A Road Safety Evaluation Method Based on Clustering Neural Network

Zhenguo Yi, Yunpeng Wang, Daxin Tian, G. Lu, Haiying Xia
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引用次数: 3

Abstract

Traffic accident records data mining is very important to understand why traffic accidents occurred frequently under some driving, environment, and vehicle conditions. There are many reasons can lead to accident, and their relationships are complex, it is very difficult to build a correct evaluation model. To overcome this problem, statistical models such as neural network, fuzzy logic, decision tree etc. have been widely used on such accident data to analyze road crashes. In this paper we present a road safety evaluation method based on the clustering neural network. This method first learn the history data, after it is stable, it can be used to evaluate the road safety. The experimental results prove that this method is effective.
基于聚类神经网络的道路安全评价方法
交通事故记录数据挖掘对于理解交通事故在某些驾驶、环境和车辆条件下频繁发生的原因非常重要。导致事故发生的原因很多,而且它们之间的关系也很复杂,很难建立正确的评价模型。为了克服这一问题,神经网络、模糊逻辑、决策树等统计模型被广泛应用于这类事故数据来分析道路碰撞。本文提出了一种基于聚类神经网络的道路安全评价方法。该方法首先学习历史数据,待其稳定后,可用于道路安全评价。实验结果证明了该方法的有效性。
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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