The COVID & Transit Operations and Management Session describes how transit operation is affected by and reacts to the COVID-19 pandemic.
Full Details about the Paper Session can be found here: COVID & Transit Operations and Management Papers
Authors: Pramesh Kumar, Alireza Khani, University of Minnesota, Eric Lind, John Levin, Metro Transit, Minneapolis-St. Paul
We study the potential spread of infectious disease through passenger encounters in a public transit system using Automatic Passenger Count (APC) data. An algorithmic procedure is proposed to evaluate three different measures to quantify these encounters. The first two measures quantify the increased possibility of disease spread from passenger interaction when traveling between different origin-destination pairs. The third measure evaluates an aggregate measure quantifying the relative risk of boarding at a particular stop of the transit route considering the likelihood of alighting at other stops. For calculating these measures, we employ compressed sensing to estimate a sparse matrix planted in the underdetermined system of equations obtained from the APC data. Using the APC data of Route 5 in Minneapolis/St. Paul region during the COVID-19 pandemic, we found that all three measures grow abruptly with the number of passengers on board. The passenger contact network is densely connected, which further increases the potential risk of disease transmission. To reduce the relative risk, we propose to restrict the number of passengers on-board and analyze its effect using a simulation framework. We found that, for the particular test on route 5, a considerable reduction in the relative risk can be achieved when the maximum number of passengers on-board is restricted below 15. To account for the reduced capacity and still maintain reasonable passenger wait times, we would then need to increase the frequency of the route.
Authors: Michael Wilbur, Anna Ouyang, Riyan Kabir, Abhiram Vadali, Abhishek Dubey, Vanderbilt University, Aifya Ayman, Vincent Poon, Aron Laszka, University of Houston, Philip Pugliese, Chattanooga Area Regional Transportation Authority, Dan Freudberg, Nashville Metro
Public transit is central to cultivating equitable communities. Meanwhile, the novel coronavirus COVID-19 and associated social restrictions has radically transformed ridership behavior in urban areas. Perhaps the most concerning aspect of the COVID-19 pandemic is that low-income and historically marginalized groups are not only the most susceptible to economic shifts but are also most reliant on public transportation. As revenue decreases, transit agencies are tasked with providing adequate public transportation services in an increasingly hostile economic environment. Transit agencies therefore have two primary concerns. First, how has COVID-19 impacted ridership and what is the new post-COVID normal? Second, how has ridership varied spatio-temporally and between socio-economic groups? In this work we provide a data-driven analysis of COVID-19's affect on public transit operations and identify temporal variation in ridership change. We then combine spatial distributions of ridership decline with local economic data to identify variation between socio-economic groups. We find that in Nashville and Chattanooga, TN, fixed-line bus ridership dropped by 66.9\% and 65.1\% from 2019 baselines before stabilizing at 48.4\% and 42.8\% declines respectively. The largest declines were during morning and evening commute time. Additionally, there was a significant difference in ridership decline between the highest-income areas and lowest-income areas (77\% vs 58\%) in Nashville.
Authors: Hongyuan Yang, Yu Nie, Northwestern University
In this study, we analyze the risk involved in riding various transit modes in the aftermath of COVID-19. The goal is to identify which factors are related to this risk, how such a relationship can be represented in a manner amenable to analysis, and what a transit operator can do to mitigate the risk while running its service as efficiently as possible. The proposed infection risk model is sensitive to such factors as prevalence of infection, baseline transmission probability, social distance, and the number of close human contacts. Built on this model, we formulate and solve, both analytically and numerically, a design problem for an operator of a bus line, to jointly optimize the vehicle capacity and the staff testing schedule. We find: (i) The optimal profit of the operator, as well as the testing frequency and the vehicle capacity, decreases when a passenger expects to come in close contact with more fellow riders in a trip; (ii) Using a larger bus and/or reducing the testing cost enables the agency to both test the drivers more frequently and allow more passengers in each bus; (iii) The higher the prevalence of infection, the less efficient the transit operation become; and (iv) The potential to improve transit service capacity is limited given the safety requirement imposed.
