Methodology to Predict the Safety Performance of Urban and Suburban Arterials (2008) / Chapter Skim
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Appendix A Survey Results
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SURVEY RESULTS This appendix presents the results of the survey of potential HSM users that was conducted as part of the research. The purpose of this survey was to obtain an assessment of potential user needs for the safety prediction methodology for urban and suburban arterials.
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Figure A-1. Questionnaire Used for Survey SURVEY ON PREDICTION OF THE SAFETY PERFORMANCE FOR URBAN AND SUBURBAN ARTERIALS NCHRP Project 17-26 The Transportation Research Board (TRB)
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Figure A-1. Questionnaire Used for Survey (Continued)
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Figure A-1. Questionnaire Used for Survey (Continued)
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Figure A-1. Questionnaire Used for Survey (Continued)
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Figure A-1. Questionnaire Used for Survey (Continued)
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Figure A-1. Questionnaire Used for Survey (Continued)
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RESPONSE RATE Tables A-1 and A-2 summarize the number of survey responses received from various agencies. Of the 278 surveys that were mailed out, 109 responses have been received, for an overall response rate of 39 percent.
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Table A-1. Response Rate for the Survey Agency/organization type Number of questionnaires mailed Number of responses received Response rate (%)
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A-10 Table A-3. Assessment of Priority Ratings for Specific HSM Applications to Urban and Suburban Arterials State highway agencies Local highway agencies MPOs TRB Task Force Application type Avg Min Max Avg Min Max Avg Min Max Avg Min Max Estimating the current safety performance for an existing arterial for which accident history data are not available or are not considered reliable 2.8 1 5 2.4 1 5 3.6 1 5 2.6 1 5 Estimating the safety performance for an existing arterial combining model predictions and actual, reliable accident history data 3.7 1 5 3.6 1 5 3.6 2 5 2.9 1 5 Forecasting the future change in safety performance that may occur on an existing facility as traffic volumes grow 3.7 1 5 3.6 1 5 4.0 1 5 4.0 2 5 Forecasting the safety effectiveness for a proposed improvement project on an existing arterial 4.5 3 5 4.5 3 5 4.1 1 5 4.6 3 5 Forecasting the safety performance of a new arterial that has not yet been constructed 3.1 1 5 2.8 1 5 2.9 1 5 3.4 1 5 Forecasting the effect on safety of new driveways or new development that may be proposed along an existing arterial (i.e., for development impact studies or driveway permit requests)
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A-11 Table A-4. Use of Existing Safety Prediction Methods by Highway Agencies and MPOs Number (percentage)
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A-12 Table A-5 presents the priority ranking of these input variable sets by state highway agencies. The potential input variables are arranged in descending order of priority ranking by the respondents; thus, the highest ranked variables are presented first.
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A-13 Table A-5. Candidate Input Variables in Descending Priority Order as Ranked by State Highway Agencies Priority rating Candidate input variable Avg Min Max Rank order ROADWAY SEGMENTS Traffic volume (AADT)
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Table A-5. Candidate Input Variables in Descending Priority Order as Ranked by State Highway Agencies (Continued)
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A-15 Table A-6. Candidate Input Variables in Descending Priority Order as Ranked by Local Highway Agencies Priority rating Candidate input variable Avg Min Max Rank order ROADWAY SEGMENTS Traffic volume in peak period (veh/h)
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Table A-6. Candidate Input Variables in Descending Priority Order as Ranked by Local Highway Agencies (Continued)
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A-17 Table A-7. Candidate Input Variables in Descending Priority Order as Ranked by Metropolitan Planning Organizations Priority rating Candidate input variable Avg Min Max Rank order ROADWAY SEGMENTS Traffic volume in peak period (veh/day)
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Table A-7. Candidate Input Variables in Descending Priority Order as Ranked by Metropolitan Planning Organizations (Continued)
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A-19 Table A-8. Candidate Input Variables in Descending Priority Order as Ranked by TRB Task Force Members Priority rating Candidate input variable Avg Min Max Rank order ROADWAY SEGMENTS Traffic volume (AADT)
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Table A-8. Candidate Input Variables in Descending Priority Order as Ranked by TRB Task Force Members (Continued)
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Table A-8. Candidate Input Variables in Descending Priority Order as Ranked by TRB Task Force Members (Continued)
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A-22 Table A-9. Summary of Priority Ratings of Candidate Input Variables Average priority rating Rank order Candidate input variable State Local MPO Task force State Local MPO Task force ROADWAY SEGMENTS Bicycle facilities 2.1 2.7 2.8 2.1 34 27 26 35 Bicycle volumes 2.2 2.3 2.6 2.2 33 33 32 34 Delineation 2.9 3.3 3.0 2.6 25 14 24 29 Design or posted speed 4.3 3.8 4.2 4.1 2 5 2 4 Grades 3.6 3.4 3.1 3.4 14 11 21 19 Horizontal curves 3.9 3.9 3.4 3.8 7 3 14 9 Lane widths 4.2 3.7 4.2 4.1 3 6 2 4 Lighting 3.5 3.2 3.4 3.7 16 17 14 11 Median type 3.5 3.3 3.4 3.7 16 14 14 11 Median width 3.8 2.8 2.7 3.7 10 23 31 11 Number and type of median openings 3.9 3.7 3.1 3.9 7 6 21 7 Number and type of driveways 4.1 3.9 3.6 4.2 5 3 8 3 Number of through lanes 4.2 3.4 3.8 4.3 3 11 5 2 Older drivers/driver population characteristics 2.4 1.9 2.8 2.3 32 35 26 33 One-way vs.
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Table A-9. Summary of Priority Ratings of Candidate Input Variables (Continued)
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Table A-9. Summary of Priority Ratings of Candidate Input Variables (Continued)
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A-25 Table A-10. Additional Candidate Input Variables in Descending Priority Order as Recommended by Those Surveyed Number of respondents citing the additional input variable (by respondent type)
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Table A-10. Additional Candidate Input Variables in Descending Priority Order as Recommended by Those Surveyed (Continued)
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A-27 In Question 6, agencies were asked to provide any additional measures of safety effectiveness that they currently utilize or recommend utilizing in the safety prediction methodologies other than the three listed above. The suggestions made are summarized in Table A-11.
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A-28 Table A-11. Additional Measures of Safety Effectiveness in Descending Priority Order as Recommended by Those Surveyed Number of respondents citing the additional input variable (by respondent type)
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A-29 Table A-12. Availability of Highway Agency Computerized Data Files for Urban and Suburban Arterials Number (percentage)
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A-30 Table A-14. Ability of Highway Agencies to Link Roadway Segment and Intersection Inventory Data Files to Accident Data Number (percentage)
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