Upload
zeki
View
38
Download
1
Tags:
Embed Size (px)
DESCRIPTION
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 - PowerPoint PPT Presentation
Citation preview
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 Evaluation study
– Calibration & Validation– Ramp metering algorithms– Evaluation results
Conclusions
Background
California PATH program Project Objective
– Evaluating ramp-metering algorithms in a micro-simulation environment
IntroductionCategories of ramp-metering control
Fixed-time Local traffic responsive
– ALINEA
Coordinated traffic responsive– BOTTLENECK– ZONE
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
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 ATMIS
Modules
Developed API Library
Advanced Algorithms
Data Handling
Routing
Ramp
Signal
CORBA
Databases
Adaptive Signal Control
Adaptive Ramp Metering
Dynamic Network Loading
Demand
MethodologiesEvaluation framework
Developed API Library
Data Handling Basic ATMIS modules Performance
Measure Time-based
Ramp
Loop data aggregator
MySQL Database
PARAMICS SIMULATION
Time-dependent Travel demands
Ramp-metering algorithms
Oracle Database
Historical loop data
Evaluation studystudy site
Irvine Central Dr SR-133 Sand canyon Dr Jeffery Dr Culver Dr I-5
Los Angeles Irvine
0.6 1.11 0.93 2.35 1.93 1.57 3.04 3.31 3.86 4.03 6.21 5.74 5.55 5.01
1 2 3 7 6 5 4
Evaluation study Network coding in PARAMICS
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)
0
20
40
60
80
100
120
0 20 40 60 80
P er c ent oc c upanc y
0
20
40
60
80
100
120
0 20 40 60 80
P er c ent oc c upanc y
Real world Simulation
Loop station @ 3.04
Evaluation study Model Validation (volume comparison)
loop station @ 3.04 (percentage error 8.7%)
0100200300400500600700800900
5-m
in v
olu
me
simulation real-world
Evaluation study ALINEA
maintaining a optimal occupancy on the downstream mainline freeway
Calibration:– KR = 70– Odesired = 20%– Location: 60 m
))(()1(~)( tOOKtrtr downstreamdesiredR
Downstream detector
On-ramp detector
Queue detector
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
Section
Area of influence Traffic direction
Mainline detectors 1 2 3 7 6 5 4
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
Traffic direction
1 2 3 7 6 5 4
Zone 1 Zone 2
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 StudyPerformance measures
Total vehicle travel time (TVTT) Average mainline travel time (AMTT) Total mainline delay (TMD) Total on-ramp delay (waiting time) (TOD)
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
Traffic direction
1 2 3 7 6 5 4
Zone 1 Zone 2
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 studyTotal vehicle travel time
-1.00%0.00%1.00%2.00%3.00%4.00%5.00%6.00%7.00%8.00%9.00%
ALI
NE
A
BO
TTLE
NE
CK
BO
TTL
EN
EC
K-
ALI
NE
A
ZON
E
ZON
E-A
LIN
EA
Tim
e s
avi
ng
(%
)
AM high demands AM low demands
Incident at congestion Incident at end of congestion
Evaluation studyAverage mainline travel time
Average mainline travel time
0.00%2.00%4.00%6.00%8.00%
10.00%12.00%
ALI
NE
A
BO
TTLE
NE
CK
BO
TTLE
NE
CK
-ALI
NE
A
ZON
E
ZON
E-
ALI
NE
ATim
e s
avi
ng
(%
)
AM high demands AM low demands
Incident at congestion Incident at end of congestion
Evaluation studyTotal mainline delay
Mainline total delay
-5.00%0.00%5.00%
10.00%15.00%20.00%25.00%30.00%
ALI
NE
A
BO
TTLE
NE
CK
BO
TTLE
NE
CK
-ALI
NE
A
ZON
E
ZON
E-
ALI
NE
A
De
lay
de
cre
ase
(%
)
AM high demands AM low demands
Incident at congestion Incident at end of congestion
Evaluation studyTotal on-ramp delay
Total on-ramp delay
0.0%20.0%40.0%60.0%80.0%
100.0%
ALI
NE
A
BO
TTLE
NE
CK
BO
TTLE
NE
CK
-ALI
NE
A
ZON
E
ZON
E-
ALI
NE
ADe
lay
incr
ea
se (
%)
AM high demands AM low demands
Incident at congestion Incident at end of congestion
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
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– 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.