Dynamic ridepooling is a promising shared mobility model that offers significant potential as a sustainable alternative to traditional transport. An analysis of New York City’s taxi data suggests that converting all existing taxi orders into dynamic ride-pooling could result in almost 100% of orders being matched with a ride. This would keep individual detour delays under two minutes and reduce transport capacity demand by 30%. This would substantially alleviate urban congestion and reduce carbon emissions. However, realizing this potential would impose stringent operational demands on platforms. The critical challenge lies in designing pricing strategies that incentivize passenger participation and driver acceptance, while ensuring the platform’s profitability. Conventional uniform discount models are inadequate due to significant variations in ride-matching success rates across different itineraries.

In response to this issue, Professor WANG Xiaolei’s research team from the Department of Management Science and Engineering has conducted a research into differentiated pricing for dynamic ridepooling services. Their seminal paper, “Optimizing OD-based up-front discounting strategies for enroute ridepooling services”, which was formally published in the prestigious journal Transportation Research Part B: Methodological, establishes a novel framework for operational optimization. Doctoral candidate WANG Siying of Tongji SEM is the first author, and PROFESSOR Wang is the corresponding author.