Performance evaluation of adaptive ramp metering algorithms in paramics simulation
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Performance Evaluation of Adaptive Ramp Metering Algorithms in PARAMICS Simulation. Lianyu Chu , Henry X. Liu, Will Recker California PATH, UC Irvine H. Michael Zhang Department of Civil and Environmental Engineering, UC Davis. Presentation Outline. Introduction Methodologies

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Performance Evaluation of Adaptive Ramp Metering Algorithms in PARAMICS Simulation

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Performance evaluation of adaptive ramp metering algorithms in paramics simulation

Performance Evaluation of Adaptive Ramp Metering Algorithms in PARAMICS Simulation

Lianyu Chu, Henry X. Liu, Will Recker

California PATH, UC Irvine

H. Michael Zhang

Department of Civil and Environmental Engineering, UC Davis


Presentation outline

Presentation Outline

  • Introduction

  • Methodologies

  • Evaluation study

    • Calibration & Validation

    • Ramp metering algorithms

    • Evaluation results

  • Conclusions


Background

Background

  • California PATH program Project

  • Objective

    • Evaluating ramp-metering algorithms in a micro-simulation environment


Introduction categories of ramp metering control

IntroductionCategories of ramp-metering control

  • Fixed-time

  • Local traffic responsive

    • ALINEA

  • Coordinated traffic responsive

    • BOTTLENECK

    • ZONE


Methodologies

Methodologies

  • Choosing an ITS-capable model (PARAMICS, VISSIM, AIMSUM2,…)

  • Developing ATMIS modules

  • Good calibration of studied network

  • Development, design, calibration and optimization of ramp-metering algorithms

  • Performance evaluation under different scenarios


Methodologies micro simulator paramics

Methodologies Micro-simulator PARAMICS

  • Scalable, high-performance microscopic traffic simulation package

  • ITS-capable

  • API programming

    => Capability enhancement through API development


Methodologies api development a hierarchical approach

Methodologies API development: A Hierarchical Approach

Provided API Library

Signal

Ramp

ATMIS Modules

Routing

Demand

Developed API Library

CORBA

Data Handling

Databases

Adaptive Signal Control

Advanced Algorithms

Adaptive Ramp Metering

Dynamic Network Loading


Methodologies evaluation framework

MethodologiesEvaluation framework


Evaluation study study site

Evaluation studystudy site


Evaluation study network coding in paramics

Evaluation study Network coding in PARAMICS


Evaluation study model calibration

Evaluation study Model calibration

  • Accurate Network Geometry

  • Vehicle characteristics & Performance

  • The proportion of vehicle types

  • Driving restrictions

  • The signposting setting for links

  • Driver behavior factors in car-following and lane-changing models


Evaluation study model validation volume occupancy

Evaluation study Model Validation (volume-occupancy)

Loop station @ 3.04

Real world

Simulation


Evaluation study model validation volume comparison

Evaluation study Model Validation (volume comparison)


Evaluation study alinea

Evaluation study ALINEA

  • maintaining a optimal occupancy on the downstream mainline freeway

  • Calibration:

    • KR = 70

    • Odesired = 20%

    • Location: 60 m


Evaluation study bottleneck

Evaluation study BOTTLENECK

  • System level metering rate

    • Occupancy at Downstream > Desired occupancy

    • Vehicle storage in the section

  • Local level metering rate:Occupancy control

  • Calibration: - Area of influence of each section - Weighting factor of each on-ramp


Evaluation study zone

Evaluation study ZONE

  • System level metering rate: volume control

  • Local level metering rate:Occupancy control

  • Calibration

    • Identify bottleneck, divide the network into zones

    • 6-level metering plan for each entrance ramp


Evaluation study assumptions experimental designs

Evaluation study Assumptions & experimental designs

  • Override strategy.

  • Metering rate restriction

  • No diversion

  • Same occupancy control calibration used in BOTTLENECK and ZONE.

  • 15 simulation runs for each scenario

  • Compared with fixed-time control


Evaluation study performance measures

Evaluation StudyPerformance measures

  • Total vehicle travel time (TVTT)

  • Average mainline travel time (AMTT)

  • Total mainline delay (TMD)

  • Total on-ramp delay (waiting time) (TOD)


Evaluation study scenarios

Evaluation studyScenarios

  • Morning peak hour (6:30-10:00)

    • highly congestion

    • lower congestion

  • Incidents: block the rightmost lane for 10 minute

    • at the beginning of congestion

    • at the end of congestion


Evaluation study algorithms to be evaluated

Evaluation studyalgorithms to be evaluated

  • ALINEA

  • Traditional BOTTLENECK

  • Improved BOTTLENECK: replacing the local control strategy, i.e. occupancy control, with ALINEA control

  • ZONE

  • Improved ZONE


Evaluation study total vehicle travel time

Evaluation studyTotal vehicle travel time


Evaluation study average mainline travel time

Evaluation studyAverage mainline travel time


Evaluation study total mainline delay

Evaluation studyTotal mainline delay


Evaluation study total on ramp delay

Evaluation studyTotal on-ramp delay


Evaluation results

Evaluation results

  • All algorithms can be used for improving freeway congestion.

  • ALINEA shows very good performance under all scenarios.

  • The two coordinated ramp-metering algorithms, i.e., BOTTLENECK and ZONE, are a little more efficient than ALINEA under normal conditions.

  • Compared with ZONE, BOTTLENECK can identify a bottleneck dynamically.

  • Coordinated algorithms can be improved by integrating a better local algorithm, such as the ALINEA algorithm .


Conclusions

Conclusions

  • A capability-enhanced micro-simulation laboratory has been developed for evaluating ramp-metering algorithms, potentially, some ATMIS applications.

  • Adaptive ramp-metering algorithms can ameliorate freeway traffic congestion effectively.

  • Compared with local algorithm, coordinated algorithms are more efficient, but the improvement is limited.


More information

More Information

  • PATH reports: http://www.path.berkeley.edu

    • Liu, X., Chu, L., and Recker, W. PARAMICS API Design Document for Actuated Signal, Signal Coordination and Ramp Control, California PATH Working Paper, UCB-ITS-PWP-2001-11

    • Zhang, H. M., Kim, T., Nie, X., Jin, W., Chu, L. and Recker, W. Evaluation of On-ramp Control Algorithm, California PATH Research Report, UCB-ITS-PRR-2001-36.


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