Real-Time Multi-Sensor Multi-Source Network Data Fusion Using Dynamic Traffic Assignment Models
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...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2010
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| Online Access: | http://hdl.handle.net/1721.1/54705 https://orcid.org/0000-0003-0203-9542 |
| _version_ | 1811088426845339648 |
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| author | Wen, Yang Antoniou, Constantinos Lopes, Jorge Alves Bento, Joao Huang, Enyang Ben-Akiva, Moshe E |
| author2 | Massachusetts Institute of Technology. Center for Transportation & Logistics |
| author_facet | Massachusetts Institute of Technology. Center for Transportation & Logistics Wen, Yang Antoniou, Constantinos Lopes, Jorge Alves Bento, Joao Huang, Enyang Ben-Akiva, Moshe E |
| author_sort | Wen, Yang |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:02:00Z |
| format | Article |
| id | mit-1721.1/54705 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:02:00Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/547052022-10-01T18:41:46Z Real-Time Multi-Sensor Multi-Source Network Data Fusion Using Dynamic Traffic Assignment Models Wen, Yang Antoniou, Constantinos Lopes, Jorge Alves Bento, Joao Huang, Enyang Ben-Akiva, Moshe E 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 Ben-Akiva, Moshe E. Ben-Akiva, Moshe E. Wen, Yang Antoniou, Constantinos Huang, Enyang travel information and guidance traffic state analysis and prediction simulation and modeling Multi-Sensor Fusion 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. Portuguese Foundation for International Cooperation in Science, Technology and Higher Education 2010-05-04T19:21:14Z 2010-05-04T19:21:14Z 2009-11 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-5519-5 http://hdl.handle.net/1721.1/54705 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 https://orcid.org/0000-0003-0203-9542 en_US http://dx.doi.org/10.1109/ITSC.2009.5309859 12th International IEEE Conference on Intelligent Transportation Systems, 2009. ITSC '09. 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. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | travel information and guidance traffic state analysis and prediction simulation and modeling Multi-Sensor Fusion Wen, Yang Antoniou, Constantinos Lopes, Jorge Alves Bento, Joao Huang, Enyang Ben-Akiva, Moshe E Real-Time Multi-Sensor Multi-Source Network Data Fusion Using Dynamic Traffic Assignment Models |
| title | Real-Time Multi-Sensor Multi-Source Network Data Fusion Using Dynamic Traffic Assignment Models |
| title_full | Real-Time Multi-Sensor Multi-Source Network Data Fusion Using Dynamic Traffic Assignment Models |
| title_fullStr | Real-Time Multi-Sensor Multi-Source Network Data Fusion Using Dynamic Traffic Assignment Models |
| title_full_unstemmed | Real-Time Multi-Sensor Multi-Source Network Data Fusion Using Dynamic Traffic Assignment Models |
| title_short | Real-Time Multi-Sensor Multi-Source Network Data Fusion Using Dynamic Traffic Assignment Models |
| title_sort | real time multi sensor multi source network data fusion using dynamic traffic assignment models |
| topic | travel information and guidance traffic state analysis and prediction simulation and modeling Multi-Sensor Fusion |
| url | http://hdl.handle.net/1721.1/54705 https://orcid.org/0000-0003-0203-9542 |
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