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 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.

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.

Optimizing Traffic Signal Timing through Network Decomposition

2005 ◽  
Vol 1925 (1) ◽  
pp. 167-175 ◽  
Author(s):  
Edward Lieberman ◽  
Jinil Chang
Keyword(s):  
Control Systems ◽  
Real Time ◽  
Traffic Control ◽  
Operating Conditions ◽  
Urban Traffic ◽  
Arterial System ◽  
Signal Timing ◽  
Network Decomposition ◽  
Traffic Demand ◽  
Grid Network

A signal control system named Real-Time/Internal Metering Policy to Optimize Signal Timing (RT/IMPOST) has been under development for several years. It is designed to compute signal timing plans for the entire range of operating conditions from under- to oversaturation for control systems ranging from first generation (GEN 1) to highly responsive advanced traffic management systems. The different flow regimes of urban traffic control are reviewed. Then the focus is on the treatment to develop cycle-based signal timing plans for inclusion in the data libraries referenced by GEN 1 real-time traffic control systems for the undersaturated-flow regime. This approach decomposes a grid network into its constituent arterial subsystems in a way that is responsive to user-specified priorities. The user may define and rank the arterial systems subsumed within a grid network so as to satisfy strategic objectives responsive to traffic demand patterns and local needs. The procedure computes optimal signal timing plans for all these arterial subsystems in a priority-ranked sequence and integrates these arterial-based timing plans to form a networkwide signal timing plan. Field and simulation test results are presented. The field tests confirm that the IMPOST timing plans improved the operational performance of traffic along an arterial system in New York State relative to a fine-tuned existing control. The simulation studies compared the performance of traffic responding to IMPOST signal timing plans with that responding to Synchro 6.0 signal timing plans. The benefits of network decomposition and of arterial priority ranking are also documented.

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 Mobile Road Traffic Management System Using Weighted Sensor

2017 ◽  
Vol 11 (5) ◽  
pp. 147 ◽  
Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Traffic Information ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Management System

<p><strong>Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion ha</strong><strong>s</strong><strong> be</strong><strong>come</strong><strong> a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model </strong><strong>was</strong><strong> simulated and the performance was </strong><strong>assessed</strong><strong> using response time, delay and throughput. Results showed that</strong><strong>,</strong><strong> mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.</strong></p>

scholarly journals Ant Colony Optimization for Real Time Traffic Lights Control on a Single Intersection

2020 ◽  
Vol 14 (02) ◽  
pp. 196 ◽  
Author(s):  
Nouha Rida ◽  
Mohammed Ouadoud ◽  
Aberrahim Hasbi
Keyword(s):  
Road Traffic ◽  
Green Light ◽  
Optimal Solution ◽  
Ant Colony ◽  
Traffic Data ◽  
Traffic Light ◽  
Traffic Lights ◽  
The Road ◽  
Real Time Traffic ◽  
On The Road

In this paper, we present a new scheme to intelligently control the cycles and phases of traffic lights by exploiting the road traffic data collected by a wireless sensor network installed on the road. The traffic light controller determines the next phase of traffic lights by applying the Ant Colony Optimazation metaheuristics to the information collected by WSN. The objective of this system is to find an optimal solution that gives the best possible results in terms of reducing the waiting time of vehicles and maximizing the flow crossing the intersection during the green light. The results of simulations by the SUMO traffic simulator confirm the preference of the developed algorithm over the predefined time controller and other dynamic controllers.

Upper Level for Real Time Urban Traffic Control Systems

1990 ◽  
Vol 23 (8) ◽  
pp. 473-476 ◽  
Author(s):  
A. Kessaci ◽  
J.L. Farges ◽  
J.J. Henry
Keyword(s):  
Control Systems ◽  
Real Time ◽  
Traffic Control ◽  
Urban Traffic ◽  
Upper Level ◽  
Urban Traffic Control

Intelligent Traffic Control at Intersection Based upon Real-Time Vehicle Detection

2013 ◽  
Vol 313-314 ◽  
pp. 343-346
Author(s):  
Guo Zhong Yao
Keyword(s):  
Information Transmission ◽  
Real Time ◽  
Traffic Control ◽  
New Method ◽  
System Operation ◽  
Traffic Condition ◽  
Traffic Lights ◽  
Ism Band ◽  
Pass Through ◽  
Real Traffic

A new method of traffic control based upon real-time number of the vehicles at the intersection was pulled forward. Serial vehicle detectors were used to detect the number of the vehicle waiting to pass through the intersection. One or more control boxes were used to control the traffic lights according to real traffic condition and transreceived data between itself and the detectors. The information transmission between the detectors and the controllers is based upon 2.4G ISM band. Arithmetic on the system operation, which took the pedestrians and the situation of excessive vehicles into account, is introduced.

scholarly journals Model petri net of adaptive traffic lights and its collaboration with a special event

2018 ◽  
Vol 147 ◽  
pp. 02005
Author(s):  
Tomi Tristono ◽  
Setiyo Daru Cahyono ◽  
Sutomo ◽  
Pradityo Utomo
Keyword(s):  
Petri Net ◽  
Fixed Time ◽  
Time Interval ◽  
System Control ◽  
Model Design ◽  
Modeling Framework ◽  
Urban Network ◽  
Traffic Demand ◽  
Traffic Lights ◽  
Collaboration Model

Traffic lights have an important role as the system control of vehicles flow on the urban network. Commonly, most countries still using fixed time strategy. Our research proposes the adaptive traffic lights model to response the traffic demand. It uses basic Petri net as a general modeling framework. Foractuating method of minimum and maximum green signal time interval, the green traffic lights have three-time extension units. Next, we collaborate on a case of the existence of railways that crosses on the southern arm of an intersection. We introduce both of collaboration model design of traffic lights and the railway's gate which always closes while a train passing. Verification and validation of the model are based on the simulation result of vehicles queue. The collaboration model design of traffic lights has excellent performance, and it can resolve the congestion problem better than conventional schedule.

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