Deployment and Calibration of Large-Scale Mesoscopic Dynamic Traffic Assignment Model of Melbourne, Australia

Deployment of a large-scale simulation-based Dynamic Traffic Assignment (DTA) model requires a large number of demand and supply input parameters to be calibrated. In this paper, the authors report the ongoing efforts on calibration of a large-scale mesoscopic DTA model of Melbourne, Australia. The authors utilize multiple data sets consisting of big traffic data from freeway loop detectors, SCATS, and probe travel times. The authors classify and calibrate the traffic flow fundamental diagrams against empirical data from a large number of sites. For the demand-side calibration, a bi-level optimization problem is solved to estimate time-dependent origin-destination matrices, where the upper level aims to minimize the discrepancy between the estimated and observed traffic data and the lower level generates the assignment proportions matrix using the simulation-based DTA model. Calibration results of the Melbourne Central Business District (CBD) are presented. Results demonstrate a 50% reduction in root mean square error (RMSE) and a significant increase in R-squared from 0.53 to 0.92 after implementation of an iterative and data-rich calibration process.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 17p
  • Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01632425
  • Record Type: Publication
  • Report/Paper Numbers: 17-04108
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 8 2016 11:34AM