01047479

Component

A pervasive problem in many areas of transport analysis is the need to integrate information from multiple incomplete data sources (e.g. conventional sample surveys, census records, operational data streams and data generated by ITS systems to name but a few) in the estimation of underlying parameters of interest. Researchers working in different areas have developed a wide range of approaches of varying levels of sophistication and applicability. However, due to the lack of appropriate general purpose techniques, the process of data integration is often done in practice in an ad hoc and unsatisfactory way. This paper presents a Bayesian framework for such information integration problems. This framework is designed to enable the use of existing structural knowledge (in the form of existing transport models) and existing measurement knowledge (in the form of characterizations of sampling and non-sampling errors) to inform the data integration task. Existing knowledge is encoded in a Bayesian Belief Network (BBN). A simple illustrative application to the problem of estimating OD matrices from multiple information sources is outlined.

01042056

07-2165

English

17p

2007

Transportation Research Board 86th Annual Meeting

CD-ROM

Figures; Maps; References; Tables (1)

Information dissemination; Information systems; Integrated systems; Intelligent transportation systems; Origin and destination; Real time information; Surveys; Travel surveys; Traveler information and communication systems

Bayesian analysis; Data integration

Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2007 Paper #07-2165

TRIS, TRB

Feb 8 2007 6:50PM