为解决高速公路收费站间非平稳交通流状态下因卡尔曼滤波算法自适应性能差而导致的旅行时间预测精度不稳定的问题，提出等间距插值和 Sage- Husa 自适应卡尔曼滤波相结合的预测算法。融合人工半自动收费和电子不停车收费数据计算平均旅行时间; 引入等间距插值方法重构实时与历史旅行时间之间的时间序列; 利用最小二乘法原理构建 Sage- Husa 自适应预测模型; 开发旅行时间预测应用系统，实时主动预测高速公路站间旅行时间。在某示范路段的应用表明: 在正常、事故、小长假3 种交通流状态下，所提方法的所有周期平均相对误差均在 7．5%内，事故周期平均相对误差均在10%内．

Poor adaptability of Kalman filtering algorithm may result in inaccurate prediction of expressway traveltime when the traffic flow between two expressway toll stations is non- stationary.In order to solve this problem,aprediction algorithm based on the equidistant interpolation and the Sage- Husa adaptive Kalman filtering is proposed.In the investigation,first,data from manual toll collection and electronic toll collection are merged together to cal-culate the average travel time.Then,the time series between real- time and historical travel time is reconstructedvia the equidistant interpolation,and a prediction model based on the Sage- Husa adaptive Kalman filtering is con-structed with the help of the least square method.Moreover,a prediction system of expressway travel time is deve-loped and is finally applied to the real- time prediction of the travel time between two toll stations.Case study resultsof an expressway section show that,in the three states,namely,the normal state,the accident state,and the holi-day state,the proposed algorithm is able to restrict the average relative error of all periods or of a random accidentwithin 7.5% or 10%,respectively.