TY - GEN
T1 - Real-time multi-sensor multi-source network data fusion using dynamic traffic assignment models
AU - Huang, E.
AU - Antoniou, C.
AU - Wen, Y.
AU - Ben-Akiva, M.
AU - Lopes, J.
AU - Bento, J.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Multi-sensor fusion
KW - Simulation and modeling
KW - Traffic state analysis and prediction
KW - Travel information and guidance
UR - http://www.scopus.com/inward/record.url?scp=72449196756&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2009.5309859
DO - 10.1109/ITSC.2009.5309859
M3 - Conference contribution
AN - SCOPUS:72449196756
SN - 9781424455218
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 533
EP - 538
BT - 2009 12th International IEEE Conference on Intelligent Transportation Systems, ITSC '09
T2 - 2009 12th International IEEE Conference on Intelligent Transportation Systems, ITSC '09
Y2 - 3 October 2009 through 7 October 2009
ER -