Modifying the service patterns of public transport vehicles to account for the COVID-19 capacity

TL;DR

This paper presents a dynamic nonlinear programming model to optimize public transport service patterns during COVID-19, aiming to reduce overcrowding while managing passenger wait times and unserved demand.

Contribution

It introduces a novel decision support model that adjusts vehicle routes considering capacity constraints and passenger waiting times during the pandemic.

Findings

Model reduces vehicle overcrowding effectively

Analysis of trade-offs between unserved demand and waiting times

Demonstrated improvements on a real bus line in the Netherlands

Abstract

As public transport operators try to resume their services, they have to operate under reduced capacities due to COVID-19. Because demand can exceed capacity at different areas and across different times of the day, drivers have to refuse passenger boardings at specific stops. Towards this goal, many public transport operators have modified their service routes by avoiding to serve stops with high passenger demand at specific times of the day. Given the urgent need to develop decision support tools that can prevent the overcrowding of vehicles, this study introduces a dynamic integer nonlinear program that proposes service patterns to individual vehicles that are ready to be dispatched. In addition to the objective of satisfying the imposed vehicle capacity due to COVID-19, the proposed service pattern model caters for the waiting time of passengers. Our model is tested in a bus line connecting the university of Twente with its surrounding cities demonstrating the improvement in terms of vehicle overcrowding, and analyzing the potential negative effects related to unserved passenger demand and excessive waiting times.