Traffic Flow Combination Forecasting Method Based on Improved LSTM and ARIMA

TL;DR

This paper introduces a novel traffic flow forecasting method combining an improved LSTM neural network with ARIMA, enhancing accuracy and adaptability for intelligent traffic systems.

Contribution

It proposes a new combined prediction model based on SDLSTM-ARIMA that improves traffic flow forecasting stability and accuracy over existing methods.

Findings

The SDLSTM-ARIMA model outperforms traditional ARIMA and LSTM models in accuracy.

The embedded system demonstrates high reliability and low cost.

The method has wide application prospects in intelligent traffic systems.

Abstract

Traffic flow forecasting is hot spot research of intelligent traffic system construction. The existing traffic flow prediction methods have problems such as poor stability, high data requirements, or poor adaptability. In this paper, we define the traffic data time singularity ratio in the dropout module and propose a combination prediction method based on the improved long short-term memory neural network and time series autoregressive integrated moving average model (SDLSTM-ARIMA), which is derived from the Recurrent Neural Networks (RNN) model. It compares the traffic data time singularity with the probability value in the dropout module and combines them at unequal time intervals to achieve an accurate prediction of traffic flow data. Then, we design an adaptive traffic flow embedded system that can adapt to Java, Python and other languages and other interfaces. The experimental results demonstrate that the method based on the SDLSTM - ARIMA model has higher accuracy than the similar method using only autoregressive integrated moving average or autoregressive. Our embedded traffic prediction system integrating computer vision, machine learning and cloud has the advantages such as high accuracy, high reliability and low cost. Therefore, it has a wide application prospect.