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《Journal of Transport Information and Safety》 2015-02
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Short Period Urban Traffic Volume Forecasts Using Speed Data

ZHANG Runmin;XIA Jingxin;HUANG Wei;Intelligent Transportation System Research Center,Southeast University;  
A short period traffic volume forecasting model,which based on Kalman filtering algorithm and without assuming the state variables to be stationary,is proposed with considering the characteristics of speed variation.On the basis of the spatial temporal evolution relationship between the traffic flow of upstream and downstream in urban road network,a time variant state transition matrix is developed from the average speed data collected in field.The new state transition matrix will replace the constant state transition matrix of the existing short period traffic volume forecasting model based on Kalman filtering algorithm.Traffic volume forecasts of 4days on 2real road sections were conducted,the results show that the improved model has a better overall forecasting accuracy than the original model due to the enhancement of dynamic performance.The mean absolute relative error(MARE)decreased from 7.64%to 7.25% and from 16.04%to 15.75%;equality coefficient(EC)increased from 0.957 2to 0.960 2and from 0.925 0to 0.926 8.For those time periods when the traffic volume changed rapidly,the improvement is even more significant.In this case,the mean absolute relative error reduced by 14.8%.The results also verified the effectiveness of the proposed approach.
【CateGory Index】: U491.14
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