T-Ridership: a web tool for reprogramming public transportation fleet to minimize COVID-19 transmission

Saba Imani, Majid Vahed and Mohammad Vahed

No casb7, SocArXiv from Center for Open Science

Abstract: Introduction As the outbreak of novel coronavirus disease (COVID-19) continues to spread rapidly throughout the world, steps are being taken to limit the impact on public health. In the realm of infectious diseases like COVID-19, social distancing is effective to avoid exposure to the virus and reduce its spread. However, current studies about public transit did not consider social distancing which plays a fundamental role in the current outbreak. Therefore, it is vital to study how to optimally manage public transit systems in order to minimize risks related to COVID-19. Methods In this study we present a novel web-based application, T-Ridership based on a hybrid optimized dynamic programming inspired by neural networks algorithm to optimize public transit for safety with respect to COVID-19. Two main steps are taken in the analysis through Metropolitan Transportation Authority (MTA): the first is detecting high-density stations by input data normalization, and then, using these results, the T-Ridership tool automatically determines optimal station order planning. Results We evaluated the performance of our web tool by comparing the results with real data extracted from MTA. The number of passengers in a route dropped significantly after normalization by T-Ridership. These results can be used in expanding on and improving policy in public transit, to better plan the scheduled time of trains in a way that prevents high-volume human contact, increasing social distance and reducing the possibility of disease transmission (available at: http://t-ridership.com).

Date: 2021-05-05
New Economics Papers: this item is included in nep-tre
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://osf.io/download/609189e619183d036755028f/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:casb7

DOI: 10.31219/osf.io/casb7

Access Statistics for this paper

More papers in SocArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().