When Liteng Zha was choosing his undergraduate major at Southeast University in China, he thought the choice was obvious.

“I had family suggest computer science and different engineering fields,” Zha said. “But transportation always felt more relevant to me because you deal with it every day.”

Within his studies, Zha has focused on the development of emerging technologies.

“Transportation research is no longer just about counting cars and signal timing,” he explained. “There is so much more now going on such as automated technologies with an increasing need in the models and tools to understand them particularly with the ubiquitous data.”

As a master’s student in civil engineering at the Southwest Region University Transportation Center (SWUTC) at Texas A&M, his research focused specifically on traffic simulation. Working on a SWUTC-funded project, Zha assisted on developing a safety performance monitoring system by building a simulation test bed and developing algorithms to extract safety performance indicators. In another joint project between the Federal Highway Administration, the Batelle Memorial Institute and Siemens Corporation, he built a simulation platform for connected highway and vehicle systems.

Now two years into his Ph.D. program at the University of Florida Transportation Institute (UFTI), Zha’s research has shifted focus to ride-sourcing applications such as Uber and Lyft. Under UFTI faculty member Dr. Yafeng Yin, he has been working on a National Science Foundation (NSF)-funded project related to this topic.

Zha’s focus on this NSF project is whether ride-sourcing applications should be regulated and how. Some questions being asked include: Is competition between ride-sharing platforms and regular taxis socially beneficial? Given the drivers have more flexibility in determining daily shifts and platform, has the authority in implementing price instruments such as surge pricing? What does the temporal and spatial distributions of drivers and customers looks like?

With the assistance of 2015 TRIP Intern Stephen Spana, Zha also designed a ride-sourcing vehicle simulation using an agent-based simulation program called NetLogo. In the simulation, taxis searched for customers within a simplified transportation network. They observed how properties like vehicle search time and customer wait time changed as the distance over which customers could find taxis increased. At lower matching distances the simulation behaved like a traditional network of street hailing (where the ability to find a taxi is limited by sight distance), and at higher matching distances the simulation behaved like a network of ride-sourcing vehicles (where customers can find vehicles via smartphones).

“I think that undergraduates interested in Transportation benefit a lot from internship programs like TRIP,” Zha said. “It’s a lot of hard work but they learn so much about what is involved in research.”

Zha is currently aiming to finish his Ph.D. in a year. Ideally, he would like a position in the industry, where he can use his transportation skills with emerging technologies.