摘要
分析了BRT(Bus Rapid Transit)环境下进行车辆行程时间预测的特点,建立了相应的预测模型。并针对经典Kal-man滤波器在进行车辆行程时间预测时存在的不足,提出了一种在BRT环境下利用对历史数据进行Fuzzy回归计算来修正Kalman滤波结果的方法。最后本文针对2008年10月9日北京市南中轴快速公交线的实际数据进行了对比实验,结果表明,改进后的滤波器有效降低了原算法的误差。
The paper first analyzes the characteristic of predicting BRT vehicle travel time, and builds the prediction model. Then contraposed to the disadvantages of the tradition Kalman filter in predicting travel time, the paper presents an improved Kalman filter based on the fuzzy regression adaptive historical data samples of vehicle travel time. Finally the paper uses actual data collected from BRT Transport of South Axis Street in Beijing on Oct 9, 2008 for experiment. The results show that the improved filter effectively reduces the error of the original algorithm.
出处
《计算机与现代化》
2009年第12期1-3,9,共4页
Computer and Modernization
基金
国家"十五"科技攻关计划基金资助项目(2005BA414B04)
