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.
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.
A reporter asked me what I thought about Columbus being ranked the sixth best city for commuters in the US. As you can see in the article, I am not impressed.
Columbus ranks sixth-best city for commuters – NBC4 Columbus
New paper: Stiles, J., Li, Y. and Miller, H.J. (2022) “How does street space influence crash frequency? An analysis using segmented street view imagery,” Environment and Planning B: Urban Analytics and City Science, https://doi.org/10.1177/23998083221090962
Abstract. Road crashes in metropolitan areas are challenging to prevent because they stem from the interactions of drivers and other system users in intricate built environments. Recent theories indicate that features of the built environment may induce unsafe driving by shaping users’ expectations and behaviors. The availability of street view imagery and methods of scene parsing create new possibilities for understanding how features of the built environment influence crash incidence. Most previous crash research using street imagery has applied manual processing methods. In this paper, we develop and apply automated machine parsed imagery in conjunction with self-explaining roads theory to consider how the street space visible to drivers influences crash frequency, using data from Columbus, Ohio, USA. While controlling for road network and area characteristics, we model the association of individual street elements with crash frequency. We then conduct a cluster analysis to define four types of street spaces, which are used in a subsequent model. We find that an Open Road type of metropolitan street space, characterized by more visible sky, roadway, and signage are associated with the greatest increase in crashes, and that the majority of these spaces exist on arterial or collector class road segments. We theorize that the visual similarity of this type of street space to highways promotes faster, less careful driving, which combines with their mixed land uses to make them the least safe. This points to the importance of traffic calming for such roads in high-activity areas, and the need to differentiate environments of non-highways from highways to promote careful driving.