Ph.D. Final Oral Exam: Ye Tian

Event
Speaker: 
Ye Tian
Wednesday, May 4, 2022 - 1:15pm
Event Type: 

Trip-vehicle Matching and Vehicle Routing Optimization for Ride-Sharing

In recent years, ride-sharing systems have emerged as one of the quintessential examples of sharing economy that can effectively leverage excessive and under-utilized vehicle resources to address many challenges in modern transportation systems (e.g., CO$_2$ emissions, growing fuel prices) and achieve "triple-win'' between the riders, enlisted drivers, and the ride-sharing platform. Lying at the heart of most ride-sharing systems is the problem of joint trip-vehicle matching and routing optimization, which is highly challenging and results in this area remain rather limited. In this thesis, we studied both planning setting and real-time setting for ride-sharing problems.

In the planning setting, available drivers and rider demands are given beforehand. We proposed an analytical framework to state the problem and reformulate it as a mixed-integer linear program, which can be solved by global optimization methods. To efficiently solve large-sized problem instances, we developed a memory-augmented time-expansion (MATE) approach which leverages the special problem structure to facilitate approximate (or even exact) algorithm designs.

In the real-time setting, rider demands are not available in advance. We proposed a reinforcement learning formulation for ride-sharing that jointly optimizes the rewards and experiences from the perspectives of the drivers and riders, respectively.  Then we developed a reactive model-free deep RL approach based on proximal policy optimization (PPO) to solve the joint trip-vehicle matching and routing optimization problem. Finally, we conduct extensive simulations to analyze and verify the performance of our ride-sharing system using real-world datasets. Simulations results show that the proposed framework outperforms a greedy and the receding horizon control (RHC) algorithms under all testing demand patterns. 

Committee: Jia Liu (co-major professor), Hridesh Rajan (co-major professor), Pavan Aduri, Qi Li, and Wensheng Zhang

Join on WebEx: https://iastate.webex.com/iastate/j.php?MTID=m41edb00b66db6bdac1057ce7db3714cf