Application Research of Passenger Traffic Prediction Model based on ARIMA Model and Exponential Smoothing

DOI

10.2991/978-94-6463-514-0_29How to use a DOI?

Keywords

Passenger traffic prediction; ARIMA model; exponential smoothing; Model weighted combinations; Cascade model

Abstract

The purpose of this study is to explore the application of passenger volume prediction model based on Autoregressive Moving Average Model (ARIMA) and exponential smoothing method in the transportation field. First, the parameters and trends required in the model were identified by analysing the historical passenger traffic data. Secondly, The ARIMA model is used to capture the autocorrelation and moving average properties in time series data to improve the accuracy of forecasting. At the same time, combined with the exponential smoothing method, the change trend of the data was effectively fitted and predicted. The results show that the passenger traffic prediction model based on ARIMA and exponential smoothing method shows good prediction effect in practical application, which can provide accurate passenger traffic prediction information for traffic management departments and provide a scientific basis for transportation planning and resource allocation.

Copyright

© 2024 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  - Qishun Song
AU  - Changsheng Luo
PY  - 2024
DA  - 2024/09/28
TI  - Application Research of Passenger Traffic Prediction Model based on ARIMA Model and Exponential Smoothing
BT  - Proceedings of the 2024 7th International Symposium on Traffic Transportation and Civil Architecture (ISTTCA 2024)
PB  - Atlantis Press
SP  - 274
EP  - 281
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-514-0_29
DO  - 10.2991/978-94-6463-514-0_29
ID  - Song2024
ER  -