Conference paper
DynaMIT2.0: architecture design and preliminary results on real-time data fusion for traffic prediction and crisis management
The ability to monitor and predict in real-time the state of the transportation network is a valuable tool for both transportation administrators and travellers. While many solutions exist for this task, they are generally much more successful in recurrent scenarios than in non-recurrent ones. Paradoxically, it is in the latter case that such tools can make the difference.
Therefore, the dynamic traffic assignment and simulation based prediction system such as DynaMIT (1) demonstrates high effectiveness in the context of sudden network disturbance or demand pattern changes. This paper presents the design, development and implementation of new components and modules of DynaMIT 2.0 which is an extension of its predecessor with recent enhancements on online calibration, context mining, scenario analyser and strategy simulation capability.
Also, some preliminary results are presented using Singapore expressway to show the actual benefit of the system.
Language: | English |
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Publisher: | IEEE |
Year: | 2015 |
Pages: | 2250-2255 |
Proceedings: | 18th International IEEE Conference on Intelligent Transportation Systems |
ISBN: | 1467365955 , 1467365963 , 1467365971 , 9781467365956 , 9781467365963 and 9781467365970 |
ISSN: | 21530017 and 21530009 |
Types: | Conference paper |
DOI: | 10.1109/ITSC.2015.363 |
ORCIDs: | Pereira, Francisco Camara |
Calibration Computational modeling Data models DynaMIT2.0 Prediction algorithms Predictive models Real-time systems Sensors Singapore expressway Transportation architecture design context mining crisis management data handling data mining demand pattern changes dynamic traffic assignment dynamic traffic simulation emergency management real-time data fusion road traffic scenario analyser sensor fusion strategy simulation capability sudden network disturbance traffic information systems traffic prediction transportation administration transportation network state monitoring transportation network state prediction travellers