Workshop 174: What Are the Decision Support …...Multiple devices, multiple roads, multiple agencies •Repeatability in responses •Ensuring policy is adhered to in responses Key

WORKSHOP 174: Decision Support Subsystem Requirements

for the Next Generation Traffic Management Systems and

Centers (TMCs)

Sponsoring Committees:

Freeway Operations [AHB20]

Regional TSMO Committee [AHB10]

ITS Committee [AHB15]

Artificial Intelligence and Advanced Computing Applications Committee [AHJ70]

Traffic Signal Systems Committee [AHB25]

ATM Joint Subcommittee [AHB20-5]

Sunday January 7, 2018

1:30-4:30pm

Salon A, Convention Center

1

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AGENDA FOR THE DAY

1:30-1:35 pm • Welcome and Workshop Purpose

1:35-2:50 pm• Opportunities to Integrate Decision Support Subsystems into

Traffic Management Systems and TMCs

2:55-4:05 pm• What Research is Needed to Advance Using Decision Support

Subsystems for Traffic Management Systems and TMCs?

4:05-4:25 pm• Action Planning – Identify Topics to Pursue Possible NCHRP

Research Problem Statements

4:25-4:30 pm • Review Action Plan & Next Steps for Committees3

WELCOME: Introductions and Workshop Purpose

1:30-1:35pm: Welcome

1:35-2:50pm: Presentations

2:55-4:05pm: Discussion

4:05-4:25pm: Action Planning

4:25-4:30pm: Wrap-up

Moderators:

Jon Obenberger, Ph.D., P.E., FHWA, Chair, Freeway Operations TRB

Committee

Les Jacobson, P.E., WSP | Parsons Brinckerhoff, Chair, Regional TSMO

Committee [AHB10]

4

PRESENTATIONS TO FRAME THE DISCUSSION: Opportunities

to Integrate Decision Support Subsystems into Traffic

Management Systems and TMCs

Moderator:

Dan Lukasik, Parsons






5

PRESENTATION SCHEDULE

Decision Support, DS Tools, DS Subsystems, and Applications for Active Traffic Management

Emanuel Robinson, Westat

Evolution of Using Decision Support Tools to Actively Manage Traffic

Phil Masters, Parsons

The Australian Approach and Architecture for Decision Support Subsystems,

Adam Myers, TRANSMAX, and John Gaffney, VicRoads

How Might Decision Support Subsystems Vary from Different Traffic Management Systems

Matthew Juckes, Kapsch

Examples of Decision Support Subsystems Used by Traffic Management Systems & TMCs to

Actively Manage Traffic

Doug Gettman, Kimley-Horn

6

Decision Support, DS Tools and Subsystems,and DSS for Active Traffic Management

Emanuel Robinson, Ph.D.

Workshop: What are the DSS Requirements for Next Gen Traffic Management Systems

and Centers (TMCs)

January 7, 2018

Why do our decisions need support?

• Factors affecting decision-making: Fatigue, stress, pressure, eating habits, caffeine, lack of info, uncertainty

• Decision makers under stress:

— Seek out certainty (less tolerant of ambiguity), fast decisions

— Experience greater conflict

— Narrow perception due to sensory overload (“tunnel vision”)

— Perception distortion and sub-optimal judgment

— Less able to handle complex or difficult tasks

— Focus on short-term survival goals, sometimes at the expense

of long-term benefits

— Choose risky alternatives

8

Decision Support Tools• Decision support tools can range from mechanical to digital

• Early history based on decision support tools, and then evolved into

systems (Robinson, 2017)

9

Decision Support Subsystems• Support business, organizational functions or actions

— Useful for unstructured and semi-structured problems

— Limits situations prone to biases

— Encourages consistency & improves efficiency of decisions

• Part of overall system

— Utilizes input data

— Provides options to the decision-maker or decision making process

— Computer-based

— The “brain” of the overall system

— Designed to meet needs of specific decisions or systems

1

0

Major DSS Components:

Data and Database

APIs to enable exchanging & using data between components

Software/computing (“rules”)

Analysis (e.g., prediction, simulation)

Evaluation (not as common)

