Improving Mobility Options through Transit Signal Priority (TSP)

Term Start:

June 1, 2024

Term End:

May 31, 2025

Budget:

$200,000

Keywords:

Mode Choice, Transit Service Quality, Transit Signal Priority, Travel Time Reliability, Underserved Communities

Thrust Area(s):

Data Modeling and Analytic Tools, Equity and Understanding User Needs

University Lead:

Georgia Institute of Technology

Researcher(s):

Michael Hunter

TSP seeks to optimize the interaction between busses and the infrastructure, creating a minimum resistance path for transit buses through signalized intersections. TSP may improve travel time reliability (TTR), schedule adherence, and ultimately the quality of service and ridership for transit systems. Bhat and Sardesai explicitly includes of TTR in mode choice, demonstrating the significance of this measure, with higher sensitivity seen for those with less flexible work schedules. The importance of TTR in mode choice is further shown through survey results by Li et al., as well as in the consideration of individual’s public transport route selection by Swierstra et al. Thus, improving transit service quality has high potential benefits, with transit disproportionately serving essential workers and traditionally underserved communities. Additionally, improved quality of service may encourage traveler mobility choice behavior to switch from personal vehicles to transit, with associated benefits of reduced congestion, vehicle miles traveled, and emissions. An efficiently designed TSP system can lead to a more sustainable and equitable transportation system.

Past TSP research has mainly focused on developing optimization strategies to improve bus performance, typically while limiting impacts to the general traffic. Although, the acceptable level of impact to general traffic (typically single occupancy vehicles) is a policy decision that should be explicitly considered by agencies, while recognizing the potential constraints such policies place on transit performance. Adaptive TSP with online optimization has been studied and are increasingly being piloted. Existing adaptive TSP algorithms are primarily analytical models and mathematical programming, integrating real-time data for traffic state definition and actuation triggering. Most of the proposed strategies need further evaluation and testing to achieve field ready status. Additionally, for widespread adoption, more efficient real-time optimization algorithms need to be developed.

Focusing on real-time operational control, this project will develop and test novel AI/ML based TSP actuation and optimization algorithms in a simulated environment. The algorithms shall seek to integrate automatic passenger counting (APC), automatic vehicle location (AVL), connected vehicle (CV) data, and real time signal phasing and timing (SPaT) data. The project builds on a recently completed Georgia Department of Transportation (GDOT) funded study that explored TSP fundamental principles in a simulated environment and laid the groundwork for more advanced TSP algorithms.

Project scope

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