Modeling of Optimal Bus Service Under the Influence of Epidemic Outbreaks

Epidemic outbreaks such as the COVID-19 pandemic could pose prodigious challenges to bus transit operations in that mitigating infection risks and sustaining mobility of travelers need to be simultaneously addressed. The current study introduces a bi-level model to help derive the optimal bus service strategy under the influence of epidemic outbreaks. This is achieved by successively executing the lower- and upper-level models along with solution algorithms for transit assignment and infected traveler estimation to minimize the weighted total travel cost of travel times of all travelers and medical treatments for newly infected travelers. Computational experiments are conducted for a 4-km urban bus transit corridor and a 7-square-km central business district (CBD) bus transit network, respectively. In absence of vaccination coverage, it reveals that the total travel cost is expected to increase for higher infection rates. Mandating social distancing rules is largely more effective than adjusting bus fleet sizes where a larger social distance becomes essential for higher infection rates. Infection risks could be significantly reduced when the vaccination coverage reaches certain threshold levels where no social distance becomes necessary. Model enhancements could be made to handle temporal and spatial variabilities of epidemic impacts and susceptible travelers.