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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
TRB January 2006 J. L. Schofer Northwestern University 4
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?
TRB January 2006 J. L. Schofer Northwestern University 5
Basics – The Role of Transportation
• Mobility (access to opportunity)
• Economy– Opportunities– Efficiency– Sustainability
• Security– Resistance &
resilience
TRB January 2006 J. L. Schofer Northwestern University 6
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
TRB January 2006 J. L. Schofer Northwestern University 8
Influences on Decisions
Objective Information Problems Options Outcomes
Subjective information Values Opinions Biases
Noise
DecisionDecisionprocessprocess Decisions
TRB January 2006 J. L. Schofer Northwestern University 9
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
TRB January 2006 J. L. Schofer Northwestern University 10
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
TRB January 2006 J. L. Schofer Northwestern University 11
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
TRB January 2006 J. L. Schofer Northwestern University 12
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
TRB January 2006 J. L. Schofer Northwestern University 13
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
TRB January 2006 J. L. Schofer Northwestern University 14
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
TRB January 2006 J. L. Schofer Northwestern University 15
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)• …
TRB January 2006 J. L. Schofer Northwestern University 16
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!
TRB January 2006 J. L. Schofer Northwestern University 17
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
TRB January 2006 J. L. Schofer Northwestern University 18
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
TRB January 2006 J. L. Schofer Northwestern University 19
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!
TRB January 2006 J. L. Schofer Northwestern University 20
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
TRB January 2006 J. L. Schofer Northwestern University 21
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
TRB January 2006 J. L. Schofer Northwestern University 22
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
TRB January 2006 J. L. Schofer Northwestern University 23
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
TRB January 2006 J. L. Schofer Northwestern University 24
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!