On September 2, 2024, the second "Liyuan Mingli" Cross-Innovation Graduate Forum was successfully held online via Tencent Meeting. Professor Wang Xiaolei delivered an insightful academic report titled “Designing a Forward-Looking Matching Policy for Dynamic Ridepooling Service.” The forum was hosted by Associate Professor Wang Hong from SZUCM, and was attended by 64 faculty members, doctoral students, and master’s students from the College of Management.

Professor Wang Xiaolei is a professor at the School of Economics and Management, Tongji University. She received her bachelor's degree from the University of Science and Technology of China in 2008 (awarded the Guo Moruo Scholarship) and her Ph.D. from the Hong Kong University of Science and Technology in 2012 (awarded the HKUST SENG PhD Research Excellence Award). Her research focuses on urban transportation system optimization, particularly in the operational optimization of shared mobility services and urban traffic management under shared mobility. She has published over 30 papers in major SCI/SSCI journals in the field of transportation, including 13 in top-tier journals such as Transportation Research Part B and Transportation Science, with an average citation of over 80. She has led key, outstanding youth, general, and youth projects funded by the National Natural Science Foundation of China, and is a core member of the "Comprehensive Transportation System Operation Management" innovation group. In 2023, she received funding from the CCF-DiDi Gaia Scholars Research Fund. She serves as the chair of the Shared and On-demand Mobility Technology Committee at the World Transport Convention, a member of the Transportation Management Branch of the Society of Management Science and Engineering, and an editorial board member of Transportation Research Part E.

In her presentation, Professor Wang Xiaolei provided an in-depth introduction to the significant impact of on-demand dynamic ride-sharing service dispatch strategies on the platform's profitability and passenger experience. She highlighted the limitations of existing dispatch strategies, which often overlook upcoming pairing opportunities. She then detailed a forward-looking vehicle dispatch strategy, explaining how predictive information can be integrated into the dispatch algorithm to enhance order matching efficiency. By incorporating the expected distance savings from predicted future orders into the weight design, she proposed a method to set passenger pick-up priorities, optimizing the dispatch strategy to achieve better performance in response rates and total distance savings. Additionally, she validated the model's superior performance through experiments using grid networks and real-world road networks in Haikou.

Following the presentation, there was a lively discussion where faculty and students engaged with Professor Wang Xiaolei on topics such as the accuracy of prediction models, factors influencing priority weights, practical applications of the model, and potential future improvements, including handling dynamic uncertainties. Professor Wang provided patient and detailed responses to all questions, greatly benefiting the attendees.