Integrated design framework for on-demand transit system based on spatiotemporal mobility patterns

Document Type

Journal Article

Publication Date


Subject Area

place - urban, place - asia, mode - demand responsive transit, ridership - demand, technology - intelligent transport systems, operations - performance, planning - integration, planning - methods


On-demand transit, Spatiotemporal mobility pattern, Flexible transit service, Real-time vehicle routing, Demand classification, Passenger convenience, Operation efficiency


On-demand transit is a flexible transit service designed to adjust the service schedule and route based on passengers’ dynamic demand. The operation of on-demand transit operates in accordance with physical and socioeconomic environments, and demand patterns. In order to meet the diverse mobility needs in urban areas, integrating different transit services is essential to improve both passenger convenience and operational efficiency simultaneously. We propose a data-driven design framework for an on-demand transit system that operates three types of services: planned-and-inflexible (PI), planned-and-flexible (PF), and unplanned-and-flexible (UF), each with varying levels of responsiveness to real-time demand. We classify historical demand data into three classes based on their spatiotemporal density. Then, we use the trip data of each class to plan and operate the PI, PF, and UF services. The performance of the proposed system is evaluated using real public transit data from Sejong city. Simulation studies reveal that the proposed system outperforms the existing on-demand transit system. Specifically, we observe that the PI and PF services, which are planned based on the historical spatiotemporal mobility patterns, highly compatible with requests that follow the major mobility patterns. At the same time, the UF service, which offers real-time routing without prior planning, covers areas and times beyond those served by the PI and PF services that do not correspond to major mobility patterns. Furthermore, we found that the proposed system is flexible enough to accommodate various real-world demand patterns by proving suggestions on the optimal vehicle operation for each service.


Permission to publish the abstract has been given by Elsevier, copyright remains with them.


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