Abstract

In the current literature, data is aggregated for the estimation of functions to explain or predict crash patterns using either clustering analysis, regression analysis, or stage-wise models. Typically, analysis sites are grouped into site subtypes based on predefined characteristics. The assumption is that sites within each subtype experience similar crash patterns as a function of prespecified explanatory characteristics. To develop functions to estimate crashes, all data points are clustered only as a function of associated site characteristics. As a consequence, estimated parameters may be based on different crash patterns that represents various trends that could be better captured by using multiple functions. To address this limitation, this study proposes a mathematical program utilizing clusterwise regression to assign sites to clusters and simultaneously seek sets of parameter values for the corresponding estimation functions, so as to maximize the probability of observing the available data. A simulated annealing, coupled with maximum likelihood estimation, was used to solve the mathematical program. Results were analyzed for two site subtypes with fatal and all injury crashes: (1) roadway segments for urban multilane divided segments and (2) urban four-leg signalized intersections. Clusterwise regression improved the predicted number of crashes with multiple estimation functions within the same site subtype.

摘要

在当前文献中，使用聚类分析，回归分析或阶段模型对数据进行汇总以估计功能，以解释或预测碰撞模式。通常，根据预定义的特征将分析站点分组为站点子类型。假设每个子类型内的站点都会经历类似的崩溃模式，这是预先确定的解释特征的函数。为了开发估算事故的功能，所有数据点仅根据相关站点的特征进行聚类。结果，估计的参数可以基于表示各种趋势的不同崩溃模式，这些崩溃趋势可以通过使用多个功能更好地捕获。为了解决这个限制，这项研究提出了一种数学程序，利用聚类回归将站点分配给聚类，并同时为相应的估计函数寻找参数值集，从而最大程度地提高了观测可用数据的可能性。模拟退火与最大似然估计一起用于求解数学程序。分析了具有致命事故和所有伤害事故的两种工地亚型的结果：（1）城市多车道划分路段的道路路段和（2）城市四路信号交叉口。聚类回归使用同一站点子类型中的多个估计函数改进了预计的崩溃数量。模拟退火与最大似然估计一起用于求解数学程序。分析了具有致命事故和所有伤害事故的两种工地亚型的结果：（1）城市多车道划分路段的道路路段和（2）城市四路信号交叉口。聚类回归使用同一站点子类型中的多个估计函数改进了预计的崩溃数量。模拟退火与最大似然估计一起用于求解数学程序。分析了具有致命事故和所有伤害事故的两种工地亚型的结果：（1）城市多车道划分路段的道路路段和（2）城市四路信号交叉口。聚类回归使用同一站点子类型中的多个估计函数改进了预计的崩溃数量。