1. TRB January 2006J. L. Schofer Northwestern University1 Decision-driven Framework for a National Transportation Data Program Joseph L. Schofer Northwestern University The Transportation Center © New Yorker 1976

2. TRB January 2006J. L. Schofer Northwestern University2 Performance evaluation, Problem Identification, Agenda setting, Action choices Condition, system performance, Benefits, Costs, Distribution over space, social groups, time DecisionsInformationAnalysisData 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

3. TRB January 2006J. L. Schofer Northwestern University3 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

4. TRB January 2006J. L. Schofer Northwestern University4 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?

5. TRB January 2006J. L. Schofer Northwestern University5 Basics – The Role of Transportation Mobility (access to opportunity) Economy –Opportunities –Efficiency –Sustainability Security –Resistance & resilience

6. TRB January 2006J. L. Schofer Northwestern University6 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 outcomes Knowledge supports… Action decisions to change systems & services This is a management process and it feeds on data!

7. TRB January 2006J. L. Schofer Northwestern University7 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

8. TRB January 2006J. L. Schofer Northwestern University8 Influences on Decisions Objective Information Problems Options Outcomes Subjective information Values Opinions Biases Noise Decisionprocess Decisions

9. TRB January 2006J. L. Schofer Northwestern University9 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

10. TRB January 2006J. L. Schofer Northwestern University10 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

11. TRB January 2006J. L. Schofer Northwestern University11 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

12. TRB January 2006J. L. Schofer Northwestern University12 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

13. TRB January 2006J. L. Schofer Northwestern University13 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

14. TRB January 2006J. L. Schofer Northwestern University14 Data-related errors 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

15. TRB January 2006J. L. Schofer Northwestern University15 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) …

16. TRB January 2006J. L. Schofer Northwestern University16 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!

17. TRB January 2006J. L. Schofer Northwestern University17 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: MSA 2 Situational data: Land use, density Transportation services LOS Location Design Condition Utilization LOS

18. TRB January 2006J. L. Schofer Northwestern University18 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

19. TRB January 2006J. L. Schofer Northwestern University19 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!

20. TRB January 2006J. L. Schofer Northwestern University20 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

21. TRB January 2006J. L. Schofer Northwestern University21 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 Data Analysis for primary decision Analysis task Analysis for secondary decision Costs of Data Benefits of Data to DM B = C Max B/C Max B-C

22. TRB January 2006J. L. Schofer Northwestern University22 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

23. TRB January 2006J. L. Schofer Northwestern University23 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

24. TRB January 2006J. L. Schofer Northwestern University24 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!