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 An Agent-Based Simulation to Explore Communication in a System to Control Urban Traffic with Smart Traffic Lights

2021 ◽  
Vol 10 (3) ◽  
pp. 209-225
Author(s):  
Marcos De Oliveira ◽  
Robson Teixeira ◽  
Roberta Sousa ◽  
Enyo José Tavares Gonçalves
Keyword(s):  
Traffic Control ◽  
Traffic Congestion ◽  
Urban Traffic ◽  
Transport Infrastructure ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Traffic Lights ◽  
Intelligent Software ◽  
Smart Traffic ◽  
Urban Traffic Control

Populational growth increases the number of cars and makes the transport infrastructure increasingly saturated. The control of traffic lights by intelligent software is a promising way to solve the problem caused by this situation. This article addresses this problem that occurs in urban traffic. An agent-based simulation of an urban traffic control system is proposed. The solution is offered as intelligent traffic lights as agents to alleviate traffic congestion at a given location. Each agent controls a crossing and maintains communication with agents from other corners. Thus, they can have greater control of a larger area and identify patterns that can help them to solve congestion problems. The results of our simulated experiments point to the improvement of the urban traffic when using the proposed Multiagent System, in comparison with an approach that uses crossing agents without communication and other that implements static traffic lights.

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.

Traffic Microsimulation for Flexible Utilization of Urban Roadways

2019 ◽  
Vol 2673 (10) ◽  
pp. 92-104 ◽  
Author(s):  
Aleksandar Stevanovic ◽  
Nikola Mitrovic
Keyword(s):  
Traffic Control ◽  
Simulation Models ◽  
Current Method ◽  
Fixed Time ◽  
Urban Traffic ◽  
Future Research ◽  
Major Barrier ◽  
Urban Networks ◽  
Traffic Efficiency ◽  
Traffic Systems

The current method of organizing traffic flows in urban networks uses directional right-of-way links to move traffic between urban intersections. Conflict resolution between vehicles is almost exclusively exercised at the intersections, which turns them into bottlenecks of our urban traffic systems. Even an attempt to model a different organization of traffic hits a major barrier, because the traditional simulation models do not offer enough flexibility to model bidirectional traffic on individual links in the network. This paper presents flexible arterial utilization simulation modeling (FAUSIM), a novel microsimulation platform designed to address this deficiency of traditional tools. The outputs from this tool are validated, successfully, in comparison with a commonly utilized Vissim model. The paper then illustrates the ability of FAUSIM to model conventional and unconventional traffic control scenarios. A combined alternate-direction lane assignment and reservation-based intersection control (CADLARIC) scenario is where directional driving paths are altered between neighboring lanes to align vehicles for decreased conflict for left and right turns at intersections where a reservation-based algorithm is utilized to process conflicts. This is compared with a conventional fixed-time (FT) control. The results of the experiments, executed on a small three-intersection corridor, show that CADLARIC significantly outperforms conventional driving with the FT control in relation to traffic efficiency (delays and stops). While the FT control generates fewer (potential) conflicting events, the CADLARIC confidently handles conflicting situations inside and outside the intersections. Future research should further validate the FAUSIM platform and investigate several other unconventional traffic scenarios with connected and automated vehicles.

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.

Agent-Based Approach in Roadway Traffic and Transportation Systems: State of the Art

2007 ◽  
Author(s):  
Bo Chen ◽  
Harry H. Cheng ◽  
Joe Palen
Keyword(s):  
Large Scale ◽  
Dynamic Environment ◽  
Congestion Management ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Agent Technology ◽  
Future Research ◽  
Traffic Signal Control ◽  
Multi Agent Systems ◽  
Agent Based

Agent technology is rapidly emerging as one of the powerful technologies for the development of large-scale distributed systems to deal with the uncertainty in a dynamic environment. The domain of traffic and transportation systems is well suited for an agent-based approach because systems are usually geographically distributed in dynamic changing environments. Our literature survey shows that the techniques and methods resulted from the field of agent and multi-agent systems have been applied to many aspects of traffic and transportation systems, including modeling and simulation, dynamic routing and congestion management, intelligent traffic management, and urban traffic signal control. This paper examines agent-based approach and its applications in roadway traffic and transportation systems, and discusses several future research directions towards successful deployment of agent technology in traffic and transportation systems.

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.

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 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.

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