Abstract

The study presented an approach that integrated kernel density estimation (KDE) algorithm and spatial autocorrelation analysis, which helped to determine traffic accident (TA) hotspot locations and simultaneously evaluate the statistical significance of the hotspot clusters. Firstly, hotspots were identified by applying a GIS-based KDE algorithm. Secondly, the hotspot clusters were evaluated in terms of statistical significance by applying the Moran’s I statistic indices. Finally, hotspots were arranged according to their significance. TA data in the (2015–2017) period in Hanoi, Vietnam were applied to test this approach. Importantly, the study proposed a validation process for the results by applying the Gi* statistics method to validate the (H-H) clusters and the EPDO method to validate the ranking of hotspots. The results showed that this integration overcame the drawbacks of the KDE method. The statistical test process of clusters helped to hinder the occurrence of too many clusters that were determined by the KDE method because they were not really dangerous. This approach was helpful and precise in identifying TA hotspots with the statistical meaning. These outcomes will not only enable traffic authorities to comprehensively understand the reasons for each accident but also to help them manage and deal with hazardous areas according to the prior order in case of limited expenses and allocate traffic safety sources accordingly.

摘要

该研究提出了一种集成核密度估计（KDE）算法和空间自相关分析的方法，有助于确定交通事故（TA）热点位置，同时评估热点集群的统计显着性。首先，通过应用基于 GIS 的 KDE 算法识别热点。其次，通过应用 Moran's I 统计指数来评估热点集群的统计显着性。最后，热点根据其重要性进行排列。应用越南河内 (2015-2017) 期间的 TA 数据来测试这种方法。重要的是，该研究提出了一个结果验证过程，通过应用 Gi* 统计方法来验证 (HH) 集群和 EPDO 方法来验证热点的排名。结果表明，这种集成克服了 KDE 方法的缺点。集群的统计测试过程有助于阻止由 KDE 方法确定的过多集群的发生，因为它们并不是真正危险的。这种方法在识别具有统计意义的 TA 热点方面是有帮助和精确的。这些成果不仅可以让交通管理部门全面了解每起事故的原因，还可以帮助他们在费用有限的情况下，按照优先顺序管理和处理危险区域，并相应地分配交通安全资源。这种方法在识别具有统计意义的 TA 热点方面是有帮助和精确的。这些成果不仅可以让交通管理部门全面了解每起事故的原因，还可以帮助他们在费用有限的情况下，按照优先顺序管理和处理危险区域，并相应地分配交通安全资源。这种方法在识别具有统计意义的 TA 热点方面是有帮助和精确的。这些成果不仅可以让交通管理部门全面了解每起事故的原因，还可以帮助他们在费用有限的情况下，按照优先顺序管理和处理危险区域，并相应地分配交通安全资源。