Bicyclists are among the most vulnerable road users in the urban transportation system. It is critical to investigate the contributing factors to bicycle-related crashes and to identify the hotspots for efficient allocation of treatment resources. A grid-cell-based modeling framework was used to incorporate heterogeneous data sources and to explore the overall safety patterns of bicyclists in Manhattan, New York City. A random parameters (RP) Tobit model was developed in the Bayesian framework to correlate transportation, land use, and sociodemographic data with bicycle crash costs. It is worth mentioning that a new algorithm was proposed to estimate bicyclist-involved crash exposure using large-scale bicycle ridership data from 2014 to 2016 obtained from Citi Bike, which is the largest bicycle sharing program in the United States. The proposed RP Tobit model could deal with left-censored crash cost data and was found to outperform the Tobit model by accounting for the unobserved heterogeneity across neighborhoods. Results indicated that bicycle ridership, bicycle rack density, subway ridership, taxi trips, bus stop density, population, and ratio of population under 14 were positively associated with bicycle crash cost, whereas residential ratio and median age had a negative impact on bicycle crash cost. The RP Tobit model was used to estimate the cell-specific potential for safety improvement (PSI) for hotspot ranking. The advantages of using the RP Tobit crash cost model to capture PSI are that injury severity is considered by being converted into unit costs, and varying effects of certain explanatory variables are accounted for by incorporating random parameters. The cell-based hotspot identification method can provide a complete risk map for bicyclists with high resolution. Most locations with high PSIs either had unprotected bicycle lanes or were close to the access points to protected bicycle routes.

骑自行车的人是城市交通系统中最脆弱的道路使用者之一。重要的是要调查与自行车相关的撞车事故的成因，并确定热点以有效分配治疗资源。基于网格单元的建模框架用于合并异构数据源，并探索纽约市曼哈顿自行车手的总体安全模式。在贝叶斯框架中开发了随机参数（RP）Tobit模型，以将运输，土地使用和社会人口统计学数据与自行车撞车成本相关联。值得一提的是，提出了一种新算法，该算法使用2014年至2016年从美国最大的自行车共享计划Citi Bike获得的大规模自行车出行数据估算自行车骑行者所遭受的碰撞风险。所提出的RP Tobit模型可以处理左删失的碰撞成本数据，并且通过考虑邻域之间未观察到的异质性，发现该模型优于Tobit模型。结果表明，自行车出行成本，自行车架密度，地铁出行率，出租车出行，公交车站密度，人口以及14岁以下人口的比例与自行车出行成本成正相关，而居住比例和中位年龄对自行车出行成本有负影响。 。RP Tobit模型用于估计针对热点排名的特定于小区的安全改进潜力（PSI）。使用RP Tobit撞车成本模型来捕获PSI的优点是，可以通过将伤害严重性转换为单位成本来考虑，并通过合并随机参数来考虑某些解释变量的不同影响。基于细胞的热点识别方法可以为自行车手提供高分辨率的完整风险图。大多数PSI较高的位置要么没有未保护的自行车道，要么靠近通往受保护的自行车道的入口。