User interface

11

1.1.1 Expert Rules Subsubsystem

1.1.3 Prediction Subsubsystem

1.1.2 Evaluation SubsubsystemIncidents

ConstructionVMS Status Data

Weather Alert Data

Parking Lot Data

Link DataSpecial Events

Traffic Signal Status Data

Res

pons

e P

lan

Req

uest

Agen

cy U

ser R

espo

nse

HOV Status Data

Inci

dent

sC

onst

ruct

ion

VMS

Stat

us D

ata

Wea

ther

Ale

rt D

ata

Park

ing

Lot D

ata

Link

Dyn

amic

Dat

aSp

ecia

l Eve

nts

Traf

fic S

igna

l Sta

tus

Dat

a

HO

V St

atus

Dat

a

Pred

icte

d N

etw

ork

Con

ditio

ns

Rec

omm

ende

d R

espo

nse

Plan

Plan

Dec

isio

n R

esul

t

Mea

sure

s of

Effe

ctiv

enes

s

Agen

cy S

tatu

s D

ata

Agen

cy S

tatu

s R

eque

st

Rec

omm

ende

d R

espo

nse

Plan

Recommended Response Plan

His

toric

al D

ata

IncidentsConstruction

VMS Status Data

Weather Alert Data

Parking Lot Data

Link DataSpecial Events

Traffic Signal Status Data

HOV Status Data

O/D

Mat

rix D

ata

Stat

ic N

etw

ork

Dat

aSt

atic

Tra

ffic

Sign

al D

ata

DS Subsystem Components (Dallas DSS example)

Example – DALLAS DSS

DS Subsystem Design Issues

14

Design issues: Methods, architecture, computer specs and hardware

• Interfaces between components

• Innovative technologies to incorporate (e.g., AI, machine learning, data fusion)

• Translation/storing/use of archived and real-time data

Prediction capabilities

Analytical capabilities and requirements

Software and integration

• Open source

• Off-the-shelf

DS Subsystem Design Issues (cont.)

15

Content requirements

• Unique traffic signal and TMC requirements (including non-traffic functions)

• Different decisions which are automated or supported real-time

• Ramp metering

• Speed management

• Incident detection

• Managed lanes

Monitoring/tracking performance

Modification and maintenance

User issues

• Knowledge

• Skills

• Abilities needed to maintain and modify

• Interface

DSS Evolution and Agency Needs• DSS needs to meet system requirements while

providing a foundation

• Agency should make modifications to meet evolving needs of agency and system

• Range of DSS possible for traffic management, so needs to be designed and implemented in a way that allows for easy construction or modifications.

16

Thank you!

Additional questions or comments:

Emanuel Robinson, Ph.D.

[email protected]

240-314-5812

17

mailto:[email protected]

Evolution of Using Decision Support Tools to Actively Manage Traffic

Phil MastersSenior Consultant, ITS

Timeline

19

Decision Support

Automated

Tabl

e Lo

okup

Expe

rt S

yste

mRu

le B

ased

Even

t Sce

nario

Mat

rixM

odel

Driv

en

20101950 1960 1970 1980 1990 2000 20101950 1960 1970 1980 1990 2000

Com

pute

r Tra

ffic

Sig

nal S

yste

m

Syst

em B

ased

Ram

p M

eter

ing

SCO

OT

SCA

TS

Cong

estio

n M

anag

emen

tTr

avel

Tim

esQ

ueue

War

ning

Early Decision Support Implementations

• Burlington Skyway ATMS, Ontario, Canada – 1985

Corridor Management System centred on a skyway bridge with no shoulders

Included freeway, highway, arterials with traffic signals and lift bridge

20

Operators input incident location & details (e.g. lanes blocked, incident type)

DSS supplied a complete response plan

Based on table lookup

Also had multiple incident de-confliction

Early Decision Support Implementations continued

• INFORM, Long Island, NY – 1987

Corridor Traffic Management System

Data from 1400 sensors in a 35 mile corridor processed to balance use of freeways and arterials

“Sphere of Influence” concept to group individual ITS devices into response plans which prompted operators for approval

21

Based on modified table lookup

VMS message templates with fill-in to reduce number of discrete messages in table

Early Decision Support Implementations continued

• COMPASS, Ontario, Canada – 1991

Major C/D Highway up to 18 lanes

Single incident response includes multiple VMS

Operators enter incident details (e.g., location, lanes blocked, incident type, estimated severity

