ABSTRACT

Due to the complex structure of observation based traffic accident data and the absence of an analytic model to define their characteristics, different models are required. Accident characteristics have been modeled for different road segments with two different methods: evolutionary data clustering method and resilient neural networks. In the first method, observation data was clustered using an evolutionary search-based clustering algorithm. The first method is based on determining whether observation based test data have the conditions of a possible death or injury accident based on the distance to the cluster centers obtained. The second one is a regression method that predicts whether an accident will cause death or injury according to observation based traffic data in test road segments by using resilient neural networks. Experiment results show that data analysis methods are very effective in determining the existence of the conditions that may cause accidents resulting in death or injury.

摘要

由于基于观测的交通事故数据结构复杂，并且缺乏定义其特征的分析模型，因此需要不同的模型。已经使用两种不同的方法对不同路段的事故特征进行了建模：进化数据聚类方法和弹性神经网络。在第一种方法中，使用基于进化搜索的聚类算法对观测数据进行聚类。第一种方法是基于获得的聚类中心的距离来确定基于观察的测试数据是否具有可能发生死亡或受伤事故的条件。第二种是回归方法，它通过使用弹性神经网络根据测试路段中基于观察的交通数据来预测事故是否会导致死亡或伤害。