Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models

Srinivasa Ravi Chandra · Haitham M. Al-Deek

363 citations0/0 files storedJournal of Intelligent Transportation Systems

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Traffic Prediction and Management Techniques Transportation Planning and Optimization Traffic control and management

Abstract

Short-term traffic prediction on freeways is one of the critical components of advanced traveler information systems. The traditional methods of prediction have used univariate Auto-Regressive Integrated Moving Average (ARIMA) time series models, based on the autocorrelation function of the time series of traffic variable at a location; however, the effect of upstream and downstream location information has been largely neglected or underutilized in the case of freeway traffic prediction. It is the purpose of this article to demonstrate the effect of upstream as well as downstream locations on the traffic at a specific location. To achieve this, a section of five stations extending over 2.5 miles on I-4 in the downtown region of Orlando, Florida is selected. The speeds from a station at the center of this location are then checked for crosscorrelations with stations upstream and downstream. Cross correlation function is analogous to autocorrelation function extended to two variables. It indicates whether the past values of an input series influence the future values of a response series. It was found that the past values of upstream as well as downstream stations influence the future values at a station and, therefore, can be used for prediction. A vector auto regressive model was found appropriate and better than the traditional ARIMA model for prediction at these stations.

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References

1977Book Review: Time Series Analysis: Forecasting and Control 2002Introduction to Time Series and Forecasting 2003Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results 1993Introduction to Multiple Time Series Analysis 1971Time Series Analysis Forecasting and Control 2008Introduction to time series analysis and forecasting 1984Dynamic prediction of traffic volume through Kalman filtering theory 2002Comparison of parametric and nonparametric models for traffic flow forecasting

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