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公交站点短时的客流预测是智能公交调度系统中重要的决策基础与技术支持. 在对短时客流特性进行分析的基础上，提出了以卡尔曼滤波作为公交站点短时客流的预测模型，并给出了模型的求解过程. 选用了一条实际公交线路中客流量较大、客流变化明显具有代表性的站点进行了采集数据和实例分析，数据结果的平均绝对误差为5.1771，均方误差为0.7961，表明提出的模型与算法可以有效地对短时公交客流进行预测. 与人工神经网络预测结果比较，在相同的实例数据下，其平均绝对误差为10.4770，均方误差为1.6724，结果表明使用卡尔曼滤波建立的模型比较准确，说明本文所提出的方法预测误差小，具有现实的应用意义.

Kalman Filter-Based Short-Term Passenger Flow Forecasting on Bus Stop

Short-term passenger flow forecasting on bus stop is an important base and technical support for decision-making on the intelligent bus dispatch system.A Kalman filter method is developed to forecast short-term passenger flow based on the characteristic analysis,and the solving algorism is presented.In the real bus line,a typical bus station with large and significantly changed passenger flow is chosen to be an example.The prediction method and the artificial neural network had been compared with the result...