22

DSS provides complete response plan for operator approval

Based on modified table lookup

VMS message templates with fill-in to reduce number of discrete messages in table

Key Reasons for Using Decision Support Subsystems

• Facilitating decisions in complex traffic management environments

Multiple devices, multiple roads, multiple agencies

• Repeatability in responses

• Ensuring policy is adhered to in responses

Key to getting buy-in from partner agencies

• Elimination of errors

• Free operators to undertake other tasks

• Reduction in time taken to achieve a response

23

Decision Support System Development – Table Lookup

• Earliest systems used table lookup where criteria allowed the system to get to the right cell in the table

Require constant maintenance to stay up to date

Tables grow exponentially with the size of the ATMS

24

DMS Message Sign ID Post Mile

Event Location

PM 0.0-0.5

Event Location

PM 0.5-1.0

Event Location

PM 1.0-1.5

Event Location

PM 1.5-2.0

Event Location

PM 2.0-2.5

Event Location

PM 2.5-3.0

Dynamic Messsage Sign

Number 11.00

ACCIDENT AHEAD

1 RT LANE BLOCKED

ACCIDENT AHEAD

1 MILE

1 RT LANE BLOCKED


Number 2 2.25

ACCIDENT AHEAD

2 MILES

1 RT LANE BLOCKED

ACCIDENT AHEAD

2 MILES

1 RT LANE BLOCKED

ACCIDENT AHEAD

2 MILES

1 RT LANE BLOCKED

ACCIDENT AHEAD

1 MILE

1 RT LANE BLOCKED


Number 33.85

ACCIDENT AHEAD

EXPECT CONGESTION

4 MILES

ACCIDENT AHEAD

EXPECT CONGESTION

4 MILES

ACCIDENT AHEAD

3 MILES

1 RT LANE BLOCKED

ACCIDENT AHEAD

2 MILES

1 RT LANE BLOCKED

ACCIDENT AHEAD

2 MILES

1 RT LANE BLOCKED

ACCIDENT AHEAD

1 MILE

1 RT LANE BLOCKED


Number 44.20

ACCIDENT AHEAD

4 MILES

EXPECT CONGESTION

ACCIDENT AHEAD

4 MILES

EXPECT CONGESTION

ACCIDENT AHEAD

3 MILES

EXPECT CONGESTION

ACCIDENT AHEAD

3 MILES

EXPECT CONGESTION

ACCIDENT AHEAD

4 MILES

1 RT LANE BLOCKED

ACCIDENT AHEAD

2 MILES

1 RT LANE BLOCKED


Number 5 6.35

ACCIDENT AHEAD

5 MILES

EXPECT CONGESTION

ACCIDENT AHEAD

5 MILES

EXPECT CONGESTION

ACCIDENT AHEAD

4 MILES

EXPECT CONGESTION

ACCIDENT AHEAD

4 MILES

EXPECT CONGESTION


Number 68.40

ACCIDENT AHEAD

5 MILES

EXPECT CONGESTION


Number 7 10.10


Number 8 12.50


Number 9 14.00

Traffic Blockage on Route X, Dynamic Message Sign Messaging, One right lane blocked

Some systems used message formats with fill-in fields to reduce the table size

{NumLanes} BLOCKED

IN {Express/Collector}

BEYOND {Street}

Decision Support System Development – Rules Based

• Expert system based DSS uses an off-the-shelf expert system engine such as Gensym G2 which uses reasoning technologies including rules, workflows and procedures to determine a response

More complex initial setup than table based,

• Rules based systems determine the response for every situation based on a set of rules

Changes in the operation only require a change to one or a few rules

Number of rules is related to the complexity of

• Key difference from table based new response plan is generated for each incident

25

Decision Support System Development – Model Driven

• Latest iteration is model driven rules based systems that use a model to predict future traffic conditions and determine the effectiveness of multiple response options

Model can predict upcoming problems to respond ahead of congestion forming

26

For incidents model can review a number of optional responses developed and identify the best

Human in the Loop?

• Why have we had the human in the loop?

Enhanced detection

Filling in gaps in operations concepts

Lack of trust in DSS

Policies or legislative issues

...

• If we solve these problems, what prevents us from moving to fully automated operations?

