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.

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 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.

Traffic Monitoring and Congestion Management in the SCOOT Urban Traffic Control System

1998 ◽  
Vol 1634 (1) ◽  
pp. 118-122 ◽  
Author(s):  
David Bretherton ◽  
Keith Wood ◽  
Neil Raha
Keyword(s):  
Control System ◽  
Traffic Control ◽  
Detection System ◽  
Congestion Management ◽  
Traffic Monitoring ◽  
Urban Traffic ◽  
Incident Detection ◽  
The European Union ◽  
Traffic Control System ◽  
Urban Traffic Control

The SCOOT Urban Traffic Control system is now operating in over 170 cities worldwide, including 7 systems in North America. Since the first system was installed, there has been a continuous program of research and development to provide new facilities to meet the requirement of the traffic manager. The latest version of SCOOT (Version 3.1) incorporates a traffic information database, ASTRID, and an incident-detection system, INGRID, and provides a number of facilities for congestion control. The traffic monitoring facilities of SCOOT, including a new facility to estimate emissions from vehicles, and the current program of work to enhance the incident-detection system and to provide additional facilities to manage incidents and congestion are reported in this paper. The work is being carried out as part of the European Union, DGXIII 4th Framework project, COSMOS, with additional funding from the UK Department of Transport. The enhanced system is to be installed in the Kingston Borough of London, where it will be tested in combination with congestion warning information provided by variable message signs.

Current Developments in SCOOT: Version 3

1996 ◽  
Vol 1554 (1) ◽  
pp. 48-52 ◽  
Author(s):  
Dave B retherton
Keyword(s):  
Control System ◽  
Data Base ◽  
Traffic Control ◽  
Detection System ◽  
Urban Traffic ◽  
Incident Detection ◽  
Bus Priority ◽  
Current Information ◽  
Traffic Control System ◽  
Urban Traffic Control

The SCOOT urban traffic control system is now operating successfully in more than 130 towns and cities worldwide. The latest version of SCOOT has been extended to include support for bus priority, the automatic SCOOT traffic information data base (ASTRID) system, and the INGRID incident detection system and has been given added flexibility, particularly for use in incident conditions. Bus priority in SCOOT was developed within the European Union DRIVE 2 project PROMPT. This software has now been issued as part of the latest SCOOT version, following the field trials in London and Southampton, United Kingdom, which showed that significant benefits to buses could be obtained. The ASTRID data base has now been integrated with SCOOT and can run in the same machine as the urban traffic control system. As well as providing current and historical information to traffic engineers, ASTRID now can feed historic information back into SCOOT, providing a substitute cyclic flow profile that can be used for optimization when there are faulty detectors. The INGRID incident detection system contains two algorithms to provide an indication of an incident; taking current information directly from SCOOT, INGRID detects abnormal changes in flow and occupancy, and comparing current information with historic information stored in the ASTRID data base, INGRID detects abnormal patterns in these parameters. The SCOOT optimizers have been made more flexible and can now make larger changes to the signal timings if required. This facility can be switched on or off and would be particularly useful where an incident has been detected.

Vehicle Detection and Classification Using Model-Based and Fuzzy Logic Approaches

2005 ◽  
Vol 1935 (1) ◽  
pp. 154-162
Author(s):  
Heng-Da Cheng ◽  
Haining Du ◽  
Liming Hu ◽  
Chris Glazier
Keyword(s):  
Fuzzy Logic ◽  
Traffic Control ◽  
Vehicle Detection ◽  
Classification Systems ◽  
Vehicle Classification ◽  
Highway Traffic ◽  
Control Center ◽  
Model Based ◽  
Fixed Orientation ◽  
Classification Information

Vehicle detection and classification information is invaluable in many transportation issues. Vehicle feature extraction and detection are the preprocesses required for vehicle classification. Current automatic vehicle classification systems have deficiencies: low accuracy, special requirements, fixed orientation of the camera, or additional hardware and devices. This paper discusses a vehicle detection and classification system using model-based and fuzzy logic approaches. The system was tested with the use of a variety of images captured by the highway traffic control center of the Utah Department of Transportation. In comparison with existing systems, major advantages of the proposed system are ( a) no special orientation of the camera is required, ( b) no additional devices are needed, and ( c) high classification accuracy is provided. Experimental results show that the performance of the proposed system exceeds that of the existing video-based vehicle classification systems.

scholarly journals SUMO Based Platform for Cooperative Intelligent Automotive Agents

