Volume-based Probabilistic Approaches to Determining When to Turn on and off Signal Coordination Plans

Traffic signal coordination is an engineering tool used to enhance the quality of traffic flow, increase traffic throughput, reduce travel time and delay, and reduce number of stops by providing good progression along major arterials. While vehicles on major arterials benefit from signal coordination, side-street vehicles may infer additional delays since coordination plans demand longer cycle lengths than natural cycle lengths. Therefore, traffic engineers always face a significant question that is at what time of day signal coordination plans should be activated to maximize the benefits of vehicles on major arterials and minimize the disutility accrued to side-street vehicles? Basically, traffic volume, intersection spacing, and pedestrian volume are key factors in determining when coordination plans should be implemented. A few attempts have been previously made to develop models through consideration of those factors to determine when adjacent signals should be coordinated. However, it is shown that in practice these models are inefficient and instead traffic engineers should use their experience and engineering judgment to determine when to turn on coordination plans. More importantly, previous studies did not consider the delay of side-street vehicles and number of stops of major-street vehicles which are significant measure of effectiveness in assessing the performance of arterials.This study focused on developing guidelines for signals coordination from two aspects: (1) the delay that side-street vehicles experience at the stop bar without seeing vehicles pass by on major streets during coordination plans, and (2) the expected number of stops that vehicles on major streets will make when signals operate in actuated modes. Afterwards, a survey was conducted to find out at what level of traffic volume, traffic agencies tend to activate coordination plans. Then, the models' outputs and the results of the survey were compared with each other and consolidated to develop volume-based guidelines for signal coordination.

Level thesis:degree_level

Doctorate Degree

Year dc:date.issued

2015

Author dc:creator

• Andalibian, Rasool

Advisor dc:contributor.advisor

• Tian, Zong Z.

Committee members dc:contributor.committeemember

• Gibby, Reed
• Panorska, Anna
• Song, Shunfeng
• Xu, Hao

• Number of stops
• Probabilistic models
• Side-street delay
• Signal coordination
• Traffic volume

Statement dc:rights

• In Copyright(All Rights Reserved)

Handle dc:identifier.uri

http://hdl.handle.net/11714/2537

OAI identifier oai:identifier

oai:scholarwolf.unr.edu:11714/2537