scholarly journals Regulation Models of Crossroad Based on Wireless Sensor Networks and Notions of the Fluid Mechanics

2020 ◽  
Vol 9 (3) ◽  
pp. 2536-2541
Keyword(s):  
Traffic Control ◽  
Traffic Congestion ◽  
Information Technologies ◽  
Smart Cities ◽  
Urban Traffic ◽  
Wireless Sensor ◽  
Effective Control ◽  
Control Mechanisms ◽  
Traffic Light ◽  
Traffic Lights

The permanent growth of the population in smart cities has increased the number of vehicles. Consequently the problem of traffic congestion has become one of the main problems to be solved by today's traffic control systems, especially at traffic intersections. In fact, the traditional method which avoids the congestion in a crossroads is the classic command (Timing) by means of traffic lights. However, the traffic light management modes are sometimes based on classic models which make them unsuitable for the treatment of different experienced situations in traffic (either dense or fluid traffic). Fortunately, thanks to the significant progress made, especially the use of New Information Technologies and Communications for example Wireless Sensor Network, for the regulation of traffic, are solutions become central in the field of urban traffic management. They have made it possible to propose more effective control mechanisms to reduce the effects of traffic congestion. In this article, we will present the continuation of our work [1], the objective is to offer to the users of the road a crossing time as long as possible, while preventing the car cap to propagate over a distance that is set between two wireless sensors, to do this, we can act on the setting of the traffic light to regulate traffic in intersections.

scholarly journals Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6218
Author(s):  
Rodrigo Carvalho Barbosa ◽  
Muhammad Shoaib Ayub ◽  
Renata Lopes Rosa ◽  
Demóstenes Zegarra Rodríguez ◽  
Lunchakorn Wuttisittikulkij
Keyword(s):  
Deep Learning ◽  
Traffic Control ◽  
Detection System ◽  
Lightweight Design ◽  
Vehicle Detection ◽  
Activation Function ◽  
Urban Traffic ◽  
Vehicle Classification ◽  
Traffic Light ◽  
Traffic Lights

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.

scholarly journals Bi-Level Optimization Model for Urban Traffic Control

2021 ◽  
Vol 21 (3) ◽  
pp. 108-126
Author(s):  
Krasimira Stoilova ◽  
Todor Stoilov ◽  
Stanislav Dimitrov
Keyword(s):  
Traffic Control ◽  
Optimization Model ◽  
Dynamical Behavior ◽  
Control Variable ◽  
Green Light ◽  
Urban Traffic ◽  
Traffic Light ◽  
Urban Network ◽  
Traffic Lights ◽  
Urban Traffic Control

Abstract The urban traffic control optimization is a complex problem because of the interconnections among the junctions and the dynamical behavior of the traffic flows. Optimization with one control variable in the literature is presented. In this research optimization model consisting of two control variables is developed. Hierarchical bi-level methodology is proposed for realization of integrated optimal control. The urban traffic management is implemented by simultaneously control of traffic light cycles and green light durations of the traffic lights of urban network of crossroads.

Smart Cities: Study and Comparison of Traffic Light Optimization in Modern Urban Areas Using Artificial Intelligence

2018 ◽  
Vol 8 (2) ◽  
pp. 10
Author(s):  
Mustapha Kabrane ◽  
Salah-ddine Krit ◽  
Lahoucine El Maimouni
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Road Network ◽  
Smart Cities ◽  
Optimization Techniques ◽  
Traffic Light ◽  
Large Cities ◽  
Traffic Lights ◽  
The Road ◽  
Road Signs

In large cities, the increasing number of vehicles private, society, merchandise, and public transport, has led to traffic congestion. Users spend much of their time in endless traffic congestion. To solve this problem, several solutions can be envisaged. The interest is focused on the  system of road signs: The use of a road infrastructure is controlled by a traffic light controller, so it is a matter of knowing how to make the best use of the controls of this system (traffic lights) so as to make traffic more fluid. The values of the commands computed by the controller are determined by an algorithm which is ultimately, only solves a mathematical model representing the problem to be solved. The objective is to make a study and then the comparison on the optimization techniques based on artificial intelligence1 to intelligently route vehicle traffic. These techniques make it possible to minimize a certain function expressing the congestion of the road network. It can be a function, the length of the queue at intersections, the average waiting time, also the total number of vehicles waiting at the intersection

scholarly journals Smart Cities and Data Analytics for Intelligent Transportation Systems: An Analytical Model for Scheduling Phases and Traffic Lights at Signalized Intersections

