Conference proceeding
Mean-Field Learning for Day-to-Day Departure Time Choice with Mode Switching
2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC, pp.4136-4141
IEEE Conference on Decision and Control
01/01/2023
DOI: 10.1109/CDC49753.2023.10384044
Abstract
Understanding travelers' day-to-day departure time choice (DDTC) is vital for managing traffic congestion, especially in multi-modal transportation systems. While providing real-time traffic information and alternative trip plans brings convenience to travelers, their collective travel patterns may conversely lead to unstable traffic equilibrium states. We investigate a DDTC problem with mode switching in this paper. A group of heterogeneous agents can adaptively choose their modes and departure times to minimize total travel costs in a dynamic game. Using a customized hierarchical soft actor-critic (HSAC) algorithm with a continuum approximation of other agents, the traffic dynamics will converge to an approximate Markovian Perfect Equilibrium (MPE). Our findings also shed light on changes in long-term travel behavior due to the widespread deployment of emerging mobility and travel information technology. This approach serves as a foundation for promoting intelligent travel plans through adaptive traffic control policies.
Details
- Title: Subtitle
- Mean-Field Learning for Day-to-Day Departure Time Choice with Mode Switching
- Creators
- Ben Wang - University of Michigan–Ann ArborQi Luo - Clemson UniversityYafeng Yin - University of Michigan–Ann Arbor
- Resource Type
- Conference proceeding
- Publication Details
- 2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC, pp.4136-4141
- Publisher
- IEEE
- Series
- IEEE Conference on Decision and Control
- DOI
- 10.1109/CDC49753.2023.10384044
- ISSN
- 0743-1546
- eISSN
- 2576-2370
- Number of pages
- 6
- Grant note
- CMMI-1904575; CMMI-2308750 / National Science Foundation; National Science Foundation (NSF)
- Language
- English
- Date published
- 01/01/2023
- Academic Unit
- Business Analytics
- Record Identifier
- 9984696710702771
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