10.29007/sc13 ◽  
2019 ◽  
Author(s):  
Levente Alekszejenkó ◽  
Tadeusz P. Dobrowiecki
Keyword(s):  
Traffic Control ◽  
Urban Traffic ◽  
Future Research ◽  
Complex Solution ◽  
Agent Based ◽  
Traffic Lights ◽  
Lane Changes ◽  
Control Scheme ◽  
Variable Topology ◽  
Intelligent Communication

Starting from the problems of nowadays’ urban traffic (congestions, imperfect timing of traffic lights, high impact of lane changes) we investigate the feasibility of a cooperative intelligent agent based solution as an overall control scheme governing the car flow in congested urban intersections.The proposed complex solution features both the intelligent traffic control and the car platooning. In order to test and verify the merits of the proposed solution in urban intersection of a widely variable topology, but also to support our future research aims, a simulation platform, extending the basic functionalities of SUMO with the options of intelligent communication and cooperative co-acting, was designed and developed.

scholarly journals Traffic Control Prediction Design Based on Fuzzy Logic and Lyapunov Approaches to Improve the Performance of Road Intersection

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2205
Author(s):  
Sadiqa Jafari ◽  
Zeinab Shahbazi ◽  
Yung-Cheol Byun
Keyword(s):  
Traffic Control ◽  
Fuzzy Controller ◽  
Fixed Time ◽  
Urban Traffic ◽  
Urban Transport ◽  
Traffic Light ◽  
Traffic System ◽  
Input Variables ◽  
Space Dynamics ◽  
Takagi Sugeno

Due to the increasing use of private cars for urbanization and urban transport, the travel time of urban transportation is increasing. People spend a lot of time in the streets, and the queue length of waiting increases accordingly; this has direct effects on fuel consumption too. Traffic flow forecasts and traffic light schedules were studied separately in the urban traffic system. This paper presents a new stable TS (Takagi–Sugeno) fuzzy controller for urban traffic. The state-space dynamics are utilized to formulate both the vehicle’s average waiting time at an isolated intersection and the length of queues. A fuzzy intelligent controller is designed for light control based upon the length of the queue, and eventually, the system’s stability is proved using the Lyapunov theorem. Moreover, the input variables are the length of queue and number of input or output vehicles from each lane. The simulation results describe the appearance of the proposed controller. An illustrative example is also given to show the proposed method’s effectiveness; the suggested method is more efficient than both the conventional fuzzy traffic controllers and the fixed time controller.

Multiagent Learning on Traffic Lights Control

2009 ◽  
pp. 307-321 ◽  
Author(s):  
Denise de Oliveira ◽  
Ana L.C. Bazzan
Keyword(s):  
Traffic Control ◽  
Multiagent System ◽  
Partial Information ◽  
Urban Traffic ◽  
Multiagent Learning ◽  
Traffic Lights ◽  
Traffic System ◽  
Exchange Of Information ◽  
Urban Traffic Control

In a complex multiagent system, agents may have different partial information about the system’s state and the information held by other agents in the system. In a distributed urban traffic control, where each junction has an independent controller, agents that learn can benefit from exchanging information, but this exchange of information may not always be useful. In this chapter the authors analyze how agents can benefit from sharing information in an urban traffic control scenario and the consequences of this cooperation in the performance of the traffic system.

scholarly journals Public Transport Priority for Multimodal Urban Traffic Control

2015 ◽  
Vol 15 (5) ◽  
pp. 17-36 ◽  
Author(s):  
Neila Bhouri ◽  
Fernando J. Mayorano ◽  
Pablo A. Lotito ◽  
Habib Haj Salem ◽  
Jean Patrick Lebacque
Keyword(s):  
Public Transport ◽  
Traffic Control ◽  
Optimization Problem ◽  
Urban Traffic ◽  
Linear Quadratic ◽  
The Public ◽  
Traffic Lights ◽  
Lq Optimal Control ◽  
Regulation Strategies ◽  
Urban Traffic Control

Abstract In order to improve the travel time of surface public transport vehicles (bus, tramway, etc.), several cities use Urban Traffic Control (UTC) systems enabling to give priority to public transport. This paper reviews these systems. Further on after a debate on their insufficiencies in the global regulation of the urban traffic on a whole network, the paper proposes intermodal regulation strategies, operating on intersection traffic lights to regulate the traffic, favouring the public transport. All these strategies are based on the Linear Quadratic (LQ) optimal control theory, but they are different in their ways of taking into account the public transport in the optimization problem. The simulation tests are carried out in a network of eight intersections and two public transport lines.

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