Department of Civil Engineering, the University of British Columbia, 6250 Applied Science Lane, Vancouver, BC V6T 1Z4, Canada.
Department of Civil Engineering, the University of British Columbia, 6250 Applied Science Lane, Vancouver, BC V6T 1Z4, Canada.
Accid Anal Prev. 2021 Sep;159:106263. doi: 10.1016/j.aap.2021.106263. Epub 2021 Jun 26.
Crash data is usually aggregated over time where temporal correlation contributes to the unobserved heterogeneity. Since crashes that occur in temporal proximity share some unobserved characteristics, ignoring these temporal correlations in safety modeling may lead to biased estimates and a loss of model power. Seasonality has several effects on cyclists' travel behavior (e.g., the distribution of holidays, school schedules, weather variations) and consequently cyclist-vehicle crash risk. This study aims to account for the effect of seasonality on cyclist-vehicle crashes by employing two groups of models. The first group, seasonal cyclist-vehicle crash frequency, employs four vectors of the dependent variables for each season. The second group, rainfall involved cyclist-vehicle crash frequency, employs two vectors of the dependent variables for crashes that occurred on rainy days and non-rainy days. The two model groups were investigated using three modeling techniques: Full Bayes crash prediction model with spatial effects (base model), varying intercept and slope model, and First-Order Random Walk model with a spatial-temporal interaction term. Crash and volume data for 134 traffic analysis zones (TAZ's) in the City of Vancouver were used. The results showed that the First-Order Random Walk model with spatial-temporal interaction outperformed the other developed models. Some covariates have different associations with crashes depending on the season and rainfall conditions. For example, the seasonal estimates for the bus stop density are significantly higher for the summer and spring seasons than for the winter and autumn seasons. Also, the intersection density estimate for a rainy day is significantly higher than a non-rainy day. This indicates that on a rainy day each intersection to the network adds more risk to cyclists compared to a non-rainy day.
碰撞数据通常随时间聚合，时间相关性导致未观测到的异质性。由于时间上接近的碰撞具有一些未被观察到的特征，如果在安全建模中忽略这些时间相关性，可能会导致有偏差的估计和模型能力的损失。季节性对自行车出行行为（例如，假期分布、学校时间表、天气变化）有多种影响，进而影响自行车与车辆碰撞的风险。本研究旨在通过采用两组模型来考虑季节性对自行车与车辆碰撞的影响。第一组模型，季节性自行车-车辆碰撞频率，为每个季节的四个依赖变量向量；第二组模型，降雨相关的自行车-车辆碰撞频率，为雨天和非雨天的两个依赖变量向量。使用三种建模技术研究了这两个模型组：具有空间效应的全贝叶斯碰撞预测模型（基础模型）、变截距和斜率模型以及具有时空交互项的一阶随机游走模型。研究使用了温哥华市 134 个交通分析区（TAZ）的碰撞和流量数据。结果表明，具有时空交互项的一阶随机游走模型优于其他开发的模型。一些协变量与碰撞的关联取决于季节和降雨条件。例如，夏季和春季的公共汽车站密度季节性估计值明显高于冬季和秋季。此外，雨天的交叉口密度估计值明显高于非雨天。这表明在雨天，与非雨天相比，网络中的每个交叉口对自行车手的风险增加。
