01370055

Component

Traffic assignment methods distribute Origin-Destination (OD) flows throughout the links of a given network according to procedures related to specific deterministic or stochastic modeling assumptions. In this paper, the authors propose a methodology that enhances the information provided from traffic assignment models, in terms of delivering stochastic estimates for traffic flows on links. Stochastic variability is associated to the initial uncertainty related to the OD matrix used as input into a given assignment method, and therefore the proposed methodology is not constrained by the choice of the assignment model. The methodology is based on Bayesian estimation methods which provide a suitable working framework for generating multiple OD matrices from the corresponding predictive distribution of a given statistical model. Predictive inference for link flows is then straightforward to implement, either by assigning summarized OD information or by performing multiple assignments. Interesting applications arise in a natural way from the proposed methodology, as is the identification and evaluation of critical links by means of probability estimates. A real-world application is presented for the road network of the northern, Dutch-speaking region of Flanders in Belgium, under the assumption of a deterministic user equilibrium model.

This paper was sponsored by TRB committee ADB40(1) Emerging Methods Methods and Developments in Urban Activity and Travel Analysis

01362476

12-0359

English

19p

2012

Transportation Research Board 91st Annual Meeting

Digital/other

Figures; Maps; References; Tables

Applications; Bayes' theorem; Origin and destination; Traffic assignment; Traffic models; Uncertainty

Flanders (Belgium)

Highways; Planning and Forecasting; I71: Traffic Theory

Transportation Research Board Annual Meeting 2012 Paper #12-0359

TRIS, TRB

Feb 8 2012 4:54PM