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Contextual Bandit-Based Sequential Transit Route Design under Demand Uncertainty

Cited 6 time in Web of Science Cited 8 time in Scopus

Yoon, Gyu Geun; Chow, Joseph Y. J.

Issue Date
US National Research Council
Transportation Research Record, Vol.2674 No.5, pp.613-625
While public transit network design has a wide literature, the study of line planning and route generation under uncertainty is not so well covered. Such uncertainty is present in planning for emerging transit technologies or operating models in which demand data is largely unavailable to make predictions on. In such circumstances, this paper proposes a sequential route generation process in which an operator periodically expands the route set and receives ridership feedback. Using this sensor loop, a reinforcement learning-based route generation methodology is proposed to support line planning for emerging technologies. The method makes use of contextual bandit problems to explore different routes to invest in while optimizing the operating cost or demand served. Two experiments are conducted. They (1) prove that the algorithm is better than random choice; and (2) show good performance with a gap of 3.7% relative to a heuristic solution to an oracle policy.
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  • College of Engineering
  • Department of Civil & Environmental Engineering
Research Area Business & Economics, Environmental Sciences & Ecology, Transportation Engineering, 교통공학, 비즈니스 경제학, 환경 과학 및 생태학


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