Enhancing mass transit passenger safety during a pandemic via in-vehicle time minimization

The risk of infection in a pandemic increases with duration of close contact with an infected person. Since the application of social distancing in public transit vehicles is challenging, minimization of in-vehicle time can help to protect passengers from getting infected. Skip-stop operation is a viable strategy to reduce in-vehicle time as opposed to the conventional all-stop operation, and therefore can provide safer mobility for passengers. In this paper, a mixed integer linear programming model is formulated to minimize the in-vehicle time of passengers while operating an A/B stopping pattern (one of the most popular skip-stop strategies in the literature and in practice). Since the number of direct trips is necessarily decreased when using such a skip-stop operation, this paper quantifies the tradeoff between the in-vehicle time of passengers as a pandemic-based safety measure and the number of direct trips as a measure of passenger satisfaction. In order to efficiently solve problems with a large number of transit stops, a multi-start genetic algorithm is developed. Rigorous numerical experiments indicate that the proposed approach can reduce in-vehicle time by up to 34%. Furthermore, Pareto optimal solutions are obtained to exhibit the tradeoff between in-vehicle time savings and percentage of direct trips. •Sustainable operation for public transit to enhance passenger safety in a pandemic.•In-vehicle time minimization is adopted based on guidelines by health organizations.•A tradeoff is considered between in-vehicle time and percentage of direct trips.•Other benefits include compensating for absent drivers and easing social distancing.•An efficient solution methodology is developed for practical application.