Applicability of Signal Control Method at Roundabout

2007 ◽  
Author(s):  
Kun Xue ◽  
Yu Bai ◽  
Xiao guang Yang
Keyword(s):  
Control Method ◽  
Signal Control

scholarly journals Deep Reinforcement Learning Based Left-Turn Connected and Automated Vehicle Control at Signalized Intersection in Vehicle-to-Infrastructure Environment

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 77 ◽  
Author(s):  
Juan Chen ◽  
Zhengxuan Xue ◽  
Daiqian Fan
Keyword(s):  
Reinforcement Learning ◽  
Control Method ◽  
Signalized Intersection ◽  
Signal Control ◽  
Left Turn ◽  
Automated Vehicle ◽  
Whole Process ◽  
Policy Gradient ◽  
Experience Replay ◽  
Automated Vehicle Control

In order to solve the problem of vehicle delay caused by stops at signalized intersections, a micro-control method of a left-turning connected and automated vehicle (CAV) based on an improved deep deterministic policy gradient (DDPG) is designed in this paper. In this paper, the micro-control of the whole process of a left-turn vehicle approaching, entering, and leaving a signalized intersection is considered. In addition, in order to solve the problems of low sampling efficiency and overestimation of the critic network of the DDPG algorithm, a positive and negative reward experience replay buffer sampling mechanism and multi-critic network structure are adopted in the DDPG algorithm in this paper. Finally, the effectiveness of the signal control method, six DDPG-based methods (DDPG, PNRERB-1C-DDPG, PNRERB-3C-DDPG, PNRERB-5C-DDPG, PNRERB-5CNG-DDPG, and PNRERB-7C-DDPG), and four DQN-based methods (DQN, Dueling DQN, Double DQN, and Prioritized Replay DQN) are verified under 0.2, 0.5, and 0.7 saturation degrees of left-turning vehicles at a signalized intersection within a VISSIM simulation environment. The results show that the proposed deep reinforcement learning method can get a number of stops benefits ranging from 5% to 94%, stop time benefits ranging from 1% to 99%, and delay benefits ranging from −17% to 93%, respectively compared with the traditional signal control method.

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 A Novel Left-Turn Signal Control Method for Improving Intersection Capacity in a Connected Vehicle Environment

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1058 ◽  
Author(s):  
Chuanxiang Ren ◽  
Jinbo Wang ◽  
Lingqiao Qin ◽  
Shen Li ◽  
Yang Cheng
Keyword(s):  
Traffic Safety ◽  
Control Strategy ◽  
Control Method ◽  
Control Model ◽  
Signal Control ◽  
Signal Phase ◽  
Phase Duration ◽  
Left Turn ◽  
Intersection Signal Control ◽  
And Control

Setting up an exclusive left-turn lane and corresponding signal phase for intersection traffic safety and efficiency will decrease the capacity of the intersection when there are less or no left-turn movements. This is especially true during rush hours because of the ineffective use of left-turn lane space and signal phase duration. With the advantages of vehicle-to-infrastructure (V2I) communication, a novel intersection signal control model is proposed which sets up variable lane direction arrow marking and turns the left-turn lane into a controllable shared lane for left-turn and through movements. The new intersection signal control model and its control strategy are presented and simulated using field data. After comparison with two other intersection control models and control strategies, the new model is validated to improve the intersection capacity in rush hours. Besides, variable lane lines and the corresponding control method are designed and combined with the left-turn waiting area to overcome the shortcomings of the proposed intersection signal control model and control strategy.

scholarly journals Development of a conflict-free unsignalized intersection organization method for multiple connected and autonomous vehicles

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0249170
Author(s):  
Qinglu Ma ◽  
Shu Zhang ◽  
Qi Zhou
Keyword(s):  
Traffic Control ◽  
Autonomous Vehicles ◽  
Control Method ◽  
Operating Efficiency ◽  
Signal Control ◽  
Vehicle Speed ◽  
Operating Characteristics ◽  
Right Of Way ◽  
Unsignalized Intersections ◽  
Traffic Organization

An effective traffic control strategy will improve travel reliability in urban transportation networks. Lack of coordination between vehicles, however, exacerbates congestion due mainly to frequent stops at unsignalized intersections. It is beneficial to develop a conflict-free cooperation method that collects basic safety message from multiple approaching Connected and Autonomous Vehicles (for short, CAVs) and guarantees efficient unsignalized intersection operations with safe and incident free vehicle maneuvers. This paper proposes an interspersed traffic organization method under controlled constraints. Firstly, relied on shared location technology and considered the operating characteristics of CAVs at unsignalized intersections to detect and analyze traffic conflicts to establish a right-of-way judgment model for CAVs. In order to further ensure the safety and operating efficiency of the vehicle, based on the judgment results of right-of-way judgment model, a vehicle speed guidance model is established for different traffic conditions. Taking the real city standard intersection as the experimental analysis object, through data collection and simulation experiment, the signal control method and the organization method proposed in this paper are compared and analyzed. The results showed that the traffic organization method proposed in this paper improves the operational efficiency of 46%, the average travel time is reduced by 6.54s, which is not only better than the signal control method, but also supports the development of car networking technology.

scholarly journals Self-organisation in traffic signal control algorithms under light traffic conditions

ORiON ◽  
2019 ◽  
Vol 35 (1) ◽  
pp. 57-87
Author(s):  
SJ Movius ◽  
JH Van Vuuren
Keyword(s):  
Simulation Model ◽  
Traffic Simulation ◽  
Control Method ◽  
Fixed Time ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Light Traffic ◽  
Traffic Conditions ◽  
Self Organisation

Fixed-time control and vehicle-actuated control are two distinct types of traffic signal control. The latter control method involves switching traffic signals based on detected traffic flows and thus offers more flexibility (appropriate for lighter traffic conditions) than the former, which relies solely on cyclic, predetermined signal phases that are better suited for heavier traffic conditions. The notion of self-organisation has relatively recently been proposed as an alternative approach towards improving traffic signal control, particularly under light traffic conditions, due to its flexible nature and its potential to result in emergent behaviour. The effectiveness of five existing self-organising traffic signal control strategies from the literature and a fixed-control strategy are compared in this paper within a newly designed agent-based, microscopic traffic simulation model. Various shortcomings of three of these algorithms are identified and algorithmic improvements are suggested to remedy these deficiencies. The relative performance improvements resulting from these algorithmic modifications are then quantified by their implementation in the aforementioned traffic simulation model. Finally, a new self-organising algorithm is proposed that is particularly effective under lighter traffic conditions.

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