Tri-level programming model for combined urban traffic signal control and traffic flow guidance

2016 ◽  
Vol 23 (9) ◽  
pp. 2443-2452 ◽  
Author(s):  
Zhi-yuan Sun ◽  
Hua-pu Lu ◽  
Wen-cong Qu
Keyword(s):  
Traffic Flow ◽  
Programming Model ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Signal Control

Node Importance Assessment of Traffic Complex Network Based on C-Means Clustering

2011 ◽  
Vol 211-212 ◽  
pp. 963-967 ◽  
Author(s):  
Li Wang ◽  
Lu Wei ◽  
Yong Zhong Zhang ◽  
Zheng Xi Li
Keyword(s):  
Traffic Flow ◽  
Complex Network ◽  
Control Area ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Node Degree ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Node Importance ◽  
Importance Assessment

Hub nodes of urban traffic complex network are very important for region traffic signal control. Traditionally, region traffic signal control system like SCOOT/SCATS use traffic flow, vehicle queue and distance between junctions as reference in sub-control-area selection practice, in which process engineers’ experience should play importance roles. In this paper, node degree, node betweeness and high peak hour traffic flow are selected as indexes for traffic network node importance assessment. Moreover, C-Means clustering is applied to analysis which junction could be act as a hub node for regional traffic control. To test the effectiveness of this method, urban network around Chang’an Street in Beijing including almost fifty nodes, China is used as trail field. Data result shows Changchunjie, FuyoujieNankou and Hepingmen junction have high clustering characteristics when clustering number are 3, 4 and 5. And the clustering center shows very similar prosperities with real hub node in practice. In conclusion, the multi-index and clustering analysis could provide theoretical support for urban traffic complex network hub node assessment.

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.

The effect of road traffic signal control cooperated with railroad crossing on surrounding traffic flow

2019 ◽  
Vol 2019.28 (0) ◽  
pp. 1012
Author(s):  
Kento OOE ◽  
Ryo ISHII ◽  
Bo YANG ◽  
Tsutomu KAIZUKA ◽  
Toshiyuki SUGIMACHI ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Traffic ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control

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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