27

Where to Next?

• Issues that caused the first DSS to be used are more significant today

“With increasing complexity of our systems, the DSS capability that seems like a luxury now will become a foundational NECESSITY in the future” – Dan Baxter

• Need to resolve barriers and further automate operation of systems

“We should be talking about decision subsystems not decision supportsubsystems” – Dr. Eil Kwon

• Modelling and prediction will play a greater role

“We need to get ahead of congestion” – Dave Roper

28

29

[email protected]

Thanks

The Australian approach and architecture for decision support systems

“By DOTs for DOTs”

• ITS is often an afterthought atthe end of a large civil project

• And there are so many options available

• It’s a B2G dominated industry,driven by commercial results

• Often one-off bespokeprojects

The result is a lot of independent black-box technology systems with noguarantee that they can work together for a good outcome

STREAMS® is a common platform for connecting devices and data to algorithms andpeople.

“One Network – One Platform”

By DOTs for DOTs…

STREAMS®

The Queensland Department of Transport

and Main Roads created and wholly owns

an unlisted proprietary limited company

This was so that development could

emphasise road performance and safety

Road agencies across Australia form a

combined customer group, and work

towards a shared vision and development

programme for the development of

STREAMS ®

1. Use a strategic outcomes focused approach rather than a technology approach“What are you trying to do?”

2. Everything needs to be integrated together“One network – One platform”

3. Make academia, industry and government work together“Does it actually work?”

Tricks to Australia’s best ITS outcomes:

The road operator must provide

safe and good quality journeys to

as many people as possible on

constrained infrastructure

1. Initial

2. Managed

3. Defined

4. Measured

5. Optimizing

Maturity Model:

To go up the maturity ladder:

Continuous performance improvement

Leverage pre-existing capabilities

Build on what other’s have done

Don’t:

Keep re-inventing the wheel

Visionary approach starting from productivity, reliability

& safety outcomes…

Design traffic systems, not just roads…

Supported by industry

STREAMS®

And other DOTs

Fault monitoring

Diagnostics

Dynamic message signs

Traveller information

Travel time

Lane use signs

Variable speed signs

Ramp signals

Vehicle detection

Traffic performance monitoring

Weather monitoring

Camera feeds

Incident detection systems

Incident response

Event planning

Analytics

And more

VicRoads used to have 28 separate systems:

Now VicRoads have a single motorway system

And when they want to do something new,

just plug in the new service and build upon

what is there

per-lane productivity

Application

Server

Business

Intelligence

Public

Traffic Data

STREAMS

Gateway

STREAMS

Explorer

STREAMS

Connect

Historical

Data

Server

Devices

Other

Traffic

Systems

Expandabl

e Map

Layers

Experimental

Algorithms

Traveller Information

Systems

Custom Reporting

Tools

Real-time Data

Applications

Fault Management

Systems

Custom Data

Exports

How to achieve whole-of-network optimisation…(A technologist’s view…)

How to achieve whole-of-network optimisation…

1. Eliminate vendor lock-in

Use a device-driver model to communicate to devices viatheir native protocol

Both bespoke and

standard protocols

(like NTCIP)

Common diagnostics

for increasingly more

interesting types



2. Graft data onto a live transport network

To make your data and algorithms

understand each other

Mesh different types of data together

Provide value-added abilities




3. Do it in real time!

Give the system access to live data and give the data to system optimisers and the academic community




3. Do it in real time!

4. Plug in algorithms

A platform – not a product!

Support the scheduling, arbitration, delegation and diagnosis of algorithms.

Let them know what each other is doing.

Optimise for what makes sense at the time.

Let the best ones win.

Share them with others.

Vehicle Detectors

Measure Evaluate Adjust

Detector Site

Movements

Routes

Network

Liv

e T

ransp

ort

Ne

two

rkM

ode

l

Unusual Congestion

Travel Time

Queue Detection

Incident Detection

Statistical Profiling

Stimulus Response

Ramp Management

Variable Speed Limits

Vehicle Pre-emption

Congestion Charging

Services build upon each other’s abilities. And you can plug-in new ones or alternatives.

