations, which can achieve traffic optimization in urban simulations. This paper presents the results of applying this novel model to a large-scale real-world case in different urban traffic congestion situations. Extensive experimental simulations in various actual cities have been carried out. Our results in the extreme case are encouraging: The prediction accuracy of traffic congestions is almost 89%, and the variance of prediction road density is less than 0.15. [ABSTRACT FROM AUTHOR]

Advancement in technology causes the rise in smart systems. City authorities want to make their cities smarter by making an intelligent decision at real time without the involvement of humans. Monitoring and controlling city traffic is one of the major challenges faced by the authorities. These days, city traffic is monitored by static network cameras deployed on few places of highways. Most of the vehicles are also equipped with cameras to store the videos as a black box. However, monitoring and controlling city traffic by using these thousands of cameras produce an overwhelming amount of high-speed videos, which is challenging to process at real time. Therefore, in this paper, we proposed a system to control city traffic by identifying illegal traffic behaviour, such as illegal U-turn, through continuous monitoring of city traffic. The continuous city traffic is monitored by the network static cameras placed on the road as well as by all the vehicles' cameras. An architecture is proposed to handle high-speed vast volume of real-time videos efficiently. For that, the two-level parallelism is achieved with the combination of Hadoop and graphics processing unit (GPU) while processing each frame using parallel environment of Hadoop and each block of a frame using GPU. MapReduce Hadoop programming paradigm is not suitable for real-time processing. Thus, we proposed a parameter calculation algorithm that is equivalent to MapReduce mechanism for image processing while dividing the images/frames into fixed-size blocks. We analyzed the city road traffic, which is collected by static cameras placed on various roads and also by vehicles' cameras while running on the road. Later, the illegal traffic behaviour are recognized, e.g. illegal U-turn, drunken drive, zig-zag drive, over-speed, etc. Finally, the efficiency of the designed system and algorithms are tested by considering the overall running time and system's throughput with respect to video duration as well as the number of frames. The findings indicate that the proposed architecture with GPU-based algorithm over the Hadoop system perform extraordinary. [ABSTRACT FROM AUTHOR]

The article offers information on aspects related to acoustics. Topics discussed include an overview of policy for road traffic noise in Australia in 2016, the use of algebraic graph for analysis and modelling of urban traffic noise system, and implementation of facade treatments in compliance of South Australian Road Traffic Noise Guidelines.

Traffic congestion is a growing problem in many countries around the world. It has been recognized that instead of constructing more roads and freeways to counter this problem it is prudent to improve the utilization of existing road network through a judicious combination of advances in control engineering, communication and information technology. The traffic control architecture proposed in this paper is a combination of communicating Urban Traffic Control Architecture (UTCA) and Freeway Traffic Control Architecture (FTCA). The UTCA combines context-awareness, Cyber-Physical Systems (CPS) principles, and Autonomic Computing System (ACS) principles to optimize traffic congestion and enforce safety in urban traffic network. The UTCA includes a network of adaptive intersection traffic controllers and their immediate supervisory systems, who are also networked. The central piece of each traffic controller is an arbiter, which is a mini CPS. It is aware of the traffic dynamics at the intersection managed by it, by virtue of continuous input from monitoring sensors. Due to this context-awareness ability and its communication ability to exchange traffic information with its neighbors, it can execute policy-based reactions in order to enable safe and efficient traffic throughput at its intersection. Each urban traffic supervisory system is designed with ACS principles in order to minimize downtime caused by environmental emergencies and maximize security of the subsystem under it. A supervisory subsystem will also collect global traffic flow information and contextual constraints from its neighbors. Based on this input it will modify policies and communicate them to its traffic controller for timely adaptation. The urban traffic flowing into freeway traffic will be mediated by Intelligent Ramp Meters (IRM). An IRM interacts with the urban traffic control system and its nearest Intelligent Roadside Unit (IRSU) to regulate the flow of traffic from urban to freeway network. The FTCA consists of a network of mutually interacting IRSUs which monitor traffic flow, communicate with IRMs for providing traffic guidance for freeway drivers. An IRSU will communicate with the vehicles in the zone managed by it in order to provide information on rerouting when road and weather conditions warrant it. It also facilitates exchange of information between vehicles, guide them in lane changes and maintaining safe distance in order to avoid collision. [ABSTRACT FROM AUTHOR]

In this study, dynamic traffic control strategies, namely dynamic ramp metering and dynamic speed limit control, have been examined through microscopic traffic simulation based on site measurements. In this context, the traffic flow data at a particular highway intersection have been analyzed to determine the pattern of the traffic. Then, the traffic model has been built in a traffic micro-simulation software and calibrated with the field data. The foci of the study are to measure the efficiency of the dynamic traffic control strategies and to compare it with the uncontrolled case considering various performance indicators such as total travel time, average delay time per vehicle, and average number of stops per vehicle. For the dynamic ramp metering strategies, the ALINEA (Asservissement Lineaire d'Entree Autoroutiere - French for Linear Utilization for Highway Entrances) control algorithm is implemented with different fixed-time cycle lengths. It has been observed that various ramp metering implementations decreased the average delay time per vehicle up to 30%. The dynamic speed limit control strategies are set according to the occupancy rates that are measured at the bottleneck downstream. The examined speed limit control strategies decreased the average delay time per vehicle to around 7%. The results also revealed that the implemented dynamic traffic control strategies help alleviate congestion by increasing the capacity of the bottleneck section. [ABSTRACT FROM AUTHOR]

We consider basic notions of the theory of S-hypernetworks. We show that in terms of that theory, one can describe many systems of network structure adequately enough to find solutions for the problems at hand. In fact, an analytic setting for the analysis and synthesis problems for the structures of the said systems is virtually an imitational model that can be used for problems related to optimization and control of city traffic systems. [ABSTRACT FROM AUTHOR]