01660320

Component

https://doi.org/10.1177/0361198118791360

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.

18-04982

English

pp 79-92

2018-12

Transportation Research Record: Journal of the Transportation Research Board

Digital/other

Figures (5) ; References (23) ; Tables (1)

Calibration; Dynamic traffic assignment; Improvements; Real time control

Highways; Operations and Traffic Management

TRIS, TRB, ATRI

Jan 8 2018 11:14AM

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