The long term behaviour of day-to-day traffic assignment models

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The long term behaviour of day-to-day traffic assignment models. / Smith, Mike; Hazelton, Martin L.; Lo, Hong K.; Cantarella, Giulio E.; Watling, David P.

In: Transportmetrica A: Transport Science, Vol. 10, No. 7, 2014, p. 647-660.

Research output: Contribution to journalArticle

Harvard

Smith, M, Hazelton, ML, Lo, HK, Cantarella, GE & Watling, DP 2014, 'The long term behaviour of day-to-day traffic assignment models', Transportmetrica A: Transport Science, vol. 10, no. 7, pp. 647-660. https://doi.org/10.1080/18128602.2012.751683

APA

Smith, M., Hazelton, M. L., Lo, H. K., Cantarella, G. E., & Watling, D. P. (2014). The long term behaviour of day-to-day traffic assignment models. Transportmetrica A: Transport Science, 10(7), 647-660. https://doi.org/10.1080/18128602.2012.751683

Vancouver

Smith M, Hazelton ML, Lo HK, Cantarella GE, Watling DP. The long term behaviour of day-to-day traffic assignment models. Transportmetrica A: Transport Science. 2014;10(7):647-660. https://doi.org/10.1080/18128602.2012.751683

Author

Smith, Mike ; Hazelton, Martin L. ; Lo, Hong K. ; Cantarella, Giulio E. ; Watling, David P. / The long term behaviour of day-to-day traffic assignment models. In: Transportmetrica A: Transport Science. 2014 ; Vol. 10, No. 7. pp. 647-660.

Bibtex - Download

@article{9b93901b32204f70b34267557aaa2b36,
title = "The long term behaviour of day-to-day traffic assignment models",
abstract = "The dynamical behaviour of deterministic process, day-to-day traffic assignment models is sometimes characterised by convergence to a variety of different fixed equilibrium points dependent upon the initial flow pattern, even though individual trajectories are unique for a given start point. This non-uniqueness is seemingly in sharp contrast to the evolution of stochastic process, day-to-day models; under certain assumptions these converge in law to a unique stationary distribution, irrespective of the start point. In this article, we show how models may be constructed which exhibit characteristics of both deterministic models and stochastic models, and illustrate the ideas by using a simple example network.",
keywords = "control, deterministic, dynamics, stochastic",
author = "Mike Smith and Hazelton, {Martin L.} and Lo, {Hong K.} and Cantarella, {Giulio E.} and Watling, {David P.}",
year = "2014",
doi = "10.1080/18128602.2012.751683",
language = "English",
volume = "10",
pages = "647--660",
journal = "Transportmetrica A: Transport Science",
issn = "2324-9935",
publisher = "Taylor and Francis",
number = "7",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - The long term behaviour of day-to-day traffic assignment models

AU - Smith, Mike

AU - Hazelton, Martin L.

AU - Lo, Hong K.

AU - Cantarella, Giulio E.

AU - Watling, David P.

PY - 2014

Y1 - 2014

N2 - The dynamical behaviour of deterministic process, day-to-day traffic assignment models is sometimes characterised by convergence to a variety of different fixed equilibrium points dependent upon the initial flow pattern, even though individual trajectories are unique for a given start point. This non-uniqueness is seemingly in sharp contrast to the evolution of stochastic process, day-to-day models; under certain assumptions these converge in law to a unique stationary distribution, irrespective of the start point. In this article, we show how models may be constructed which exhibit characteristics of both deterministic models and stochastic models, and illustrate the ideas by using a simple example network.

AB - The dynamical behaviour of deterministic process, day-to-day traffic assignment models is sometimes characterised by convergence to a variety of different fixed equilibrium points dependent upon the initial flow pattern, even though individual trajectories are unique for a given start point. This non-uniqueness is seemingly in sharp contrast to the evolution of stochastic process, day-to-day models; under certain assumptions these converge in law to a unique stationary distribution, irrespective of the start point. In this article, we show how models may be constructed which exhibit characteristics of both deterministic models and stochastic models, and illustrate the ideas by using a simple example network.

KW - control

KW - deterministic

KW - dynamics

KW - stochastic

UR - http://www.scopus.com/inward/record.url?scp=84900461036&partnerID=8YFLogxK

U2 - 10.1080/18128602.2012.751683

DO - 10.1080/18128602.2012.751683

M3 - Article

VL - 10

SP - 647

EP - 660

JO - Transportmetrica A: Transport Science

JF - Transportmetrica A: Transport Science

SN - 2324-9935

IS - 7

ER -