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.