Research

Current WORK 

Emerging mobility services, such as on-demand hailed or shared rides and time-sensitive deliveries are empowered by the recent advancement in digital technologies. Many of them are proven to fulfill mobility needs with improved quality and convenience. Future mobility options are anticipated to be further transformed by the development and maturity of connected, autonomous, shared, and electrified (CASE) vehicles. Novel service concepts and vehicular technologies are jointly reshaping the supply of mobility options and thus inducing a significantly increased level of demand, that is often beyond the capacity of existing systems and infrastructures. Overloaded systems are prone to excessive congestion and emission, leading to degradation of system performance and detrimental impacts on the environment. 

My research is thereby motivated to inform system design and operations in response to disruptive demand, which are efficient, effective, sustainable, resilient, and robust against uncertainties. In the following sections, I introduce 2 streams of my current research that address the planning of emerging mobility systems from different but complementary perspectives. 

• The first line of work presents stochastic and computational modeling frameworks to evaluate the dynamic capacity of transportation facility design alternatives in steady state. The service capacity, as a critical component of transportation supply, is estimated and maximized subject to limited resources. I am honored to be supported by the Airport Cooperative Research Program (ACRP) Graduate Research Award (sponsored by the Federal Aviation Administration) and recognized by the Autonomous Transportation and Connected Roads (ATCR) Seed Grant Program, which is a collaborative force by Georgia Tech Research Institute, the city of Peachtree Corners in Georgia, and Delta. More details can be found here (click it to be re-directed).

• The second line of work leverages interpretable machine learning techniques to enhance understanding of mobility demand and travel behavior to inform transportation planning and policymaking. Specifically, my work unravels how socioeconomic and demographic factors influence the adoption of ride-hailing services and informs socially aware policymaking for low-income groups. Another paper considers the problem of demand inference based on incomplete public transit data using optimization methods.  

For more details, please feel free to email me and ask for manuscripts or presentation slides.