TOPL (Tools for Operations Planning): I-80 Freeway ...robotics.eecs.berkeley.edu/~varaiya/papers_ps.dir/090708-TOPL.pdf · TOPL (Tools for Operations Planning): I-80 Freeway Corridor

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TOPL (Tools for Operations Planning):I-80 Freeway Corridor Monitoring and Control

Andy ChowGunes DervisogluGabriel GomesRoberto HorowitzAlex A KurzhanskiyJaimyoung KwonXiao-Yun LuAjith MuralidharanT Qi

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Tony QiuRene O. SanchezDongyan SuPravin Varaiya

090609

Outline

The AURORA architecture

Part I: System Overview

Part II: TOPL applied to the I-80 freeway

D t ll ti d d l lib ti

Part III: TOPL applied to the I-80/San Pablo corridor

Data collection and model calibrationImputating missing ramp flowsUsing Google Earth to refine the modelSimulating HOV lanesOnramp meteringPlan for VSL near the maze

Using GIS mapsModeling arterial traffic

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Modeling arterial traffic

Part IV: Future directionsTowards a Smart Corridor TMC

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Part I AURORA architectureAlex KurzhanskiyAlex Kurzhanskiy

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The TOPL system

manual inspection Scenarios

HOV lanesramp meteringVSLincidentsspecial events

Preliminary Aurora XML

The TOPL System

OpenJUMP filtering

GIS Importer

Aurora XML

Aurora Configurator

Aurora Simulator

Reports

Calibrationof model

parameters

Imputationof missing ramp flows

etc.

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The TOPL System

network data, calibration & imputation

final model simulation & analysis

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Part II I-80 Data collection, model calibrationGunes Dervisoglou Gabriel GomesGunes Dervisoglou, Gabriel Gomes

OpenJUMP filtering

GIS Importer


HOV lanesramp meteringVSLincidentsspecial events etc.

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The TOPL System

Aurora XML

Aurora Configurator

Aurora Simulator

Reports

Calibrationof model

parameters


I-80 study section

I-80 Eastbound

23 miles

26 on-ramps and off-ramps26 on ramps and off ramps

loop detection at 55 locations

HOV lane with some HOV entrances/exits

I-80 Westbound23 miles29 on-ramps and 24 off-rampsloop detection at 57 locations

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loop detection at 57 locationsHOV lane with some HOV entrances/exits

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Model calibration

Step 1: Download daily PeMS detector health data for the study section, determine good days (73 good days in Jan – Sept 2008)

Step 2: TOPL Freeway Modeler generates freeway geometry andStep 2: TOPL Freeway Modeler generates freeway geometry and configuration along with a loop health ranking for the good days

Step 3: Choose calibration data from the good days

Step 4: TOPL Freeway Modeler automatically calibrates the model parameters and imputes missing ramp flows

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Step 5: TOPL Freeway Modeler generates configuration file for the AURORA simulation

Freeway Geometry (I-80 E)

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Link [int] Up Node [int]

Dn Node [int] Length [ft] Lanes [int] Auxlanes

[float] ML det. [vds] Has ML [bool]

1 1 2 792 6 0 401892 1

2 2 3 792 4 0 401892 0

3 3 4 1452 4 0 401657 1

4 4 5 1452 3 0 401657 0

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Select good days (I-80E)

Criteria:

– Detector Health: days with over 80% health during Jan-Sept 2008 C i S

EB

– Congestion Status

Moderate CongestionBoundaries in free-flow

– ‘Normal’ operating conditions

No special eventsHigh max. flow is indicator

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Calibrating fundamental diagram

Calibration: Process of fitting a fundamental diagram to density/flow data

For each vehicle detector station estimate:

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For each vehicle detector station estimate:

– Free flow speed

– Capacity

– Congestion wave speed

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Data collection and model calibration

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1. Free flow speed: Least square fit of data points with speed above 55 mph.


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1. Free flow speed: Least square fit of data points with speed above 55 mph.

