Dynamic Model for estimating the Macroscopic Fundamental Diagram

Hoai Nam Nguyen; Barak Fishbain; Eilyan Bitar; David Mahalel; Per Olof Gutman

doi:10.1016/j.ifacol.2016.07.050

Dynamic Model for estimating the Macroscopic Fundamental Diagram

Hoai Nam Nguyen, Barak Fishbain, Eilyan Bitar, David Mahalel, Per Olof Gutman

Research output: Contribution to journal › Article › peer-review

Abstract

The Macroscopic Fundamental Diagram (MFD) relates the number of circulating vehicles (or accumulation) to a neighbourhood's average speed or flow. In theory the MFD has a well-defined maximum which remains invariant over time. Recent studies, however, suggest that in practice this is not the case, and the MFD does present a variations over time. These variations in the MFD render traffic simulations, modelling and control schemes inaccurate, as these tools do not capture the dynamic nature of the MFD. This paper presents a dynamic model for estimating the MFD, so it does capture the MFD's time varying nature. A mathematical Kalman-filter based framework for solving the model and estimating the MFD are also presented. The application of the method on a small scale example shows the potential of the method.

Original language	English
Pages (from-to)	297-302
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	49
Issue number	3
DOIs	https://doi.org/10.1016/j.ifacol.2016.07.050
State	Published - 2016

Keywords

Dynamic Estimation
Kalman-Filter
Macroscopic Fundamental Diagram (MFD)
Traffic Control
Traffic Network State

ASJC Scopus subject areas

Control and Systems Engineering

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10.1016/j.ifacol.2016.07.050

Cite this

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title = "Dynamic Model for estimating the Macroscopic Fundamental Diagram",

abstract = "The Macroscopic Fundamental Diagram (MFD) relates the number of circulating vehicles (or accumulation) to a neighbourhood's average speed or flow. In theory the MFD has a well-defined maximum which remains invariant over time. Recent studies, however, suggest that in practice this is not the case, and the MFD does present a variations over time. These variations in the MFD render traffic simulations, modelling and control schemes inaccurate, as these tools do not capture the dynamic nature of the MFD. This paper presents a dynamic model for estimating the MFD, so it does capture the MFD's time varying nature. A mathematical Kalman-filter based framework for solving the model and estimating the MFD are also presented. The application of the method on a small scale example shows the potential of the method.",

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AU - Fishbain, Barak

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AU - Mahalel, David

AU - Gutman, Per Olof

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Dynamic Model for estimating the Macroscopic Fundamental Diagram

Abstract

Keywords

ASJC Scopus subject areas

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