论文标题
空间多分析分析方法以识别崩溃热点并估算崩溃风险
Spatial multiresolution analysis approach to identify crash hotspots and estimate crash risk
论文作者
论文摘要
在本文中，作者评估了空间多分析分析（SMA）方法的性能，该方法的行为就像可变的带宽内核密度估计（KDE）方法，用于危险的道路段识别（HRSI）和崩溃风险（预期的崩溃数量）。所提出的SMA类似于KDE方法，其额外好处是，根据各个段的同质性，在不同的道路段中允许带宽不同。此外，仅根据数据最小化均方误差的无偏估计，每个路段的最佳带宽仅基于数据确定。作者将SMA方法与实践崩溃分析方法（经验贝叶斯方法（EB）方法）相提并论，就其HRSI的能力及其预测未来崩溃的能力而言。结果表明，至少在本文中使用的整个弗吉尼亚州州际网络的崩溃数据中，SMA可能胜过EB方法。 SMA可在Excel电子表格中实现，该电子表格可自由下载。
In this paper, the authors evaluate the performance of a spatial multiresolution analysis (SMA) method that behaves like a variable bandwidth kernel density estimation (KDE) method, for hazardous road segments identification (HRSI) and crash risk (expected number of crashes) estimation. The proposed SMA, is similar to the KDE method with the additional benefit of allowing for the bandwidth to be different at different road segments depending on how homogenous the segments are. Furthermore, the optimal bandwidth at each road segment is determined solely based on the data by minimizing an unbiased estimate of the mean square error. The authors compare the SMA method with the state of the practice crash analysis method, the empirical Bayes (EB) method, in terms of their HRSI ability and their ability to predict future crashes. The results indicate that the SMA may outperform the EB method, at least with the crash data of the entire Virginia interstate network used in this paper. The SMA is implemented in an Excel spreadsheet that is freely available for download.
