Journal
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
Volume 131, Issue -, Pages 84-105Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trb.2019.11.005
Keywords
Ride-sourcing; Online matching; Matching time interval; Matching radius
Categories
Funding
- Hong Kong Research Grants Council [HKUST16208619]
- NSFC/RGC [N_HKUST627/18]
- Didi Chuxing
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With the availability of the location information of drivers and passengers, ride-sourcing platforms can now provide increasingly efficient online matching compared with physical searching and meeting performed in the traditional taxi market. The matching time interval (the time interval over which waiting passengers and idle drivers are accumulated and then subjected to peer-to-peer matching) and matching radius (or maximum allowable pick-up distance, within which waiting passengers and idle drivers can be matched or paired) are two key control variables that a platform can employ to optimize system performance in an online matching system. By appropriately extending the matching time interval, the platform can accumulate large numbers of waiting (or unserved) passengers and idle drivers and thus match the two pools with a reduced expected pick-up distance. However, if the matching time interval is excessively long, certain passengers may become impatient and even abandon their requests. Meanwhile, a short matching radius can reduce the expected pick-up distance but may decrease the matching rate as well. Therefore, the matching time interval and matching radius should be optimized to enhance system efficiency in terms of passenger waiting time, vehicle utilization, and matching rate. This study proposes a model that delineates the online matching process in ride-sourcing markets. The model is then used to examine the impact of the matching time interval and matching radius on system performance and to jointly optimize the two variables under different levels of supply and demand. Numerical experiments are conducted to demonstrate how the proposed modeling and optimization approaches can improve the real-time matching of ride-sourcing platforms. (C) 2019 Elsevier Ltd. All rights reserved.
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