Real-time traffic accident prediction based on AdaBoost classifier

doi:10.11772/j.issn.1001-9081.2017.01.0284

Abstract

Abstract: The traditional road traffic accident forecast mainly uses the historical data, including the number and the loss of traffic accidents, to predict the future trend, however, the traditional method can not reflect the relationship between the traffic accident and real-time traffic characteristics, and it also can not prevent accidents effectively. In order to solve the problems above, a real-time traffic accident prediction method based on AdaBoost classifier was proposed. Firstly, the road traffic states were divided into normal conditions and dangerous conditions, and the real-time collection of traffic flow data was used as the characteristic variable to characterize the different states, so the real-time prediction problem could be converted to a classification problem. Secondly, the Probability Density Function (PDF) of traffic flow characteristics under the two conditions in different time scales were estimated by Parzen window nonparametric estimation method, and the estimated density function was analyzed by the separability criterion based on probability distribution, then the sample data with appropriate characteristic variable and time scale could be determined. Finally, the AdaBoost classifier was trained to classify different traffic conditions. The experimental results show that the correct ratio by using standard deviation of traffic flow characteristics to classify test samples is 7.9% higher than that by using average value. The former can reflect the differences of different traffic states better, and also get better classification results.

Key words: intelligent transportation, accident prediction, classifier, traffic flow characteristic, Parzen window, separability criterion

摘要： 传统的道路交通事故预测是对交通事故次数及其造成的损失的历史趋势进行预测，针对其不能反映交通事故与实时交通特性关系、不能有效地预防事故发生的问题，提出一种基于AdaBoost分类器的交通事故实时预测的方法。首先，将交通道路划分为正常、危险两种交通状态，利用实时采集的交通流数据作为特征变量对不同的状态进行表征，将事故的实时预测问题转化为分类问题；然后，采用Parzen窗非参数估计的方法对两种状态在不同时间尺度下候选交通流特征的概率密度函数（PDF）进行估计，利用基于概率分布的可分性判据分析估计的密度函数，选择合适的特征变量及时间尺度，确定样本数据；最后，根据样本数据训练AdaBoost分类器对不同的交通状态进行分类识别。实验结果表明，采用交通流特性的标准差特征对测试样本分类的正确率比平均值特征高7.9%，更能反映不同交通状态的差别，获得更好的分类结果。

关键词: 智能交通, 事故预测, 分类器, 交通流特性, Parzen窗, 可分性判据

CLC Number:

TP391.4

ZHANG Jun, HU Zhenbo, ZHU Xinshan. Real-time traffic accident prediction based on AdaBoost classifier[J]. Journal of Computer Applications, 2017, 37(1): 284-288.

张军, 胡震波, 朱新山. 基于AdaBoost分类器的实时交通事故预测[J]. 计算机应用, 2017, 37(1): 284-288.

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