Paper ID:250655F5

Authos:  Daniel Price

Title: Evaluating the Speed Advantage of Urban Elevated Small Vehicle Arterials (SVAs): A Case Study of Chicago, New York City, and San Francisco

​Publisher: Algorithm_Lab.

Conference: ICAITD 2025: The Second International Conference of AI new Technology and open Discussion

Location: Pleasanter Lounge  Nakano, Tokyo Japan

Date: 1-4 june 2025

Editor: Kazuo Ohzeki (Algorithm_Lab.  Professor Emeritus of Shibaura Institute of Technology, Professor of Emeritus of International Professional University of Technology in Tokyo)

Citation: Proceedings: ICAITD  2025  

https://doi.org/10.63211/j.p.25.645305 

​pages:  37-43

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Abstract:

This study investigates the potential of urban elevated Small Vehicle Arterials (SVA(s)) to alleviate urban traffic congestion and reduce car dependency. Building upon previous research highlighting the need for further exploration of SVA routing and transit times, this paper presents a rudimentary analysis of three diverse US metropolitan areas: Chicago, New York City, and San Francisco. Using Google Maps data, travel times for automobiles, mass transit, and proposed SVA routes were compared during a Friday evening rush hour. Results indicate that while SVA travel speeds consistently exceed those of mass transit, automobile speeds show greater variability, sometimes exceeding and sometimes falling short of SVA speeds. The findings suggest that while SVA offer a potentially faster alternative in many urban contexts, further research with larger, more robust datasets is needed to fully understand the impact of factors such as city geography, cultural norms, and existing infrastructure on SVA effectiveness. This research highlights the need for future studies to quantify observed average modal speed nuances and identify optimal locations for SVA implementation worldwide.
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Keywords: Urban, Highways, Bicycle, Elevated, Small, Vehicle, Arterial, Modal, Spee

Category: Full paper

Review process: Two reviewers

Publication date: July 2nd 2025

First received date: Mar. 12th 2025
Copyright :Author(Full), AlgorithmLab.(First-in-the-world publishing rights granted by the author as the Prceedings of ICAITD 2025)
Licence:Viewer can download and view this review paper, but cannot secondary distribute (redistribute) it. (It is not Creative Commons License, nor MIT Licence) In other words, "Do not distribute" is the License

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