Conference proceeding
The Rich and the Poor: A Markov Decision Process Approach to Optimizing Taxi Driver Revenue Efficiency
CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, Vol.24-28-, pp.2329-2334
01/01/2016
DOI: 10.1145/2983323.2983689
Abstract
Taxi services play an important role in the public transportation system of large cities. Improving taxi business efficiency is an important societal problem since it could improve the income of the drivers and reduce gas emissions and fuel consumption. The recent research on seeking strategies may not be optimal for the overall revenue over an extended period of time as they ignored the important impact of passengers' destinations on future passenger seeking. To address these issues, this paper investigates how to increase the revenue efficiency (revenue per unit time) of taxi drivers, and models the passenger seeking process as a Markov Decision Process (MDP). For each one-hour time slot, we learn a different set of parameters for the MDP from data and find the best move for a vacant taxi to maximize the total revenue in that time slot. A case study and several experimental evaluations on a real dataset from a major city in China show that our proposed approach improves the revenue efficiency of inexperienced drivers by up to 15% and outperforms a baseline method in all the time slots.
Details
- Title: Subtitle
- The Rich and the Poor: A Markov Decision Process Approach to Optimizing Taxi Driver Revenue Efficiency
- Creators
- Huigui Rong - Hunan UniversityXun Zhou - University of IowaChang Yang - Hunan UniversityZubair Shafiq - University of IowaAlex Liu - Michigan State University
- Resource Type
- Conference proceeding
- Publication Details
- CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, Vol.24-28-, pp.2329-2334
- Publisher
- Assoc Computing Machinery
- DOI
- 10.1145/2983323.2983689
- Number of pages
- 6
- Grant note
- 1566386 / Direct For Computer & Info Scie & Enginr; National Science Foundation (NSF); NSF - Directorate for Computer & Information Science & Engineering (CISE)
- Language
- English
- Date published
- 01/01/2016
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380526002771
Metrics
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