A Bayesian Approach to Modeling Uncertainty in Transport Infrastructure Project Forecasts

The increased participation of the private sector in the delivery of transport infrastructure projects has increased the emphasis placed on understanding the accurate, and uncertainty, of traffic demand forecasts. Transport models which provide these traffic forecasts rely on simplified assumptions usually involving a combination of physical, socioeconomic, environmental and individual factors for a modeled base and future time period. Uncertainty in the value of input parameters and their conditional relationships results in uncertainty in the outturn forecasts. The accuracy of model predictions is normally tested through a number of quantitative and statistical methods. This paper presents a summary of the approaches used to model uncertainty in practice including scenario testing, sensitivity testing and statistical risk analysis using Monte-Carlo methods. However, other techniques are now available, and may offer superior insight into the structure of the underlying problem. In this paper, Bayesian belief networks, together with Monte Carlo Markov Chain techniques, are applied as an alternative method for modeling uncertainty in transport modeling. The paper illustrates the technique on a simplified toll road case study, based on a motorway in São Paulo, Brazil, in which the authors compute equilibrium solutions for traffic flow, travel time and cost for fixed demand and elastic demand problem formulations. The paper concludes on a comparison between the Bayesian belief network and a more conventional sensitivity analysis and discusses the relative merits of each approach.

Language

  • English

Media Info

  • Media Type: DVD
  • Features: Figures; Maps;
  • Pagination: 23p
  • Monograph Title: TRB 89th Annual Meeting Compendium of Papers DVD

Subject/Index Terms

Filing Info

  • Accession Number: 01155786
  • Record Type: Publication
  • Report/Paper Numbers: 10-2959
  • Files: TRIS, TRB
  • Created Date: Jan 25 2010 11:27AM