| 基于决策树理论的交通流参数短时预测 |
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| 引用本文： | 薛红军,陈广交,李鑫民,顾理.基于决策树理论的交通流参数短时预测[J].交通信息与安全,2016,34(3):64-71. |
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| 作者姓名： | 薛红军 陈广交 李鑫民 顾理 |
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| 作者单位： | 南通市公路管理处 江苏 南通 226001 |
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| 摘 要： | 针对现有交通流参数短时预测方法的不足,考虑到交通流数据序列的非线性特征,提出一种基于决策树理论的非参数预测方法。采用时间序列滞后项将交通流参数序列转化成非参数模型能处理的数据格式。考虑到交通流参数之间存在长期协整关系,构建流量速度滞后项的组合向量,为预测模型提供基础数据。构建基于分类回归树(CART)的交通流参数短时预测模型。基于实际采集的道路交通流数据,对模型在不同等级道路不同速度区间下的性能进行评估。结果表明,所提出的模型相较于常用的时间序列模型,精度有所提高;速度预测准确性普遍高于流量,速度平均绝对百分比误差基本小于13%,而流量预测则达到了30%;采用工作日和周末数据分别建模能够有效提升预测性能;不同速度区间下的预测性能评估显示,模型在各等级道路中速区间的预测结果具有较高的准确性与稳定性。
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| 关 键 词： | 城市交通 交通流参数 短时预测 决策树 非参数模型 |
A Short-term Forecasting of Traffic Flow Parameters Based on Decision Tree Theory |
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| Abstract: | Due to the non-linearity feature of data series of traffic flow,a nonparametric predicting method based on the theory of decision tree is proposed to overcome the deficiencies of current short-term forecasting methods of traffic flow.Using time series lags,the data series of traffic flow are converted into a format which can be recognized by a non-parameter model.Considering the long-term co-integration relationship between traffic flow parameters,combinational vectors of volume and speed lags are established,providing a basis for this forecasting model.Then a decision tree model based on classification and regression tree (CART)is developed for the prediction for the parameters of short-term traffic flow.Based on actual data of traffic flow,the performance of this CART decision tree model is evaluated under different types of road and speed intervals.The results show that the CART decision tree model is superior to the general time se-ries model in terms of the prediction accuracy.In addition,the accuracy of speed prediction is generally higher than that of volume prediction.The mean average percentage error (MAPE)of speed prediction is less than 13% while the MAPE of volume prediction is 30%.Besides,this model can perform better when it is constructed with the data from weekdays or weekends separately.The evaluation of performance under different speed intervals indicates that the CART decision tree model presents high accuracy and stability in medium intervals speed for all types of road. |
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