2. Capacity : Maximum observed flow.

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1. Free flow speed: Least square fit of data points with speed above 55 mph

2. Capacity : Maximum observed flow

3. Congestion line: 3.1. Divide congested data into “bins”3.2. Take maximum non-outlier point as representative of each bin3.3. Fit line pivoted at the maximum flow point

Assign parameters to all links

links without

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links withoutdetector

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Problems with the Berkeley Highway LabGabriel GomesGabriel Gomes

OpenJUMP filtering

GIS Importer



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The TOPL System

Aurora XML

Aurora Configurator

Aurora Simulator

Reports

Calibrationof model

parameters


Imputating missing ramp flowsAjith MuralidharanAjith Muralidharan

OpenJUMP filtering

GIS Importer



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The TOPL System

Aurora XML

Aurora Configurator

Aurora Simulator

Reports

Calibrationof model

parameters


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Imputation of missing ramp flows

Onramp and offramp flows are essential for simulation

But ramps often lack a functioning detector station

Motivation

But ramps often lack a functioning detector station

Use mainline measurements to infer ramp flows (as best we can)

What ramp flows will produce measured mainline flows in CTM?

Algorithm based on adaptive learning control theory

Approach

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Missing ramp flows

No ramp measurements are available

48 segments are lumped into 36 cells with working detectors

Impute onramp and offramp flows for ramps with missing/incorrect measurements

Simulate freeway operation using the imputed flows

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Simulated versus measured velocity contours

I-80E: Feb 19, 2009 (Th)

Simulated Speed0 70

PeMS Speed0 70

Tim

e [h

r]

5

10

15

20

20

30

40

50

60

Tim

e [h

r]

5

10

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30

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50

60

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PostMile10 15 20 25 30

20

0

10

PostMile10 15 20 25 30

20

0

10

Simulated versus measured density, flow contours

Density Error = 4.18%e [h

r]

Simulated Density [veh/mile]0

5

10 200

250

300

e [h

r]

PeMS Density [veh/mile]0

5

10 200

250

300

PostMile

Tim

e

10 15 20 25 30

15

20 50

100

150

PostMile

Tim

e

10 15 20 25 30

15

20 50

100

150

Simulated Flow [veh/hr]0

9000

PeMS Flow [veh/hr]0

9000

20

Flow Error = 7.57 %

PostMile

Tim

e [h

r]

10 15 20 25 30

5

10

15

20

0

1000

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3000

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5000

6000

7000

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PostMile

Tim

e [h

r]

10 15 20 25 30

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10

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2000

3000

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5000

6000

7000

8000

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Simulated versus measured aggregate performance

VMT : 4.0% VHT : 1.5% Delay : 20%

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14 x 104

500

600

3000

3500Error is 1.6885

0 5 10 15 20 250

2

4

6

8

10

Time [hr]

VMT

[mile

s]

0 5 10 15 20 25

0

100

200

300

400

Time [hr]

Del

ay [h

r]

0 5 10 15 20 25

0

500

1000

1500

2000

2500

Time [hr]

VHT

[hr]

– PeMSSimulated

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– Simulated

Using Google Earth to refine networkDongyan SuDongyan Su

OpenJUMP filtering

GIS Importer



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The TOPL System

Aurora XML

Aurora Configurator

Aurora Simulator

Reports

Calibrationof model

parameters


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On ramp from EB Cutting Blvd.

onramp E10 1. Confirm PeMS VDS and ramp information. Make note of missing ramps

Refining the model

On ramp from EB Cutting Blvd.

onramp E09

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E05Gilman St. onramp 1. Confirm PeMS VDS and

ramp information. Make note of missing ramps

Refining the model

2. Use Google Earth to gather onramp length and number of lanes.

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1. Confirm PeMS VDS and ramp information. Make note of missing ramps

Refining the model

3. Enter the information (missing ramps and onramp details) into the TOPL input spreadsheet

2. Use Google Earth to gather onramp length and number of lanes

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Simulating HOV lanesAjith Muralidharan Alex KurzhanskiyAjith Muralidharan, Alex Kurzhanskiy,

OpenJUMP filtering

GIS Importer


HOV lanesramp meteringVSL incidentsspecial events etc.

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The TOPL System

filtering

Aurora XML

Aurora Configurator

Aurora Simulator

Reports

Calibrationof model

parameters


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HOV model in Aurora

Aurora permits

– Multiple vehicle types

– Multiple lanes as separate links

HOV models have

– Two vehicle types - HOV’s and SOV’s

– Freeway link consists of HOV and ML (general purpose) lanes

– Two modes of operation

HOV actuated- no SOVs in HOV lane

HOV not actuated– HOV lane operates as general purpose lane

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HOV not actuated HOV lane operates as general purpose lane

Split ratio Policy (I)

