article

Urban Network-Wide Traffic Variables and Their Relations

Transportation Science, Volume 21, Issue 1

Pages 1 - 16

https://doi.org/10.1287/trsc.21.1.1

Published: 01 February 1987 Publication History

Abstract

<P>Time-lapse aerial photography over the Central Business Districts CBD of Austin and Dallas, Texas, has been employed to determine the averages of concentration, speed and fraction of vehicles stopped and to examine the relations among such network-wide averages including the flow which was measured on the ground simultaneously. The results have indicated that the average flow in a street network may indeed be expressed as the product of the space mean speed and concentration. Simultaneous ground experiments have also been conducted in the Austin CBD to investigate the reasonableness of the assumptions of the “two-fluid model,” a curvilinear relation between the trip time and stop time per unit distance, which may be used in characterizing the quality of traffic service in urban street networks. As a result of these simultaneous ground experiments and aerial observations, the assumptions of the model have been verified. Moreover, relations between the fraction of vehicles stopped and concentration as well as between speed and concentration have allowed the two-fluid model to be used to compare the quality of traffic service in various street networks under the same level of concentration. The two-fluid model may then be used to predict, for a given change in vehicular concentration in a street network, the resulting changes in the averages of speed, fraction of vehicles stopped, flow, etc. This is particularly useful as a performance model in urban planning where for a given concentration it is desirable to predict the resulting traffic conditions.</P>

References

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Digital Library

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Cited By

Chai DZhang DSun YYang S(2020)Research on the City Network Structure in the Yellow River Basin in China Based on Two-Way Time Distance Gravity Model and Social Network Analysis MethodComplexity10.1155/2020/66809542020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/6680954
Diveev ASofronova EMikhalev VBelyakov A(2017)Intelligent Traffic Flows Control Software for MegapolisProcedia Computer Science10.1016/j.procs.2017.01.004103:C(20-27)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.procs.2017.01.004
Artimy MRobertson WPhillips WLaberteaux KHartenstein HJohnson DSengupta R(2005)Assignment of dynamic transmission range based on estimation of vehicle densityProceedings of the 2nd ACM international workshop on Vehicular ad hoc networks10.1145/1080754.1080761(40-48)Online publication date: 2-Sep-2005
https://dl.acm.org/doi/10.1145/1080754.1080761

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cover image Transportation Science

Transportation Science Volume 21, Issue 1

February 1987

61 pages

ISSN:1526-5447

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INFORMS

Linthicum, MD, United States

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Published: 01 February 1987

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Cited By

Chai DZhang DSun YYang S(2020)Research on the City Network Structure in the Yellow River Basin in China Based on Two-Way Time Distance Gravity Model and Social Network Analysis MethodComplexity10.1155/2020/66809542020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/6680954
Diveev ASofronova EMikhalev VBelyakov A(2017)Intelligent Traffic Flows Control Software for MegapolisProcedia Computer Science10.1016/j.procs.2017.01.004103:C(20-27)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.procs.2017.01.004
Artimy MRobertson WPhillips WLaberteaux KHartenstein HJohnson DSengupta R(2005)Assignment of dynamic transmission range based on estimation of vehicle densityProceedings of the 2nd ACM international workshop on Vehicular ad hoc networks10.1145/1080754.1080761(40-48)Online publication date: 2-Sep-2005
https://dl.acm.org/doi/10.1145/1080754.1080761

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