Abstract:

A combined dynamic modeling was proposed to forecast the traffic volume time series. Taking the characteristic of the road traffic volume into account, the traffic volume time series was decomposed into the cyclic item, the tendentious item, and the chaotic disturbing item. The sum of cyclic and tendentious items was forcast by the seasonal index smoothing method. The cycle of the computation was set at one day and one week, and the weights were determined by the recursive least square method with the forgetting factor. The chaotic item was forecast by the adjacent domain method. The results of forecasting the real traffic volume time series show that the traffic volume is interrelated with the states of the preceding day and preceding week. The remnant after removal by the seasonal index smoothing forecast is chaotic. The averag relative error of the one week traffic volume forecast with different statistic gaps is less than 9%.

Key words: engineering of communications and transportation, intelligence transportation system, traffic volume time series, recursive least square method