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

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).