Real-Time Multi-Sensor Multi-Source Network Data Fusion Using Dynamic Traffic Assignment Models
Name
Huang-2009-Real-time multi-sensor multi-source network data fusion using dynamic traffic assignment models.pdf
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Author(s)
Wen, Yang
Antoniou, Constantinos
Lopes, Jorge Alves
Bento, Joao
Huang, Enyang
Ben-Akiva, Moshe E
Date Issued
November 2009
Journal
12th International IEEE Conference on Intelligent Transportation Systems, 2009. ITSC '09.
Publisher
Institute of Electrical and Electronics Engineers
Citation
Huang, E. et al. “Real-time multi-sensor multi-source network data fusion using dynamic traffic assignment models.” Intelligent Transportation Systems, 2009. ITSC '09. 12th International IEEE Conference on. 2009. 1-6. © 2009 IEEE
Version
Final published version
Abstract
This paper presents a model-based data fusion framework that allows systematic fusing of multi-sensor multi-source traffic network data at real-time. Using simulation-based Dynamic Traffic Assignment (DTA) models, the framework seeks to minimize the inconsistencies between observed network data and the model estimates using a variant of the Hooke-Jeeves Pattern Search. An empirical validation is provided on the Brisa A5 Inter-City Motorway in the West coast of Portugal. The real-time network data provided by loop detectors, video cameras and toll counters is collected and fused within DynaMIT, a state-of-the-art DTA system. State estimation is first performed, yielding consistent approximation of the network condition. This is then followed by network state forecast, showing significantly improved Normalized Root Mean Square Error (RMSN) over alternative predictive systems that do not use real-time information to correct themselves.
Subjects
travel information and guidance
traffic state analysis and prediction
simulation and modeling
Multi-Sensor Fusion
MIT Department
Massachusetts Institute of Technology. Center for Transportation & Logistics
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Intelligent Transportation Systems Laboratory
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1109/ITSC.2009.5309859
