Enhanced Monitoring and Planning of Network Infrastructure with Remote Data Collection Progress Report Mark Hickman The University of Arizona Pitu Mirchandani

Enhanced Monitoring and Planning ofNetwork Infrastructure with Remote

Data Collection

Progress Report

Mark HickmanThe University of Arizona

Pitu MirchandaniArizona State University

March 10, 2011

Outline

1. Brief Scope of Project

2. Status of Task 1

3. Status of Task 2

4. Status of Task 3

Brief Scope of Project

Three Major Tasks:

1. Data fusion of simultaneous data collected from airborne and ground sensors for infrastructure monitoring2. Use of remotely collected data for developing better models for network planning and emergency operations3. Develop tools and enhance “enabling” technologies for airborne data collection.

Brief Scope

Task 1

Task 2

Task 3

Potential Benefits of Research

Availability of affordable technologies to do the above for routine and commercial applications.

Better calibration of infrastructure planning models: weaving, estimating queue lengths, traffic impacts of development, bottleneck analysis, etc.Real-time estimation of network conditions using multiple data sources including airborne sensorsLogistics for airborne systems for real-time monitoring and incident/disaster management.

Brief Scope

Task 1

Task 2

Task 3

Project Team

PI’sMark Hickman (UA)Pitu Mirchandani (ASU)Ron Askin (ASU)Yi- Chang Chiu (UA)

Other ResearchersDavid Lucas (Research Engineer, ASU,

MALTA related research)Xueyan Du (RA, UA, TRAVIS–related )Xianbiao Hu (RA, UA, MALTA- related)Peng Sun (RA, ASU, TRAVIA-related)Zouyang Zhou (RA, ASU, data fusion))

Brief Scope

Task 1

Task 2

Task 3

Technical Advisory Board

Tom Buick – Consultant (was with Maricopa DOT)Dave Gibson – FHWA Turner FairbanksGreg Jordan – Skycomp IncSarath Joshua –MAG (Maricopa planning agency)Jane Lappin – Volpe Center, USDOTScott Nodes – Arizona DOTJim Schoen – Kittelson Inc.Aichong Sun – PAG (Pima planning agency)Dale Thompson – FHWA Turner Fairbanks

(First TAB meeting was held April 27, 2010)

Brief Scope

Task 1

Task 2

Task 3

Task 1: Near Real-Time Traffic Monitoring

with Ground-based and Remotely Sensed Data Major focus on Data Collection and

Data Fusion.Existing data sources: • 1-sec loop data from City of Tucson, City of Tempe, Maricopa County DOT• Video data from Living Laboratory (City of Tucson)• Historical travel times on freeways and major arterials from probe vehicles, from MAG and PAG congestion studies• Historical traffic volumes on freeways and major arterials, from MAG and PAG count programs•Probe vehicle data from buses, from Sun Tran (City of Tucson) and Valley Metro (City of Tempe)

Task 1.1: New Simultaneous Data Collection • 20 hours of flight time (10 hours per year) for airborne surveillance on Speedway and I-10 in Tucson and in Tempe and US 60 in Phoenix• GPS based tracking during airborne data collection

Brief Scope

Task 1

Task 2

Task 3

Data Collection Technologies


with Ground-based and Remotely Sensed Data



Brief Scope

Task 1

Task 2

Task 3



Videos:Swan and Speedway IntersectionI-10 / Miracle Mile

Brief Scope

Task 1

Task 2

Task 3



Task 1.2: Develop Model-based Statistical Approaches to

estimate (off-line data fusion) • Flows• Speeds• Queue lengths on arterials• Densities on freeways. Calibrate model with ground truth

Develop software tool based on data fusion approach

Brief Scope

Task 1

Task 2

Task 3


with Ground-based and Remotely Sensed Data Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

Objective: Estimate system state represented by (density) and v (speed) in each cell of the freeway segmentMeasurements w/o remote sensing:We may get detectormeasurements of flow (r) and speed (v) at the positions of the dark boxes.

