| Citation: | SHEN Leixiao, LU Yuhang, GUO Jianhua. Adaptability of Kalman Filter for Short-time Traffic Flow Forecasting on National and Provincial Highways[J]. Journal of Transport Information and Safety, 2021, 39(5): 117-127. doi: 10.3963/j.jssn.1674-4861.2021.05.015
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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.
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