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. 2021 Mar 10:9:42985-42993.
doi: 10.1109/ACCESS.2021.3065298. eCollection 2021.

Seat Assignments With Physical Distancing in Single-Destination Public Transit Settings

Jane F Moore  1 , Arthur Carvalho  1 , Gerard A Davis  2 , Yousif Abulhassan  3 , Fadel M Megahed  1
Affiliations

Seat Assignments With Physical Distancing in Single-Destination Public Transit Settings

Jane F Moore et al. IEEE Access. .

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.

Keywords: Airplane boarding; COVID-19; mixed integer programming (MIP) model; operations research; prescriptive analytics; public transport; school bus seating.

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Figures

FIGURE 1.
FIGURE 1.
Bird’s-eye view of the proposed application. The numbers in parentheses represent the execution order.
FIGURE 2.
FIGURE 2.
User interface of the proposed application.
FIGURE 3.
FIGURE 3.
Input data concerning a vehicle’s layout.
FIGURE 4.
FIGURE 4.
Sample output for the “No Household” model.
FIGURE 5.
FIGURE 5.
Sample output for the “Household” model.
FIGURE 6.
FIGURE 6.
Sample output for an Airbus A220 seating plan.
See this image and copyright information in PMC

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