Authors: Mary Rose Fissinger, Chicago Transit Authority, John Attanucci, Jinhua Zhao, Massachusetts Institute of Technology (MIT)
Several months after the initial stay-at-home orders were issued across the country in response to the COVID-19 pandemic, transit agencies are still seeing ridership levels at around a quarter of their pre-pandemic levels. Charting a path forward will be extremely difficult, but it must be rooted in an understanding of who a system’s riders are so that policies can be crafted with these individuals in mind. This work uses the Chicago Transit Authority as a case study, first examining the impact of COVID-19 on system ridership in terms of trips, and then using a customer segmentation approach to uncover how distinct behavioral responses from different groups of riders drove the overall drop in trips on the system. Two groups of riders are examined in particular: frequent off-peak bus riders with a high proportion of trips involving transfers and frequent peak rail riders, as these two groups represented particularly large percentages of trips pre-COVID. We find that these two groups responded to the pandemic in very different ways, with two-thirds of the former group continuing to ride during the pandemic while only 20% of the latter group used transit at all during the periods we analyzed after the COVID outbreak. Based on these and other findings, a series of policy recommendations are laid forth for the recovery of transit ridership in Chicago, considering each group in turn and addressing their particular mobility needs and challenges.
Authors: Fabian Diaz, Sarmad Abbasi, University of Saskatchewan, Daniel Fuller, Memorial University of Newfoundland, Ehab Diab, University of Saskatchewan
Over the past few months, transit agencies across Canada have been rushed to implement a range of strategies in response to the COVID-19 pandemic, with no standardized guidelines to direct their efforts. This study explores the initial response of transit agencies serving the 25 most populous Canadian cities by understanding the distinct types of response measures implemented between March 1 st and May 31 st , 2020. It also explores to what extent information related to these measures was accessible and usable, and how transit agencies used social media to communicate their efforts to the public. To achieve these goals, a detailed review of Canadian transit agencies websites and social media accounts was performed. The findings suggest that larger transit agencies across Canada implemented the most measures to respond to COVID-19, but not necessarily provided the most accessible information regarding the measures. Overall, while all transit agencies reduced the offered service’s frequency and capacity and implemented rear door boarding/existing, the implementation of other physical and communication measures varied considerably between agencies. Information related to the number of COVID-19 cases within the workforce was least accessible across agencies. Transit agencies’ twitter platforms were used more by larger agencies. While most of transit agencies tend to employ tweets that include some type of graphics, very few agencies employed videos and animations to communicate important information to the public. This paper provides transit planners and policymakers with comprehensive information regarding the initial response of Canadian transit agencies to maintain operations in such critical times.
Authors: Huan Lu, Hongcheng Gan, University of Shanghai for Science and Technology
Ensuring the necessary service functions of public transport under the influence of the epidemic and providing support for blocking the spread of epidemics is an important research issue in urban traffic emergency management. In this paper, based on the public transport network and epidemic information data, considering the two aspects of bus stops and epidemic sites, integrating the spatial analysis methods such as the topological model of public transport network, the centrality model of public transport network and nuclear density analysis, we have constructed the risk assessment method of public transport exposure, and done the case study of COVID-19 in Wuhan, China. The results show that the overall exposure risk of bus stops presents a "multi center circle" structure; high-risk and low-risk stops mainly rely on urban trunk roads and branches; high-risk and relatively high-risk stops are mostly traffic hubs and shopping malls, accounting for 35.63%, and medium and low-risk stops account for 64.37%, which are mainly distributed in the peripheral areas of urban core area; high-risk and low-risk sites are mainly distributed in the peripheral areas of urban core areas. Finally, according to the difference of public transport exposure risk level, the study puts forward to formulate classified public transport control measures to achieve differentiated precise prevention and control, so as to provide theoretical basis and decision-making reference for urban traffic management departments to carry out risk management and formulate management and control policy.
Authors: Thomas Koch, Elenna Dugundji, Vrije Universiteit, Amsterdam
Using modern transit routing algorithms it became feasible to compute all fastest travel options between all traffic zones in larger cities, allowing for an in-depth analysis how frequencies affect journeys between different origins, destinations and departure times. We use the rooftop method to calculate a realistic model of how a public transit user may perceive travel time, taking into account waiting time and/or adaption time to fit appointments in someone's schedule. The higher the frequencies, the lower those waiting times will be and vice versa. The rooftop method calculates a travel impedance for any given moment in the travel time. Furthermore, as few journeys will start and end at a transit stop, some walk component is often also involved. We sample 6693 addresses for 799 zones to compute travel times door-to-door in Amsterdam and surrounding area, explicitly including walk access and egress time to and from transit. In this study we focus on the transit timetables before, during, and in the current phase of the COVID-19 pandemic in order to investigate the effect of changed schedules on accessibility and mobility by public transit. This is particularly relevant for services that have been reduced and may remain reduced for the near future moving ahead. We expect this application of methods outlined in this paper to be of interest to public authorities and transit providers in making difficult decisions during COVID-19.