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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Transportation Resea...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Transportation Research Record Journal of the Transportation Research Board
Article . 2007 . Peer-reviewed
License: SAGE TDM
Data sources: Crossref
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Impact of Traffic Flow on Travel Time Variability of Freeway Corridors

Authors: Huizhao Tu; J.W.C. van Lint; H.J. van Zuylen;

Impact of Traffic Flow on Travel Time Variability of Freeway Corridors

Abstract

Travel time variability is determined by variations in demand and capacity. Knowledge about these demand and supply factors can help improve the reliability of travel time and hence the quality of traveling. The precise nature of the relationship between, for example, variations in inflows and travel time variation is still largely unknown. This paper uses empirical traffic data from both Regiolab-Delft in the Netherlands and the Beijing Olympic area to analyze the variability of travel times depending on inflow conditions. Preliminary analysis shows that travel time variability is a function of inflow characterized by two so-called critical inflows (critical transition inflow λ1 and critical capacity inflow λ2, which are both lower than capacity). These critical inflow levels subdivide traffic into a fluent traffic region, a transition traffic region, and a capacity traffic region. Variation of inflow has little or no effect on travel time variation below λ1. But both demand and capacity variations have a positive correlation with travel time variability in between λ1 and λ2. When volumes are above λ2, the inflow slightly affects the travel time variability. Under all inflow levels, the variation in capacity appears to have more impact on travel time variability than does the variation of traffic flow.

Related Organizations
Subjects by Vocabulary

Microsoft Academic Graph classification: Meteorology Reliability (computer networking) Inflow Traffic flow Supply and demand Preliminary analysis Travel time Transport engineering Beijing Fluent Environmental science

Keywords

Mechanical Engineering, Civil and Structural Engineering

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    53
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
53
Top 10%
Top 10%
Top 10%
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