Conference Proceedings 26 - Transportation Research Board

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Workshop SummaryBrenda Berg, Trans TechJoy Dahlgren, Partners for Advanced Transit and Highways, University of California, BerkeleyBecky Duke, Montana Department of TransportationMark Hallenbeck, Washington State Transportation Center, University of WashingtonShirley Hsiao, Orange County Transportation AuthorityPatricia Hu, Oak Ridge National LaboratoryDavid Kuehn, Federal Highway AdministrationJoseph Segale, Chittenden County Metropolitan Planning OrganizationChuck Sonday, Nebraska Department of RoadsRonald Tweedie, New York State Department of TransportationSTATE OF THE PRACTICETraffic data warehousing is being done in several locations(e.g., Wisconsin, Florida, Los Angeles, andPhoenix). Loop detectors are commonly used in California,but a fair percentage are generally out of service,many because of communications failures. It isdifficult to find resources to maintain them. Agenciesare experimenting with laser detection. Warehousedinformation is made available in the form of publishedreports and CD-ROMs.Some locations, including Florida, are linking geographicand accident information. The First Systemin Los Angeles will link incident information to ageographic information system (GIS) database. Atleast one state tried to set up a centralized data systemand found it did not work. At least two statesnow hold the information collectors responsible forstoring and providing the data they collect.ISSUESMeasure Selection• There are different perspectives on performancemeasures, such as customer versus agency (e.g., busbreakdown versus preventive maintenance measures)and national versus local.• Selection of appropriate performance measuresneeds to be a combination of top-down (needsdriven)concerns and bottom-up (data-driven) capabilities.• Performance measures should not be portrayedas the absolute criteria in decision making.• There is a tendency to overmeasure. It is importantto balance data-acquisition costs and data availability.• Performance measures should be selected to bestinform policy makers.• How do we build effective measures, from conceptual(word based, descriptive) to operational definitions(quantitative) and from input to output, tooutcome, to second-order outcomes?• Performance measures need to be related to strategicgoals. Thus, new performance measures shouldbe defined, and obsolete performance measuresshould be dropped.• The key is to know how you are going to usethe performance measures before knowing whetherthe measures are the ‘‘right’’ ones. A process needs tobe established to determine which measures to ultimatelyuse.• Performance drives data needs and priorities.• When communicating data between manage-100
WORKSHOP SUMMARY: SELECTING MEASURES, DATA NEEDS, AND ANALYTICAL ISSUES 101ment systems (e.g., in New Jersey through GIS, inCalifornia through a central office), where is the formatdecreed? What is an appropriate level of centralization?• Segment and collect information by coordinatepoints versus line segment or shape.• Level of accuracy and consistency for higherlevel(policy) purposes: is the ‘‘real’’ number important?Or is it the trend? the consistency in method?• Performance measures for policy analysis shouldbe suited for their purpose with the following characteristics:–Have an appropriate level of accuracy (is the‘‘real’’ number important or is the trend mostcritical?);–Be measures that are easy to explain and havea level of accuracy that can be explained;–Help in the decision-making process; and–Be flexible enough to respond to a changingdecision and policy environment.Data and Data Needs• Assured quality of data;• Integration of data from different sources anddifferent geographic or political jurisdictions;• Data compatibility (avoid comparisons between‘‘apples and oranges’’);• Data consistency;• Uniform format;• Standardized definitions;• Location referencing systems;• Integration tools;• Key data elements of bicycle and pedestrian datato evaluate system reliability;• Resources to support necessary data collectionand analysis;• Hierarchy of surrogate measures for measuresthat are too difficult, intangible, or expensive to measure(e.g., quality of life, societal costs);• Use of outcome surrogates (e.g., bike networkgaps versus number of riders) from greater to morespecific (e.g., quality of life to more train riders toone-time performance, cleanliness) because outcomemeasures may be harder to assess as they becomemore general and policy oriented and more subjectto external factors;• Survey of users about qualities that can be affectedby the transportation agency and tailored todifferent markets (locations and demographicgroups) because different groups have different needsand priorities;• Coordination of data collection within a singleagency to maximize efficiency;• Data ownership: Who collects the data? Whoowns the data?;• Understanding the difference between projectionsand actual observations (projections typicallysubject to forecast uncertainty; actual observationssubject to measurement errors);• Future data needs (difficult to anticipate);• Proper data and tools (to justify revenue increaseswith performance measures);• Right amount of data: How much data isenough?; and• Worth of data collection: Does it pay for itself?Analytical Issues• We need to understand the variability anderrors.• Do the decisions we make fully appreciate andconsider the variability and errors in the data?• Do we have the right tools (and data) to compareperformance among different modes for investmentdecisions?• We need better tools to quantify trade-offsamong different performance goals.• Although it is difficult, it is important to measurethe relationship between transportation and land use,livable communities, economic vitality, and otherbroad social goals.• We do not understand the relationships amongcommodities, mode choice, and travel characteristics;we could use more research.• There are a lot of data but not enough analysis.We need to know what the data are telling us.OPPORTUNITIES AND CONSTRAINTS• Enhance data collection with leverage-evolvingtechnologies [e.g., intelligent transportation systems(ITS), imaging, Global Positioning System (GPS), cellularphone, wireless communications].• Validate the reliability of data generated byevolving technologies.• Identify the abundant opportunities to integratethese data with traditional data.• Begin a dialogue with the Archived Data UserService (ADUS) community.• Decide which and how much ITS data areneeded (planners).• Understand the risks of using these data.• Use the Internet for data sharing and agencywidecoordination.• Collect data that serve multiple purposes.
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National Academy of SciencesNationa
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Workshop Summary ..................
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General OverviewIntroductionExecuti
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4 PERFORMANCE MEASURES TO IMPROVE T
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10 PERFORMANCE MEASURES TO IMPROVE
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Linking Performance Measures withDe
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Panel DiscussionJohn Poorman, Capit
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Workshop SummaryJohn Basilica, Loui
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RESOURCE PAPERImplementing Performa
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Page 67 and 68: Panel DiscussionJennifer Finch, Col
Page 69 and 70: FIGURE 2Level of measures and align
Page 71 and 72: PANEL DISCUSSION: AGENCY IMPLEMENTA
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Page 96 and 97: Panel Discussion, Part 1Anita Vande
Page 102 and 103: Panel Discussion, Part 2Douglas Zim
Page 104 and 105: FIGURE 11 Example data
Page 113 and 114: RESOURCE PAPERMeasuring That Which
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Page 117 and 118: TABLE 1Sustainability MeasuresNewma
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Page 143 and 144: Workshop SummaryScott Bassett, Oreg
Page 145: Freight Performance MeasuresCurrent
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APPENDIX ASummaries of 20 Poster Se
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SUMMARIES OF 20 POSTER SESSIONS 157
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TABLE 1(continued) WSDOT Outcomes,
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FIGURE 2 Sacramento Regional Transi
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FIGURE 3Goal evaluation table: Chit
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TABLE 6Mobility Performance Measure
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FIGURE 13 MPAH lane-miles by roadwa
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SUMMARY OF PEER EXCHANGE ON DATA FO
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APPENDIX C: RESEARCH STATEMENTS DEV
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LIST OF PARTICIPANTS 217University,
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LIST OF PARTICIPANTS 219Darwin Stua
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Conference Proceedings 26 - Transportation Research Board

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