Design of heterogeneous flexible-route public transportation networks under low demand
place - asia, mode - other, planning - network design, planning - methods, ridership - demand
Public transportation, Network design, Flexible service, Continuum approximation, Spatially heterogeneous
This paper presents design methods for a flexible-route transit system, in which vehicles travel within predetermined areas to provide door-to-door service. The main advantage of this system is that passengers no longer have to access transit stations in order to gain service. This system is suitable for low and heterogeneous passenger demand distribution as it features a hybrid system layout that includes both a hub-and-spoke network in the peripheral region and a grid network in the central region, along with heterogeneous local routes that address local demand variations. Continuum approximation (C.A.) is used to reduce the computation burden by formulating the design problem with respect to a few decision variables. We compare the performance of the proposed transit system with (i) the typical fixed-route system, (ii) the homogeneous flexible-route grid system, and (iii) the flexible-route grid system with local routes, in hypothetical settings. It is found through our numerical examples that the integration of the three proposed features (i.e. flexible transit, local tubes, and hybrid structure), as compared to counterparts with only two or fewer features, yields lower combined agency and user costs under the assumed low heterogeneous demand distribution. We then apply the design framework to a more realistic case for the City of Changzhi, China. An implementable design for Changzhi is developed, and its performance verified with simulations, demonstrating accuracy and applicability of the proposed continuum approximation model.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Petit, A., & Ouyang, Y. (2022). Design of heterogeneous flexible-route public transportation networks under low demand. Transportation Research Part C: Emerging Technologies, Vol. 138, 103612.