Data-driven Distributionally Robust Optimization for Vehicle Balancing of Mobility-on-Demand Systems
Document Type
Article
Date of Original Version
1-1-2021
Abstract
With the transformation to smarter cities and the development of technologies, a large amount of data is collected from sensors in real time. Services provided by ride-sharing systems such as taxis, mobility-on-demand autonomous vehicles, and bike sharing systems are popular. This paradigm provides opportunities for improving transportation systems' performance by allocating ride-sharing vehicles toward predicted demand proactively. However, how to deal with uncertainties in the predicted demand probability distribution for improving the average system performance is still a challenging and unsolved task. Considering this problem, in this work, we develop a data-driven distributionally robust vehicle balancing method to minimize the worst-case expected cost. We design efficient algorithms for constructing uncertainty sets of demand probability distributions for different prediction methods and leverage a quad-tree dynamic region partition method for better capturing the dynamic spatialoral properties of the uncertain demand. We then derive an equivalent computationally tractable form for numerically solving the distributionally robust problem. We evaluate the performance of the data-driven vehicle balancing algorithm under different demand prediction and region partition methods based on four years of taxi trip data for New York City (NYC). We show that the average total idle driving distance is reduced by 30% with the distributionally robust vehicle balancing method using quad-tree dynamic region partitions, compared with vehicle balancing methods based on static region partitions without considering demand uncertainties. This is about a 60-million-mile or a 8-million-dollar cost reduction annually in NYC.
Publication Title, e.g., Journal
ACM Transactions on Cyber-Physical Systems
Volume
5
Issue
2
Citation/Publisher Attribution
Miao, Fei, Sihong He, Lynn Pepin, Shuo Han, Abdeltawab Hendawi, Mohamed E. Khalefa, John A. Stankovic, and George Pappas. "Data-driven Distributionally Robust Optimization for Vehicle Balancing of Mobility-on-Demand Systems." ACM Transactions on Cyber-Physical Systems 5, 2 (2021). doi: 10.1145/3418287.