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

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 in PARAMICS Simulation

  • Introduction

  • Methodologies

  • Evaluation study

    • Calibration & Validation

    • Ramp metering algorithms

    • Evaluation results

  • Conclusions


Background
Background in PARAMICS Simulation

  • California PATH program Project

  • Objective

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


Introduction categories of ramp metering control
Introduction in PARAMICS SimulationCategories of ramp-metering control

  • Fixed-time

  • Local traffic responsive

    • ALINEA

  • Coordinated traffic responsive

    • BOTTLENECK

    • ZONE


Methodologies
Methodologies in PARAMICS Simulation

  • 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 in PARAMICS SimulationMicro-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 in PARAMICS SimulationAPI 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
Methodologies in PARAMICS SimulationEvaluation framework


Evaluation study study site
Evaluation study in PARAMICS Simulationstudy site


Evaluation study network coding in paramics
Evaluation study in PARAMICS SimulationNetwork coding in PARAMICS


Evaluation study model calibration
Evaluation study in PARAMICS SimulationModel 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 in PARAMICS SimulationModel Validation (volume-occupancy)

Loop station @ 3.04

Real world

Simulation


Evaluation study model validation volume comparison
Evaluation study in PARAMICS SimulationModel Validation (volume comparison)


Evaluation study alinea
Evaluation study in PARAMICS SimulationALINEA

  • maintaining a optimal occupancy on the downstream mainline freeway

  • Calibration:

    • KR = 70

    • Odesired = 20%

    • Location: 60 m


Evaluation study bottleneck
Evaluation study in PARAMICS SimulationBOTTLENECK

  • 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 in PARAMICS SimulationZONE

  • 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 in PARAMICS SimulationAssumptions & 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 Study in PARAMICS SimulationPerformance 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 study in PARAMICS SimulationScenarios

  • 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 study in PARAMICS Simulationalgorithms 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 study in PARAMICS SimulationTotal vehicle travel time


Evaluation study average mainline travel time
Evaluation study in PARAMICS SimulationAverage mainline travel time


Evaluation study total mainline delay
Evaluation study in PARAMICS SimulationTotal mainline delay


Evaluation study total on ramp delay
Evaluation study in PARAMICS SimulationTotal on-ramp delay


Evaluation results
Evaluation results in PARAMICS Simulation

  • 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 in PARAMICS Simulation

  • 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 in PARAMICS Simulation

  • 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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