Contextual Bandit-Based Sequential Transit Route Design under Demand Uncertainty
economics - operating costs, ridership - demand, planning - methods, planning - network design, planning - route design
Costs, Demand, Feedback control, Investments, Optimization, Planning, Public transit, Ridership, Routes and routing, Transit operating agencies, Uncertainty
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.
Permission to publish the abstract has been given by SAGE, copyright remains with them.
Yoon, G, & Chow, J.Y.J. (2020). Contextual Bandit-Based Sequential Transit Route Design under Demand Uncertainty. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2674(5), pp. 613-625.