随机交通流的组合控制预测模型和方法

为了解决交通流预测中的时变性、随机性和不确定性等问题,提出了一种新的组合控制预测算法.该方法利用多尺度的小波分析方法将交通流量分解为均匀部分和随机部分,并根据状态的动态变化构建了组合控制预测模型,分别利用支持向量机回归和Markov链法来预测交通流的均匀序列和随机序列,同时基于准动态规划方法来计算最优控制向量和相应的控制矩阵,从而动态选择训练数据得到最优预测结果,并采用实际数据进行实验验证与比较.结果表明,本文方法得到的平均相对误差和均方差比其他方法低74%和85%,得到的均等系数高6%,从而验证了本文方法的可行性和有效性.

Combined control predictive model and method for stochastic traffic flow

In order to solve the problem of time variability, randomness and uncertainty in the traffic flow forecasting, a new combined control predictive algorithm was proposed. The proposed method divided the traffic flow into uniform part and random part with the multi-scale wavelet analysis method. The combined control predictive model was established according to the dynamic change of states. The uniform and random sequences of traffic flow were predicted with the support vector machine regression and Markov chain method, respectively. Based on the quasi dynamic programming method, the optimal control vector and the corresponding control matrix were calculated. Hence, the optimal prediction results could be obtained through the dynamic selection of training data. Furthermore, the experimental verification and comparison were implemented with the actual data. The results show that the average relative error and mean square deviation obtained with the proposed method are 74% and 85% lower than those of other methods, and the obtained equality coefficient is 6% higher than that of other methods. Therefore, the feasibility and effectiveness of the proposed method can be verified.