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Algorithm fusion method to enhance automatic incident detection on Melbourne freeways

机译:算法融合方法增强墨尔本高速公路自动事件检测

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摘要

This paper addresses the transferability issue faced by many practitioners in developing an effective and efficient automatic incident detection algorithm for different freeways. An algorithm fusion procedure developed for the Central Expressway in Singapore is evaluated to demonstrate its transferability potential in detecting lane-blocking incidents along freeways in Melbourne, Australia. This study observes that the flow-based algorithm fusion options that use a set of different detection threshold values for various pre-incident traffic flow conditions possess promising transferability potential. They give a reasonably high detection rate of above 80% with false alarm rate levels below 0.2% with mean-time-to-detect values less than 150 seconds. These flow-based algorithm fusion options significantly outperform a model specifically developed for traffic conditions on freeways in Melbourne. In conclusion, this method is capable of providing an alternative to the commonly practiced methods in detecting incidents along different sites.
机译:本文解决了许多从业者在开发针对不同高速公路的有效且高效的自动事件检测算法时面临的可转移性问题。对为新加坡中央高速公路开发的算法融合程序进行了评估,以证明其在检测澳大利亚墨尔本高速公路沿线阻塞事件方面的可转移性潜力。这项研究发现,针对各种事前交通流情况使用一组不同的检测阈值的基于流的算法融合选项具有广阔的可移植性潜力。它们提供了80%以上的合理高检测率,误报率低于0.2%且平均检测时间小于150秒。这些基于流量的算法融合选项明显优于专门针对墨尔本高速公路交通状况开发的模型。总之,该方法能够为检测沿不同地点的事件提供一种替代常规方法的方法。

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