TRB January 2006 J. L. Schofer Northwestern University 1

Decision-driven Framework for a National Transportation Data Program

Joseph L. Schofer

Northwestern University

The Transportation Center

© New Yorker 1976

TRB January 2006 J. L. Schofer Northwestern University 2

Performance evaluation, Problem Identification, Agenda

setting, Action choices

Condition, system performance, Benefits, Costs, Distribution over

space, social groups, time

Decisions Information Analysis Data

Other factors

Data for Decision Making

• Need data to support informed transportation choices• Based on logic, planning theory, long federal policy history

This is the Data-Decision Supply Chain

TRB January 2006 J. L. Schofer Northwestern University 3

Demand Grows for National Data Programs

• SAFETEA-LU mandates• Critical transportation

issues – Congestion– Safety– Infrastructure condition &

vulnerability– Energy & environment– Equity– Finance– Human & intellectual capital– institutions

• Opportunities– Technologies – ITS– Policies & strategies -

privatization• Support uncertain for

national data programs– CFS, NHTS

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Sell Data Programs on Outcomes

What are data used for?• What decisions will they

support• What debates will they

feed?• How will better data make

transportation better?• How will it make life better?

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Basics – The Role of Transportation

• Mobility (access to opportunity)

• Economy– Opportunities– Efficiency– Sustainability

• Security– Resistance &

resilience

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Achieving Transportation Objectives

Need to know…• Condition of facilities &

services now• Trends in demand, supply,

costs, performance & impacts

• Risks– Will trends continue?– Vulnerabilities

• Options & their outcomesKnowledge supports…• Action decisions to

change systems & services

This is a management process and it feeds on data!

TRB January 2006 J. L. Schofer Northwestern University 7

Data Needs to Achieve Transportation Objectives

• Condition of facilities & services now

• Quality, quantity & distribution of service to users

• Trends in demand, supply, costs, performance & impacts

• Risks– Will trends continue?– Vulnerabilities

• Options & their outcomes– Facilities, services, policies

• Actions to change systems & services

Timely condition, service & mobility data

Time series condition, mobility, LOS data

Prior outcomes, forecasts

Projected demand, supply, performance & impacts.

Manifest vulnerabilities

Learning from actions

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Influences on Decisions

Objective Information Problems Options Outcomes

Subjective information Values Opinions Biases

Noise

DecisionDecisionprocessprocess Decisions

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Data Informs the Debate

• Data will be used by various protagonists

• Good data can raise the debate– Deal with substance, facts– Distinguish between values

and facts

• Data (alone) rarely determines the decision…

• But no data - or poor data - can lead to trouble!

Data: problems, options, impacts

Protagonist 3

Protagonist 2

Protagonist 1

Protagonist 4

DecisionsDebate

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Models of Decision Making

• Rational-comprehensive– Ideal (all information)

• Satisficing– Rational, limited view

(limited information)

• Projects vs. outcomes– “…I choose rail because it’s

rail” (biased information)

• Field of dreams– Stupidity, cupidity or vision?

(what information?)

Benefits

Costs

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Field of Dreams Decision Model

• If we build it, will they ride?– Sometimes they do…– Can we advance without

dreams?

• Sometimes planners don’t see the goal– Only provide information– Limited perspective

• Value of data when dreaming – Behavior, markets– Avoiding disasters

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Data and Decisions

• The best data don’t assure good decisions– Other factors are important– Decision makers aren’t

perfect, either• Sometimes its

advantageous for DMs to be unencumbered by objective information

• But DMs don’t want to be wrong – Poor or absent data can’t

help– opens door for good data

Beware of Train Wrecks

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Decision Errors in Transportation

• Error in eyes of beholder– Not just failure to take advice– Every mismatch isn’t failure

• Performance, costs, impacts different than expected/desired

• Important, unintended, undesired outcomes (vs. noise)

• Failure to act in face of credible information

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Dat

a-re

late

d e

rro

rs

Sources of Decision Errors• Forecasting errors

– Data– Models– Assumptions

• External factors– Unexpected changes

• Information delivery– Didn’t understand…

• Decision maker action– Ignoring information– Poor decision making– Diabolical motives