Flow exchange between lanes at nodes

Split ratios

– Specified separately for HOV’s and SOV’sp p y

– Unknown flow exchanges between HOV and SOV lane

Split ratio policy

– Indicate unknown split ratios in Aurora

– Specify partially filled split ratio matrices

Automatic policy to determine the split ratios

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Automatic policy to determine the split ratios

– Split ratios determined to maximize flows/ use downstream space

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Split ratio policy (II)

Indicate unknown split ratios

HOV HOV

Mainline Mainline

HOV vehicle split policy SOV vehicle split policy

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From \ To HOV Mainline Offramp

HOV * * 0.15

Mainline * * 0.15

Onramp * * 0

From \ To HOV Mainline Offramp

HOV 0 0.85 0.15

Mainline 0 0.85 0.15

Onramp 0 1 0

HOV lane actuated

I80E Model with HOV

HOV acutation hours: 5:00 AM – 10:00 AM and 3:00 PM -7:00 PM

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I80E Simulation: 1 in 5 vehicles is HOV capable

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Larger delay on HOV, smaller delay on ML in 1 in 4 vs.1 in 5 scenario

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Onramp meteringGabriel GomesGabriel Gomes

OpenJUMP filtering

GIS Importer



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The TOPL System

Aurora XML

Aurora Configurator

Aurora Simulator

Reports

Calibrationof model

parameters


Aurora onramp metering algorithms

Aurora can be used to compare onramp metering algorithms

Algorithms currently implemented in Aurora:

– Time of day metering

– ALINEA

– SWARM 1/2a/2b (no dynamic bottlenecks yet)

TOD and Alinea are isolated controllers, SWARM is coordinated

Isolated controller Coordinated controller

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ALINEA ramp metering and queue override

Downstream mainline detectors

Onramp queue detector

Onramp merge detector

… Critical mainline density = capacity / freeflow speed… Alinea gain (we used 50 mph)

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Alinea:

Queue override: If onramp queue detector is occupied, set

No ramp metering

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ALINEA without queue override

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ALINEA with queue override

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Percent reduction in total travel time

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Case TTT [veh.hr] % reduction

No control 30,869 -

Alinea no override 30,452 1.35%

Alinea with override 30,538 1.07%

Percent reduction in delay

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Case Delay [veh.hr]

% reduction

No control 4,945 -

Alinea no override 2,513 49.2%

Alinea with override 3,292 33.4%

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Ramp queue as percent of ramp storage

200

250

300

350

400

onra

mp

stor

age

Case 1: No ramp control

200

250

300

350

400

onra

mp

stor

age

Case 2: Alinea without queue override

0 5 10 15 200

50

100

150

Per

cent

age

of

Time of day0 5 10 15 20

0

50

100

150

Per

cent

age

of

Time of day

350

400

Case 3: Alinea with queue override

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0 5 10 15 200

50

100

150

200

250

300

Per

cent

age

of o

nram

p st

orag

e

Time of day

queue override

Part II VSL control on I-80 W near MacArthur MazeXiao Yun Lu Tony QiuXiao-Yun Lu, Tony Qiu

OpenJUMP filtering

GIS Importer



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The TOPL System

Aurora XML

Aurora Configurator

Aurora Simulator

Reports

Calibrationof model

parameters


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Part III I-80/San Pablo corridor

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Using GIS mapsAndy Chow Jaimyoung KwonAndy Chow, Jaimyoung Kwon


OpenJUMP filtering

GIS Importer


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The TOPL System

Aurora XML

Aurora Configurator

Aurora Simulator

Reports

Calibrationof model

parameters


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Construction of corridor network from GIS data

Test network: I-80/San Pablo corridor Starting point: Tele Atlas North America (TANA) GIS filesFour tools:

– OpenJUMP: Open source GIS tool Basic GIS manipulationOpenJUMP: Open source GIS tool. Basic GIS manipulation– TOPL GIS Importer. Export to AURORA format– AURORA Configurator: Manual adjustments

– AURORA Simulator: Run scenarios

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OpenJUMP

Initial cropping of GIS information

Joining Alameda and Contra Costa GIS files.

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GIS Importer

Retain desired street types

Retain desired street names (San Pablo, University, Solano, etc)

Simplify edges

Export to AURORA format

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GIS Importer for I-80/San Pablo corridor

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Filtered and simplified fileRaw GIS file

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GIS Importer for I-80/San Pablo corridor

Matlab-based experimental algorithm – only include I-80, San Pablo, and major streets /ramps connecting them

Parker Ave.Richmond PkwyHilltop Dr.El Portal Dr.

