The application of space-time ARIMA model on traffic flow forecasting

Traffic flow data are in the form of spatial time series and are collected at specific locations at constant intervals of time. Space-time autoregressive time series modeling is a promising inductive method that uses a small number of parameters and can be used for online monitoring and prediction....

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 6; pp. 3408 - 3412
Main Authors Shu-Lan Lin, Hong-Qiong Huang, Da-Qi Zhu, Tian-Zhen Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Constraint theory
Cybernetics
Data engineering
Educational institutions
Forecasting
Intelligent Transport Systems
Machine learning
Predictive models
Random variables
STARIMA
Telecommunication traffic
Traffic control
Traffic flow network
Vectors
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Summary:Traffic flow data are in the form of spatial time series and are collected at specific locations at constant intervals of time. Space-time autoregressive time series modeling is a promising inductive method that uses a small number of parameters and can be used for online monitoring and prediction. In this paper, we develop space-time autoregressive models for urban traffic flow network scenarios. We evaluate the ability of the space-time autoregressive models to model the spatial and temporal correlations in the traffic network and show that the space-time model performs well.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212785