MODELING AND FORECASTING VEHICULAR TRAFFIC FLOW AS A SEASONAL STOCHASTIC TIME SERIES PROCESS

Abstract

Extensive data collection is now commonplace for urban freeway and street network systems. Research efforts are underway to unleash the system management potential inherent in this unprecedented access to traffic condition data. A key element in this research area is traffic condition forecasting. Reliable and accurate condition forecasts will enable transportation management systems to dynamically anticipate the future state of the system rather than merely respond to the current situation. Recent traffic flow prediction efforts have focused on application of neural networks, nonparametric regression using nearest neighbor algorithms, multiple class linear regression based on automatic clustering, time series analysis techniques, and hybrid models combining two or more of these approaches. Seasonal time series methods, such as seasonal ARIMA models and Holt-Winters smoothing, have not been among the investigated forecasting techniques. The need to explore these techniques is motivated by a strong theoretical expectation that they will provide accurate and parsimonious traffic condition models. The findings presented in this report establish the traffic flow prediction superiority of seasonal time series methods, especially seasonal ARIMA modeling, over the recently developed methods. The research also contributes a specific application of time series outlier modeling theory to vehicular traffic flow data. This outlier detection and modeling procedure uncovered a common ARIMA model form among the seasonally stationary series used in this research. This common model form is ARIMA (1,0,1)(O,1,1)S, where S is the length of the series seasonal cycle. A glossary of terms is included.