  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.
