enhanced monitoring and planning of network infrastructure with remote data collection presentation...

Enhanced Monitoring and Planning ofNetwork Infrastructure with Remote

Data CollectionPresentation to Technical Advisory Board

by the Research Team:

The University of ArizonaArizona State UniversityGerman Aerospace Research Center (DLR)

April 27, 2010

Outline

1. Brief Scope of Proposed Project

2. Background and Review of

Experience

3. Details of Proposed Tasks – Task 1



6. Role of partners and collaborators

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

Background

Task 1

Task 2

Task 3

Role of Partners

Will give details shortly!

Key Investigators

German Aerospaceo Martin Ruhé (Dipl.-Ing, Stuttgart) Associate Head, Institute of

Transportation Research, DLR

o Reinhart Kühne (Ph.D. in Physics, University of Stuttgart), Director and Head of the Institute for Transportation Studies, DLR

University of Arizonao Mark Hickman (PI) (PhD, MIT) Associate professor in

transportation engineering in the Department of Civil Engineering and Engineering Mechanics (CEEM).

o Yi-Chang Chiu (PhD, U. Texas). Assistant professor in transportation engineering in CEEM.

Arizona State Universityo Pitu Mirchandani (Sc.D., MIT). Professor in the

Department of Computation, Informatics, and Decision Sciences (CIDS).

o Ron Askin (PhD, Georgia Tech). Professor and Department Head on CIDS..

Research: Use of airborne imaging technologies to analyze traffic flows, both for level-of-service analysis and for detailed vehicle trajectory data.

Research: Traffic Modeling and Simulation

Research: Remote sensing of traffic. Theories, models and algorithms for decision making in transportation and traffic systems .

Research: Applied Statistics

Research: Automated image analysis for traffic data collection and pollution measurement.

Research: Traffic flow theory. Transportation engineering.

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

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 sensors

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

Logistics for airborne systems for real-time monitoring and incident/disaster management.

Background and Review of Experience

o Mark Hickman and Pitu Mirchandani were part of the NCRST-F team, where they (1) developed preliminary algorithms for stabilizing images from moving platforms, (2) automatically tracked vehicles, (3) computed vehicle speeds and queues from these tracked vehicles, (4) developed approaches to compute measures of traffic performance on freeways and arterials, …

o Hickman-Mirchandani were also part of the CRESTA consortium where we used remote sensing to study queues and delays at border crossings

Will discuss some results shortly!

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

Past RITA/USDOT sponsored research


o Hickman-Mirchandani-Chiu have been developing and studying evacuation models and scenarios, with support from Arizona DOT and NSF, where they (1) developed simulation models, (2) developed strategies for mass evacuation, and (3) analyzed hypothetical scenarios in the Phoenix area

o Mirchandani and colleagues at the ATLAS center have data fusion approaches for real-time prediction of travel times on arterials, and developed a web-based interface

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners


Data Collection Technologies RITA- USDO

T


NCRST-F: Traffic performance measures and studies

o Traffic flow, density, speed, travel timeo Intersection delayo Freeway level of serviceo Origin-destination flows

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

RITA- USDO

T


NCRST-F: Tracking individual vehicleso Travel times on urban arterialso Trajectories of vehicleso Estimating speeds

CheckpointDistance

(mi)

T ravel

T ime

Cumulative Avg.

Speed (mph)

Link Speed

(mph)

Cumulative

LOS

Link

LOS

Euclid Ave. -

Park Ave. 0.16 0:01:03 9.4 9.4 F F

Mountain Ave. 0.43 0:02:18 11.1 12.6 F F

Cherry Ave. 0.67 0:02:43 14.9 35.5 E A

Campbell Ave. 0.93 0:04:11 13.3 10.5 E F

Ped Crossing #1 1.17 0:04:31 15.6 43.3 E A

Tucson Blvd. 1.42 0:04:58 17.2 33.3 D B

Ped Crossing #2 1.67 0:05:21 18.7 38.6 D A

Country Club Rd 1.91 0:05:51 19.6 29.1 D B

Ped Crossing #3 2.21 0:06:21 20.9 36.7 D A

Ped Crossing #4 2.54 0:06:47 22.5 45.7 C A

Ped Crossing #5 2.7 0:07:00 23.1 41.6 C A

Alvernon W ay 2.92 0:07:36 23 22.2 C C

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

RITA- USDO

T

TIME-SPACE DIAGRAM Speedway between Park and Euclid

(200)

