Abstract:
This paper concerns the exploration of statistical models for the analysis of observational freeway flow data, and the development of empirical models to capture and predict short-term changes in traffic flow characteristics on sequences of links in a partially detectorised freeway network. A first set of analyses explores regression models for minute-by-minute traffic flows, taking into account time of day, day of the week, and recent upstream detector-based flows. Day- and link-specific random effects are used in a hierarchical statistical modeling framework. A second set of analyses captures day-specific idiosyncrasies in traffic patterns by including parameters that may vary throughout the day. Model fit and short-term predictions of flows are thus improved significantly. A third set of analyses includes recent downstream flows as additional predictors. These further improvements, though marginal in most cases, can be quite radically useful in cases of very marked breakdown of freeway flows on some links. These three modeling stages are described and developed in analyses of observational flow data from a set of links on Interstate Highway 5 (I-5) near Seattle.
Keywords:
Bayesian inference; Hierarchical regression models; Short-term prediction; Dynamic hierarchical regression models; Trafficc ow.
tr111.pdf