Conference proceeding
TrajGAIL: Trajectory Generative Adversarial Imitation Learning for Long-Term Decision Analysis
2020 IEEE International Conference on Data Mining (ICDM), Vol.2020-, pp.801-810
11/2020
DOI: 10.1109/ICDM50108.2020.00089
Abstract
Mobile sensing and information technology have enabled us to collect a large amount of mobility data from human decision-makers, for example, GPS trajectories from taxis, Uber cars, and passenger trip data of taking buses and trains. Understanding and learning human decision-making strategies from such data can potentially promote individual's well-being and improve the transportation service quality. Existing works on human strategy learning, such as inverse reinforcement learning, all model the decision-making process as a Markov decision process, thus assuming the Markov property. In this work, we show that such Markov property does not hold in real-world human decision-making processes. To tackle this challenge, we develop a Trajectory Generative Adversarial Imitation Learning (TrajGAIL) framework. It captures the long-term decision dependency by modeling the human decision processes as variable length Markov decision processes (VLMDPs), and designs a deep-neural-network-based framework to inversely learn the decision-making strategy from the human agent's historical dataset. We validate our framework using two real world human-generated spatial-temporal datasets including taxi driver passenger-seeking decision data and public transit trip data. Results demonstrate significant accuracy improvement in learning human decision-making strategies, when comparing to baselines with Markov property assumptions.
Details
- Title: Subtitle
- TrajGAIL: Trajectory Generative Adversarial Imitation Learning for Long-Term Decision Analysis
- Creators
- Xin Zhang - Worcester Polytechnic InstituteYanhua Li - Worcester Polytechnic InstituteXun Zhou - University of IowaZiming Zhang - Worcester Polytechnic InstituteJun Luo - Lenovo
- Resource Type
- Conference proceeding
- Publication Details
- 2020 IEEE International Conference on Data Mining (ICDM), Vol.2020-, pp.801-810
- Publisher
- IEEE
- DOI
- 10.1109/ICDM50108.2020.00089
- ISSN
- 1550-4786
- eISSN
- 2374-8486
- Grant note
- 69A3551747131 / U.S. Department of Transportation (10.13039/100000140) IIS-1942680,CNS-1952085,CMMI-1831140,DGE-2021871,CCF-2006738 / NSF (10.13039/100000001)
- Language
- English
- Date published
- 11/2020
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380545802771
Metrics
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