Urban traffic signal control with connected and automated vehicles: A survey

2019 ◽  
Vol 101 ◽  
pp. 313-334 ◽  
Author(s):  
Qiangqiang Guo ◽  
Li Li ◽  
Xuegang (Jeff) Ban
Keyword(s):  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Automated Vehicles

Extremum-Seeking for Adaptation of Urban Traffic Signal Control

2014 ◽  
Vol 47 (3) ◽  
pp. 5067-5072 ◽  
Author(s):  
Ronny Kutadinata ◽  
Will Moase ◽  
Chris Manzie ◽  
Lele Zhang ◽  
Tim Garoni
Keyword(s):  
Traffic Signal ◽  
Urban Traffic ◽  
Extremum Seeking ◽  
Signal Control ◽  
Traffic Signal Control

scholarly journals Software-defined Architecture for Urban Regional Traffic Signal Control

2020 ◽  
Vol 32 (2) ◽  
pp. 229-236
Author(s):  
Songhang Chen ◽  
Dan Zhang ◽  
Fenghua Zhu
Keyword(s):  
Traffic Congestion ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Signal Control ◽  
The Novel ◽  
Simulation Experiments ◽  
Microscopic Traffic Simulation ◽  
Control Devices ◽  
Innovation Platform

Regional Traffic Signal Control (RTSC) is believed to be a promising approach to alleviate urban traffic congestion. However, the current ecology of RTSC platforms is too closed to meet the needs of urban development, which has also seriously affected their own development. Therefore, the paper proposes virtualizing the traffic signal control devices to create software-defined RTSC systems, which can provide a better innovation platform for coordinated control of urban transportation. The novel architecture for RTSC is presented in detail, and microscopic traffic simulation experiments are designed and conducted to verify the feasibility.

Analysis of Trends in Data on Transit Bus Dwell Times

2017 ◽  
Vol 2619 (1) ◽  
pp. 64-74 ◽  
Author(s):  
Isaac K. Isukapati ◽  
Hana Rudová ◽  
Gregory J. Barlow ◽  
Stephen F. Smith
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Distribution Model ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Real Time Control ◽  
Arrival Times ◽  
On The Road

Transit vehicles create special challenges for urban traffic signal control. Signal timing plans are typically designed for the flow of passenger vehicles, but transit vehicles—with frequent stops and uncertain dwell times—may have different flow patterns that fail to match those plans. Transit vehicles stopping on urban streets can also restrict or block other traffic on the road. This situation results in increased overall wait times and delays throughout the system for transit vehicles and other traffic. Transit signal priority (TSP) systems are often used to mitigate some of these issues, primarily by addressing delay to the transit vehicles. However, existing TSP strategies give unconditional priority to transit vehicles, exacerbating quality of service for other modes. In networks for which transit vehicles have significant effects on traffic congestion, particularly urban areas, the use of more-realistic models of transit behavior in adaptive traffic signal control could reduce delay for all modes. Estimating the arrival time of a transit vehicle at an intersection requires an accurate model of dwell times at transit stops. As a first step toward developing a model for predicting bus arrival times, this paper analyzes trends in automatic vehicle location data collected over 2 years and allows several inferences to be drawn about the statistical nature of dwell times, particularly for use in real-time control and TSP. On the basis of this trend analysis, the authors argue that an effective predictive dwell time distribution model must treat independent variables as random or stochastic regressors.

scholarly journals A Systematic Literature Review of Researches on Urban Traffic Strategy Development

2017 ◽  
Vol 3 (1) ◽  
pp. 14
Author(s):  
Zhang Lin ◽  
Cheng Wei ◽  
Wang Wei ◽  
Li Yinan ◽  
Xiao Haochen
Keyword(s):  
Traffic Signal ◽  
Urban Traffic ◽  
Current Status ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Network ◽  
Optimization Strategy ◽  
Advantages And Disadvantages ◽  
Research Perspectives ◽  
Traffic Optimization

Abstract—With the advancement of computer science and the development of urban economy, the interest of human research on urban traffic strategy has been promoted. Number of vehicles in urban traffic network in a sharp increase, in order to solve the current status of China's traffic congestion, we hope to reduce urban vehicles greenhouse gas emissions and to reduce waiting time is a serious problem currently facing the city traffic. In order to solve this problem, it can be from two aspects. On the one hand, traffic signal control of traffic network, the other is to optimize the route of the vehicle. This paper respectively from tells the development of the traffic signal control strategy and vehicle routing process, and compares their advantages and disadvantages. The paper summarizes the urban traffic strategy and traffic optimization strategy in recent years, and systematically summarizes the present situation and existing problems of urban traffic optimization strategies at home and abroad, summarizes the development prospects of urban traffic optimization strategies, and provides the strategies for traffic optimization. In order to provide the strategy of scholars engaged in the transportation of new research perspectives and research data.

scholarly journals Evaluation and Application of Urban Traffic Signal Optimizing Control Strategy Based on Reinforcement Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Yizhe Wang ◽  
Xiaoguang Yang ◽  
Yangdong Liu ◽  
Hailun Liang
Keyword(s):  
Reinforcement Learning ◽  
Hierarchical Control ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Learning Ability ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Multidimensional Space ◽  
High Flows ◽  
Multiple Intersections

Reinforcement learning method has a self-learning ability in complex multidimensional space because it does not need accurate mathematical model and due to the low requirement for prior knowledge of the environment. The single intersection, arterial lines, and regional road network of a group of multiple intersections are taken as the research object on the paper. Based on the three key parameters of cycle, arterial coordination offset, and green split, a set of hierarchical control algorithms based on reinforcement learning is constructed to optimize and improve the current signal timing scheme. However, the traffic signal optimization strategy based on reinforcement learning is suitable for complex traffic environments (high flows and multiple intersections), and the effects of which are better than the current optimization methods in the conditions of high flows in single intersections, arteries, and regional multi-intersection. In a word, the problem of insufficient traffic signal control capability is studied, and the hierarchical control algorithm based on reinforcement learning is applied to traffic signal control, so as to provide new ideas and methods for traffic signal control theory.

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