A data mining driven risk profiling method for road asset management

D. Emerson, J. Weligamage, R. Nayak
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引用次数: 1

Abstract

Road surface skid resistance has been shown to have a strong relationship to road crash risk, however, applying the current method of using investigatory levels to identify crash prone roads is problematic as they may fail in identifying risky roads outside of the norm. The proposed method analyses a complex and formerly impenetrable volume of data from roads and crashes using data mining. This method rapidly identifies roads with elevated crash-rate, potentially due to skid resistance deficit, for investigation. A hypothetical skid resistance/crash risk curve is developed for each road segment, driven by the model deployed in a novel regression tree extrapolation method. The method potentially solves the problem of missing skid resistance values which occurs during network-wide crash analysis, and allows risk assessment of the major proportion of roads without skid resistance values.
基于数据挖掘的道路资产管理风险分析方法
路面防滑阻力已被证明与道路碰撞风险有很强的关系,然而,应用目前使用调查级别来识别容易发生碰撞的道路的方法是有问题的,因为它们可能无法识别正常以外的危险道路。该方法利用数据挖掘技术分析复杂且以前难以理解的道路和碰撞数据量。该方法可以快速识别出碰撞率较高的道路,这可能是由于防滑缺陷造成的,以便进行调查。在一种新的回归树外推方法中部署的模型驱动下,为每个路段开发了一个假设的防滑/碰撞风险曲线。该方法潜在地解决了在全网碰撞分析中出现的滑阻值缺失问题,并允许对大部分没有滑阻值的道路进行风险评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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