Short-term traffic flow prediction using a methodology based on autoregressive integrated moving average and genetic programming
The accurate short-term traffic flow forecasting is fundamental to both theoretical and empirical aspects of intelligent transportation systems deployment. This study aimed to develop a simple and effective hybrid model for forecasting traffic volume that combines the AutoRegressive Integrated Movin...
Main Authors: | Chengcheng Xu, Zhibin Li, Wei Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
Vilnius Gediminas Technical University
2016-09-01
|
Series: | Transport |
Subjects: | |
Online Access: | https://journals.vgtu.lt/index.php/Transport/article/view/1495 |
Similar Items
-
Annual forecasting of inflation rate in Iran: Autoregressive integrated moving average modeling approach
by: Samrad Jafarian‐Namin, et al.
Published: (2021-04-01) -
Exploring Hybrid Models For Short-Term Local Weather Forecasting in IoT Environment
by: Toai Kim Tran, et al.
Published: (2023-12-01) -
FORECASTING OF COVID–19 WITH AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) METHOD IN EAST JAVA PROVINCE
by: Yeni Baitur Roziqoh, et al.
Published: (2023-05-01) -
A Novel Hybrid Autoregressive Integrated Moving Average and Artificial Neural Network Model for Cassava Export Forecasting
by: Warut Pannakkong, et al.
Published: (2019-09-01) -
COVID-19 prevalence forecasting using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN): Case of Turkey
by: Gülhan Toğa, et al.
Published: (2021-07-01)