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
Epidemics and pandemics such as the COVID-19 outbreak have clear geographic dimensions due to the vector spreading the virus (human contact), demographics and co-morbidity factors that vary geographically, the distributed and heterogeneous nature of health care systems, and the highly variable response and interventions from political authorities and the public-at-large. The decline and shifts in human activity also affect broader social, economic and environmental systems to varying degrees. Geospatial information can play vital roles in crafting effective government and societal responses at the operational, tactical and strategic levels.
On 17 June 2020, the Mapping Science Committee of the National Academies of Science, Engineering and Medicine will host an online workshop on Geospatial Needs for a Pandemic-Resilient World. This workshop will explore the needs of federal agencies, organizations, and scientists for geospatial data science to understand and respond to epidemics/pandemics and developing infrastructure and policies that facilitate effective management and graceful recovery from these types of shocks. This workshop is free and open to the public.
New publication: Park, Y., Mount, J., Liu, L., Xiao, N. and Miller, H.J. (2020) “Assessing public transit performance using real-time data: Spatio-temporal patterns of bus operation delays in Columbus, Ohio, USA,” International Journal of Geographical Information Science, 34, 367-392.
ABSTRACT: Public transit vehicles such as buses operate within shared transportation networks subject to dynamic conditions and disruptions such as traffic congestion. The operational delays caused by these conditions can propagate downstream through scheduled transit routes, affecting system performance beyond the initial delay. This paper develops an approach to measuring and assessing vehicle delay propagation in public transit systems. We fuse data on scheduled bus service with real-time vehicle location data to measure the originating, cascading and recovery locations of delay events across space with respect to time. We integrate the resulting patterns to construct stop-specific delay propagation networks. We also analyze the spatiotemporal patterns of propagating delays using parameters such as 1) transit line-based network distance, 2) total propagating delay size, and 3) distance decay. We apply our methodology using publicly available schedule and real-time location data from the Central Ohio Transit Authority (COTA) public bus system in Columbus, Ohio, USA. We find that delay initiation is spatially and temporally uneven, concentrating on specific stops in downtown and specific suburban locations. Core stops play a critical role in propagating delays to a wide range of connected stops, eventually having a disproportional impact on the on-time performance of the bus system.
KEYWORDS: Mobility, urban applications, public bus delay propagation, public transport reliability assessment, spatio-temporal data modelling