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Transportation Research Part C Emerging Technologies
Article . 2008 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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
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Estimation of the distribution of travel times by repeated simulation

Authors: Hollander, Y.; Liu, R.H.;

Estimation of the distribution of travel times by repeated simulation

Abstract

In recent years, the reliability of transport systems has been widely recognised as a key issue in transport planning and evaluation. To analyse the level of reliability we need information about the distribution of travel times. Transport analysts are in a serious need for tools to estimate this distribution in hypothetical scenarios, but there are currently few such tools. In this paper we raise the question of whether it is possible to look at the outputs of each single run of a traffic microsimulation model as estimates of traffic conditions on a single day, while accounting for the fact that randomness and heterogeneity are in the nature of traffic phenomena. If it is possible to establish an analogy between a single run and a single day, then the distribution of outputs between runs can be used as an estimate of the respective distribution in the real network. Investigating this issue is vital since many practitioners wrongly assume that such analogy can be taken for granted. We discuss here methodological, statistical and computational aspects that this question brings in, and illustrate them in a series of experiments, where a special procedure for calibrating the microsimulation model has a key role.

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selected citations
These citations are derived from selected sources.
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!
86
Top 10%
Top 1%
Top 10%
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