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Spatial Aggregation Issues in Traffic Assignment Models

Author

Listed:
  • Ouassim Manout

    (ENTPE, LAET
    Polytechnique Montreal)

  • Patrick Bonnel

    (ENTPE, LAET)

  • François Pacull

    (Architecture and Performance)

Abstract

Most transport models rely on a discrete description of space, and are, therefore, subject to spatial aggregation bias. Spatial aggregation induces the use of centroid connectors and the omission of intrazonal trips in traffic assignment. This practice is shown to bias main traffic assignment outcomes, especially in spatially coarse models. To address these modeling errors, the literature suggests some solutions but no clear-cut conclusion on the contribution of these solutions is available. In the current research, we undergo a detailed investigation of the contribution of some of these modeling solutions in order to provide useful and practical recommendations to academics and policy makers. Different assignment strategies that are deemed to mitigate the impacts of spatial aggregation in traffic assignment are explored in different case studies. Findings from this research outline that demand-side assignment strategies outperform supply-side methods in addressing the spatial aggregation problem. The results also suggest that the inclusion of intrazonal demand in traffic assignment is not sufficient to overcome aggregation biases. The definition of connectors is also of importance.

Suggested Citation

  • Ouassim Manout & Patrick Bonnel & François Pacull, 2021. "Spatial Aggregation Issues in Traffic Assignment Models," Networks and Spatial Economics, Springer, vol. 21(1), pages 1-29, March.
    • Handle: RePEc:kap:netspa:v:21:y:2021:i:1:d:10.1007_s11067-020-09505-6
    DOI: 10.1007/s11067-020-09505-6
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