Kalman-type filter:Ref: Wang and Papageorgiou (2005)



1 1 1 1

1 1 1 1

Conservation Equation

( 1) ( ) [[ ( ) ( ) ( )]

[ ( ) ( ) ( )] ( ) ( ) [ ( ) ( ) ( )]]

Dynamic Speed Equation

( 1) (

qi i i i i i

i i

q qi i i i i i i i i i

i i

Tk k k v k k

k v k k r k k k v k k

v k v k

1

1

1) [[ exp[ ]] ( )] ( )[ ( ) ( )]

[ ( ) ( )] ( ) ( ) ( )

( ) ( )

a

f i i i icr i

vi i i ii

i i i i i

T Tv v k v k v k v k

a

vT k k T r k v kk

k k

Macroscopic model: Second order traffic flow model known as MetaNet model [validated by Papageorgiou in Paris, 1989]



1 1 2 2

0 0 1 1 1

1 1 1

[ ]

[ ]

[ ]

[ ]

TN N

TN N N

Tf cr

q v q vN N

v v v

q v r r

v a

z

d

p

1( 1) [ ( ), ( ), ( ), ( )]k k k k k z g z d p

Convert boundary variables d(k) and model parameters p(k) into state variables

2

3

1 2 3

( 1) ( ) ( )

( 1) ( ) ( )

[ ]

[ ]

T T T T

T

k k k

k k k

d d

p p

x z d p

ξ

( 1) ( ( ), ( ))k k k x f x ξ



PM

Example: Estimation at a detection point [ref: Wang and Papageorgiou, 2005]

Brief Scope

Task 1

Task 2

Task 3

199965

199970

199975

199980

199985

199990

199995

200000

200005

200010

200015

200020

200025

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200055

0 250 500 750 1000 1250 1500 1750 2000 2250 2500 2750 3000 3250 3500 3750

Distance along Roadway (m)

Tim

e (

Se

c)

Data from airborne sensors



Brief Scope

Task 1

Task 2

Task 3



Data fusion architecture with airborne dataBrief Scope

Task 1

Task 2

Task 3



Data fusion architecture with airborne data

o Measurements: trajectories through remote sensing (speed, density, spacing, ..)

o System state: speed, densityo The spacing usually represents the safety distance

related to speed.o The spacing and density are related.o A new mesoscopic model will be used only to

process the remote image area, the rest of the freeway is still estimated using the macroscopic model



Task 1.3: Additional Data Collection

Task 1.4 : Develop Model-based Statistical Approaches for real-time

estimation

Task 1.5: Mobile platform routing and scheduling logistics

Task 1.6: Logistics of mobile sensors, considering placement of new fixed detectors

TO DO

TO DO

TO DO

NEXT

Brief Scope

Task 1

Task 2

Task 3



Develop routing and scheduling logistics for mobile platforms • With current fixed sensors

• With new fixed sensors (include location decisions in logistics model)

Testing and demonstration of algorithms: Simulate traffic network using DynusT. Monitor density and speed on each link iIf di > dmax or vi < vmin then link will be monitored remotely by air Define monitoring route (currently heuristically). Visualize in Google Earth.

Brief Scope

Task 1

Task 2

Task 3

Demonstration

Beaverton, OR, network simulated in DynusT. (currently traffic

flow is not activated)

Some locations are assumed to “need” monitoring

Route determined by current heuristic. Red link = active monitoring; Blue link = deadheading to next link to be monitored.

Run demonstation(will load command window, followed by Google

Earth)

Brief Scope

Task 1

Task 2

Task 3

Task 2: Use of Airborne Sensors for Enhanced Network Planning and

Emergency Operations2.1 Enhanced Calibration: Currently we calibrate MALTA with census and ground data. In this task we will also use data from remote sensors – airborne imagery and vehicles with GPS locations.

Interactions

Supply Demand

Traffic Flow

NetworkConfigurations

TrafficControls

Participation

DepartureTime

Route

PerformanceMeasure

Strategies Information

JUST STARTED

Brief Scope

Task 1

Task 2

Task 3


Emergency Operations

– Detailed vehicle trajectories– Datasets range from light to

congested traffic conditions– Expect to have 100k+ data points

Brief Scope

Task 1

Task 2

Task 3

199965

199970

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199985

199990

199995

200000

200005

200010

200015

200020

200025

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200055

0 250 500 750 1000 1250 1500 1750 2000 2250 2500 2750 3000 3250 3500 3750



o Quadratic Optimization

Brief Scope

Task 1

Task 2

Task 3

observedi

calculatedi

observedcalculated

VV

VV

X

11

XXf T

KVL bf 2

1min

,,,

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,

qb

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bf

bf

cal

kkkkk

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v,1

,

Greenshield Type 1

Greenshield Type 2



Brief Scope

Task 1

Task 2

Task 3

0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

SIR Density(veh/ml/ln)

Spe

ed(m

ph)

The AMS Model Speed-Density Relationship(SIR length=225 feet)

AMSAMS+Greenshields model 1AMS+Greenshields model 2

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

100 125 150 175 200 225 250 275 300 325 350 375 400 425 450 475 500

SIR length (feet)

Th

e V

alu

e o

f fu

nct

ion

(m

l*m

l/h

/h)

Greenshields Model 1

Greenshields Model 2

Data


Emergency OperationsTask 2.2: Enhanced Traffic Management: to include arterial contra-flow and signal metering at intersections for evacuation.

