Contextual Bandit-Based Sequential Transit Route Design under Demand Uncertainty
| Autor: | Joseph Y.J. Chow, Gyugeun Yoon |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
021103 operations research Operations research business.industry Computer science Mechanical Engineering 05 social sciences 0211 other engineering and technologies 02 engineering and technology Network planning and design Public transport 0502 economics and business business Transit (satellite) Civil and Structural Engineering |
| Zdroj: | Transportation Research Record: Journal of the Transportation Research Board. 2674:613-625 |
| ISSN: | 2169-4052 0361-1981 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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