交通流预测的马尔科夫粒子滤波方法研究

智能交通系统中，短时交通流预测是实现先进的交通控制和交通诱导的关键技术之一.针对目前马尔科夫交通流量预测模型在精度方面的不足，以及交通流量随机性、波动性的特点，提出马尔科夫粒子滤波交通流预测模型.一方面，将对交通流量预处理后的样本数据应用于马尔科夫模型中预测未来交通流量，能够较好地描述交通流量的变化趋势；另一方面，针对该预测结果精度的不足及对非线性预测不稳定的缺点，引用粒子滤波算法，将预测结果及权值进行不断更新，以及样本重选样过程，经过多次迭代，使样本粒子更加逼近真实预测值，从而提高预测精度.最后，以北京昌平区某检测器检测到的交通量进行仿真，将预测结果与传统马尔科夫链进行误差对比分析.结果表明，本文提出的马尔科夫粒子滤波交通流预测模型 5 min间隔误差为6.14%、1 h间隔误差为6.04%，预测精度高，具有更好的适用性和稳定性.

Markov Particle Filter Traffic Flow Prediction Model

In intelligent traffic system, short-term traffic flow prediction is one of the key technologies of traffic control and traffic guidance. Due to the inaccuracy of Markov traffic flow prediction model, according to the characteristics of traffic flow, a Markov particle filter traffic flow prediction model is proposed. On the one hand, after pretreatment of traffic flow, it can be used as sample data to predict future traffic flow in Markov model, which can better describe the trend of traffic flow. On the other hand, in view of the inaccuracy of the prediction results and the disadvantages of non- linear prediction instability, particle filter algorithm is used to update the prediction results and weights, and the sample re-selection process. After several iterations, the sample particles are closer to the actual prediction result, thus improving the prediction accuracy. Finally, the traffic flow detected by a detector in Changping District of Beijing is simulated, and the prediction results are compared with the traditional Markov chain. The results show that the 5-minute interval error and 1-hour interval error of the proposed Markov particle filter traffic flow prediction model are 6.14% and 6.04% respectively. It shows that the model has better applicability and stability, and the prediction accuracy is high.