1 Optimization of ALINEA Ramp-metering Control Using Genetic Algorithm with Micro-simulation Lianyu Chu and Xu Yang California PATH ATMS Center University.

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Optimization of ALINEA Ramp-metering Control Using Genetic Algorithm with

Micro-simulation

Lianyu Chu and Xu Yang

California PATH ATMS Center

University of California, Irvine

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Overview

• Background: ALINEA• Genetic Algorithm• Optimization Framework• Simulation Modeling• Optimization Study• Conclusion Remarks

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Background

• ALINEA, proposed by Papageorgiou in 1990s• A local feedback ramp-metering strategy • Remarkably simple, highly efficient and easily

implemented• Good performance

– Field tests– Simulation-based studies

• Potential applications

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Background: ALINEA

))(*()(~)( tOOKttrtr R

Downstream detector

On-ramp detector

Queue detector

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Background : ALINEA

• Parameter values in field tests:– Desired occupancy O* : 0.18 -- 0.31

– KR =70, in real-world experiments

– Downstream detector location: 40 m -- 500 m downstream

– Update cycle t: 40 seconds -- 5 minutes

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Background: Purposes

• How to optimize ALINEA’s operational parameters in order to maximize its performance?

• Method: -> Hybrid method: simulation + GA

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Genetic Algorithm

• Mimic the the mechanics of natural selection and evolution

• Proven to be a useful method for optimization

• Useful when there are too many parameters to be considered

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Optimization Framework

MOE

GA

Time-dependentTravel demands

PARAMICSsimulation

ALINEAramp-metering algorithm

PerformanceMeasure

Ramp MeteringController

Loop DataAggregator

ParameterValues

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Simulation Modeling

• Study site

Traffic direction

Irvine Central Dr

SR-133

Sand Cnyn. Jeffery Dr Culver Dr

6.21 5.74 5.55 5.01 4.03 3.86 3.31 3.04 2.35 1.93 1.57 1.11 0.93 0.6 (post-mile)

1 2 3 7 6 5 4

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Simulation Modeling

• Model Calibration

Loop station @ postmile 3.04 (simulation)

0

20

40

60

80

100

0 20 40 60 80

Percent occupancy

30-s

ec v

olum

e

Loop station @ postmile 3.04 (real world)

0

20

40

60

80

100

0 20 40 60 80

Percent occupancy

30-s

ec v

olum

e

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Optimization Study

• MOE: Total vehicle travel time (TVTT)Ni,j: total number of vehicles that actually traveled

between origin i and destination j

Di,j: travel demand from origin i to destination j for the whole simulation time (Di,j is not equal to Ni,j because of the randomness of the micro-simulation)

Tki,j: travel time of the kth vehicle that traveled from origin i to destination j

)/( ,, 1

,,

,

jiji

N

k

kjiji NTDTVTT

ji

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Optimization Study

• Setup the range of calibrated parameters for ALINEA

Parameter RangeRegulator KR 10 ~ 300Desired occupancy 10% ~ 40%Update cycle of metering rate 10~300 secLocation of downstream detector 0~600 m

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Optimization Study

• The best, worst and average fitness values of each generation

1.46

1.48

1.5

1.52

1.54

1.56

1.58

1 2 3 4 5 6 7 8 9 10

Best Average Worst Fixed-time

Generation

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Optimization Study

• The results of optimized ALINEA parameters

Parameter RangeRegulator KR 70~200Desired occupancy 19~21%

30~31%Update cycle of metering rate 30~60 secLocation of downstream detector 120~140 m

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Findings

• When the regulator KR, used for adjusting the constant disturbances of the feedback control, is within the range from 70 to 200, the metering system is found to perform well.

• The optimal location of the downstream detector is found to be between 120~140 meters downstream of the on-ramp nose in our simulation study.

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Findings

• The update cycle of the metering rate implementation gives the best system performance when it ranges from 30 to 60 seconds in our study.

• The desired occupancy of the downstream detector station is found to be within two ranges, either from 19% to 21% or around 30% to 31%. Finally, 19% to 21% is selected for its better network reliability performance.

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Findings

0102030405060708090

100

0 10 20 30 40 50 60

Percent occupancy

Vo

lum

e

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Conclusions

• This paper presents a hybrid GA-simulation method to find the optimized parameter values for the ALINEA control. This method is effective to find the optimized parameter values.

• Practitioners can use our optimization results as a basic operational reference if they implement ALINEA control in the real world.

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Conclusions

• This study shows that micro-simulation can be used to calibrate and optimize the operational parameters of ramp metering control. Potentially, micro-simulation may also be used to fine-tune parameters for various other ITS strategies.

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Thank you