计算机与现代化 ›› 2009, Vol. 1 ›› Issue (12): 1-3,9.doi: 10.3969/j.issn.1006-2475.2009.12.001
• 人工智能 • 下一篇
马 云，熊桂喜
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MA Yun, XIONG Gui-xi
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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Kalman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。
关键词: 模糊回归计算, 卡尔曼滤波器, 行程时间预测
Abstract: 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.
Key words: fuzzy regression, Kalman filter, travel time prediction
中图分类号:
TP391
马云;熊桂喜. BRT环境下车辆行程时间预测分析[J]. 计算机与现代化, 2009, 1(12): 1-3,9.
MA Yun;XIONG Gui-xi. Analysis of Travel Time Prediction Based on BRT Environment[J]. Computer and Modernization, 2009, 1(12): 1-3,9.
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http://www.c-a-m.org.cn/CN/Y2009/V1/I12/1
