2021/05/31 by Koichi Ito, Filip Biljecki · 2 citations
Computer Science · Environmental Science · Social Sciences · #Urban Green Space and Health #Urban Heat Island Mitigation #Urban Transport and Accessibility #cs.CV
paper · pdf · doi:10.1016/j.trc.2021.103371
published as Transportation Research Part C 132: 103371, 2021
arxiv created 2021/09/20 · openalex publication_date 2021/09/20 · crossref created 2021/09/20 · arxiv updated 2021/09/21 · crossref issued 2021/11/01 · crossref published 2021/11/01 · crossref published-print 2021/11/01 · openalex created_date 2025/10/10 · crossref deposited 2026/05/11 · crossref indexed 2026/07/09 · openalex updated_date 2026/08/01
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.