Decision-driven Framework for a National Transportation Data Program

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

2. Otherfactors Decisions Information Analysis Data Condition, system performance, Benefits, Costs, Distribution over space, social groups, time Performance evaluation, Problem Identification, Agenda setting, Action choices 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 J. L. Schofer Northwestern University

3. 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 Demand Grows for National Data Programs 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? J. L. Schofer Northwestern University

5. Basics – The Role of Transportation • Mobility (access to opportunity) • Economy • Opportunities • Efficiency • Sustainability • Security • Resistance & resilience 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 outcomes Knowledge supports… • Action decisions to change systems & services This is a management process and it feeds on data! J. L. Schofer Northwestern University

7. 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 Projected demand, supply, performance & impacts. Manifest vulnerabilities Prior outcomes, forecasts Learning from actions Data Needs to Achieve Transportation Objectives J. L. Schofer Northwestern University

8. Decisions Decision process Influences on Decisions Objective Information Problems Options Outcomes Subjective information Values Opinions Biases Noise J. L. Schofer Northwestern University

9. Protagonist 1 Protagonist 2 Data: problems, options, impacts Debate Decisions Protagonist 3 Protagonist 4 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! J. L. Schofer Northwestern University

10. Costs Benefits 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?) 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 J. L. Schofer Northwestern University

12. Beware of Train Wrecks 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 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 J. L. Schofer Northwestern University

14. 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 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) • … 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! J. L. Schofer Northwestern University

17. People, commodities • Demographics/attributes • O-D: MSA2 • Situational data: Land use, density • Transportation services • LOS • Location • Design • Condition • Utilization • LOS 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 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 J. L. Schofer Northwestern University

19. LA airport makes plans to deal with people with bird flu symptoms Prevention Of Infectious Disease Outbreaks & Bioterrorism In Air Travel To Be Focus Of Congressional Hearing Hawaii Begins Influenza Surveillance at Honolulu International Airport SYNERGIES! 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 J. L. Schofer Northwestern University

20. Stockholm Congestion Charge Trial 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! J. L. Schofer Northwestern University

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

22. 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 Collecting Better, Cheaper Data 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 J. L. Schofer Northwestern University

24. Good decisions mean mobility, logistics efficiency, and security Data- Decision Supply Chain! 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? J. L. Schofer Northwestern University