卡尔曼滤波短时交通流预测普通国省道适应性研究

短时交通流预测是提高普通国省道交通运行效率和安全的关键技术之一。普通国省道具有分布地域广、情况复杂的特点，要求短时交通流预测方法具有良好的适应性，然而，针对短时交通流预测算法适应性及其机制的系统性研究尚不多见。选取1种自适应卡尔曼滤波算法，系统分析其适应性和适应机制。获取江苏省徐州市普通国省道路网中8个交通调查站所采集的实际交通流数据开展实例分析，结果表明:在不同的交通流量水平下，所选算法均值预测的平均绝对百分比误差在10.98%~15.92%之间，区间预测的无效覆盖率在5.21%~6.15%之间，表明所选的自适应卡尔曼滤波算法在不同交通流水平下都具有良好的预测性能；对所选算法的参数进行分析发现，算法参数能够随交通流水平的变化而自动调整，具有良好的自适应机制；所选算法能够在预测初期实现有效的性能调整和收敛。 

Adaptability of Kalman Filter for Short-time Traffic Flow Forecasting on National and Provincial Highways

The forecast of short-term traffic flow is one of the significant technologies to improve safety of national and provincial highways. General national and provincial highways have the characteristics of wide distribution and complex conditions, which requires good adaptability of short-time traffic flow forecasting methods. However, there are few systematic studies on this adaptability and associated mechanism. Out of many forecasting algorithms of short-term traffic flow, the adaptive Kalman filter algorithm is selected to investigate adaptability and its adaptive mechanism. The empirical analysis is conducted using traffic flow data collected from 8 traffic survey stations of the national and provincial road network in Xuzhou, Jiangsu, China. Under different traffic flows, the average absolute percentage errors for mean prediction of the selected algorithm ranged from 10.98% to 15.92%. Furthermore, the invalid coverage of the interval generation ranged from 5.21% to 6.15% under different traffic levels. The results indicate the selected adaptive Kalman filter algorithm has good overall performance under different traffic levels. After analyzing the parameters of the selected algorithm, it can be found that the algorithm parameters can be adjusted automatically with the change of traffic flow level, presenting a good adaptive mechanism; in the early stage of prediction, the selected algorithm can achieve effective performance adjustment and convergence.