Abstract: Mobility is central to urbanity, and urbanity is central to our common future as the world’s population crowds into urban areas. This is creating a global urban mobility crisis due to the unsustainability of our 20th century transportation systems for an urban world. Fortunately, the science and planning of urban mobility is transforming away from infrastructure as the solution towards a sustainable mobility paradigm that manages rather than encourages travel, diminishes mobility and accessibility inequities, and reduces the harms of mobility to people and environments. In this essay, I discuss the contributions over the past decade of movement analytics to sustainable mobility science and planning. I also highlight two major challenges to sustainable mobility that should be addressed over the next decade.
Keywords: movement analytics, mobility science, animal movement ecology, sustainable mobility, urbanity
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
New paper: Lee, J., Porr, A. and Miller, H.J. (2020) “Evidence of increased vehicle speeding in Ohio’s major cities during the COVID-19 pandemic,” Transportation Findings, June. https://doi.org/10.32866/001c.12988
Abstract. This paper compares the speeding patterns before and after the COVID-19 pandemic in three major cities in Ohio, USA: Columbus, Cincinnati, and Cleveland. Using high-resolution and real-time INRIX traffic data, we find evidence of increased speeding in all three cities. In particular, we observe an increase in the spatial extent of speeding as well as in the average level of speeding. We also find the mean differences in speeding before and after the COVID-19 outbreak are statistically significant within the study areas.