(100)

-

100

200

300

400

500

600

700

800

900

1,000

- 3 6 9 12 15 18 21 24 27 30 33 36

Time (s)

Sp

ac

e (

ft)

V2 V3 V4 V5 V6 V7 V8 V9 V10 V11

Speedway & Euclid

Speedway & Park

Flow

Density

Distance Headway

Time Headway

Platoon Dispersion


NCRST-F: Tracking individual vehicleso Travel times on urban arterialso Trajectories of vehicles

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

RITA- USDO

T



2 sec sample frames 2 sec sample frames

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners



Georeferencing

RITA- USDO

T


NCRST-F: Technology Development of TRAVIS Vehicle Tracking Software

Data Collection

Video Image Processing

Trajectory Processing

Application Post-processing

Raw Video Vehicle in Image

Vehicle Position and Time

Registration Vehicle detection Vehicle tracking

Scaling Road mask

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

RITA- USDO

T


Current TRAVIS User Interface for off-line Tracking

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

RITA- USDO

T

Arterial Video

Unregistered Video orig_tuc.avi

Registered Video tuc_int_340.avi

Registered Video with Tracking track_cars_cc.avi


Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners


DLR: ANTAR and TrafficFinder


o DLR’s TrafficFinder’s outputs (real-time)

199965

199970

199975

199980

199985

199990

199995

200000

200005

200010

200015

200020

200025

200030

200035

200040

200045

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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)

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners


o DLR TrafficFinder’s predicted trajectories

Congested flow Synchronized flow Impeded free flow Free flow

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners


As members of the CRESTA consortium, Hickman and Mirchandani used remote-sensing technologies for investigating queues and delays at border crossings

Composite PhotoFeb. 27, 2008

2:00 pm

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

RITA- USDO

T


1

2

3

4 5

6

1: Pre-screening stations

2: Primary inspection

3: Secondary inspection

4: X-ray inspection

5: Arizona DOT inspection

6: Last weight check station

Mariposa Port of Entry OperationsBrief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

RITA- USDO

T


Results from simulation modeling and calibration

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

RITA- USDO

T


Queuing Analysis at StationsBrief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

RITA- USDO

T


-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

0 10 20 30 40

Manually Observed Queue

Image Analysis for Queue LengthsRITA- USDO

T

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners


CRESTA Project : Using DLR TrafficFinder for tracking vehicles

RITA- USDO

T


CRESTA Project : Using DLR TrafficFinder for tracking vehicles

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

RITA- USDO

T


Chiu-Hickman-Mirchandani have also done extensive simulation and traffic management for evacuations

Safe zone

HypotheticalSink Node

EvacuationZones

EvacuationOrigin Nodes

EvacuationDestination Nodes

IntermediateNodesIntermediate

Zones

Need a fast WAY OUTfrom evacuation zones to safe zones


Model-basedEstimation ofSystem State

Sensor media

Fixed and Mobile Sensorsmeasurements

Feedback& Actions

Model-basedOptimization

of Traffic Flow

Objective:e.g. min evacuation.

time

Management and control decisions

Safe zone


EvacuationZones




Zones

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners


Simulation plays major role in the systematic evaluation – For tactical/operational planning – off-

line “what if” scenario analysis– For analysis of operations decisions –

used as testing real-time strategies (since strategies cannot be easily tested during real events)

The research team is developing MALTA simulation software for this purpose.

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners


MALTA (Multi-resolution assignment and loading of traffic activities) simulation architecture

Interactions

Supply Demand

Traffic Flow

NetworkConfigurations

TrafficControls

Participation

DepartureTime

Route

PerformanceMeasure

Strategies Information

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners


Procedure for simulation-based analysis

o collect network and traffic datao develop network modelo calibrate model with available datao develop evacuation scenarioso develop evacuation strategieso study strategies based on simulation model outputso as case studies:

Operational and tactical planning for no-notice bomb threat at a Phoenix Cardinals game

Feedback control for management of evacuating traffic during floods (short-notice)

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners


SPARTA : System for prediction and analysis of real-time traffic on arterials: real-time data collection, real-time arterial monitoring (travel times, queues, turn ratios,…), and traffic images on the web.

