CAJ | 학술논문

针对基于单一数据源、利用卡尔曼滤波理论建立行程时间预测模型存在的不足，采用多源数据进行行程时间预测以提高精度。浮动车、固定检测器是常用的交通信息采集方法，在信息种类、数据精度等方面存在一定的互补性。因此，选择2种检测器的实时交通数据作为模型输入参数。利用卡尔曼滤波理论，以流量、占有率、行程时间作为输入量构成参数矩阵，建立城市道路网络行程时间预测模型。并通过Vissim仿真实验验证了模型的有效性。结果表明：基于多源数据的行程时间预测模型平均绝对相对误差为5．45％，其精度比单独采用固定检测器检测数据预测提高了14．4％，比单独采用浮动车数据预测提高了7．5％。
침대기우단일수거원、이용잡이만려파이론건립행정시간예측모형존재적불족，채용다원수거진행행정시간예측이제고정도。부동차、고정검측기시상용적교통신식채집방법，재신식충류、수거정도등방면존재일정적호보성。인차，선택2충검측기적실시교통수거작위모형수입삼수。이용잡이만려파이론，이류량、점유솔、행정시간작위수입량구성삼수구진，건립성시도로망락행정시간예측모형。병통과Vissim방진실험험증료모형적유효성。결과표명：기우다원수거적행정시간예측모형평균절대상대오차위5．45％，기정도비단독채용고정검측기검측수거예측제고료14．4％，비단독채용부동차수거예측제고료7．5％。
In view of the deficiencies of traditional travel time prediction models developed based on Kalman filtering technique and single data source ,the multi-source data are used to improve such models and the prediction accuracy of travel time .Floating cars and loop detectors are common ways for collecting travel time ,and the two are complementary to each other in many ways .Therefore ,the real-time traffic data from the two sources are used as the inputs of the pre-diction model .Through Kalman filtering ,flow ,occupancy and travel time are used as inputs of the proposed travel time prediction model .Finally ,the model is verified through a simulation from Vissim .The simulation results show that the average absolute relative error of the estimated travel time based on the model developed based on the multi-source data is 5 .45% ,which is 14 .4% lower than those estimated based on the loop detector data only and 7 .5% lower than those esti-mated based on the floating car data alone .