Transportation

Ghost Neighborhoods of Columbus

We have a new project at the Center for Urban and Regional Analysis – Ghost Neighborhoods of Columbus. We are using machine learning techniques to extract data from historic Sanborn Fire Atlas maps to create 3D visualizations and analysis of neighborhoods destroyed and damaged by urban highway construction in the 20th century. Read and listen all about it below:

 

Don’t not panic about Intel and transportation

I dropped more knowledge on local news about induced travel demand and traffic congestion, this time with respect to Ohio Department of Transportation’s plans to widen roads for the Intel development. People need to know that expanding roads and highways won’t solve anything.

‘Don’t panic’: Transportation officials discuss Intel traffic concerns – WBNS 10TV Columbus, September 12, 2022

 

Realizable accessibility: evaluating the reliability of public transit accessibility using high‑resolution real‑time data

New paper!  Liu, L., Porr, A. and Miller, H.J. (2022) “Realizable accessibility: Evaluating the reliability of public transit accessibility using high-resolution real-time data,” Journal of Geographical Systems, online first.

Take home message:

We develop a refined time geographic measure of accessibility via public transit using real-time vehicle location data. We also show how to use this measure with schedule data to analyze the reliability of public transit accessibility at the urban scale. To be published in a special issue on “Time Geography in the Age of Mobility Analytics” in the Journal of Geographical Systems.

Abstract:

The widespread availability of high spatial and temporal resolution public transit data is improving the measurement and analysis of public transit-based accessibility to crucial community resources such as jobs and health care. A common approach is leveraging transit route and schedule data published by transit agencies. However, this often results in accessibility overestimations due to endemic delays due to traffic and incidents in bus systems. Retrospective real-time accessibility measures calculated using real-time bus location data attempt to reduce overestimation by capturing the actual performance of the transit system. These measures also overestimate accessibility since they assume that riders had perfect information on systems operations as they occurred. In this paper, we introduce realizable real-time accessibility based on space–time prisms as a more conservative and realistic measure. We, moreover, define accessibility unreliability to measure overestimation of schedulebased and retrospective accessibility measures. Using high-resolution General Transit Feed Specification real-time data, we conduct a case study in the Central Ohio Transit Authority bus system in Columbus, Ohio, USA. Our results prove that realizable accessibility is the most conservative of the three accessibility measures. We also explore the spatial and temporal patterns in the unreliability of both traditional measures. These patterns are consistent with prior findings of the spatial and temporal patterns of bus delays and risk of missing transfers. Realizable accessibility is a more practical, conservative, and robust measure to guide transit planning.