Vehicle Detectors


Detector Site

Movements

Routes

Network

Liv

e T

ransp

ort

Ne

two

rkM

ode

l

Unusual Congestion

New! Travel Time ++

Queue Detection

Incident Detection

Stimulus Response

Ramp Management


Vehicle Pre-emption

Statistical Profiling Congestion Charging

Vehicle Detectors


Detector Site

Movements

Routes

Network

Liv

e T

ransp

ort

Ne

two

rkM

ode

l

Unusual Congestion

Travel Time

Queue Detection

Incident Detection

Statistical Profiling

Stimulus Response

Ramp Management


Vehicle Pre-emption

Congestion Charging

What happens if I close

the road?

Business rules use the transport network to automatically figure out how respond to incidents

Because it is all part of the one system, and the live transport network is a foundational service,

algorithms can automatically account for the change in capacity.




3. Do it in real time


5. Embrace science!

Academia is an untapped resource.

They need ways to get at the data and test their ideas.

Use APIs to separate concerns.

Yet again… “Does it actually work?”




3. Do it in real time


5. Embrace science!

6. Harness new technology

What good is an amazing algorithm if you can’tcompute it live? Or if it crashes…?

What if you could ramp up your computing resources at peak hour?

Government

Academia

Technologists

Optimal Traffic

Network

Performance

Scalability

Reliability

Security

Clever ideas

Scientific method

Outcomes based approach

Operational priority

Adam Myers

Principal Architect

Transmax (developer of STREAMS®)

[email protected]

www.transmax.com.au


http://www.transmax.com.au/

How Might Decision Support Subsystems Vary forDifferent Traffic Management Systems

Transportation Research Board 97th Annual Meeting

Workshop #174 – What Are the Decision Support Subsystem Requirements for the

Next Generation Traffic Management Systems and Centers (TMCs)

January 7, 2018

Components of a Decision Support Subsystem

CORE Components

Rules

Engine

Static

Databases

Real-Time

Data Sources

Prediction

Engine

Simulation

Engine

Evaluation

Engine

Decisions

ATMS

(Actions &

GUI)

Planning

Tools

DSS

DF Database

Operators

Data

Fusion

(DF)

01/07/2017 TRB 2018 - Workshop 174 www.kapsch.net | 54

http://www.kapsch.net/

Choosing the Right DSS

What are the needs, decisions and Goals of the DSS?

Stakeholder input and needs definition

Defined Functional Requirements

What are the current capabilities?

ITS device functionality

Automation and staffing

Current ATMS and future operations

Modular system

What is the level of sophistications desired?

Predictions & Forecasts

Modeling and Simulation

Planning support



ATMS and DSS SubsystemsWhen to leverage the toolbox of subsystems

Knowledge Base Only

Suitable for problems with well-defined operationalstrategies

Prediction and modeling is not necessary

Knowledge Base + Prediction

Some chance of variability

Limited responses

Knowledge Base + Prediction + Modeling

Higher severity and intensity of trafficcongestion

Potential multiple options for response planning

Multi-agency / Multi-modal response plans

Visualization Only

Lots of data and no operational strategies

Rules Engine

Simulation

Analytics/

Prediction

Visualization



The DSS design process


Stakeholder

Inputs

Current

Capabilities

Available

Data

Sources

User Need Definition and

Gap Analysis

Concept of Operations and Functional Requirements

(Modular vs Customization, COTS vs OS, Automation

vs Operator, Realtime vs Prediction vs Simulation)

System design,

Development andTesting

External Systems

Upgrades

Soft Launch


Single Subsystem – Rule Based ApplicationKnowledge Base / Expert System

DSS used to ensure optimal and consistent decisions based on operational experience of subject matter experts

Freeway Incident Management – Rules engine to recommend optimal strategies

Search radius

Which ITS devices to include in response plan

What messages to post on DMS and send to511

Stakeholders Notifications

Service patrol dispatch


Rules to

Plans


Single Subsystem – Performance Metrics and ITS PlanningData Visualization and Business Intelligence

