Real-time rescheduling and disruption management for public transit

Abstract

This research is motivated by the operations of a public transit company in Hong Kong. We investigate how real-time information can be utilized in combination with historical data to improve routing and scheduling decisions practically. A dynamic integrated vehicle and crew scheduling problem is studied where travel times are stochastic and time-dependent. The objective is to maximize the route frequencies and mileage to provide good passenger service and simultaneously minimize crew overtime and meal-break delays. To mitigate unexpected delays due to uncertainties in operations, various mathematical models are proposed for revising the schedules in real time under a rolling-horizon framework. Their efficiency and effectiveness are evaluated via simulation using real-world data. The simulation results also identify the potential benefits of revising the schedule dynamically in real time using optimization models. The results show that the proposed approaches can significantly reduce motormen overtime and meal-break delays while maintaining coverage and route frequency requirements.

Passenger-oriented rescheduling of trains and rolling stock for handling large passenger demand: linearized models with train capacity constraint

“…The eigenmodel is a bi-objective model, which iterates between the three planning stages, defining different paths for the planning process based on the sequence of solving the problems. With a different focus, Lai and Leung (2018) studied the integrated problem of vehicle and crew scheduling, where travel times are time dependent and highly stochastic. The objective is to maximize the route frequencies and mileage to provide good passenger services and simultaneously minimize crew overtime and meal-break delays.…”

Bus scheduling considering trip‐varying travel times, vehicle availability and capacity

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Effect of track geometry quality on tram transport system indicators

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