Impacts of Shared Bikes on Pedestrian and Cyclist Safety: A Macroscopic Analysis
Macroscopic traffic safety models that predict crash frequency over regions of a transportation network are becoming increasingly common. However, perhaps due to data availability, these models tend to focus only on vehicle exposure attributes to the detriment of non-motorized vehicle information. A handful of studies have integrated single explanatory variables that capture non-motorized transportation use into macroscopic safety prediction models. This study seeks to extend these works by incorporating several exposure metrics that capture non-motorized and public transportation use. A macro-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 (POI) as measures of travel exposure. The models are developed using negative binomial regression and various functional forms are considered. The results show that the number of shared bike trips and POI visits are positively associated 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. These include Health Care, Miscellaneous and Grocery Stores, Schools, Transportation, and Motor Vehicle, Food and Drinking, Public Services. Route length, route density and traffic surrogate datasets are more influential to the motorists involved crashes.
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Work Title | Impacts of Shared Bikes on Pedestrian and Cyclist Safety: A Macroscopic Analysis |
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License | In Copyright (Rights Reserved) |
Work Type | Article |
Publication Date | January 1, 2022 |
Deposited | March 14, 2023 |
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