CEE Professor Song Gao Selected as a PIT Fellow

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The Public Interest Technology Initiative at UMass Amherst (PIT@UMass) has selected Professor Song Gao of the Civil and Environmental Engineering (CEE) Department for the 2023 class of PIT Fellows. Gao’s PIT project is titled “An Efficient and Scalable Cooperative for Ride-Sourcing Drivers (COOP).” Ride-sourcing is a digital platform, utilized by private automobile owners, to offer on-demand, door-to-door rides for users requesting transportation.

The PIT initiative seeks to develop critical thinking, tech and social literacy, and pragmatic strategies needed to promote personal and professional social responsibility through the development of curricular, research, experiential, and outreach offerings across the diverse schools and colleges of UMass Amherst. Each PIT Fellowship provides the research team with from $5,000 to $8,000 in seed funding to support the groundwork or strengthen ongoing work that will ultimately be submitted for external funding.

Some of the prerequisites for PIT Fellow projects are that they address a complex problem with public interest impacts, involve the responsible use of information technologies, and reduce cultural, economic, and other societal disparities.

According to Gao, her PIT research “aims to generate behavioral insights and algorithmic tools for a ride-sourcing driver cooperative (COOP) to enable effective driver organization and eventual market efficiency and enhanced societal welfare.”

As Gao says about the need for her research, “The emergence of ride-sourcing platforms disrupts the transportation sector by linking passengers with ordinary car owners/drivers via smartphone apps. As cities and states grapple with their impacts on the taxi industry, transit ridership, and traffic congestion and emissions, issues of driver rights and protection have been in national headlines.”

Gao says she has three major research objectives with her PIT project. First of all, she seeks to understand drivers’ learning and choice behaviors using dynamic discrete choice models grounded on psychologically sound learning theories.

Secondly, Gao wants to develop model-based and model-free algorithms to optimize decisions on when to start and end working, where to search for passengers, and whether to accept a ride request, scalable to the level of driver participation.

Gao’s third and final objective is to implement a prototype of COOP with real drivers in the real world with behaviorally informed driver guidance synthesizing results from the two previous objectives.

In general, Gao’s research lab studies transportation system optimization and econometric models of traveler behavior, with applications in smart and shared mobility, transportation planning, and sustainable transportation systems.

Gao earned her M.S. and Ph.D. in Transportation from the Massachusetts Institute of Technology and her B.E. in Civil Engineering at Tsinghua University in Beijing, China. (April 2023)