Optimizing Customized Bus Routing and Maximum Seat Occupancy Rate Under the Influence of Epidemic Outbreaks

Customized bus (CB) played an important role in mitigating the widespread transmission of viruses on public transit during epidemic outbreaks. However, there is limited research on the operational strategies of CB considering the influence of epidemics on passengers’ travel behavior. This paper presents a customized bus route planning and maximum seat occupancy rate setting optimization model under the assumption of elastic demand while considering the in-vehicle infection risk cost of passengers. The constraints are linearized, and the model is transformed into a mixed-integer quadratic programming problem. A hybrid algorithm based on genetic algorithm (GA) combined with simulated annealing (SA) operations and embedded descent local search is proposed to solve the route planning and maximum seat occupancy rate problem. Numerical studies are conducted to examine the model properties and the effectiveness of the hybrid algorithm. Results indicate that jointly optimizing routing and maximum seat occupancy rate of CB can significantly improve the profitability of CB while controlling total infection risk of passengers during epidemic outbreaks. Passengers’ perception on in-vehicle infection risk is helpful in reducing system infection risk. Neglecting this perception may prevent CB operators from achieving optimal operational efficiency and profitability due to overestimation of passenger demand. Notably, tightening the route length limit of CB is not always an effective way to mitigate system infection risk during an epidemic. The M-SAGA proposed in this paper is able to achieve significantly better solution quality especially for large-size networks.