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Title:

A Bayesian Approach to Modeling Uncertainty in Transport Infrastructure Project Forecasts

Accession Number:

01155786

Record Type:

Component

Availability:

Transportation Research Board Business Office

500 Fifth Street, NW
Washington, DC 20001 United States

Abstract:

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.

Monograph Accession #:

01147878

Report/Paper Numbers:

10-2959

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Cheung, Kevin
Polak, John W

Pagination:

23p

Publication Date:

2010

Conference:

Transportation Research Board 89th Annual Meeting

Location: Washington DC, United States
Date: 2010-1-10 to 2010-1-14
Sponsors: Transportation Research Board

Media Type:

DVD

Features:

Figures; Maps

Uncontrolled Terms:

Bayesian analysis

Geographic Terms:

Sao Paulo (Brazil)

Subject Areas:

Highways; Planning and Forecasting; I72: Traffic and Transport Planning

Source Data:

Transportation Research Board Annual Meeting 2010 Paper #10-2959

Files:

TRIS, TRB

Created Date:

Jan 25 2010 11:27AM