On July 29th, I participated in a webinar on the Impacts of COVID-19 on Mobility, organized by The Ohio State University College of Arts and Sciences, the College of Engineering and the OSU Alumni Association. The recording is posted below. Very interesting discussion; worth watching!
Faculty and industry experts have a conversation about the potential implications of COVID-19 on the design of our communities and various modes of transportation including air travel, personal vehicles, public transit, micro-mobility and ride-hailing services.
Chris Atkinson, Director, The Ohio State University Smart Mobility Program
Jennifer Clark, Professor and Section Head, City and Regional Planning
Harvey Miller, Reusche Chair in Geographic Information Science; Director, Center for Urban and Regional Analysis
Sophia Mohr, Chief Innovation Officer, Central Ohio Transit Authority
Stephanie Morgan, Executive Director, Air Transportation and Aerospace Campus
Giorgio Rizzoni, Center for Automative Research, Ford Motor Company Chair, Mechanical & Aerospace Engineering
New paper: Liu, L. and Miller, H.J. (2020) “Measuring risk of missing transfers in public transit systems using high-resolution schedule and real-time bus location data,” Urban Studies (Special issue on Big Data in the City) https://doi.org/10.1177/0042098020919323
Abstract: The emergence of urban Big Data creates new opportunities for a deeper understanding of transportation within cities, revealing patterns and dynamics that were previously hidden. Public transit agencies are collecting and publishing high-resolution schedule and real-time vehicle location data to help users schedule trips and navigate the system. We can use these data to generate new insights into public transit delays, a major source of user dissatisfaction. Leveraging open General Transit Feed Specification (GTFS) and administrative Automatic Passenger Counter (APC) data, we develop two measures to assess the risk of missing bus route transfers and the consequent time penalties due to delays. Risk of Missing Transfers (RoMT) measures the empirical probability of missed transfers, and Average Total Time Penalty (ATTP) shows overall time loss compared to the schedule. We apply these measures to data from the Central Ohio Transit Authority (COTA), a public transit agency serving the Columbus, Ohio, USA metropolitan area. We aggregate, visualise and analyse these measures at different spatial and temporal resolutions, revealing patterns that demonstrate the heterogeneous impacts of bus delays. We also simulate the impacts of dedicated bus lanes reducing missing risk and time penalties. Results demonstrate the effectiveness of measures based on high-resolution schedule and real-time vehicle location data to assess the impacts of delays and to guide planning and decision making that can improve on-time performance.
Keywords: automatic passenger counter data, General Transit Feed Specification data, public transit, risk of missing transfer