利用基于角度的离群点检测方法和滑动窗口机制来实时识别碰撞风险

IF 2.4 3区 工程技术 Q3 TRANSPORTATION
Zhen Gao, Jingning Xu, Rongjie Yu, Lei Han
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引用次数: 0

摘要

开发实时碰撞风险模型是一个热门的研究课题,因为它可以识别碰撞前兆,从而触发主动交通管理策略。目前,碰撞风险识别模型主要是基于监督学习技术开发的,这需要大量的历史碰撞数据样本。然而,在现实世界中,崩溃是罕见的事件,在这种情况下,监督学习方法的性能可能会严重下降,以处理不平衡的样本。此外,数据异构问题是另一个关键挑战。本研究引入无监督学习方法来解决样本不平衡和数据异质性问题,实验结果验证了该方法的有效性。利用上海市城市快速路系统数据进行实证分析。对几种无监督学习方法进行了测试,其中基于角度的异常点检测(ABOD)模型表现最佳,灵敏度为80.4%,虚警率(FAR)为25.4%。考虑到交通流分布的变化,进一步提出了带滑动窗口的动态ABOD方法,该方法的灵敏度提高了6.3%,FAR降低了8.1%。最后,利用本文提出的模型构建个性化道路水平模型,在样本量小、样本不平衡严重的情况下取得了较好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilizing angle-based outlier detection method with sliding window mechanism to identify real-time crash risk
Developing real-time crash risk models has been a hot research topic as it could identify crash precursors and thus triggering active traffic management strategies. Currently, crash risk identification models were mainly developed based upon supervised learning techniques, which requires large sample size of historical crash data. However, crashes are rare events in the real world, where the performance of supervised learning methods can be severely degraded to deal with the imbalanced sample. Besides, the data heterogeneity issue is another critical challenge. In this study, the unsupervised learning approach has been introduced to address unbalanced samples and data heterogeneity issues, and the experimental results has verified the effectiveness of the method. Data from the Shanghai urban expressway system were utilized for the empirical analyses. Several unsupervised learning methods were tested, among which, Angle-Based Outlier Detection (ABOD) model showed the best performance with 80.4% sensitivity and 25.4% false alarm rate (FAR). Considering the varying traffic flow distribution, dynamic ABOD with sliding window is further proposed, which improves the sensitivity by 6.3% and reduces the FAR by 8.1%. Finally, the proposed model is used to construct personalized road-level models, which achieve good performance despite the small sample size and severe sample imbalance.
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来源期刊
CiteScore
6.00
自引率
15.40%
发文量
38
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