Data-driven On-demand Service Dispatching
Emerging on-demand transport services, such as Uber and GoGoVan, usually face the dilemma of demand-supply imbalance, meaning that the spatial distributions of orders and drivers are imbalanced. Due to such imbalance, much supply resource is wasted while a considerable amount of order demand cannot be met in time. To address this dilemma, knowing the unmet demand in the near future is of high importance for service providers because they can dispatch their vehicles in advance to alleviate the impending demand-supply imbalance, we develop a general framework for predicting the unmet demand in the future time slots. Under this framework, we first evaluate the predictability of unmet demand in on-demand transport services and find that unmet demand is highly predictable. Then, we extract both static and dynamic urban features relevant to unmet demand from data sets in multiple domains. Finally, multiple prediction models are trained to predict unmet demand by using the extracted features.
- Wengen Li, Jiannong Cao, Jihong Guan, Shuigeng Zhou, Guanqing Liang, Winnie K. Y. So, and Michal Szczecinski: A General Framework for Unmet Demand Prediction in On-Demand Transport Services. IEEE Transactions on Intelligent Transportation Systems 2018: 2820-2830
- Yuqi Wang, Jiannong Cao, Lifang He, Wengen Li, Lichao Sun, and Philip S. Yu: Coupled Sparse Matrix Factorization for Response Time Prediction in Logistics Services. ACM CIKM 2017: 929-947
Dr. Wengen Li and Yu Yang.
Previous members include Dr. Yuqi Wang. Thanks for his contributions.