Journal article
cGAIL: Conditional Generative Adversarial Imitation Learning-An Application in Taxi Drivers' Strategy Learning
IEEE transactions on big data, Vol.8(5), pp.1288-1300
10/01/2022
DOI: 10.1109/TBDATA.2020.3039810
Abstract
Smart passenger-seeking strategies employed by taxi drivers contribute not only to drivers' incomes, but also higher quality of service passengers received. Therefore, understanding taxi drivers' behaviors and learning the good passenger-seeking strategies are crucial to boost taxi drivers' well-being and public transportation quality of service. However, we observe that drivers' preferences of choosing which area to find the next passenger are diverse and dynamic across locations and drivers. It is hard to learn the location-dependent preferences given the partial data (i.e., an individual driver's trajectory may not cover all locations). In this article, we make the first attempt to develop conditional generative adversarial imitation learning (cGAIL) model, as a unifying collective inverse reinforcement learning framework that learns the driver's decision-making preferences and policies by transferring knowledge across taxi driver agents and across locations. Our evaluation results on three months of taxi GPS trajectory data in Shenzhen, China, demonstrate that the driver's preferences and policies learned from cGAIL are on average 36.2 percent more accurate than those learned from other state-of-the-art baseline approaches.
Details
- Title: Subtitle
- cGAIL: Conditional Generative Adversarial Imitation Learning-An Application in Taxi Drivers' Strategy Learning
- Creators
- Xin Zhang - Worcester Polytechnic InstituteYanhua Li - Worcester Polytechnic InstituteXun Zhou - University of IowaJun Luo - [Machine Intelligence Center, Lenovo Group Limted, Telegraph Bay, Hong Kong, Hong Kong, (e-mail: jluo1@lenovo.com)]
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on big data, Vol.8(5), pp.1288-1300
- Publisher
- IEEE
- DOI
- 10.1109/TBDATA.2020.3039810
- ISSN
- 2332-7790
- eISSN
- 2332-7790
- Grant note
- IIS-1942680 (CAREER); CNS-1952085; CMMI-1831140; DGE-2021871 / National Science Foundation (10.13039/501100008982) Safety Research using Simulation University Transportation Center 69A3551747131 / U.S. Department of Transportation (10.13039/100000140)
- Language
- English
- Date published
- 10/01/2022
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380410902771
Metrics
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