Traffic accidents are a common problem in any transportation network. Road traffic accidents are predicted to be the seventh leading cause of deaths by the year 2030. Recently research in the integration of geographical information systems (GIS) for analyzing accidents, road design and safety management has increased considerably. The perpetual use of GIS tools, lead this study to propose the identification of accident hotspots by exploiting GIS technology coupled with kernel density estimation (KDE). This paper proposes the use of KDE technique and GIS technology to automatically identify the accident hotspots using UK as the study area. Analysis shows that most of the accidents occur when there is a 30 mph speed limit, a weekend, in the evening time, during the months of October and November, on the single carriageway, where there is ‘T’ or staggered junction and on ‘A’ road class. Moreover, this study also proposed techniques to classify the accident severity that is classified as either fatal, serious or slight. The driver behavior and environmental features achieved an accuracy up to 85% on the severity classification with Bagging technique. Further, the shortcomings, limitations and recommendations for future work are also identified.

中文翻译：

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利用GIS技术和核密度估计的道路事故热点时空聚类与分析。

交通事故是任何运输网络中的常见问题。预计到2030年，道路交通事故将是造成死亡的第七大原因。最近，有关用于分析事故，道路设计和安全管理的地理信息系统（GIS）集成的研究已经大大增加。GIS工具的永久使用使本研究提出了利用GIS技术结合核密度估计（KDE）来确定事故热点的方法。本文提出了使用KDE技术和GIS技术以英国为研究区域自动识别事故热点的方法。分析表明，大多数事故是在10月和11月的单个行车道上，当速度限制为30英里/小时，一个周末，晚上的十月和十一月期间发生的，有“ T”或交错路口且道路等级为“ A”的地方。此外，这项研究还提出了将事故严重程度分类为致命，严重或轻度的技术。使用装袋技术可将驾驶员的行为和环境特征的严重性分类的准确度提高到85％。此外，还指出了未来工作的不足，局限和建议。