01023501

Component

https://doi.org/10.3141/1959-10

http://scholar.google.com...aiya&publication_year=2006

A method is presented to divide the total congestion delay in a freeway section into six components: the delay caused by incidents, special events, lane closures, and adverse weather; the potential reduction in delay at bottlenecks that ideal ramp metering can achieve; and the remaining delay, caused mainly by excess demand. The fully automated method involves two steps. First, the components of nonrecurrent congestion are estimated by statistical regression. Second, the method locates all bottlenecks and estimates the potential reduction in delay that ideal ramp metering can achieve. The method can be applied to any site with minimum calibration. It requires data about traffic volume and speed; the time and location of incidents, special events, and lane closures; and adverse weather. Applied to a 45-mi section of I-880 in the San Francisco Bay Area in California, the method reveals that incidents, special events, rain, potential reduction by ideal ramp metering, and excess demand respectively account for 13.3%, 4.5%, 1.6%, 33.2%, and 47.4%, respectively, of the total daily delay. The delay distribution of the various components is different between the morning and evening peak periods and between the two freeway directions. Quantifying the components of congestion at individual freeway sites is essential for developing effective congestion mitigation strategies.

01036675

English

pp 84-91

2006

Transportation Research Record: Journal of the Transportation Research Board

0309099684

Print

Figures (3) ; References (18) ; Tables (2)

Bottlenecks; Freeways; Incident management; Lane closure; Nonrecurrent congestion; Ramp metering; Special events; Traffic congestion; Traffic delays; Traffic incidents; Traffic mitigation; Travel demand; Weather conditions

San Francisco Bay Area

Highways; Operations and Traffic Management; Society; I71: Traffic Theory; I73: Traffic Control

TRIS, TRB, ATRI

Mar 3 2006 10:26AM

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