Waiting time and headway modeling considering unreliability in transit service. (January 2022)

• Record Type: Journal Article
• Authors: Ansari Esfeh, Mohammad; Saidi, Saeid; Wirasinghe, S.C.; Kattan, Lina
• Is Part Of: Transportation research. Volume 155(2022)
• Page Start: 219
• Page End: 233
• Publication Date: 2022-01
• Subjects: Waiting time -- Headway modeling -- Transit system -- Optimum frequency -- Service irregularity -- Service reliability -- Demand-responsive service; Transportation -- Research -- Periodicals; 388.011
• DOI: 10.1016/j.tra.2021.11.015
• Languages: English
• ISSNs: 0965-8564

Abstract

Highlights: Waiting time models for feeder-trunk and demand responsive service are revisited. New formulation is developed to estimate mean waiting time for unreliable services. Waiting time of passengers at destination is overlooked in previous studies. Most of the previous studies underestimate the mean waiting time of passengers. Classic optimum headway formulations are updated using the proposed approach. Abstract: Waiting cost is usually considered as the highest cost imposed to passengers in transit systems. Efficient planning and operations of transit systems require accurate estimation of passengers' waiting time. While the assumption of half the headway, as the mean waiting time experienced by transit users, has been extensively used in waiting time cost estimation, it is not always a realistic assumption considering heterogeneous passengers, service irregularities, and different types of transit services. In addition, many transit studies considered the waiting times of passengers only at the origin, while waiting times can also be incurred at transfer points and the destination, the latter for passengers with fixed arrival time at their destination. In this paper, we developed new mean waiting time formulations for different transit systems, including feeder-trunk service, Dial-a-Ride service, and single route with unreliable service. All possible combinations of types of passenger (planning and non-planning, with fixed and flexible arrival time at destination) and service types (schedule-based/frequency-based and high-frequency/low-frequency) are considered to capture the underlying dynamics of transit systems. The developed approach in this paper could be utilised in transit studies to better model these underlying dynamics and to achieve better designs and more efficient operations.