Intelligent Vehicular Traffic Signal Control for Vehicular AdHoc Networks

2021 ◽  
Vol 10 (2) ◽  
pp. 664-674
Keyword(s):  
Light Signal ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Light ◽  
Vehicle To Vehicle ◽  
City Regions ◽  
Vehicle To Infrastructure ◽  
Distance Vector ◽  
Traffic Regulator

Current traffic regulator in remote is vehicle impelled, pre-coordinated, and webster’s technique, which produce more deferral at higher traffic. The chance of sending a keen and constant versatile traffic light regulator, which gets data from vehicles, for example, the position and speed of the vehicle, and then use this information to streamline the traffic light signal at the convergence for vehicle to vehicle(V2V) and vehicle to infrastructure(V2I) communication. The traffic board framework utilizing the AdHoc Ondemand Distance Vector (AODV) convention for VANET is sufficiently used in this work. It has been seen that practically all routes demand communication arrive at the target, a couple over significant distances with center vehicle thickness fizzled. Nonetheless, the load on the association starting from the unsophisticated transmission is gigantic. Therefore, it additionally prompts rapidly developing postponements and connection disappointment. A few trajectory answers don’t come through considering the way that telecomunication is as yet going on. This is a basic issue, particularly in city regions with high vehicle thickness. Based on the information in this paper, appropriate traffic signal control is developed to minimize the congestion at the intersections

scholarly journals Chula-SSS: Developmental Framework for Signal Actuated Logics on SUMO Platform in Over-saturated Sathorn Road Network Scenario

10.29007/t895 ◽  
2018 ◽  
Author(s):  
Chaodit Aswakul ◽  
Sorawee Watarakitpaisarn ◽  
Patrachart Komolkiti ◽  
Chonti Krisanachantara ◽  
Kittiphan Techakittiroj
Keyword(s):  
Software Package ◽  
Road Network ◽  
Light Signal ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Panel ◽  
Traffic Light ◽  
Traffic Police ◽  
Human Operators

In this paper, Chula-Sathorn SUMO Simulator (Chula-SSS) has been proposed as an educational tool for traffic police and traffic engineers. The tool supports our framework to develop actuated traffic signal control logics in order to resolve urban traffic congestion. The framework design aims to incorporate the tacit traffic control expertise of human operators by trying to extract and extend the human-level intelligence in actuating logically traffic signal controls. In this regard, a new software package has been developed for the microscopic-mobility computer simulation capability of the SUMO (Simulation of Urban MObility) platform. Using the SUMO TraCI, our package implements the graphical user interface (GUI) of actual traffic light signal control panel, recently introduced in Bangkok (Thailand) for traffic police deployment in the Chulalongkorn University’s Sathorn Model project under the umbrella of Sustainable Mobility Project 2.0 of the World Business Council for Sustainable Development (WBCSD). The traffic light signal control panel GUI modules can communicate via TraCI in real-time to SUMO in order both to retrieve the raw traffic sensor data emulated within SUMO and to send the desired traffic light signal phase manually entered via GUI by the module users. Each of the users could play a role of traffic police in charge of actuating the traffic light signal at each of the controllable intersections. To demonstrate this framework, Chula-SSS has been implemented with the calibrated SUMO dataset of Sathorn Road network area. This area is one of the most critical areas in Bangkok due to the immense traffic volume with daily recurring traffic bottlenecks and network deadlocks. The simulation comprises of 2375 intersection nodes, 4517 edges, 10 main signalised intersections. The provided datasets with Chula-SSS cover both the morning and evening rush-hour periods each with over 55,000 simulated vehicles based on the comprehensive traffic data collection and SUMO mobility model calibration. It is hoped that the herein developed framework and software package can be not only useful for our Thailand case, but also readily extensible to those developing and least- developed countries where traffic signal controls rely on human operations, not yet fully automated by an area traffic controller. In those cases, the framework proposed herein is expectedly an enabling technology for the human operators to practice, learn, and evolve their traffic signal control strategies systematically.

scholarly journals An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4291 ◽  
Author(s):  
Qiang Wu ◽  
Jianqing Wu ◽  
Jun Shen ◽  
Binbin Yong ◽  
Qingguo Zhou
Keyword(s):  
Reinforcement Learning ◽  
Control Method ◽  
Communication Protocol ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Light Control ◽  
Traffic Light ◽  
Traffic Light Control ◽  
Multi Agent

With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment.

