Integrating COVID-19 health risks into crowding costs for transit schedule planning. (March 2022)

• Record Type: Journal Article
• Authors: Devasurendra, K.W.; Saidi, Saeid; Wirasinghe, S.C.; Kattan, Lina
• Is Part Of: Transportation research interdisciplinary perspectives. Volume 13(2022)
• Publication Date: 2022-03
• Subjects: COVID-19 and transit -- Optimum headway -- Pandemic health risks -- Transit scheduling; Transportation -- Periodicals; 388.05
• DOI: 10.1016/j.trip.2021.100522
• Languages: English
• ISSNs: 2590-1982

Abstract

Highlights: Finds the optimum headway of a bus line during a pandemic. Proposes methods to adapt transit operations to dynamic demand. A mathematical model is developed to represent the average cost of crowding. A methodology is proposed to incorporate the cost of perceived health risks. Models the value of ride time as a function of crowding and pandemic severity. Abstract: The public transport sector worldwide experienced the worst impact in recent history, in terms of ridership loss, due to the COVID-19 pandemic. The pandemic negatively affected passengers' perceptions of public transport and is likely to make a lasting impact on ridership, trip patterns, and modal share. Without any supportive changes to transit operations, ridership is likely to decline. This study explores the setting of frequencies in transit lines and proposes a two-part methodology that addresses the changing perceptions of users, especially in a health-related context. The first part develops a mathematical model that expresses the pre-COVID-19 cost of passenger crowding as an integral part of user costs to determine the optimal headway that considers the trade-offs between user and operator costs. A continuum approximation for the demand of the bus line has been used in the derivation. The second part extends the developed model to include both the costs of the health risks associated with the COVID-19 pandemic and crowding. The developed models will help transit planners and operators to plan and adapt operations to changing health risks during the pandemic and post-pandemic. Several numerical examples are provided to describe the uses and applications of the analytical models using information obtained from the literature.