|Table of Contents|

K-nearest neighbor model of short-term traffic flow forecast(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2012年02期
Page:
105-111
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
K-nearest neighbor model of short-term traffic flow forecast
Author(s):
YU Bin1; WU Shan-hua1; WANG Ming-hua1; ZHAO Zhi-hong2
1.School of Transportation Management,Dalian Maritime University,Dalian 116026,Liaoning,China;2.School of Information Engineering,Chang’an University,Xi’an 710064,Shaanxi,China
Keywords:
traffic information engineering;  short-term traffic flow forecast;  K-nearest neighbor model;  space-time parameters;  exponent weight
PACS:
U491.14
DOI:
-
Abstract:
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.

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Last Update: 2012-04-30