Interactions

Supply Demand

Traffic Flow


TrafficControls

Participation

DepartureTime

Route

PerformanceMeasure


TO DO

Brief Scope

Task 1

Task 2

Task 3


Emergency OperationsTask 2.3 Simulate Data Collection: will allow us to simulate data collection from airborne sensors for performance measurement.

Interactions

Supply Demand

Traffic Flow


TrafficControls

Participation

DepartureTime

Route

PerformanceMeasure


TO DO

Brief Scope

Task 1

Task 2

Task 3


Emergency OperationsIt is essential that one monitors the unfolding evacuating scenario in developing evacuation strategies.

Task 2.4 Experiments on Emergency Evacuations: will allow us to test different sensor configurations during emergencies, e.g., strategies with only ground-based sensor data, with only airborne sensor data, and a some intermediate levels of both sensor types .

Model-basedEstimation ofSystem State

Model-basedEstimation ofSystem State

Sensor media

Fixed and Mobile Sensors

Sensor media

Fixed and Mobile Sensorsmeasurementsmeasurements

Feedback& ActionsFeedback& ActionsFeedback& Actions

Model-basedOptimization

of Traffic Flow

Objective:e.g. min evacuation.

time

Model-basedOptimization

of Traffic Flow

Objective:e.g. min evacuation.

time

Management and control decisions

Management and control decisions

Safe zone

HypotheticalSink Node

EvacuationZones

EvacuationOrigin Nodes

EvacuationDestination Nodes

IntermediateNodesIntermediate

Zones

Safe zone

HypotheticalSink Node

EvacuationZones

EvacuationOrigin Nodes

EvacuationDestination Nodes

IntermediateNodesIntermediate

Zones

TO DO

Brief Scope

Task 1

Task 2

Task 3

Task 3: Enhancement of Software Tools for Individual Vehicle Tracking and

Simulation The goal of Task 3 is to develop affordable technologies to monitor traffic performance for routine and commercial applications.

Subtask 3.1: Enhancement of GUI and manual tracking features

We will improve the interface for manual tracking, so that a user may be allowed to identify existing vehicles and vehicles subsequently entering the field of view, so that they are tracked by the software.

Brief Scope

Task 1

Task 2

Task 3


Simulation Subtask 3.1: Enhancement of GUI and manual tracking features





1. Auto Detect Mode

2. Manual Detect Mode•Menus for parameters and options• Making sure you don’t exit inadvertently

•


Simulation Subtask 3.1: Enhancement of GUI and manual tracking featuresHelp menus:


Simulation

Subtask 3.2: Development and testing of road mask concepts

Exact identification of vehicle positions from airborne imagery, to better locate vehicles within specific lanes, requires the clear identification of roadways.

Road masks will allow us to do this in real-time since it will decrease image size for processing.

Brief Scope

Task 1

Task 2

Task 3


Simulation Subtask 3.2: Development and testing of road mask conceptsBrief Scope

Task 1

Task 2

Task 3

•Procedure:

•Have a geo-referenced MAP of area•Take image of traffic (with approximate compass N)•Match image with map and identify location of camera image•Include mask by cropping of non-road part of image•Process remainder of image•Output processed data

Demo for Location Identification


Simulation

Subtask 3.3: Investigation of remote sensing for monitoring vehicle emissions

DLR is investigating remote sensors for monitoring emissions and integrating emissions sensing in ANTAR.

The UA-ASU-DLR research team will study the incorporation of remote emission sensing also in TRAVIS.

TO DO

Brief Scope

Task 1

Task 2

Task 3

Summary

Summary

The proposed project consisting of 3 major tasks:

• Data fusion for traffic and infrastructure monitoring

• Use of remotely collected data for developing better models for network planning and emergency operations

• Develop “enabling” technologies for airborne data collectionThe anticipated benefits are:

• Better calibration of infrastructure planning models

• Real-time estimation of network conditions in emergency and disaster conditions

• Development of affordable technologies for airborne traffic

monitoring for routine, emergency and commercial applications

Conclusion

End of Presentation!

Any questions?

Brief Scope

Task 1

Task 2

Task 3

Documents

Enhanced Monitoring and Planning of Network Infrastructure with Remote Data Collection Progress Report Mark Hickman The University of Arizona Pitu Mirchandani