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Freeway incident detetion using fuzzy ART

机译：基于模糊ART的高速公路事故检测

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Pattern recognition techniques such as artificial neural networks continue to offer potential solutions to many of the existing problems associated with freeway incident detection algorithms. This study focuses on the application of Fuzzy ART neural networks to incident detection on freeways. Unlike backpropagation models, Fuzzy ART is capable of fast stable learning of recognition categories. It is an incremental approach that has the potential for on-line implementation. Fuzzy ART is trained with traffic patterns that are represented by 30-second loop detector data of occupancy, speed, or a combination of both. To reduce the false alarm rate that rsults from occasional misclassification of traffic patterns, a persistence time period of 3 minutes was arbitrarily selected. The algorithm performance improves when the temporal size of traffic patterns increases from one to two 30-second periods for all traffic parameters. An interesting finding is that the speed patterns produced better results than occupancy patterns. However, when combined in one pattern, occupancy and speed patterns yield the best results with 100
机译：模式识别技术（例如人工神经网络）继续为与高速公路事故检测算法相关的许多现有问题提供潜在的解决方案。这项研究的重点是模糊ART神经网络在高速公路事件检测中的应用。与反向传播模型不同，模糊ART能够快速稳定地学习识别类别。这是一种增量方法，具有在线实施的潜力。对模糊ART进行流量模式训练，该模式由30秒的占用率，速度或两者的组合的循环检测器数据表示。为了减少由于偶尔错误分类交通模式而导致的误报率，任意选择了3分钟的持续时间。对于所有流量参数，当流量模式的时间大小从1个30秒周期增加到2个30秒周期时，算法性能会提高。一个有趣的发现是，速度模式比占用模式产生更好的结果。但是，如果以一种模式组合使用，则占用率和速度模式会产生100的最佳结果

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