Calibration of large-scale transport planning models: a structured approach

Ali Najmi (), Taha H. Rashidi (), James Vaughan () and Eric J. Miller ()
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Ali Najmi: The University of New South Wales
Taha H. Rashidi: The University of New South Wales
James Vaughan: University of Toronto
Eric J. Miller: University of Toronto

Transportation, 2020, vol. 47, issue 4, No 12, 1867-1905

Abstract: Abstract Traditionally, transport planning model systems are estimated and calibrated in an unstructured way, which does not allow for interactions among included parameters to be considered. Furthermore, the computational burden of model systems plays a key role in choosing a calibration approach, and usually forces modellers to calibrate demand-side and network models separately. Also, trial-and-error methods and expert opinion are currently the backbones of transport model calibration, which leaves room for error in the calibrated parameters. This paper addresses these challenges and suggests a structured approach for determining optimal calibrated transport model parameters. This approach involves joint estimation and calibration of demand and network models, with a major focus on avoiding any manipulation of the OD matrix. The approach can be applied to static or dynamic traffic assignments. The approach is applied by calibrating GTAModel—an example of a large-scale agent-based model system from Toronto, Canada.

Keywords: Model calibration; Transport; Response surface methodology; Parameter adjustment (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11116-019-10018-6

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