A model for modifying the public transport service patterns to account for the imposed COVID-19 capacity

• Public transport service pattern model that accounts for the pandemic-imposed capacity. Determine dynamically which stops of line should be served or skipped. Efficient can solved to global optimality lines with up 60 stops. Investigation trade-off between satisfying capacity and increasing passenger travel times. As public operators try resume their services, they have operate under reduced capacities due COVID-19. Because demand exceed at different areas across times day, drivers refuse boardings specific avoid overcrowding. Given urgent need develop decision support tools prevent overcrowding vehicles, this study introduces a dynamic integer nonlinear program derive optimal patterns individual vehicles are ready dispatched. In addition objective imposed vehicle COVID-19, proposed waiting time passengers. Our is tested in bus connecting University Twente its surrounding cities demonstrating in-vehicle crowding levels excessive unserved