Statistical Analyses of Freeway Traffic Flows

Claudia Tebaldi, Mike West and Alan F Karr

Journal of Forecasting, 2002, vol. 21, issue 1, 39-68

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 detectorized 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 modelling 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 modelling stages are described and developed in analyses of observational flow data from a set of links on Interstate Highway 5 (I-5) near Seattle. Copyright © 2002 by John Wiley & Sons, Ltd.

Date: 2002
References: Add references at CitEc
Citations: View citations in EconPapers (4)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:21:y:2002:i:1:p:39-68

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing () and Christopher F. Baum ().