Statistical Analyses of Freeway Traffic Flows

This paper concerns the exploration of statistical modelling tools for the analysis of observational freeway ow data, and the development of empirical models to capture and predict short-term changes in tra c ow characteristics on sequences of links in a partially detectorised freeway network. A rst set of analyses explores regression models for minute-by-minute tra c ows, taking into account time of day, day of the week, and recent upstream detector-based ows. Relationships between days are captured through the use of dayand link-speci c random e ects in an hierarchical statistical modelling framework. A second set of analyses re nes these models to include parameters that may vary moderately throughout the course of the day to capture day-speci c idiosyncracies in tra c patterns. These are found to improve model t and short-term predictions of ows signi cantly. A third set of analyses includes further model extensions to include recent downstream ows as additional predictors, and these are found to provide further improvements that, though marginal in most cases, can be quite radically useful in cases of very marked breakdown of freeway ows on some links. Each of these modelling stages is described and developed in analyses of observational ow data from a set of links on Interstate Highway 5 (I-5) near Seattle. Summary conclusions and open questions conclude the paper.