Abstract：
Methodological advancements in road safety research reveal an increasing inclination toward integrating spatial approaches in hot spot identification, spatial pattern analysis, and developing spatially lagged models. Previous studies on hot spot identification and spatial pattern analysis have overlooked crash severities and the spatial autocorrelation of crashes by severity, missing valuable insights into crash patterns and underlying factors. This study investigates the spatial autocorrelation of crash severity by taking two capital cities, Addis Ababa and Berlin, as a case study and compares patterns in low and high-income countries. The study used three-year crash data from each city. It employed the average nearest neighbor distance (ANND) method to determine the significance of spatial clustering of crash data by severity, Global Moran\'s I to examine the statistical significance of spatial autocorrelation, and Local Moran\'s I to identify significant cluster locations with High-High (HH) and Low-Low (LL) crash severity values. The ANND analysis reveals a significant clustering of crashes by severity in both cities, except in Berlin\'s fatal crashes. However, different Global Moran\'s I results were obtained for the two cities, with a strong and statistically significant value for Addis Ababa compared to Berlin. The Local Moran\'s I result indicates that the central business district and residential areas have LL values, while the city\'s outskirts exhibit HH values in Addis Ababa. With some persistent HH value locations, Berlin\'s HH and LL grid clusters are intermingled on the city\'s periphery. Socio-economic factors, road user behavior and roadway factors contribute to the difference in the result. Nevertheless, it is interesting to note the similarity of significant HH value locations on the outskirts of both cities. Finally, the results are consistent with previous studies and indicate the need for further investigation in other locations.
摘要：
道路安全研究的方法进步表明，在热点识别中集成空间方法的倾向越来越大，空间模式分析，并开发空间滞后模型。以往关于热点识别和空间模式分析的研究忽视了碰撞严重度和碰撞严重度的空间自相关,缺少对崩溃模式和潜在因素的宝贵见解。本研究以两个省会城市为研究对象，研究了撞车严重程度的空间自相关，亚的斯亚贝巴和柏林,作为案例研究，并比较了低收入和高收入国家的模式。该研究使用了每个城市的三年撞车数据。它采用了平均最近邻距离（ANND）方法来确定按严重程度对碰撞数据进行空间聚类的重要性，全球莫兰I检验了空间自相关的统计意义，和LocalMoran'sI来识别具有高-高(HH)和低-低(LL)崩溃严重性值的重要集群位置。ANND分析显示，在这两个城市中，按严重程度划分的撞车事故都有显著的聚类，除了柏林的致命车祸.然而,这两个城市获得了不同的全球莫兰I结果，与柏林相比，亚的斯亚贝巴具有很强的统计意义。当地莫兰I的结果表明，中央商务区和住宅区具有LL值，而该市郊区在亚的斯亚贝巴展示HH值。对于一些持久的HH值位置，柏林的HH和LL网格集群混合在城市的外围。社会经济因素，道路使用者行为和道路因素导致了结果的差异。然而，有趣的是，注意到两个城市郊区的重要HH值位置的相似性。最后,结果与之前的研究一致,表明需要在其他地点进行进一步调查.
