Short-term Traffic Flow Prediction for Expressway based on ARIMA: Compared with LSTM

DOI

10.2991/978-94-6239-648-7_9How to use a DOI?

Keywords

Highway traffic; Short-term traffic flow; ARIMA model; prediction; LSTM model

Abstract

Predicting short-term traffic flow holds significant value for the operation and management of highway traffic. As a result, it’s crucial to develop a feasible short-term traffic flow forecast model and use traffic flow data for prediction in an efficient manner. In this study, for predicting traffic flow, the Autoregressive Integrated Moving Average (ARIMA) model is employed. Firstly, the original sequence of highway traffic flow data is differenced and the ADF test is used to verify the stationarity of the sequence. Subsequently, the value range of p and q is preliminarily determined by the graphical feature analysis method, and then the model is constructed and evaluated according to the AIC and BIC criteria to determine the values of p and q. Next, to estimate parameters, utilize maximum likelihood estimation and construct a complete ARIMA model. After the model is established, a white noise test is performed on the residuals to ensure a high degree of model fit. Finally, a static forecasting method is employed to predict real traffic flow data from Luxembourg, and the outcomes of the model’s predictions are assessed. The experimental findings show that the ARIMA model predicts short-term highway traffic flow with great accuracy and dependability.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jinghan Zou
PY  - 2026
DA  - 2026/04/24
TI  - Short-term Traffic Flow Prediction for Expressway based on ARIMA: Compared with LSTM
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 69
EP  - 79
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_9
DO  - 10.2991/978-94-6239-648-7_9
ID  - Zou2026
ER  -