Macroscopic Analysis of the Impacts of Shared Bikes on Traffic Safety

Crashes involving vulnerable roadway users (VRUs), such as pedestrians and cyclists, have increased in recent years. Understanding relationships between these crashes and explanatory factors is critical to reversing this trend. However, most statistical models of VRU-related crashes rely primarily on vehicular exposure alone. VRU exposure is difficult to capture because of a lack of available data. To address this concern, this study incorporates several exposure metrics that capture nonmotorized and public transportation use at the census-tract level. A macroscopic-level crash-prediction model for the Manhattan area of New York City is developed that considers roadway and demographic variables, as well as bike-share trip information, subway flows, taxi movements, and person trips to various points of interest (POIs) as measures of travel exposure. The models are developed using geographically weighted negative binomial regression and various functional forms are considered for three different types of crash frequency. The results show that the number of shared-bike trips and POI visits are positively correlated with increases in pedestrian and cyclist crash frequencies; however, these features are less descriptive of motorist crash frequency. In addition, the explanatory power of POI information can be improved by considering only a subset of POI categories that represent essential trips. The spatial variation between motorist-related crash frequency and the exposure metric is observed to be more significant than the pedestrian- and cyclist-related crash frequency are to their exposure, and the shared-bike trips' influence on the pedestrian- and cyclist-related crash frequency appears to be homogeneous across the Manhattan area.

Automated summary

Crashes involving vulnerable roadway users have been on the rise recently. This study incorporates exposure metrics related to nonmotorized and public transportation use to develop a crash-prediction model. The results show a positive correlation between shared-bike trips and POI visits with increases in pedestrian and cyclist crash frequencies.