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交通流的可预测时间间隔是实现交通控制和诱导的关键。为考察交通流时间序列在不同时间间隔下的可预测性,以北京市二环路2min、4min、6min、8min、10min、12min、14min和16min间隔的交通量时间序列为研究对象,应用R/S分析法计算不同观测尺度下的Hurst指数值,发现同一天内Hurst指数值随观测尺度的变大而增大,同一观测尺度下,Hurst指数随着样本量的增加而降低。最后,采用近似熵法计算序列的复杂度,发现复杂度指数有相似的变化规律,证明观测尺度大小与序列的随机性强弱存在负相关。
Predictable traffic flow time interval is the key to traffic control and guidance. In order to investigate the predictability of traffic flow time series at different time intervals, the time series of traffic volume at intervals of 2min, 4min, 6min, 8min, 10min, 12min, 14min and 16min in Beijing Second Ring Road were used as research objects. S method was used to calculate Hurst index values at different observation scales. It was found that the Hurst index value increased with the observation scale on the same day, and Hurst index decreased with the increase of sample size on the same observation scale. Finally, using the approximate entropy method to calculate the complexity of the sequence, it is found that the complexity index has a similar variation rule, which proves that there is a negative correlation between the size of the observation scale and the randomness of the sequence.