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San Pablo Dam RoadCuttingCentral Ave.BuchananGilmanUniversity Ave.Ashby Ave. Powell St.

Modeling arterial trafficAndy Chow Gabriel GomesAndy Chow, Gabriel Gomes


OpenJUMP filtering

GIS Importer


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The TOPL System

Aurora XML

Aurora Configurator

Aurora Simulator

Reports

Calibrationof model

parameters


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Arterial vs freeway traffic

Freeway Arterial

Maximum speed

65 mph 35 mphp

Capacity 2200 vphpl 1800 vphpl

Congestion Bottleneck induced Signal induced

Network Simple OD patterns with single routes.

Complex OD patterns with multiple routes.

Signals Single phase Up to 8 phases

Measurements Aggregate Aggregate + individual actuations

Al ith R l ti l i l R l ti l l (A t t d

calibration

OD estimation

signal design

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Algorithms Relatively simple (SWARM, Alinea)

Relatively complex (Actuated, adaptive, SCOOT, RHODES)

design

AURORA representation of a signalized intersection

• Links and nodes• Detector stations• Signals

Intersection nodeTraffic splitting nodeEntering traffic

• Signals

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Through trafficLeft-turn trafficExiting traffic

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AURORA arterial signal model

Each signal has 3 possible states: GREEN, YELLOW, and RED.

Signals affect traffic by adjusting the link capacity:

{GREEN, YELLOW} ⇒ Full capacity{GREEN, YELLOW} ⇒ Full capacity

{RED} ⇒ Zero capacity

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AURORA arterial detector station model

Each phase may have an approach and/or stopline detector station

Each detector station may contain one or more loop detectors

Each loop detector can measure

– Aggregate quantities (density, flow, speed)

– Individual vehicle calls

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AURORA signal control algorithms model

Signal control algorithms currently include

– Pre-timed (multiple plans and free operation)

– Isolated actuated

– Coordinated actuated

Signal control algorithms affect the signal state by issuing hold and force-off requests for particular phases

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AURORA model of arterial traffic

DEMO: A section of San Pablo Ave in Albany, CA

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AURORA model validation

Traffic data for May 23, 2008,1-1:30PM, from wireless sensors installed by Sensys Networks, Inc.

Fundamental diagram: g

Saturation flow = 1800 vph

Free-flow speed = 30mph

Critical density = 60 vpm

Jam density = 200 vpm

Current timing plan:

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Cycle time, phase durations, offsets, and phase sequences

Travel times for San Pablo network

300

350 Mean S.D. Matched 189.4 41.3Pretimed 189.7 18.4ASC 189.5 11.8

100

150

200

250

Trav

el ti

me (

sec)

Matched data Aurora - Pretimed

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0

50

46600 46800 47000 47200 47400 47600 47800 48000 48200 48400 48600

Entry time (sec)

Matched data Aurora Pretimed

Aurora - COORD HCM

*Note: Simulated network assumes 0 cross traffic

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Comparison with Paramics simulation

300

350

100

150

200

250

Trav

el tim

e (se

c)

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0

50

46600 47100 47600 48100 48600

Entry time (sec)

Aurora - Pretimed Paramics -pretimed

Part IV Future directions Alex Kurzhanskiy Pravin VaraiyaAlex Kurzhanskiy, Pravin Varaiya

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Towards a Smart Corridor TMC

securityproductivitynon-recur.i

recurrenti

alarm alarm alarm alarm

assessmentlosscongestioncongestion

controlcenter

scenariodatabase trusted, fast corridor simulator

Road network

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SCADAtraffic state estimation

and prediction

Supervisory Control And Data Acquisition

Real-time state estimation and short-term predictionflow

Model

Model prediction Measurement correction

density

200

300

400

ty (v

pm)

ActualLower boundUpper bound

Uncertainty Measurements

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0 50 100 150 200 250 300 350 4000

100

Time (minutes)

Den

sit

0 50 100 150 200 250 300 350 4000

20

40

60

80

Time (minutes)

Inte

rval

Siz

e (v

pm)

Just measurementsMeasurements + CTM

5 am 11 am

Documents

TOPL (Tools for Operations Planning): I-80 Freeway ...robotics.eecs.berkeley.edu/~varaiya/papers_ps.dir/090708-TOPL.pdf · TOPL (Tools for Operations Planning): I-80 Freeway Corridor