Electric, Autonomous, & Green: The Future of Personal Transportation – Columbus Metropolitan Club – May 3 2023

On Wednesday, May 3 2023, I had the opportunity to participate in a panel discussion on the Future of Personal Transportation at the Columbus Metropolitan Club. Joining me on the panel were Preeti Choudhary, Executive Director, DriveOhio, Ted Angel, Director, Aerospace Affairs, Dayton Development Coalition, and  host Walker Evans, Co-Founder & CEO, Columbus Underground.

My main message – the future of personal transportation should be similar to the history of personal transportation – walking, biking and public transit. Electric vehicles, autonomous vehicles and advanced air mobility are simply continuations of the same thing we have been trying for a century – cars and car dependence.  As should be clear, cars are not working well, and we can’t solve our car problem with more car-ing.

Event Page

Video of the panel discussion

The deadly impact of urban streets that look like highways

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.