Abstract
While the importance of physical (social) distancing in reducing the spread of COVID-19 has been well-documented, implementing similar controls in public transit remains an open question. For instance, in the United States, guidance for maximum seating capacity in single-destination public transit settings, such as school buses, is only dependent on the physical distance between passengers. In our estimation, the available models/guidance are suboptimal/inefficient since they do not account for the possibility of passengers being from the same household. This paper discusses and addresses the aforementioned limitation through two types of physical distancing models. First, a mixed-integer programming model is used to assign passengers to seats based on the reported configuration of the vehicle and desired physical distancing requirement. In the second model, we present a heuristic that allows for household grouping. Through several illustrative scenarios, we show that seating assignments can be generated in near real-time, and the household grouping heuristic increases the capacity of the transit vehicles (e.g., airplanes, school buses, and trains) without increasing the risk of infection. A running application and its source code are available to the public to facilitate adoption and to encourage enhancements.
Highlights
Over the past year, the incidence of COVID-19 has increased exponentially, resulting in 91.9 million confirmed cases and 1.97 million deaths worldwide as of January 13, 2021 [?]
We show that seating assignments can be generated in near real-time, and the household grouping heuristic increases the capacity of the transit vehicles without increasing the risk of infection
To assist with the unprecedented challenges and restrictions faced by single-destination public transit operators, this paper highlights how prescriptive analytics can be used to assign passengers to seats while accounting for different social distancing requirements
Summary
The incidence of COVID-19 (caused by SARS-CoV-2) has increased exponentially, resulting in 91.9 million confirmed cases and 1.97 million deaths worldwide as of January 13, 2021 [?]. Our developed application utilizes and extends existing prescriptive analytics techniques/models to automate and optimize the seating problem accounting for different vehicle configurations and physical distancing requirements while providing flexibility to co-seat passengers from the same household. The graphical user interface developed as part of our application allows coordinators to input the data required by the models, namely: (a) a vehicle configuration; (b) the desired physical distancing; (c) a definition of groups if at least 2 passengers belong to the same household; and (d) the passenger boarding order, e.g., as determined by the bus route in a school bus schedule. The two models we consider are required to highlight that the relationship among passengers may play a significant role in determining the optimal seating arrangement, as couples or family members may sit in close proximity to each other This would not be the case for strangers during times of necessary physical distancing, such as during the COVID-19 pandemic. We share a link to the application’s source code to facilitate its adoption and the extension of our work in future research
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