Paper Info
Title
Improving the Information Disclosure in Mobility-on-Demand Systems
Abstract
ABSTRACTNowadays, the ubiquity of sharing economy and the booming of ride-sharing services prompt Mobility-on-Demand (MoD) platforms to explore and develop new business modes. Different from forcing full-time drivers to serve the dispatched orders, these modes usually aim to attract part-time drivers to share their vehicles and employ a 'driver-choose-order' pattern by displaying a sequence of orders to drivers as a candidate set. A key issue here is to determine which orders should be displayed to each driver. In this work, we propose a novel framework to tackle this issue, known as the Information Disclosure problem in MoD systems. The problem is solved in two steps combining estimation with optimization: 1) in the estimation step, we investigate the drivers' choice behavior and estimate the probability of choosing an order or ignoring the displayed candidate set. 2) in the optimization step, we transform the problem into determining the optimal edge configuration in a bipartite graph, then we develop a Minimal-Loss Edge Cutting (MLEC) algorithm to solve it. Through extensive experiments on both the simulation and the real-world data from Huolala business, the proposed method remarkably improves users experience and platform efficiency. Based on these promising results, the proposed framework has been successfully deployed in the real-world MoD system in Huolala.
Year
DOI
Venue
2021
10.1145/3447548.3467062
Knowledge Discovery and Data Mining
Keywords
DocType
Citations 
Mobility On-Demand, Information Disclosure, Dynamic Planning
Conference
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Yue Yang100.34
Yuan Shi262.24
Dejian Wang300.34
Qisheng Chen400.34
Lei Xu515421.38
Hanqian Li600.34
Zhouyu Fu700.34
Li Xin88227.55
Hao Zhang920758.59