基于自回归求和移动平均的冬季路温短临预测

Pavement Temperature Shortimpending Prediction Based on ARIMA in Winter

摘要:

挖掘冬季路面温度在其他外部变量影响下未来短时间内的波动规律，建立冬季路面温度短临预测模型.基于交通气象监测站的冬季历史监测数据，利用统计学方法确定路面温度的主要影响因素，应用自回归求和移动平均（ARIMA）模型建模分析，对未来短时间内的路面温度进行预测.结果表明：允许误差在±0.5 ℃和±1.0 ℃范围内，未来3 h的平均预测准确率分别达到81.25%和99.65%，对应的平均绝对误差为0.21 ℃和0.26 ℃；允许误差在±0.5 ℃范围内，未来第1 h的平均预测准确率最高，平均绝对误差最低，分别达到92.50%和0.15 ℃.

Abstract:

Under the effect of other external variables, the fluctuation rule of pavement temperature with time was excavated in the future. Based on the historical data of a traffic meteorological monitoring station, the main affecting factors of pavement temperature were determined, and the shortimpending prediction model of pavement temperature time series was established by applying autoregressive integrated moving average (ARIMA) model in order to predict pavement temperature in a short time. The results show that the predicted average accuracy reaches 81.25% and 99.65% respectively within allowable error range of ±0.5 ℃ and ±1.0 ℃ in the next 3 h. The predicted average accuracy and the mean absolute error are up to 92.50% and 0.15 ℃ respectively within allowable error range of ±0.5 ℃ in the next 1 h.