Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/201609
Title: Assessing bikeability with street view imagery and computer vision
Authors: Koichi Ito
Filip Biljecki 
Keywords: cs.CV
cs.CV
Issue Date: Nov-2021
Publisher: Elsevier BV
Citation: Koichi Ito, Filip Biljecki (2021-11). Assessing bikeability with street view imagery and computer vision. Transportation Research Part C: Emerging Technologies 132 : 103371. ScholarBank@NUS Repository.
Rights: Attribution-NonCommercial 4.0 International
Abstract: Studies evaluating bikeability usually compute spatial indicators shaping cycling conditions and conflate them in a quantitative index. Much research involves site visits or conventional geospatial approaches, and few studies have leveraged street view imagery (SVI) for conducting virtual audits. These have assessed a limited range of aspects, and not all have been automated using computer vision (CV). Furthermore, studies have not yet zeroed in on gauging the usability of these technologies thoroughly. We investigate, with experiments at a fine spatial scale and across multiple geographies (Singapore and Tokyo), whether we can use SVI and CV to assess bikeability comprehensively. Extending related work, we develop an exhaustive index of bikeability composed of 34 indicators. The results suggest that SVI and CV are adequate to evaluate bikeability in cities comprehensively. As they outperformed non-SVI counterparts by a wide margin, SVI indicators are also found to be superior in assessing urban bikeability and potentially can be used independently, replacing traditional techniques. However, the paper exposes some limitations, suggesting that the best way forward is combining both SVI and non-SVI approaches. The new bikeability index presents a contribution in transportation and urban analytics, and it is scalable to assess cycling appeal widely.
Source Title: Transportation Research Part C: Emerging Technologies
URI: https://scholarbank.nus.edu.sg/handle/10635/201609
ISSN: 0968-090X
Rights: Attribution-NonCommercial 4.0 International
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