DSS used to help users make decisions based on

the analysis of large amounts of data

Current and historical data

Large coverage area

Combine Business Intelligence and Data

Visualization

Support DOT investments and planning decisions

JTMC – Unplanned event locations

DFW – Incident Heat Map

TxDot Asset

Maintenance Prioritization



Data Driven Hybrid ApplicationMDSS Road maintenance System

MDSS - Road Condition and Treatment Module Logic,

Source: http://ral.ucar.edu/projects/rdwxmdss/prototype

MDSS uses both a forecasting model and a rules-engine


http://ral.ucar.edu/projects/rdwxmdss/prototype


Multiple SubsystemsICM Multimodal Response Planning

Rule-Based Components

Operational Strategies

Decision Thresholds

Data-Driven Components

State Estimation

Demand Prediction

Model-Driven Components

Simulation based evaluation

Analytical predicted performance

Source: I-210 Concept of Operations



Summary

Decision Support Systems rely on a toolbox of subsystem that can be applied alone or in combination to help with a variety

of problems

Things to consider when choosing which tools to select :

Type of problem

Complexity of the Decisions

Is the problem data Driven?

What type of modeling is required?

Size of the region or system

Budget

Type of devices associated with the system

Number of stakeholders



Matthew JuckesSolution Expert | Urban Mobility

Kapsch TrafficCom USA, Inc.

300 Lightning Way Suite 302

Secaucus, NJ, 07094

Phone: +1 347 224 9790

E-Mail: [email protected]

www.kapsch.net



Decision Support

Systems in

Arterial Traffic

Management

Douglas Gettman,

Ph.D.

1. Adaptive control systems

2. ATSPMs

3. The Future

68

69

70

20/01/2018 72

20/01/2018 73

Kadence recommended split

Range of split values on each DOW

Regional Traffic

Operations Program

(RTOP)

GDOT

Automated Traffic Signal Performance Measures

20/01/2018 74

20/01/2018 75

20/01/2018 76

20/01/2018 77

20/01/2018 78

20/01/2018 79

20/01/2018 80

The near future

20/01/2018 81

20/01/2018 82

Quality of progression in 7 of 9 cadence sections is

above 90% efficiency. The Montrose section has

dropped below 70% due to the communications

issue at Montrose and Tildenwood. We need to

investigate the issues in the East Rockville section,

it has dropped below 65% efficiency.

FACILITATED DISCUSSION: What Research is Needed to

Advance Using Decision Support Subsystems for Traffic

Management Systems and Centers (TMCs)

Moderator: John Corbin, FHWA

Recorders: Kevin Miller, Kapsch; Larry Klein, Klein & Associates1:30-1:35pm: Welcome





84

PURPOSE AND OBJECTIVES

Purpose

Identify the research and development needs to support agencies using decision support subsystems (DSS) for Traffic Management Systems and Centers (TMS)

Objectives

1. Identifying for key topics supporting DSS:

-Successful practices

-Technologies used

-Lessons learned

2. Identify research and development needs to address current gaps in practice with legacy systems and meet the future needs of the next generation systems and TMCs

85

SESSION OUTCOMES

1. Identify for key topics supporting DSS:

-Successful practices (e.g., projects, reports or presentations, contacts to get more information)

-Technologies used (e.g., what, why, was there an alternative evaluation or analysis conducted)

-Lessons learned (e.g., reports, presentations, where to get more information)

2. What are possible research & development topics

3. Identify other key topics which need additional consideration for research and

technology transfer

86

POTENTIAL DISCUSSION TOPICS

1. DSS Requirements Unique for Traffic Signal

Systems

2. DSS Requirements Unique for TMCs or

Different Systems

3. DSS Requirements for Non-Traffic

Functions/Services Systems Support

4. Methods, Design & Architecture Options

Considerations for DSS

5. Storing and Using Archived and Real-Time

Data

6. Analytical Capabilities and Requirements for

DSS

7. Requirements for Adding Prediction

Capabilities to DSS

8. Computing Requirements and Platform


9. Software and Integration Considerations for

DSS

10. Monitoring and Tracking Performance of DSS

11. DSS Capabilities that Allow Agencies to Modify

DSS

12. What Technologies Can DSS use (e.g., AI, Data

Fusion, Machine Learning)

13. What Resources Are Needed to Use, Monitor

or Manage DSS

14. Social Media and Crowdsourcing

15. Other87

VOTE FOR 3 TOPICS YOU WANT TO DISCUSS IN

DETAIL



2:50-2:55pm: Topic Voting




- Grab three stickers and put them next to the topics you want to discuss in

Session 2: Facilitated Discussion

- If you are willing to help out in the future research on any topics, add your

email on the Poster under the topic!

