分步式滤波在短时交通流预测中的应用

Application of step-by-step filtering on short-term traffic flow prediction

用基于分步式滤波的多传感器动态系统数据融合算法建立了短时段交通流量预测模型,并与传统的卡尔曼滤波算法在计算复杂度上进行了比较。利用澳大利亚某高速公路所采集的数据进行了预测仿真实验。实验结果表明该算法确保了预测精度,同时简化了计算量,提高了响应速度,可以实现对交通流的实时预测。

A short-term traffic flow prediction model is formulated through data fusion algorithm of multisensor dynamic system based on step-by-step filtering,and comparing complexity of the new algorithm with traditional Kalman Filtering（KF） algorithm is done.Some real time traffic flow data sampled from an Australian freeway are used to prediction and simulation experiments.The results from prediction experiments indicate that the new algorithm may ensure estimate accuracy and reduce the calculations.It can also be used to predict real time traffic flow.