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600451-00010

SWUTC Research Project Description

Enhanced Adaptive Signal Control using Dedicated Short Range Communications

University:  Texas A&M University

Principal Investigator:
Yunlong Zhang
Texas Transportation Institute
(979) 845-9902

Project Monitor:
Dr. Kaan Ozbay
Professor of Civil Engineering, Rutgers University
E-mail: kaan@rci.rutgers.edu

Funding Source:  USDOT and State of Texas General Revenues

Total Project Cost: $56,626

Project Number:  600451-00010

Date Started: 4/1/12

Estimated Completion Date:  3/31/13

Project Summary

Project Abstract:
The proposed project will focus on developing new adaptive signal control logic that utilizes the DSRC vehicle-to-infrastructure communications as the primary data collection method. We will explore the implications as well as the benefits that are set forth by enabling the collection of individual vehicle data and develop signal control logic using the collected data.

Project Objectives:
The proposed project will focus on developing new adaptive signal control logic that utilizes the DSRC vehicle-to-infrastructure communications as the primary data collection method. We will explore the implications as well as the benefits that are set forth by enabling the collection of individual vehicle data, investigate existing signal control logic for suitability, and develop/recommend control logic suitable for adaptive control with data from DSRC technology.

Task Descriptions:
Task 1: Literature Review
Literature review will be conducted in two areas. First is the review on current adaptive signal control algorithms. Second is to review the current version of the DSRC standard to better understand technical possibilities and constraints.

Task 2: Control Logic Development
New signal timing update algorithms will be developed based on newly available vehicle data made possible by the DSRC communication. If enhancements on existing adaptive control algorithms are found to be unbeneficial, new paradigm of adaptive signal control system will be explored.

Task 3: Data Collection for Case Study
Arterial corridors will be identified to collect time-varying traffic demand data for case studies.

Task 4: Simulation Evaluation
The developed adaptive control logic will be evaluated using microscopic simulation tools. Market penetration of DSRC units will be analyzed in the simulation to identify the threshold for the new control logics to be operational. Benefits in terms of arterial performance (e.g. travel time, total delays) will be assessed as well.

Task 5: Documentation and Reporting
The procedures, findings, lessons as well as all the algorithms developed will be documented. High-level system architectures and requirements will be identified in an initial attempt to develop field implementation plans.


Implementation of Research Outcomes:
Queue estimation is an unsolved problem, because traditionally detectors are at fixed locations.  Through this research, a model to estimate queue length using Connected Vehicle technology was developed.  In addition, market penetration ratio of the technology was also considered.

Products developed by this research:

Model Developed:  Queue estimation model and signal control logic based on estimated queue from Connected Vehicle technologies.

Journal Article:  A Real-Time Transit Signal Priority Control Model Considering Stochastic Bus Arrival Time, Yunlong Zhang, accepted for publication in the IEEE Transaction of Intelligent Transportation System Journal, 2014.

Journal Article Submitted for Review:  Queue Length Estimation Using Connected Vehicle for Adaptive Signal Control, Yunlong Zhang, submitted to IEEE Transaction of Intelligent Transportation System Journal, currently under review.


Impacts/Benefits of Implementation:

Research results could lead to a new type of adaptive signal control algorithm (based on queue lengths) which could help alleviate congestion by decreasing total delay and preventing queue overflow.


Web Links:

Final Technical Report