- If you are remote, using the voting feature to pick three topics and use the

Chat Box to indicate topics you are willing to help with

I Want

This

One!

When voting you might want to consider: Priorities, Feasibility, State of the Practice, Existing Research, Current Deployments/Implementations, Your Expertise, Ability to Act, Evolution of the Concept 88

BREAK AND VOTE FOR POTENTIAL DISCUSSION TOPICS

1. DSS Requirements Unique for Traffic Signal

Systems

2. DSS Requirements Unique for TMCs or

Different Systems

3. DSS Requirements for Non-Traffic

Functions/Services Systems Support

4. Methods, Design & Architecture Options


5. Storing and Using Archived and Real-Time

Data

6. Analytical Capabilities and Requirements for

DSS

7. Requirements for Adding Prediction

Capabilities to DSS

8. Computing Requirements and Platform


9. Software and Integration Considerations for

DSS

10. Monitoring and Tracking Performance of DSS

11. DSS Capabilities that Allow Agencies to Modify

DSS

12. What Technologies Can DSS use (e.g., AI, Data

Fusion, Machine Learning)

13. What Resources Are Needed to Use, Monitor

or Manage DSS

14. Social Media and Crowdsourcing

15. (Other)89

VOTING RESULTS!

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

0

10

20

30

40

50

60

70

80

90

100

1. Topic name

2. Topic name

3. Topic name

4. Topic name

90

SESSION FORMAT

The Moderator will introduce a topic chosen during the Topic Voting (1-2 minutes)

Group Discussion on each topic (12 minutes)

Consult the Read-Ahead Document as a Base for Discussion!

It has Topic Descriptions, Current Practices, Gaps in Practice; Research and Technology Transfer Needs; and Available Guidance,

Resources, and Practices! 91

ACTION PLANNING: Identify Topics to Pursue Possible

NCHRP Research Problem Statements

Moderator: Michael Pack, University of Maryland

Recorders: Jim Katsafanas, Michael Baker; Francois Dion, University of

California Berkley Path Program; and Larry Klein, Klein & Associates






92

OUTCOMES OF THIS SESSION

Develop a list of three or four topics to be assigned to specific TRB Committees for

the NCHRP Program

For each Topic, capture notes and ideas on:

1. Research Problem Statements

2. Research Synthesis Topics

93

SESSION FORMAT

The Moderator will provide an overview of the list of key topics identified for further

consideration in previous session (1-2 minutes)

Moderator will facilitate a discussion to identify priority research topics the

Committees and volunteers could advance developing research proposals (5-6

minutes)

The Note Takers will capture all the notes right on the slides so everyone can see and contribute! We want to identify: the Research Problem Statements and Research Synthesis Topics

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WRAP-UP: Review the Action Plan and Discuss/Distribute

Next Steps for Committees

Moderators:

Jon Obenberger, Freeway Operations Committee

Les Jacobson, Regional TSMO Committee

Ishak Sherif, Artificial Intelligence and Advanced Computing Applications

Committee

Peter Koonce, Traffic Signal Systems Committee

Greg Krueger, ITS Committee






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NEXT STEPS

Research Topics to identify

Send in Ideas for next year’s workshop (is another workshop needed)

Notes will be added to the following URL:

https://sites.google.com/site/trbfreewayops/

Note Takers send your notes to [email protected] for aggregation

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https://sites.google.com/site/trbfreewayops/


THANK YOU!

e

Thank you for your participation!

For specific feedback on the Research Topics or to volunteer to help support future research please email:

Michael Pack at [email protected] and Jim Katsafanas at [email protected]

If you have any additional feedback, please email any of the supporting TRB Committee Chairs:

- Jon Obenberger at [email protected]

- Les Jacobson at [email protected]

- Gregory Krueger at [email protected] ITS

- Peter Koonce at [email protected]

- Sherif Ishak at [email protected]

- Dan Lukasik at [email protected]

- Lisa Burgess at [email protected]

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Documents

Workshop 174: What Are the Decision Support …...Multiple devices, multiple roads, multiple agencies •Repeatability in responses •Ensuring policy is adhered to in responses Key