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Traffic Simulation and Signal Plan Design

Sponsored by: Texas Department of Transportation (total $210,698 in two phases: $60,861 + $149,837), as PI

This project aims to develop a fully collaborative vehicle-signal control system based on the integration of traffic signals, individual vehicles and on-board loop detectors. The system is anticipated to optimize network performance, including vehicle travel time, stop delay, vehicle emissions and fuel consumption and road congestion levels in the vicinities of intersections.

The research utilizes communications among detectors, connected vehicles and signals to gather real-time traffic conditions and to make short-term traffic predictions, such as short-term vehicle arrival rate and vehicle queue length predictions. Based on the prediction system, signal timing and coordination plan are determined by adaptive cycle length and phasing plan. The major objectives of the project are to mitigate traffic congestion and to reduce travel time on arterials.

The proposed signal plan was tested in VISSIM simulation using the real collected traffic data. Moreover, the package ASC/3 was directly employed in VISSIM simulation, which is able to simulate the operation of signal control box ASC/3, which is used by TxDOT in signal control.

The proposed proactive signal control was successfully tested in one testbed in December 2016. This project won Research “SWEET SIXTEEN” 2017, American Association of State Highway and Transportation Officials (AASHTO) in May 2017.

In 2017-2018, TxDOT continued to fund my group an additional $149,837 to implement this system at 30 intersections in the Houston Metropolitan Area. 

Routing and Traffic Assignment Problems in Stochastic Transportation Networks

Sponsored by: Â鶹ӳ»­Ó°Òô Research Enhancement Grant ($5,000), as PI

Since 2006, working as a Ph.D. student at Northwestern University, Evanston, IL, I started my research in the field of transportation system modeling and analysis, with a focus on the routing and traffic assignment problems in a stochastic transportation system. The uncertainty of travel time is usually caused by traffic congestion. The Federal Highway Administration estimated that 50-60% of congestion delays are non-recurrent, which makes the travel time unreliable.

To improve the travel time reliability, it is very important to provide motorists with the correct information about routes’ travel time reliability and to understand the impact of travelers’ routing behavior on the whole network flow. My findings are summarized in more than 10 papers published in Transportation Research Part B, Transportation Research Part A, Network and Spatial Economics, etc.

Recently, using the models and algorithm I proposed, my research team studied the travel time reliability of major corridors in the Houston Metropolitan Area. Results were reported as follows: