scholarly journals Traffic Signal Optimization under Connected-Vehicle Environment: An Overview

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jindong Wang ◽  
Shengchuan Jiang ◽  
Yue Qiu ◽  
Yang Zhang ◽  
Jianguo Ying ◽  
...  
Keyword(s):  
Research Field ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Future Research ◽  
Traffic Signal Control ◽  
Control Input ◽  
Connected Vehicle ◽  
Signal Optimization ◽  
The Arts ◽  
Control Pattern

Traffic signal optimization is a significant means for smoothing urban traffic flow. However, the operation of traffic signals is currently seriously constrained by the data available from traditional point detectors. In recent years, an emerging technology, connected vehicle (CV), which can percept the overall traffic environment in real time, has drawn researchers’ attention. With the new data source, traffic controllers should be able to make smarter decisions. A lot of work has been done to develop a new traffic signal control pattern under connected-vehicle environment. This paper provides a comprehensive review of these studies, aiming at sketching out the state of the arts in this research field. Several basic control problems, communication, control input, and objectives, are briefly introduced. The commonly used optimization models for this problem are summarized into three types: rule-based models, mathematical programming-based models, and artificial intelligence-based models. Then some major technical issues are discussed in detail. Finally, we raise the limitation of the existing studies and give our perspectives of the future research directions.

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.

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