Short-time traffic flow prediction with ARIMA-GARCH model

Chenyi Chen · Jianming Hu · Yi Zhang

214 citations0/0 files storedIEEE Intelligent Vehicles Symposium

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Traffic Prediction and Management Techniques Traffic control and management Forecasting Techniques and Applications

Abstract

Short-time traffic flow prediction is a significant interest in transportation study, and it is essential in congestion control and traffic network management. In this paper, we propose an Autoregressive Integrated Moving Average with Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GARCH) model for traffic flow prediction. The model combines linear ARIMA model with nonlinear GARCH model, so it can capture both the conditional mean and conditional heteroscedasticity of traffic flow series. The model is calibrated, validated and used for prediction based on PeMS single loop detector data. The performance of the hybrid model is compared with that of standard ARIMA model. The results show that the introduction of conditional heteroscedasticity cannot bring satisfactory improvement to prediction accuracy, in some cases the general GARCH(1,1) model may even deteriorate the performance. Thus for ordinary traffic flow prediction, the standard ARIMA model is sufficient.

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References

2002Introduction to Time Series and Forecasting 1979ANALYSIS OF FREEWAY TRAFFIC TIME-SERIES DATA BY USING BOX-JENKINS TECHNIQUES 1997Traffic Flow Forecasting: Comparison of Modeling Approaches 2004Short‐term traffic forecasting: Overview of objectives and methods 1999Application of Subset Autoregressive Integrated Moving Average Model for Short-Term Freeway Traffic Volume Forecasting 2007Introduction to Modern Time Series Analysis 1992Prediction in dynamic models with time-dependent conditional variances 2002Performance Evaluation of Short-Term Time-Series Traffic Prediction Model

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