Aggregation, disaggregation and decomposition methods in traffic assignment: historical perspectives and new trends

In this study the authors provide a comprehensive review of the existing literature on (dis)aggregation and decomposition methods in traffic assignment and classify them based on their characteristics. The study takes on two different perspectives. First, the authors explore existing methods and relate them to one or more traffic assignment components. It is found that there exists a clear separation between a demand modelling point of view, i.e., travel demand and (geographical) zoning on the one hand, and supply modelling-oriented methods, i.e. network topology and network loading, on the other. Further, the authors explore the existing literature on the interface between demand and supply, i.e., connector and centroid placement which is to be considered a special type of aggregation. It is found this aspect of traffic assignment has received relatively little attention in this context, even though it is shown to be of significant impact on modelling results. The second perspective in this study places the discussed aggregation methodologies in the broader perspective of clustering procedures. The authors do not necessarily explore clustering methods as such but mainly look at the classification of different types of clustering methods which can be projected onto the traffic assignment domain and aggregation procedures in particular. It is shown that most existing methods can be classified as supervised – or classification based – clustering procedures while relatively few studies explore other known approaches such as semi-supervised or unsupervised clustering techniques. Lastly, the authors discuss how aggregation techniques could be deployed to construct multi-scale modelling environments. There is however a lack of methodology to construct such models consistently. Findings are presented via an objective classification framework for existing (dis)aggregation and decomposition methods.


  • English

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  • Accession Number: 01747333
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 14 2020 3:08PM