scholarly journals A Review of the Self-Adaptive Traffic Signal Control System Based on Future Traffic Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yizhe Wang ◽  
Xiaoguang Yang ◽  
Hailun Liang ◽  
Yangdong Liu
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Control Method ◽  
Autonomous Driving ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Methods ◽  
Adaptive Traffic Signal Control ◽  
Self Learning ◽  
Self Adaptive

The self-adaptive traffic signal control system serves as an effective measure for relieving urban traffic congestion. The system is capable of adjusting the signal timing parameters in real time according to the seasonal changes and short-term fluctuation of traffic demand, resulting in improvement of the efficiency of traffic operation on urban road networks. The development of information technologies on computing science, autonomous driving, vehicle-to-vehicle, and mobile Internet has created a sufficient abundance of acquisition means for traffic data. Great improvements for data acquisition include the increase of available amount of holographic data, available data types, and accuracy. The article investigates the development of commonly used self-adaptive signal control systems in the world, their technical characteristics, the current research status of self-adaptive control methods, and the signal control methods for heterogeneous traffic flow composed of connected vehicles and autonomous vehicles. Finally, the article concluded that signal control based on multiagent reinforcement learning is a kind of closed-loop feedback adaptive control method, which outperforms many counterparts in terms of real-time characteristic, accuracy, and self-learning and therefore will be an important research focus of control method in future due to the property of “model-free” and “self-learning” that well accommodates the abundance of traffic information data. Besides, it will also provide an entry point and technical support for the development of Vehicle-to-X systems, Internet of vehicles, and autonomous driving industries. Therefore, the related achievements of the adaptive control system for the future traffic environment have extremely broad application prospects.

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.

Estimation of Crash Modification Factors for an Adaptive Traffic-Signal Control System

2016 ◽  
Vol 142 (12) ◽  
pp. 04016061 ◽  
Author(s):  
Jiaqi Ma ◽  
Michael D. Fontaine ◽  
Fang Zhou ◽  
Jia Hu ◽  
David K. Hale ◽  
...  
Keyword(s):  
Control System ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Adaptive Traffic Signal Control ◽  
Crash Modification Factors

ACS-Lite Algorithmic Architecture: Applying Adaptive Control System Technology to Closed-Loop Traffic Signal Control Systems

2003 ◽  
Vol 1856 (1) ◽  
pp. 175-184 ◽  
Author(s):  
Felipe Luyanda ◽  
Douglas Gettman ◽  
Larry Head ◽  
Steven Shelby ◽  
Darcy Bullock ◽  
...  
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Control Systems ◽  
Closed Loop ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Adaptive Control System ◽  
Traffic Conditions ◽  
Run Time ◽  
Traffic Signal Control Systems

ACS-Lite is being developed by FHWA to be a cost-effective solution for applying adaptive control system (ACS) technology to current, state-of-the-practice closed-loop traffic signal control systems. This effort is intended to make ACS technology accessible to many jurisdictions without the upgrade and maintenance costs required to implement ACS systems that provide optimized signal timings on a second-by-second basis. The ACS-Lite system includes three major algorithmic components: a time-of-day (TOD) tuner, a run-time refiner, and a transition manager. The TOD tuner maintains plan parameters (cycle, splits, and offsets) as the long-term traffic conditions change. The run-time refiner modifies the cycle, splits, and offsets of the plan that is currently running based on observation of traffic conditions that are outside the normal bounds of conditions this plan is designed to handle. The run-time refiner also determines the best time to transition from the current plan to the next plan in the schedule, or, like a traffic-responsive system, it might transition to a plan that is not scheduled next in the sequence. The transition manager selects from the transition methods built in to the local controllers to balance the time spent out of coordination with the delay and congestion that is potentially caused by getting back into step as quickly as possible. These components of the ACS-Lite algorithm architecture are described and the similarities and differences of ACS-Lite with state-of-the-art and state-of-the-practice adaptive control algorithms are discussed. Closed-loop control system characteristics are summarized to give the context in which ACS-Lite is intended to operate.

Assessment of self-learning adaptive traffic signal control on congested urban areas: independent versus coordinated perspectives

2015 ◽  
Vol 42 (6) ◽  
pp. 353-366 ◽  
Author(s):  
Hossam Abdelgawad ◽  
Baher Abdulhai ◽  
Samah El-Tantawy ◽  
Alireza Hadayeghi ◽  
Brue Zvaniga
Keyword(s):  
Urban Areas ◽  
Adaptive System ◽  
Synergetic Effect ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Base Case ◽  
Case Scenario ◽  
Adaptive Traffic Signal Control ◽  
Self Learning

In this paper, we introduce a simulation testbed framework to evaluate the performance of a self-learning adaptive traffic signal control system. The core contribution of this paper is the assessment of the system’s two modes of operations (independent versus coordinated) under different congestion levels and network configurations. The insights and conclusions of the paper are based on the synergetic effect of the following: (1) appropriate design of the adaptive system parameters, (2) seamless design of generic interfaces between the adaptive system and the simulation environment using application programming interfaces, (3) rigorously calibrated simulation model and a comprehensive set of performance and environmental measures, and (4) investigation of the system components required for building a complete functioning system in the field. The system was designed and lab-tested on two case studies in the City of Burlington, Ontario. The intersections were designed and operated using the adaptive system and compared to the actuated optimized and coordinated base case timings plans. The analysis of the simulation results shows that overall the adaptive system outperforms the base case scenario by up to 25% savings in delay at the network level, and 15% reduction in CO2 emission. On the other hand, the results of the two testbed models indicate that the performance of the adaptive system varies according to the intersection conditions and flows, network configuration, traffic volume, variability in flow arrivals, and the proximity of intersections to each other.

Sign in / Sign up

Export Citation Format

Share Document