利用速度信息改进城市道路短时交通量预测

Short Period Urban Traffic Volume Forecasts Using Speed Data

利用速度消息的时变特性,提出了1种无需假设状态变量为平稳的基于卡尔曼滤波算法的短时交通量预测模型。依据城市道路网上下游路段交通流之间的时空演化关系,利用实时采集的路段平均速度信息构建时变的状态转移矩阵来取代常数状态转移矩阵,对现有基于卡尔曼滤波算法的短时交通量预测模型进行改进。最后以2个真实路段4d的交通量进行预测试验,相关计算结果表明:由于加强了模型的动态性,改进后的预测模型较原模型的预测精度在整体上有所提高,其中平均绝对相对误差由7.64%及16.04%分别下降至7.25%及15.75%,均等系数则由0.957 2及0.925 0分别提高至0.960 2及0.926 8,而对于交通量急速变化的时段,提高的幅度更为明显,平均绝对相对误差可降低14.8%,从而验证了所提出方法的有效性。

A short period traffic volume forecasting model ,which based on Kalman filtering algorithm and without assuming the state variables to be stationary ,is proposed with considering the characteristics of speed variation .On the basis of the spatial temporal evolution relationship between the traffic flow of upstream and downstream in urban road net‐work ,a time variant state transition matrix is developed from the average speed data collected in field .The new state transition matrix will replace the constant state transition matrix of the existing short period traffic volume forecasting model based on Kalman filtering algorithm .Traffic volume forecasts of 4 days on 2 real road sections were conducted ,the results show that the improved model has a better overall forecasting accuracy than the original model due to the enhance‐ment of dynamic performance .The mean absolute relative error （MARE） decreased from 7 .64% to 7 .25% and from 16 . 04% to 15 .75% ;equality coefficient （EC） increased from 0 .957 2 to 0 .960 2 and from 0 .925 0 to 0 .926 8 .For those time periods when the traffic volume changed rapidly ,the improvement is even more significant .In this case ,the mean absolute relative error reduced by 14 .8% .The results also verified the effectiveness of the proposed approach .