Data for Transportation Decision Making
Report from the Kansas City Peer Exchange and Remarks on Things to Come Tom Bolle, Tim Lomax, Tom Palmerlee, Joe Schofer, Johanna Zmud
June 13, 2007
Data can speak truth to power! But that takes good data at the right time!
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Background & KC Peer Exchange
• Issues: securing needed data, variability in availability, quality & uses • Process: surveys, interviews, case studies, peer exchange, interpretation
– TRB EC-109
• Kansas City Peer Exchange: amplification, extension, ratification, next steps
– AASHTO - TRB – April 17-18 – Prior examples, discussion, report
• Today: overview, illustration, options
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Peer Exchange Outcomes
• Participants:
– FL, KS, MD, MI, MN, NM, NV, VA, VT, WA – 2 MPOs, AASHTO, FHWA, BTS, RITA (support)
• 23 Examples - Where data made a difference
– Inventory & allocation (15 cases)
• Asset inventory, condition, performance & outcome data • Identify problems, find solutions, set priorities, allocate $
– SSD, guard rails, pavement condition, bike routes, real time performance, ADA compliance, crash data integration
• Data Driven Resource Allocation
– Project status monitoring, management (3 cases)
• Project data dashboard, environmental data
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Where Data Made a Difference - 2
• General project planning (2 cases)
– Data for project scoping
• Bench marking (1 case)
– Staff salaries
• Traveler information (real time) (1 case) • Program Impact Assessment (1 case)
– Connect program investments to economic development
TRB Circular summarizing peer exchange is being prepared
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Data is a Transportation Asset
• Data is an asset that adds value to transportation systems, public & private
– Data plays central role producing useful information for decision – Data is often invisible… until it is not there
• Like all assets, data must be managed to ensure value
Objective of Data Asset Management: assure cost-effective availability of data required to support effective transportation decision making
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Uses for Data and Information:
data needs derive from data uses
• Assess current state
– infrastructure condition, operational performance, demand, service quality, impacts
• Track trends, problems, progress
– safety, congestion, environment
• Measure, evaluate outcomes of actions
– Before-after studies
• Forecast problems, outcomes of actions, policies
– Evaluate alternatives, surprises
• Accountability
– constituents, customers, DMs
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Data Value Comes from Data Uses
• Data can avoid poor decisions • Critical issues require data support:
– Current and predicted system condition, performance – If-then information for policy testing
• Contemporary issues demanding data, analysis
– – – – – – Congestion Safety Land use Economic development Sustainability PPP & cost sharing (e.g., I-95)
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Actions to Assure Data for Decisions
• Manage data assets with enterprise-wide data business plans • Understand data uses and sources by data mapping • Using available data sources, e.g., national databases, real-time data • Integrate new technology for data collection, analysis, display • Build institutional foundations for integrated data systems
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Managing Transportation Data Assets: The Data Business Plan
• Objective: manage data as integrated asset of transportation system • Action: Create enterprise-wide data business plan which…
– Defines data system – Matches data system to organizational needs, objectives – Links users, their data needs, and data (data mapping) – Identifies, integrates and delivers data from multiple sources…
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Data Business Plan (2)
– Supports multiple data uses: collect it once, use it many times – Identifies, manages and protects data flows – Assures user access while protecting sensitive data – Establishes consistent data policies – Supports sharing knowledge and tools among users – Lays out effective governance and funding schemes – Provides dynamic plan for managing data for changing world 10
Data Mapping for Business Planning
• Sources of data
– Local, national – New data, archived
Secretary Admin Asst Dir. Planning Chief R-O-W Dir. Construction Mgr. Urban Dir. Operations
• Data needed, used for different decision types • Data flows inside and among agencies • Data gaps
– – – – What’s missing, needed? Quality gaps Process gaps Clarity, usefulness gaps
Mgr. Rural
• Data efficiency
– Duplicate data – Multiple uses
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Leveraging Available Data Sources
US Demographic & Travel Trends
National Personal Transportation Survey 3.50 3.00 Household Measures 2.50 2.00 15,000 1.50 1.00 0.50 0.00 1969 1977 1983 1990 1995 2001 10,000 5,000 0 30,000 25,000 20,000
• National data sets
Annual Miles per Household
– Invisible foundations for decisions at all levels
• NHTS, CFS…
Persons/Household
Vehicles/Household
Workers/Household
Vehicle Miles/Household
– Importance of continuing support – making the case
• Using operations data for planning, management
– Readily available – Trend tracking, infrastructure assessment
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New Data Technology
for Coverage, Quality, Economy
• Data sources
– GPS, RFID, cell phones, sensors, displays
• Analysis
– data fusion, mining
• Display & delivery
– graphics, simulation, animation
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Enhancing Institutional Structures
• Innovative institutional arrangements
– Partnerships: private/public, interagency, regional, national & international – Data sharing, data integration and fusion, cost sharing
• Managing sensitive data Your data belongs to us!
– Private and proprietary data
• Disclosure protection • Value propositions
– Security vs. development
• E.g., border crossing data
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AASHTO-SCOP
High Payoff Actions
• Identify, disseminate best data practices
– Priority topics:
• Enterprise data business plans • Data mapping • Innovations in institutional structures for data systems • Integrating operational data in planning and management
– How to do it
• Focused peer exchanges • NCHRP Syntheses • Webinars & Listserves
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High Payoff Actions (2)
• Prepare Data Primer for (new) CEOs • Develop prototype data mapping: State or national data mapping case studies to show data-decision making linkage • Sponsor international scans (Scandinavia, EU)
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High Payoff Actions (3)
• Create templates, methods:
– Business plans, data sufficiency assessments…
• Integrate data issues into AASHTO, TRB activities
– Data gaps – How to fill them
Questions?
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