Conference proceeding
Unveiling Taxi Drivers' Strategies via cGAIL - Conditional Generative Adversarial Imitation Learning
2019 19TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2019), Vol.2019-, pp.1480-1485
IEEE International Conference on Data Mining
01/01/2019
DOI: 10.1109/ICDM.2019.00194
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 paper, 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 34.7% more accurate than those learned from other state-of-the-art baseline approaches.
Details
- Title: Subtitle
- Unveiling Taxi Drivers' Strategies via cGAIL - Conditional Generative Adversarial Imitation Learning
- Creators
- Xin Zhang - Worcester Polytechnic InstituteYanhua Li - Worcester Polytechnic InstituteXun Zhou - University of IowaJun Luo - Lenovo
- Contributors
- Jianyong Wang (Editor)Kyuseok Shim (Editor)Xindong Wu (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- 2019 19TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2019), Vol.2019-, pp.1480-1485
- Publisher
- IEEE
- Series
- IEEE International Conference on Data Mining
- DOI
- 10.1109/ICDM.2019.00194
- ISSN
- 1550-4786
- eISSN
- 2374-8486
- Number of pages
- 6
- Grant note
- CNS-1657350; CMMI-1831140; IIS-1566386 / NSF; National Science Foundation (NSF) DiDi Chuxing Inc.
- Language
- English
- Date published
- 01/01/2019
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380450602771
Metrics
20 Record Views