Genetics of traffic assignment models for strategic transport planning

Journal Article (2016)
Author(s)

Michiel C.J. Bliemer (University of Sydney)

MPH Raadsen (University of Sydney)

Luuk J N Brederode (TU Delft - Transport and Planning, DAT.Mobility)

MGH Bell (University of Sydney)

Luc J J Wismans (University of Twente, DAT.Mobility)

Mike J. Smith (University of York)

Transport and Planning
Copyright
© 2016 Michiel C J Bliemer, MPH Raadsen, L.J.N. Brederode, MGH Bell, Luc J J Wismans, Mike J. Smith
DOI related publication
https://doi.org/10.1080/01441647.2016.1207211
More Info
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Publication Year
2016
Language
English
Copyright
© 2016 Michiel C J Bliemer, MPH Raadsen, L.J.N. Brederode, MGH Bell, Luc J J Wismans, Mike J. Smith
Transport and Planning
Issue number
1
Volume number
37
Pages (from-to)
56-78
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Abstract

This paper presents a review and classification of traffic assignment models for strategic transport planning purposes by using concepts analogous to genetics in biology. Traffic assignment models share the same theoretical framework (DNA), but differ in capability (genes). We argue that all traffic assignment models can be described by three genes. The first gene determines the spatial capability (unrestricted, capacity restrained, capacity constrained, and capacity and storage constrained) described by four spatial assumptions (shape of the fundamental diagram, capacity constraints, storage constraints, and turn flow restrictions). The second gene determines the temporal capability (static, semi-dynamic, and dynamic) described by three temporal assumptions (wave speeds, vehicle propagation speeds, and residual traffic transfer). The third gene determines the behavioural capability (all-or-nothing, one shot, and equilibrium) described by two behavioural assumptions (decision-making and travel time consideration). This classification provides a deeper understanding of the often implicit assumptions made in traffic assignment models described in the literature. It further allows for comparing different models in terms of functionality, and paves the way for developing novel traffic assignment models.

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