Comparative modeling of risk factors for near-crashes from crowdsourced bicycle airbag helmet data and crashes from conventional police data.

Introduction: Conventional cycling crash data is valuable for shaping safe cycling environments but has limitations due to the rarity and under-reporting of cycling crashes. However, recent technological developments can provide information from near-crashes. the subheads should be italic, not bf. Also in the Abstract, there shouldn't be hard return between subheads, the whole section should all run together, so run up any text between subheads.

Method: With Metropolitan Copenhagen as a case, this study uses a very large crowdsourced near-crash dataset from Hövding bicycle airbag helmet users and conventional police crash data to model and identify differences in the infrastructure factors influencing rates of crashes and near-crashes in these datasets.

Results: In contrast to existing literature, our results show considerable differences in the factors influencing the frequency of crashes and near-crashes. The risk of crashes increases predominantly at intersections and roundabouts, whereas near-crashes are also associated with infrastructure types shared with pedestrians.

Conclusion: When used complementarily, crowdsourced near-crash data can enrich the data foundation and help increase the awareness of near-crash-prone infrastructure types necessary for shaping more comprehensive cycling safety policies.

Practical Applications: The findings of the study advocate for a broader perspective on cyclist safety, incorporating currently undisclosed near-crash-prone infrastructure types, such as paths shared by cyclists and pedestrians.