K近邻短时交通流预测模型

为了准确预测道路短时交通流,构建了基于K近邻算法的短时交通流预测模型。分析了K近邻算法的时间和空间参数,提出4种状态向量组合的K近邻模型:时间维度模型、上游路段-时间维度模型、下游路段-时间维度模型与时空参数模型。以贵州省贵阳市出租车的GPS数据对几种K近邻模型进行了检验。分析结果表明:带有时空参数的K近邻模型具有更高的预测精度,其预测误差最小,平均为7.26%。基于指数权重的距离度量方式能更精确的选择近邻,其预测误差最小,平均为5.57%。与神经网络和历史平均模型相比,带有指数权重的K近邻模型具有更好的预测精度,平均预测误差仅为9.43%。可见,带有时空参数与指数权重的K近邻模型可作为道路短时交通流预测的有效手段。

K-nearest neighbor model of short-term traffic flow forecast

In order to accurately forecast the short-term traffic flow,a K-nearest neighbor(K-NN) model was set up.The time and space parameters of the K-NN model were analyzed.Based on four different combinations of state vectors,the time dimension model,upstream section-time dimension model,downstream section-time dimension model and space-time dimension model were proposed.The four different models were validated by using the GPS data from taxis of Guiyang.Analysis result indicates that the K-NN model with both space and time parameters has highest forecasting precision than the other three models,and its average prediction error is about 7.26%.The distance measuring mode with exponent weight has higher accuracy in choosing the nearest neighbors,and its average prediction error is about 5.57%.The predicting performance of improved K-NN model with exponent weight and space-time parameters is best compared with the artificial neural network model and the historical average model,and its average prediction error is only 9.43%.So the improved K-NN model is an effective way for forecasting short-term traffic flow.2 tabs,10 figs,16 refs.