A novel bilevel programming model for designing feeder bus routes to simultaneously consider bus stop selection, bus routing, and passenger route/trip choice behavior is developed in this work. The objective of the upper-level model is to minimize the total in-vehicle travel time of passengers. The objective of the lower-level model is to determine bus stop locations and assign passenger demand to stops by minimizing the total passenger walking time. An ant colony optimization-based two-stage heuristic algorithm is developed to solve the bilevel programming model within an acceptance computation time. Different from conventional methods, the proposed methodology benefits from using real temporal and spatial characteristic datasets of travel demand aggregated from cellular data and traveling time and distance matrices under actual traffic conditions resulted from an open geographic information system (GIS) tool. Results of a real-life case study in Chongqing municipality, China, show that the proposed methodology could achieve a significant reduction of 2.9% total passenger travel time compared with a traditional method. Finally, sensitivity analyses are conducted to further understand the performance of the model. It is anticipated that the proposed methodology could be very useful in designing attractive and cost-effective feeder transit service in practice.

The COVID-19 pandemic has had serious adverse impacts on public transport service providers. Most public transport lines exhibit reduced ridership levels while, at the same time, some of them may exhibit passenger demand levels beyond the pandemic-imposed capacity limitations. This study models the problem of bus dispatching time optimization within a periodic rolling horizon optimization framework that reacts to travel time and passenger demand variations. This model allows public transport service providers to adjust their bus schedules periodically to avoid in-vehicle crowding beyond the pandemic-imposed capacity limit. The proposed model is a mixed-integer linear program that considers the possible changes to vehicle schedules and tries to minimize the number of vehicles required to perform the service while adhering to the COVID-19 capacity restrictions. Case study results from the implementation of our model on bus Line 2 in the Twente region in the Netherlands are provided demonstrating the potential gains when rescheduling the trip dispatching times and vehicle schedules.