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 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.

The effect of road traffic signal control cooperated with railroad crossing on surrounding traffic flow

2019 ◽  
Vol 2019.28 (0) ◽  
pp. 1012
Author(s):  
Kento OOE ◽  
Ryo ISHII ◽  
Bo YANG ◽  
Tsutomu KAIZUKA ◽  
Toshiyuki SUGIMACHI ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Traffic ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control

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 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.

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.

Study on Traffic Signal Control for Single Intersection via Fuzzy Logic

2012 ◽  
Vol 263-266 ◽  
pp. 624-628 ◽  
Author(s):  
Fu Yang Chen ◽  
Zhi Chao Chen ◽  
Feng Jiang
Keyword(s):  
Waiting Time ◽  
Control Method ◽  
Fuzzy Controller ◽  
Road Transport ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Condition ◽  
Traffic Demand ◽  
Average Waiting Time

Considering the status of the modern road transport system, traffic signal control for isolated intersection is studied in this paper. A traffic signal control method for six-phase intersection is proposed in this paper, aiming at reducing the average waiting time of vehicles. A two-stage fuzzy controller for this method is designed in the paper. This method takes into account not only the traffic condition of the current prevailing phase and the next phase, but also the traffic demand of the vehicle flow that turn right, non-motorized traffic flow and pedestrian. Taking the average waiting time of vehicles as the evaluation index, we make simulations on the control method proposed above. The result for the simulation shows that the performance of this method is superior to that of the timing signal control method.

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 Dynamic Lane Traffic Signal Control with Group Attention and Multi-Timescale Reinforcement Learning

2021 ◽  
Author(s):  
Qize Jiang ◽  
Jingze Li ◽  
Weiwei Sun ◽  
Baihua Zheng
Keyword(s):  
Reinforcement Learning ◽  
Traffic Flow ◽  
Learning Algorithms ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Model Training ◽  
Proper Strategy

Traffic signal control has achieved significant success with the development of reinforcement learning. However, existing works mainly focus on intersections with normal lanes with fixed outgoing directions. It is noticed that some intersections actually implement dynamic lanes, in addition to normal lanes, to adjust the outgoing directions dynamically. Existing methods fail to coordinate the control of traffic signal and that of dynamic lanes effectively. In addition, they lack proper structures and learning algorithms to make full use of traffic flow prediction, which is essential to set the proper directions for dynamic lanes. Motivated by the ineffectiveness of existing approaches when controlling the traffic signal and dynamic lanes simultaneously, we propose a new method, namely MT-GAD, in this paper. It uses a group attention structure to reduce the number of required parameters and to achieve a better generalizability, and uses multi-timescale model training to learn proper strategy that could best control both the traffic signal and the dynamic lanes. The experiments on real datasets demonstrate that MT-GAD outperforms existing approaches significantly.

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