Day-to-day origin-destination tuple estimation and prediction with hierarchical bayesian networks using multiple data sources

Ma, Y.; Kuik, R.; Van Zuylen, H.J.

doi:doi:10.3141/2343-07

Day-to-day origin-destination tuple estimation and prediction with hierarchical bayesian networks using multiple data sources

Title

Day-to-day origin-destination tuple estimation and prediction with hierarchical bayesian networks using multiple data sources

Author

Ma, Y.
Kuik, R.
Van Zuylen, H.J.

Faculty

Civil Engineering and Geosciences

Department

Transport and Planning

Date

2013-09-13

Abstract

Prediction of traffic demand is essential, either for an understanding of the future traffic state or so necessary measures can be taken to alleviate congestion. Usually, an origin-destination (O-D) matrix is used to represent traffic demand between two zones in transportation planning. Vehicles are assumed to be homogeneous; the trips of each vehicle are examined separately. This traditional O-D matrix lacks a behavioral basis and trip-based model structure. Another research stream of travel activity-based research addresses individual travel behaviors. This stream addresses the trip chain for travelers, but the research scope is attributes of trips, which ignores the road network. The concept of the O-D tuple, a sequence of dependent O-D pairs, is proposed for linking these two fields and for predicting traffic demand better. Through advanced monitoring systems that identify and track vehicles in the road network, the additional uncertainties of O-D tuples can be mitigated and thus reduce the underspecification more specifically. The hierarchical Bayesian networks mechanism in Gaussian space with multiprocesses is proposed for gaining the posterior of uncertain parameters. The model includes level and trend components for predicting future traffic volumes. A case study demonstrates that the proposed method can predict demand, and the path flow from cameras can reduce uncertainty in the estimation and prediction process, especially for O-D tuples.

Subject

Origin Destination Tuple
Hierarchical Bayesian Networks
multi-process
21 demand prediction
Multiple Data Sources

To reference this document use:

http://resolver.tudelft.nl/uuid:908579b3-c98b-4bbd-8167-7bdf61535252

Publisher

National Academy of Sciences

ISSN

0361-1981

Source

Transportation Research Record, 2343 (September), 2013; Authors version

Other version

https://doi.org/10.3141/2343-07