2021 ◽  
Vol 11 (15) ◽  
pp. 6816
Author(s):  
Fatih Gunes ◽  
Selim Bayrakli ◽  
Abdul Halim Zaim
Keyword(s):  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Transportation Systems ◽  
Signalized Intersections ◽  
Effective Control ◽  
Main Role ◽  
Smart Transportation ◽  
Traffic Lights ◽  
Service Rates

This paper is intended to improve the performance of signalized intersections, one of the most important systems of traffic control explained within the scope of smart transportation systems. These structures, which have the main role in ensuring the order and flow of traffic, are alternative systems depending on the different methods and techniques used. Determining the operation principles of these systems requires an extremely careful and planned study, considering their important role. Performance outputs obtained from the queue analyses made in previous studies formed the input of this study. The most important techniques are used in the effective control of intersections, such as signal timing: in particular, the use of effective green time and order of the transitions between phases. In this research, a traffic-sensitive signalized intersection control system was designed with the suggested methods against these two problems. The sample intersections were selected from three cities with the highest population density as the case study area. In the analysis of the performance of the connection arms of the selected intersections, flow intensity data were taken into consideration, as well as the arrival and service rates. Based on this, the outputs of the two proposed models regarding the use of phase transitions and effective green durations were compared with two other adaptive control systems and their positive results were shared. The results showed that signalized intersections, operating with a well-planned and correctly chosen technique, better regulate density and queuing.

scholarly journals Metaheuristic Techniques in Optimizing Traffic Control Lights: A Systematic Review

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Systematic Review ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Monitoring ◽  
Optimization Techniques ◽  
Traffic Light ◽  
Traffic Lights ◽  
The Road ◽  
Traffic Signal Timing ◽  
Metaheuristic Techniques

Traffic congestion is a serious problem on every roadway and streets in many cities around the world. This systematic review is devoted to analyze research papers that deal with the optimization of traffic signal timing. The main objective of such optimization is maximizing the number of the vehicles leaving the network in a given period of time. This will lead to enhancing the performance of the road system. In this work, we researched the most recent metaheuristic optimized traffic light control techniques. It was shown that integrating optimization techniques in the field of traffic lights control had a great impact on the performance of traffic monitoring. During our research, we found that the most used method was the Genetic Algorithm (GA).

scholarly journals PRIORITY BASED TRAFFIC LIGHTS CONTROLLER USING WIRELESS SENSOR NETWORKS

2013 ◽  
pp. 178-181
Author(s):  
K. R. SHRUTHI ◽  
K. VINODHA
Keyword(s):  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
International Standards ◽  
Wireless Sensor ◽  
Traffic Light ◽  
Traffic Lights ◽  
Adaptive Traffic Control ◽  
And Control ◽  
Multiple Intersections

Vehicular traffic is continuously increasing around the world, especially in large urban areas. The resulting congestion has become a major concern to transportation specialists and decision makers. The existing methods for traffic management, surveillance and control are not adequately efficient in terms of performance, cost, maintenance, and support. In this paper, the design of a system that utilizes and efficiently manages traffic light controllers is presented. In particular, we present an adaptive traffic control system based on a new traffic infrastructure using Wireless Sensor Network (WSN). These techniques are dynamically adaptive to traffic conditions on both single and multiple intersections. An intelligent traffic light controller system with a new method of vehicle detection and dynamic traffic signal time manipulation is used in the project. The project is also designed to control traffic over multiple intersections and follows international standards for traffic light operations. A central monitoring station is designed to monitor all access nodes..

scholarly journals Intelligent Traffic Control System Using ARM7 Controller and Image Processing Techniques

2021 ◽  
Vol 11 (4) ◽  
pp. 2726-2735
Author(s):  
Dr.E.V. Krishna Rao ◽  
N. Alekhya ◽  
K. Rupa ◽  
M. Sai Sujith ◽  
Sk Abdulla Aman Ahmed
Keyword(s):  
Control System ◽  
Time Delay ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Current Model ◽  
Urban Traffic ◽  
Emergency Vehicle ◽  
Traffic Lights ◽  
Alert Message ◽  
Traffic Control System

The urban traffic congestion is being increased day by day due to large number of vehicles are used by dense people in cities. In the current model of the Traffic Control System, the time delay of each signal light is static which leads to lot of waiting time and was tage of fuel. To overcome this problem, intelligent traffic management system of controlling the traffic lights using the ARM 7 controller and camera sensor is proposed. The camera which is installed along the pavement captures the real time video of the road. The video is then processed indifferent stages to find the number of vehicles in that particular lane using Convex hull technique and accordingly the time delay of the traffic signals has been changed dynamically. Incase, if an emergency vehicle like ambulance is detected by RF434 in a particular lane then automatically this lane will be given the highest priority to clear the traffic. Another feature is if any vehicle violates the traffic line that can also be identified by the RFID reader and automatically and an alert message will be sent to registered mobile number through GSM module which is interfaced with LPC2148.

