Road monthly traffic volume prediction method based on SARIMA-NAR combined model

Abstract

The invention discloses a road monthly traffic volume prediction method based on an SARIMA-NAR combination model, and the method comprises the steps: collecting monthly traffic volume data over the years, and building a road monthly traffic volume time sequence; performing linear prediction on the monthly traffic volume time sequence by using an SARIMA model to obtain a linear result; calculating a residual error according to a linear result obtained by the SARIMA model and an original sequence, and extracting a residual error sequence; constructing an NAR model by using the residual sequence, and performing nonlinear prediction to obtain a nonlinear result; superposing the linear result and the nonlinear result to obtain a final monthly traffic volume prediction result; and finally, evaluating the prediction result of the combined model through a plurality of indexes including a mean absolute percentage error (MAPE), a mean absolute error (MAE) and a root-mean-square error (RMSE), wherein the prediction result has high effectiveness and reliability, and the prediction process is more convenient and efficient.

Description

technical field [0001] The invention belongs to the field of road traffic volume prediction, and relates to a road monthly traffic volume prediction method based on a SARIMA-NAR combined model. Background technique [0002] Traffic volume is an important index to measure the performance status of road network and the key basis for road classification. Provides a wealth of information references. [0003] At present, research on traffic volume forecasting based on time series mainly includes Markov model, genetic algorithm, neural network, wavelet decomposition and reconstruction, gray system theory, ARIMA model, support vector regression and other methods. However, a single forecasting model cannot accurately capture all the information of the complex traffic volume time series, and has shortcomings such as one-sidedness and low reliability. Based on this, many scholars have carried out research on combined forecasting models. However, the existing traffic volume combinat...

AI Technical Summary

Problems solved by technology

However, the existing traffic volume combination forecasting methods are mainly aimed at short-term forecasting, and there are few studies on seasonal fluctuations; and the static neural network selected has no memory function and only depends on the current input, and its forecasting ability for time series is far lower than that of dynamic traffic volume. Neural Networks

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