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.