scholarly journals Deep Reinforcement Learning Model to Mitigate Congestion in Real-Time Traffic Light Networks

2021 ◽  
Vol 6 (10) ◽  
pp. 138
Author(s):  
Fábio de Souza Pereira Borges ◽  
Adelayda Pallavicini Fonseca ◽  
Reinaldo Crispiniano Garcia
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Traffic Control ◽  
High Volume ◽  
Urban Traffic ◽  
Traffic Data ◽  
Traffic Light ◽  
Urban Network ◽  
Traffic Demand ◽  
Traffic Lights

Urban traffic congestion has a significant detrimental impact on the environment, public health and the economy, with at a high cost to society worldwide. Moreover, it is not possible to continually modify urban road infrastructure in order to mitigate increasing traffic demand. Therefore, it is important to develop traffic control models that can handle high-volume traffic data and synchronize traffic lights in an urban network in real time, without interfering with other initiatives. Within this context, this study proposes a model, based on deep reinforcement learning, for synchronizing the traffic signals of an urban traffic network composed of two intersections. The calibration of this model, including training of its neural network, was performed using real traffic data collected at the approach to each intersection. The results achieved through simulations were very promising, yielding significant improvements in indicators measured in relation to the pre-existing conditions in the network. The model was able to deal with a broad spectrum of traffic flows and, in peak demand periods, reduced delays and queue lengths by more than 28% and 42%, respectively.

scholarly journals Development Of Automatic Traffic Light Based On Wireless Sensor Networks With Star Topologies

JAICT ◽  
2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Nadia Khotimatul Husna
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Traffic Control ◽  
Wireless Sensor ◽  
Traffic Light ◽  
Light Traffic ◽  
Traffic Lights ◽  
The Road ◽  
The Government ◽  
The Coordinator

Traffic jam at the intersection is caused by many things such as the number of vehicles that exceed the capacity of the highway, road users who do not obey the rules, or lights on a traffic light that is not in accordance with the road conditions. The government has implemented measures to control congestion solutif at a crossroads in Indonesia, especially in big cities by installing the ATCS (Automatic Traffic Control System) that regulate light traffic lights based on real time conditions crossroads. The weakness of this system still needed the operator to settings the traffic light lamp replacement and maintenance costs are expensive. Therefore, we need a system that can regulate light traffic lights adaptively based on the length of the queue of vehicles. In this project, created a system that is able to set the lights on the traffic light by the long queues of vehicles adaptively based wireless sensor networks. Data from XBee sent with a frequency of 2, 4 Ghz towards the coordinator node, the coordinator node data is then processed by a microcontroller arduino mega to compare the length of the queue between the road and determine the traffic light lights adaptively. The test results show a sensor capable of detecting the vehicle up to a distance of 175 cm, the system can detect the length of the queue as far as 56 meters, and can be changed adaptively in a ccordance long queues of vehicle.

scholarly journals Traffic Light Cycle Configuration of Single Intersection Based on Modified Q-Learning

2019 ◽  
Vol 9 (21) ◽  
pp. 4558 ◽  
Author(s):  
Hung-Chi Chu ◽  
Yi-Xiang Liao ◽  
Lin-huang Chang ◽  
Yen-Hsi Lee
Keyword(s):  
Traffic Congestion ◽  
Exhaustive Search ◽  
Urban Traffic ◽  
Light Cycle ◽  
Traffic Light ◽  
Large Cities ◽  
Q Learning ◽  
Traffic Lights ◽  
Vehicle Delay ◽  
Processing Steps

In recent years, within large cities with a high population density, traffic congestion has become more and more serious, resulting in increased emissions of vehicles and reducing the efficiency of urban operations. Many factors have caused traffic congestion, such as insufficient road capacity, high vehicle density, poor urban traffic planning and inconsistent traffic light cycle configuration. Among these factors, the problems of traffic light cycle configuration are the focal points of this paper. If traffic lights can adjust the cycle dynamically with traffic data, it will reduce degrees of traffic congestion significantly. Therefore, a modified mechanism based on Q-Learning to optimize traffic light cycle configuration is proposed to obtain lower average vehicle delay time, while keeping significantly fewer processing steps. The experimental results will show that the number of processing steps of this proposed mechanism is 11.76 times fewer than that of the exhaustive search scheme, and also that the average vehicle delay is only slightly lower than that of the exhaustive search scheme by 5.4%. Therefore the proposed modified Q-learning mechanism will be capable of reducing the degrees of traffic congestions effectively by minimizing processing steps.

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