Title: Traffic flow combination forecasting method based on improved LSTM and ARIMA

Authors: Boyi Liu; Xiangyan Tang; Jieren Cheng; Pengchao Shi

Addresses: College of Information Science and Technology, Hainan University, Haikou, 570228, China; University of Chinese Academy of Science, Beijing, 100000, China ' College of Information Science and Technology, Hainan University, Haikou, 570228, China; State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228, China ' College of Information Science and Technology, Hainan University, Haikou, 570228, China; State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228, China ' Mechanical and Electrical Engineering College, Hainan University, Haikou, 570228, China

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 similar methods using only autoregressive integrated moving average. 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 prospects.

Keywords: traffic flow forecasting; LSTM neural networks; embedded system; depth learning.

DOI: 10.1504/IJES.2020.105287

International Journal of Embedded Systems, 2020 Vol.12 No.1, pp.22 - 30

Received: 17 Aug 2017
Accepted: 06 Apr 2018
Published online: 24 Feb 2020 *

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