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Research Papers

Feedback Ramp Metering Using Godunov Method Based Hybrid Model

[+] Author and Article Information
Neveen Shlayan

Researcher
e-mail: shlayann@u.edu

Pushkin Kachroo

Professor
e-mail: pushkin@unlv.edu
Electrical and Computer Engineering
Department,
University of Nevada,
Las Vegas, NV 89154

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received June 24, 2011; final manuscript received October 23, 2012; published online June 10, 2013. Assoc. Editor: Eugenio Schuster.

J. Dyn. Sys., Meas., Control 135(5), 051010 (Jun 10, 2013) (11 pages) Paper No: DS-11-1195; doi: 10.1115/1.4023070 History: Received June 24, 2011; Revised October 23, 2012

In this paper, a new feedback control design for an isolated freeway ramp is presented utilizing hybrid dynamics based on Godunov's numerical technique. Previous feedback ramp metering designs have been mainly based on either discretized linearized methods such as ALINEA or nonlinear feedback designs based on ordinary differential equations for the traffic model. These models use lumped parameters, which fail to represent some details of the rarefaction wave phenomenon of the distributed model. Godunov's conditions employ the data known on both sides of each boundary in order to determine the characteristics of the boundary conditions. This paper uses Godunov's hybrid lumped model based on which feedback control design is proposed and simulation results for the model are presented. Real data is collected on one of the major freeway on-ramps in the Las Vegas area. The roadway parameters are estimated using least squares estimator then are used in the proposed Godunov based hybrid model. The proposed feedback ramp control design is compared with the actual ramp control algorithm. Self-tuning adaptive control is also performed using recursive parameter updating with and without exponential forgetting.

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References

Figures

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Fig. 3

Traffic characteristics

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Fig. 4

Shockwaves moving upstream (left) and downstream (right)

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Fig. 5

Rarefaction solution

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Fig. 7

The north-bound Tropicana Avenue to Interstate 15 location taken from FAST website http://rtcsnv.com/mpo/fast/dashboard.cfm [35]

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Fig. 8

The location of the north-bound Tropicana Avenue to Interstate 15 ramp detector taken from FAST's website http://rtcsnv.com/mpo/fast/dashboard.cfm [35]

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Fig. 9

The speed-volume plot of north-bound Tropicana Avenue to Interstate 15 taken from FAST's website http://rtcsnv.com/mpo/fast/dashboard.cfm [35]

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Fig. 10

Detector data from north-bound Tropicana Avenue to Interstate 15 taken from FAST's website http://rtcsnv.com/mpo/fast/dashboard.cfm [35]

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Fig. 11

The freeway segment density using the proposed Godunov-based control versus the existing ramp meter control

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Fig. 12

The freeway segment density and parameters values based on the self-tuning regulator, ρ0=10

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Fig. 13

The freeway segment density and parameters values based on the self-tuning regulator, ρ0=50

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Fig. 14

The freeway segment density and parameters values based on the self-tuning regulator, ρ0=70

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Fig. 15

The freeway segment density and parameters values based on the self-tuning regulator with exponential forgetting, ρ0=10

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Fig. 16

The freeway segment density and parameters values based on the self-tuning regulator with exponential forgetting, ρ0=50

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Fig. 17

The freeway segment density and parameters values based on the self-tuning regulator with exponential forgetting, ρ0=70

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