A Complex Network Methodology for Travel Demand Model Evaluation and Validation

  • Meead SaberiEmail author
  • Taha H. Rashidi
  • Milad Ghasri
  • Kenneth Ewe


Travel demand can be viewed as a weighted and directed graph where nodes are the origins and destinations and links represent the trips between nodes. This paper presents a network-theoretic methodology to evaluate and validate travel demand models. We apply the proposed method on three disaggregate travel demand models from Melbourne, Australia. Statistical properties of the modeled networks are compared against the observed networks over time. The new approach reveals the network structure and connectivity of the modeled trips that are not usually captured by traditional evaluation and validation methods. Results demonstrate the complexity involved in the development, evaluation, and validation of travel demand models, which calls for advanced evaluation techniques reflecting a wide range of attributes of the observed and modeled data, travelers, mobility patterns, and complex network characteristics.


Travel demand modeling Evaluation Validation Complex networks Structure Connectivity 



Number of nodes in the network


Number of edges in the network


Total number of trips


Network connectivity (2 L/N 2 )


Elements of the adjacency matrix


Elements of the weighted adjacency matrix


Flux of a given node i


Degree of a given node i


Weight of a given edge between node i and node j


Clustering coefficient of a given node i


Betweenness centrality of a given node i


Betweenness centrality of a given edge between node i and node j


Mean node flux in the network


Mean node degree in the network


Mean edge weight in the network


Mean clustering coefficient in the network


Mean weighted clustering coefficient in the network


Coefficient of variation of node flux in the network


Coefficient of variation of node degree in the network


Coefficient of variation of edge weight in the network


Network clustering coefficient


Average shortest path


Average weighted shortest path


Network diameter

Weighted network diameter


Network dissimilarity


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Research Centre for Integrated Transport Innovation, School of Civil & Environmental EngineeringUniversity of New South WalesSydneyAustralia
  2. 2.Australian Road Research Board (AARB)MelbourneAustralia

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