City-oriented and inclusive bicycle-vehicle crash frequency modeling through the integration of bicycle-sharing system and other surrogates

Abstract

This research explores a city-oriented and inclusive bicycle-vehicle crash frequency model through the integration of bicycle-sharing system and other surrogates pertaining to the built environment like road network, land use developments, and socio-demographic characteristics. Bicycle-vehicle crash data from 2016 to 2019, along with the road network, land use, and socio-demographic characteristics within a 1-mile radius of 62 randomly selected Capital Bikeshare (CaBi) stations (referred to as stations), was used to estimate and validate bicycle-vehicle crash frequency models (negative binomial regression models with log-link distribution) and assess the role of explanatory variables. The selected stations are geographically and fairly distributed in low-risk, moderate-risk, high-risk, and very high-risk areas while covering 99.8% of the total number of recorded bicycle-vehicle crashes. From such an inclusive approach, it was observed that bicyclists are more likely to be involved in crashes in commercial, mixed-use, and densely populated areas. In addition, they are more often involved in crashes in areas with more six-lane roads. Also, many bicycle-vehicle crashes have occurred at intersections. The nonexistence of bicycle crossings could explain this finding. The findings are substantial, and characteristics related to the built environment like road network (including vehicle and bicycle infrastructure), land use developments, and socio-demographics are good surrogates for traffic volume and bicycle count.

Keywords

• Bicycle-sharing
• Bicycle-vehicle crash
• Crash frequency
• Infrastructure
• Bicycle lane
• Capital Bikeshare