Data fusion plays a major role in computing arterial performance measures.

Also can get real-time images on the web.

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners


SPARTA uses the Living Laboratory at UA (supported by Tucson)

Cam

pbel

l

Euc

lid

Par

k

Cou

ntry

Clu

b

Tuc

son

Che

rry

Mou

ntai

n

Elm

3rd

Speedway

Grant

GarageATLAS

RHODESCommunications Cabinet

Traffic Cabinets

Video Detectors

Fiber &Wireless

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

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)New 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• Ground-based video

Problem Motivation and Existing Literature

Real-Time Tracking Problem definition

Problem P1

Problem P2

Problem P3

Conclusions and Further Steps

Need significant cooperation and support from public agencies


with Ground-based and Remotely Sensed Data

Develop Model-based Statistical Approaches to estimate• Flows• Speeds• Queue lengths on arterials• Densities on freeways. Calibrate model with ground truth

More Data Collection (year 2)• to validate model (for use in real-time data fusion)

Develop software tool based on data fusion approach

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners


with Ground-based and Remotely Sensed Data

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

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

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners


with Ground-based and Remotely Sensed Data Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

Subtask 1.5: Mobile platform routing and scheduling logistics

This subtask focuses on the real-time routing and scheduling of airborne platforms, such as helicopters, airplanes or UAVs, to collect meaningful real-time traffic data in a roadway network. Both the roadway networks in Tucson and Phoenix metro area will be used as case studies to evaluate these heuristics.- Optimization models development for determining the logistics of mobile data collection- Methods development to solve these very complicated problems.

Off-line study: for planning monitoring activities

On-line scheduling: for real-time monitoring

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

Emergency OperationsCurrently we calibrate MALTA with census and ground data. In this task (2.1) we will also use data from remote sensors – airborne imagery and vehicles with GPS locations

Interactions

Supply Demand

Traffic Flow


TrafficControls

Participation

DepartureTime

Route

PerformanceMeasure


Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners


Emergency OperationsTask 2.2 will include arterial contra-flow and signal metering at intersections for evacuation.

Interactions

Supply Demand

Traffic Flow


TrafficControls

Participation

DepartureTime

Route

PerformanceMeasure


Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners


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

Interactions

Supply Demand

Traffic Flow


TrafficControls

Participation

DepartureTime

Route

PerformanceMeasure


Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners


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

Task 2.4 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 .



Sensor media

Fixed and Mobile Sensors

Sensor media

Fixed and Mobile Sensorsmeasurementsmeasurements

Feedback& ActionsFeedback& ActionsFeedback& Actions


of Traffic Flow


time


of Traffic Flow


time



Safe zone


EvacuationZones




Zones

Safe zone


EvacuationZones




Zones

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

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.

Effectively this will enhance TRAVIS so that it is more user friendly and can be used for real-time 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

Background

Task 1

Task 2

Task 3

Role of Partners


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.

We will work with DLR to research and develop a real-time approach.

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners


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.

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

Role of Partners

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

o City of Tucson / Living Laboratory: loop detector data, probe vehicle data from buses, intersection camera imagery

o City of Tempe: loop detector data, probe vehicle data from buses

o Arizona Department of Transportation: freeway detector data, ramp meter data

o Maricopa Association of Governments: historical traffic volumes, probe vehicle data, still imagery

o Pima Association of Governments: historical traffic volumes and probe vehicle data

o Maricopa County Department of Transportation: loop detector data

The public agencies can provide data and can work with us on the calibration of models.

Summary

Brief Scope

Background

Task 1

Task 2

Task 3

Role of Partners

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

Background

Task 1

Task 2

Task 3

Role of Partners

enhanced monitoring and planning of network infrastructure with remote data collection presentation...

Documents

details of proposed

studies o traffic flow

brief scope of project

traffic data collection

phoenix area o mirchandani

role of partners logistics

dlr o reinhart khne

o ron askin phd