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Data Gaps & Decision Errors• Distinguish between

– Failure to use data• Analyst/DM failure

– Failure to have data• Data program failure

• Data gaps– Coverage: missing measures– Quality

• Accuracy• Timeliness• Resolution• Format (compatibility)• …

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Motivations for National Transportation Data Program

• Transportation: a national system

• Support Federal decisions– Trend interpretation– Problem identification– Grant decisions– Policy decisions– Legislation

• Standardize architecture for fusion & sharing

• More effective, efficient• Promote informed DM• Learn for the future!

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Data From National Perspective

• Flows of national interest– International– National– Interregional

• System condition & connectivity– Long term and real time

• Trends• Effectiveness of actions &

policies: Learning!– Building knowledge base for

future DM

• People, commodities• Demographics/attributes• O-D: MSA2

Situational data: Land use, density

• Transportation services•LOS

• Location• Design• Condition• Utilization• LOS

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Outline of Goal-Driven National Data Program

• Managing The Nation’s Transportation System for Mobility, Economy & Security– Ensuring personal mobility

• NPTS + situational data + activities + attitudes

– Supporting efficient logistics for economy & security• CFS + detail + intermodal + Infrastructure utilization + LOS +

international

– Protecting critical infrastructure• HPMS• Facility condition (public & private)• Critical infrastructure studies• Real-time system status

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Missing Elements & Opportunities• Planning & Managing Passenger

Travel for America – Long-distance travel survey

• State, national network planning & priorities to support…

• Economic development decisions (Industry, public facilities, tourism)

• Prediction & prevention of spread of diseases (e.g., avian flu) 

• Enhancing Relationships Between Transportation, Economy & Society– Linking data from multiple sources to

understand, predict:   • Consumer Expenditure Survey &

passenger, freight flow data• Passenger travel and data from

American Time Use Survey

LA airport makes plans to deal

with people with bird flu symptoms

Hawaii Begins Influenza Surveillance at Honolulu

International Airport

Prevention Of Infectious Disease Outbreaks & Bioterrorism In Air

Travel To Be Focus Of Congressional Hearing

SYNERGIES!

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Missing Elements & Opportunities II• Advancing Transportation

Through Organized Policy Innovation & Testing

– Learning through experience• Planned and naturally occurring

transportation changes– Identify best future actions– Inform decision making– Data needs:

• measures of: Interventions, outcomes, context, attributes of people

• Commitment to learning!Stockholm Congestion Charge Trial

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Using Benefit-Cost Framework for Data Programs

• (Good) data produce benefits through better choices

• Hard to distinguish incremental contributions of data

• Good data produce network of benefits

• Conceptually should think (broadly) in terms of B-C

decision

decision

decision

decision

Data

Analysis for primary decisionAnalysis taskAnalysis for secondary decision

Costs of Data

Be

ne

fits

of

Da

ta t

o D

M

B = CMax B/C

Max B-C

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Collecting Better, Cheaper Data

• Strategies– Continuous– Panels

• Technologies & tools– Internet– GPS– Hand held computers– Cell phones– RFID tags– Remote sensing

• Concerns & obstacles– Privacy

– Cooperation• Refusals

– What to do?• Protection• Credible uses• Sensible decisions

• Costs– Control, focus program

– Weigh the value, too

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Who Really Should Care?

• Decision makers• Citizens• Motivations for careful

choice:– Scarce resources– Minimize mistakes– Catastrophic risk

• Earmarking… is it all for naught?

• We need to make the case for national data program© New Yorker 1986

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Good Data Supports Good Decisions

• Good data: necessary – not sufficient – for good decisions

• Focus on outcomes & uses of data to support good choices

• Build constituencies – For good outcomes– For good decisions– For good data

• Collect examples: where have we done right, gone wrong?

Good decisions mean mobility, logistics

efficiency, and security

Data- Decision Supply Chain!