Journal article
Improving Scalability of Generic Online Calibration for Real-Time Dynamic Traffic Assignment Systems
Massachusetts Institute of Technology1
Singapore-MIT Alliance2
Technical University of Munich3
Department of Technology, Management and Economics, Technical University of Denmark4
Transport, Department of Technology, Management and Economics, Technical University of Denmark5
Machine Learning, Transport, Department of Technology, Management and Economics, Technical University of Denmark6
Flexible calibration of dynamic traffic assignment (DTA) systems in real time has important applications in effective traffic management. However, the existing approaches are either limited to small networks or to a specific class of parameters. In this light, this study presents a framework to systematically reduce the dimension of the generic online calibration problem, making it more scalable.
Specifically, a state–space formulation of the problem in the reduced dimension space is proposed. Following this the problem is solved using the constrained extended Kalman filter, which is made tractable because of the low dimensionality of the formulated problem. The effectiveness of the proposed approach is demonstrated using a real-world network leading to better state estimation by 13% and better state predictions by 11%—with a 50 fold dimensionality reduction.
Insights into choosing the right degree of dimensionality reduction are also discussed. This work has the potential for a more widespread application of real-time DTA systems in practice.
Language: | English |
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Publisher: | SAGE Publications |
Year: | 2018 |
Pages: | 79-92 |
ISSN: | 21694052 and 03611981 |
Types: | Journal article |
DOI: | 10.1177/0361198118791360 |
ORCIDs: | Pereira, Francisco C. |