基于多尺度级联森林的交通安全状态预测

Traffic Safety State Forecasting Based on Multi-Scale Cascade Forest

城市交通安全状态预测任务对交通管理和公共安全具有重要意义,针对交通流数据的时序相关特性,论文提出一种基于多尺度级联森林的交通安全状态预测方法。首先,对事故发生地点上游的交通流数据进行处理与特征选择;然后,采用K-means聚类和统计分析的方法对交通流数据进行安全状态量化;最后,使用提出的多尺度级联森林算法对交通安全状态进行预测,该方法能够有效地处理时序数据的分类问题。根据实验显示,所提出的方法在预测指标上相对于对比的方法都有了显著的提升。

The urban traffic safety state prediction task is of great significance to traffic management and public safety.Aiming at the time series related characteristics of traffic data,this paper proposes a traffic safety state prediction method based on multi-scale cascade forest.Firstly,the traffic flow data in the upstream of accident location is processed and selected.Then,the K-means clustering and statistical analysis method is used to quantify the traffic state.Finally,the proposed multi-scale cascade for⁃est algorithm is used to predict traffic safety state.This method can effectively deal with the classification problem of time series da⁃ta.According to the experiment,the proposed method has a significant improvement in the prediction index relative to the compari⁃son method.