Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling

Billy M. Williams

468 citations0/0 files storedTransportation Research Record Journal of the Transportation Research Board

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

Abstract

Short-term freeway traffic flow forecasting efforts to date have focused on predictions based solely on previous observations at the location of interest. This univariate prediction is useful for certain types of intelligent transportation system (ITS) forecasts, such as operational demand forecasts at system entry points. In addition, data from upstream sensors should improve forecasts at downstream locations. This motivates investigation of multivariate forecast models that include upstream sensor data. A candidate model is transfer functions with autoregressive integrated moving average errors, otherwise known as the ARIMAX model. The ARIMAX model was applied to motorway data from France that had been the subject of previous traffic flow forecasting research. The results indicate that ARIMAX models provide improved forecast performance over univariate forecast models. However, several issues must be addressed before widespread use of ARIMAX models for ITS forecasts is feasible. These issues include the increased complexity of model specification, estimation, and maintenance; model consistency; model robustness in the face of interruptions in the upstream data series; and variability in the cross-correlation between upstream and downstream observations. The last issue is critical because ARIMAX models assume constant transfer function parameters, whereas the correlation between upstream and downstream observations vary with prevailing traffic conditions, especially traffic stream speed. Therefore, further research is needed to investigate model extensions and refinements to provide a generalizable, self-tuning multivariate forecasting model that is easily implemented and that effectively models varying upstream to downstream correlations.

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References

2002Introduction to Time Series and Forecasting 1984Dynamic prediction of traffic volume through Kalman filtering theory 1996Combining kohonen maps with arima time series models to forecast traffic flow 1996Introduction to Time Series and Forecasting

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