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
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Introduction
Methodologies
Evaluation study
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Calibration & Validation
Ramp metering algorithms
Evaluation results
Conclusions
Background
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California PATH program Project
Objective
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Evaluating ramp-metering algorithms in a microsimulation environment
Introduction
Categories of ramp-metering control
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Fixed-time
Local traffic responsive
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ALINEA
Coordinated traffic responsive
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BOTTLENECK
ZONE
Methodologies
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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
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Scalable, high-performance microscopic traffic
simulation package
ITS-capable
API programming
=> Capability enhancement through API
development
Methodologies
API development: A Hierarchical Approach
Provided API
Library
Developed
API Library
Advanced
Algorithms
Signal
ATMIS
Modules
Ramp
Routing
Demand
Data
Handling
CORBA
Databases
Adaptive Signal Control
Adaptive Ramp Metering
Dynamic Network Loading
Methodologies
Evaluation framework
Oracle Database
Historical loop
data
Time-dependent
Travel demands
PARAMICS SIMULATION
Developed API Library
Basic ATMIS modules
Performance
Loop data
Time-based
Measure
aggregator
Ramp
Ramp- metering algorithms
Data Handling
MySQL
Database
Evaluation study
study site
Los Angeles 
Irvine 
Culver Dr
7
Jeffery Dr
6
6.21 5.74 5.55
5.01
5
4
4.03
3.86
Sand canyon Dr
SR-133
2
3
3.31
3.04
Irvine Central Dr
2.35
1.93
1.57
I-5
1
1.11 0.93
0.6
Evaluation study
Network coding in PARAMICS
Evaluation study
Model calibration
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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)
Loop station @ 3.04
Real world
Simulation
Evaluation study
Model Validation (volume comparison)
5-min volume
loop station @ 3.04 (percentage error 8.7%)
900
800
700
600
500
400
300
200
100
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
:0 5:0 0:0 5:0 0:0 5:0 0:0 5:0 0:0 5:0 0:0 5:0 0:0 5:0
0
4
5
1
2
4
5
1
2
4
5
1
2
4
5
6:
6:
7:
7:
7:
7:
8:
8:
8:
8:
9:
9:
9:
9:
simulation
real-world
Evaluation study
ALINEA
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maintaining a optimal
occupancy on the
downstream mainline
freeway
Downstream detector
On-ramp detector
Queue detector
r (t )  ~
r (t  1)  K R  (Odesired  Odownstream(t ))
Calibration:
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KR = 70
Odesired = 20%
Location: 60 m
Evaluation study
BOTTLENECK
7
6
Section
5
4
3
Area of influence
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–
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1
Mainline
detectors
Traffic direction 
System level metering rate
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2
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
Traffic direction 
7
6
5
4
Zone 2
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2
3
1
Zone 1
System level metering rate: volume control
Local level metering rate:Occupancy control
Calibration
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Identify bottleneck, divide the network into zones
6-level metering plan for each entrance ramp
Evaluation study
Assumptions & experimental designs
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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
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Total vehicle travel time (TVTT)
Average mainline travel time (AMTT)
Total mainline delay (TMD)
Total on-ramp delay (waiting time) (TOD)
Evaluation study
Scenarios
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Morning peak hour (6:30-10:00)
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highly congestion
lower congestion
Incidents: block the rightmost lane for 10 minute
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at the beginning of congestion
at the end of congestion
Traffic direction 
7
6
5
4
2
3
Zone 2
Zone 1
1
Evaluation study
algorithms to be evaluated
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ALINEA
Traditional BOTTLENECK
Improved BOTTLENECK: replacing the local
control strategy, i.e. occupancy control, with
ALINEA control
ZONE
Improved ZONE
Evaluation study
ZONE-ALINEA
ZONE
BOTTL
ENECKALINEA
BOTTLENECK
9.00%
8.00%
7.00%
6.00%
5.00%
4.00%
3.00%
2.00%
1.00%
0.00%
-1.00%
ALINEA
Time saving (%)
Total vehicle travel time
AM high demands
AM low demands
Incident at congestion
Incident at end of congestion
Evaluation study
Average mainline travel time
ZONEALINEA
ZONE
BOTTLENEC
K-ALINEA
BOTTLENEC
K
12.00%
10.00%
8.00%
6.00%
4.00%
2.00%
0.00%
ALINEA
Time saving (%)
Average mainline travel time
AM high demands
AM low demands
Incident at congestion
Incident at end of congestion
Evaluation study
Total mainline delay
ZONEALINEA
ZONE
BOTTLENEC
K-ALINEA
BOTTLENEC
K
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
-5.00%
ALINEA
Delay decrease (%)
Mainline total delay
AM high demands
AM low demands
Incident at congestion
Incident at end of congestion
Evaluation study
Total on-ramp delay
ZONEALINEA
ZONE
BOTTLENEC
K-ALINEA
BOTTLENEC
K
100.0%
80.0%
60.0%
40.0%
20.0%
0.0%
ALINEA
Delay increase (%)
Total on-ramp delay
AM high demands
AM low demands
Incident at congestion
Incident at end of congestion
Evaluation results
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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
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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

PATH reports: http://www.path.berkeley.edu
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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-200111
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.