scholarly journals Traffic Signal Control Using Hybrid Action Space Deep Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2302
Author(s):  
Salah Bouktif ◽  
Abderraouf Cheniki ◽  
Ali Ouni
Keyword(s):  
Reinforcement Learning ◽  
Traffic Signal ◽  
Discrete Control ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Continuous Control ◽  
Traffic Light ◽  
Control Approach ◽  
Hierarchical Decision ◽  
And Performance

Recent research works on intelligent traffic signal control (TSC) have been mainly focused on leveraging deep reinforcement learning (DRL) due to its proven capability and performance. DRL-based traffic signal control frameworks belong to either discrete or continuous controls. In discrete control, the DRL agent selects the appropriate traffic light phase from a finite set of phases. Whereas in continuous control approach, the agent decides the appropriate duration for each signal phase within a predetermined sequence of phases. Among the existing works, there are no prior approaches that propose a flexible framework combining both discrete and continuous DRL approaches in controlling traffic signal. Thus, our ultimate objective in this paper is to propose an approach capable of deciding simultaneously the proper phase and its associated duration. Our contribution resides in adapting a hybrid Deep Reinforcement Learning that considers at the same time discrete and continuous decisions. Precisely, we customize a Parameterized Deep Q-Networks (P-DQN) architecture that permits a hierarchical decision-making process that primarily decides the traffic light next phases and secondly specifies its the associated timing. The evaluation results of our approach using Simulation of Urban MObility (SUMO) shows its out-performance over the benchmarks. The proposed framework is able to reduce the average queue length of vehicles and the average travel time by 22.20% and 5.78%, respectively, over the alternative DRL-based TSC systems.

COORDINATED TRAFFIC SIGNAL CONTROL OF ROAD TRANSPORT FLOW ON THE HIGHWAY OF LIPETSK

2016 ◽  
Vol 3 (1) ◽  
pp. 413-416
Author(s):  
Кадасев ◽  
D. Kadasev ◽  
Полоцкий ◽  
D. Polotskiy
Keyword(s):  
Traffic Flow ◽  
Road Transport ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Light Signaling ◽  
Traffic Light ◽  
Operation Modes ◽  
Harmful Substances ◽  
Transport Flow

The article investigates traffic flow of lipetsk of Vodopyanov street. Based on the research proposes a change of operation modes of a traffic light signaling, which allow to reduce the likelihood of congestion and the amount of harmful substances in the environment.

scholarly journals Traffic Signal Control Model on Isolated Intersection Using Reinforcement Learning: A Case Study on Algiers City, Algeria

2021 ◽  
Vol 35 (5) ◽  
pp. 417-424
Author(s):  
Fares Bouriachi ◽  
Hicham Zatla ◽  
Bilal Tolbi ◽  
Koceila Becha ◽  
Allaeddine Ghermoul
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Light ◽  
Traffic Lights ◽  
On The Road ◽  
Isolated Intersection

Traffic jams and congestion in our cities are a major problem because of the huge increase in the number of cars on the road. To remedy this problem, several control methods are proposed to prevent or reduce traffic congestion based on traffic lights. There are few works using reinforcement learning technique for traffic light control and recent studies have shown promising results. However, existing works have not yet tested the methods on the real-world traffic data and they only focus on studying the rewards without interpreting the policies. In this paper, we proposed a reinforcement learning algorithm to address the traffic signal control problem in real multi-phases isolated intersection. A case study based on Algiers city is conducted the simulation results from the different scenarios show that our proposed scheme reduces the total travel time of the vehicles compared to those obtained with traffic-adaptive control.

scholarly journals A Survey on Deep Reinforcement Learning Network for Traffic Light Cycle Control

2020 ◽  
pp. 285-293
Author(s):  
V. Indhumathi ◽  
K. Kumar
Keyword(s):  
Reinforcement Learning ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Intelligent Transport System ◽  
Light Cycle ◽  
Traffic Light ◽  
The Road ◽  
Road Intersections ◽  
Traffic Signal Control Systems

A Traffic signal control is a challenging problem and to minimize the travel time of vehicles by coordinating their movements at the road intersections. In recent years traffic signal control systems have on over simplified information and rule-based methods and we have large amounts of data, more computing power and advanced methods to drive the development of intelligent transportation. An intelligent transport system to use the machine learning methods likes reinforcement learning and to explain the acknowledged transportation approaches and a list of recent literature in traffic signal control. In this survey can foster interdisciplinary research on this important topic.

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