01371122

Component

https://doi.org/10.3141/2315-06

http://scholar.google.com...nder&publication_year=2012

The primary purpose of this study is to understand better the roadway performance and the conditions that trigger congestion. Incidents of recurrent congestion were encountered at a location on New Hampshire Interstate 93 northbound, where radar measurements of speed were continuously recorded from April through November 2010. The root cause for the onset of congestion, both recurrent and nonrecurrent, is impossible to discern and explain by using exploratory data analyses alone; the data are too noisy. A time series modeling approach suggests that the magnitude of traffic flow and volatility, measured as the second (variance) and third (skewness) moments of flow residuals, can explain the triggering of congestion events for a highly variable environment that changes time by the time and day. The approach includes two types of mathematical models: generalized additive binomial probability models for roadway congestion that use functions of traffic flow and volatility as explanatory variables and functional data models that decompose and smooth traffic data to add insight to the roles that flow and volatility play in the congestion process. Most notably, the probability of congestion is shown to be a function of the short-term history of flow and volatility. The changes in these values—the first derivatives of flow and second and third moments of flow residuals derived from functional data model—are shown as well. Model selection, parameter estimation, and checking are presented.

01472354

12-2029

English

pp 54–65

2012

Transportation Research Record: Journal of the Transportation Research Board

9780309263146

Print

Figures; References

Mathematical models; Probability; Time series analysis; Traffic congestion; Traffic data; Traffic flow

New Hampshire

Highways; Operations and Traffic Management; I71: Traffic Theory

TRIS, TRB, ATRI

Feb 8 2012 5:07PM

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