An Improved Adaptive Signal Control Method for Isolated Signalized Intersection Based on Dynamic Programming

2016 ◽  
Vol 8 (4) ◽  
pp. 4-14 ◽  
Author(s):  
Shukai Chen ◽  
Daniel Jian Sun
Keyword(s):  
Dynamic Programming ◽  
Control Method ◽  
Signalized Intersection ◽  
Signal Control ◽  
Adaptive Signal Control ◽  
Adaptive Signal

scholarly journals An Adaptive Signal Control Method with Optimal Detector Locations

2019 ◽  
Vol 11 (3) ◽  
pp. 727 ◽  
Author(s):  
Senlai Zhu ◽  
Ke Guo ◽  
Yuntao Guo ◽  
Huairen Tao ◽  
Quan Shi
Keyword(s):  
Control System ◽  
Control Method ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Primary Role ◽  
Adaptive Traffic Signal Control ◽  
Adaptive Signal Control ◽  
Adaptive Signal

The adaptive traffic signal control system is a key component of intelligent transportation systems and has a primary role in effectively reducing traffic congestion. The high costs of implementation and maintenance limit the applicability of the adaptive traffic signal control system, especially in developing countries. This paper proposes a low-cost adaptive signal control method that is easy to implement. Two detectors are installed in each vehicle lane at an optimal location determined by the proposed method to detect green and red redundancy time, based on which the original signal timing is adjusted through a signal controller. The proposed method is evaluated through case studies with low and high volume-to-capacity ratio intersections. The results show that the proposed adaptive signal control method can significantly reduce total traffic delay at intersections.

scholarly journals A Platoon-Based Adaptive Signal Control Method with Connected Vehicle Technology

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ning Li ◽  
Shukai Chen ◽  
Jianjun Zhu ◽  
Daniel Jian Sun
Keyword(s):  
Control Method ◽  
Model Performance ◽  
Urban Traffic ◽  
Mixed Integer ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Connected Vehicle ◽  
Adaptive Signal Control ◽  
The Individual ◽  
Adaptive Signal

One important objective of urban traffic signal control is to reduce individual delay and improve safety for travelers in both private car and public bus transit. To achieve signal control optimization from the perspective of all users, this paper proposes a platoon-based adaptive signal control (PASC) strategy to provide multimodal signal control based on the online connected vehicle (CV) information. By introducing unified phase precedence constraints, PASC strategy is not restricted by fixed cycle length and offsets. A mixed-integer linear programming (MILP) model is proposed to optimize signal timings in a real-time manner, with platoon arrival and discharge dynamics at stop line modeled as constraints. Based on the individual passenger occupancy, the objective function aims at minimizing total personal delay for both buses and automobiles. With the communication between signals, PASC achieves to provide implicit coordination for the signalized arterials. Simulation results by VISSIM microsimulation indicate that PASC model successfully reduces around 40% bus passenger delay and 10% automobile delay, respectively, compared with signal timings optimized by SYNCHRO. Results from sensitivity analysis demonstrate that the model performance is not sensitive to the number fluctuation of bus passengers, and the requested CV penetration rate range is around 20% for the implementation.

Dynamic programming—neural network real-time traffic adaptive signal control algorithm

2006 ◽  
Vol 143 (1) ◽  
pp. 123-131 ◽  
Author(s):  
Dušan Teodorović ◽  
Vijay Varadarajan ◽  
Jovan Popović ◽  
Mohan Raj Chinnaswamy ◽  
Sharath Ramaraj
Keyword(s):  
Neural Network ◽  
Dynamic Programming ◽  
Real Time ◽  
Control Algorithm ◽  
Signal Control ◽  
Adaptive Signal Control ◽  
Real Time Traffic ◽  
Adaptive Signal

Effects of Connectivity and Traffic Observability on an Adaptive Traffic Signal Control System

2021 ◽  
pp. 036119812110130
Author(s):  
S M A Bin Al Islam ◽  
Mehrdad Tajalli ◽  
Rasool Mohebifard ◽  
Ali Hajbabaie
Keyword(s):  
Travel Time ◽  
Market Share ◽  
Signal Control ◽  
Market Penetration ◽  
Travel Time Reliability ◽  
Traffic Demand ◽  
Adaptive Signal Control ◽  
Loop Detector ◽  
Traffic Performance ◽  
Adaptive Signal

The effectiveness of adaptive signal control strategies depends on the level of traffic observability, which is defined as the ability of a signal controller to estimate traffic state from connected vehicle (CV), loop detector data, or both. This paper aims to quantify the effects of traffic observability on network-level performance, traffic progression, and travel time reliability, and to quantify those effects for vehicle classes and major and minor directions in an arterial corridor. Specifically, we incorporated loop detector and CV data into an adaptive signal controller and measured several mobility- and event-based performance metrics under different degrees of traffic observability (i.e., detector-only, CV-only, and CV and loop detector data) with various CV market penetration rates. A real-world arterial street of 10 intersections in Seattle, Washington was simulated in Vissim under peak hour traffic demand level with transit vehicles. The results showed that a 40% CV market share was required for the adaptive signal controller using only CV data to outperform signal control with only loop detector data. At the same market penetration rate, signal control with CV-only data resulted in the same traffic performance, progression quality, and travel time reliability as the signal control with CV and loop detector data. Therefore, the inclusion of loop detector data did not further improve traffic operations when the CV market share reached 40%. Integrating 10% of CV data with loop detector data in the adaptive signal control improved traffic performance and travel time reliability.

scholarly journals Adaptive Signal Control to Enhance Effective Green Times for Pedestrians: A Case Study

2020 ◽  
Vol 47 ◽  
pp. 704-711
Author(s):  
Gorkem Akyol ◽  
Ismet Goksad Erdagi ◽  
Mehmet Ali Silgu ◽  
Hilmi Berk Celikoglu
Keyword(s):  
Signal Control ◽  
Adaptive Signal Control ◽  
Adaptive Signal

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.

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