Development of Crash Modification Factors for Uncontrolled Pedestrian Crossing Treatments (2017) / Chapter Skim
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From page 24... ...
24 C H A P T E R 4 Crash Modification Factor Development for Pedestrian Treatments The aim of the analytical work was to use the most appropriate techniques for estimating CMFs or CMFunctions for pedestrians involved and all crashes, by type, for various treatments and treatment combinations. As defined in the FHWA's Guide to Developing Quality Crash Modification Factors (64)
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Data Analysis 25 and methodology used, followed by a presentation and discussion of the results. The chapter closes with a section that compares the results of the two sets of analyses and a final section with conclusions and recommendations.
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26 Development of Crash Modification Factors for Uncontrolled Pedestrian Crossing Treatments 5. Used the following equations to calculate the EB estimate of the expected crashes in the before period at each site, where x is the count of crashes in the before period at a treatment site and m is the expected number of crashes in the before period after correcting for possible bias due to RTM: , (1)
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Data Analysis 27 • Milwaukee, Wisconsin • New York, New York • Phoenix, Scottsdale, and Tucson, Arizona Tables 4-1 through 4-3 show the summary statistics for the following treatment groups: • Refuge island (68 sites) • Advanced YIELD or STOP markings and signs (69 sites)
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28 Development of Crash Modification Factors for Uncontrolled Pedestrian Crossing Treatments after for the four different crash types that were considered. These statistics are included to provide the reader with some basic information about the sample size and the range of the different variables.
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Table 4-4. Estimated crash modification factors from the before-after evaluation.
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30 Development of Crash Modification Factors for Uncontrolled Pedestrian Crossing Treatments • EB estimates (before)
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Data Analysis 31 limited for a robust before-after study. The results from the two approaches are compared in a later section.
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32 Development of Crash Modification Factors for Uncontrolled Pedestrian Crossing Treatments The cross-sectional analysis applied Generalized Linear Modeling (GLM)
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Data Analysis 33 a PHB was installed in 2009. Because of the multiple changes, the data for all years cannot be combined.
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34 Development of Crash Modification Factors for Uncontrolled Pedestrian Crossing Treatments the other three treatments present when looking at the safety effects of a single treatment. This approach sought to reduce the number of confounding variables that the regression modeling needed to account for.
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From page 35... ...
Data Analysis 35 Site Type Variable Frequency Percentage Reference Midblock_Int Intersection – 2,970 Midblock – 897 Intersection – 76.8 Midblock – 23.2 BusStop No – 2,047 Yes – 1,820 No – 52.9 Yes – 47.1 Lighting No – 840 Yes – 3,027 No – 21.7 Yes – 78.3 Refuge Island Presence Yes – 0 No – 3,867 Yes – 0.0 No – 100.0 SchoolCrossing No – 3,529 Yes – 338 No – 91.3 Yes – 8.7 Median No – 3,867 Yes – 0 No – 100.0 Yes – 0.0 AreaType Suburban – 3,522 Urban – 345 Suburban – 91.1 Urban – 8.9 TraffDir One-Way – 97 Two-Way – 3,770 One-Way – 2.5 Two-Way – 97.5 Treatment Midblock_Int Intersection – 813 Midblock – 341 Intersection – 70.5 Midblock – 29.5 BusStop No – 626 Yes – 528 No – 54.3 Yes – 45.7 Lighting No – 135 Yes – 1,019 No – 11.7 Yes – 88.3 Refuge Island Presence No – 0 Yes – 1,154 No – 0.0 Yes – 100.0 CrossingGuard No – 1,147 Yes – 7 No – 99.4 Yes – 0.6 SchoolCrossing No – 1,079 Yes – 75 No – 93.5 Yes – 6.5 Median No – 492 Yes – 662 No – 42.6 Yes – 57.4 AreaType Suburban – 956 Urban – 198 Suburban – 82.8 Urban – 17.2 TraffDir One-Way – 49 Two-Way – 1,105 One-Way – 4.2 Two-Way – 95.8 Table 4-7. Frequency tallies for refuge island dataset.
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From page 36... ...
36 Development of Crash Modification Factors for Uncontrolled Pedestrian Crossing Treatments Site Type Variable Frequency Percentage Reference Midblock_Int Intersection – 2,970 Midblock – 897 Intersection – 76.8 Midblock – 23.2 BusStop No – 2,047 Yes – 1,820 No – 52.9 Yes – 47.1 Lighting No – 840 Yes – 3,027 No – 21.7 Yes – 78.3 Presence of Advanced YIELD or STOP Markings and Signs Yes – 0 No – 3,867 Yes – 0 No – 100.0 SchoolCrossing No – 3,529 Yes – 338 No – 91.3 Yes – 8.7 Median No – 3,867 Yes – 0 No – 100.0 Yes – 0.0 AreaType Suburban – 3,522 Urban – 345 Suburban – 91.1 Urban – 8.9 TraffDir One-Way – 97 Two-Way – 3,770 One-Way – 2.5 Two-Way – 97.5 Treatment Midblock_Int Intersection – 417 Midblock – 123 Intersection – 77.2 Midblock – 22.8 BusStop No – 347 Yes – 193 No – 64.3 Yes – 35.7 Lighting No – 32 Yes – 508 No – 5.9 Yes – 94.1 Presence of Advanced YIELD or STOP Markings and Signs Yes – 540 No – 0 Yes – 100.0 No – 0 CrossingGuard No – 537 Yes – 3 No – 99.4 Yes – 0.6 SchoolCrossing No – 517 Yes – 23 No – 95.7 Yes – 4.3 Median No – 413 Yes – 127 No – 76.5 Yes – 23.5 AreaType Suburban – 388 Urban – 152 Suburban – 71.9 Urban – 28.1 TraffDir One-Way – 30 Two-Way – 510 One-Way – 5.6 Two-Way – 94.4 Table 4-9. Frequency tallies for advanced YIELD or STOP markings and signs dataset.
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Data Analysis 37 Site Type Variable Frequency Percentage Reference Midblock_Int Intersection – 2,349 Midblock – 780 Intersection – 75.1 Midblock – 24.9 BusStop No – 1,828 Yes – 1,301 No – 58.4 Yes – 41.6 Lighting No – 630 Yes – 2,499 No – 20.1 Yes – 79.9 PHB Presence Yes – 0 No – 3,129 Yes – 0.0 No – 100.0 SchoolCrossing No – 2,979 Yes – 150 No – 95.2 Yes – 4.8 Median No – 3,012 Yes – 117 No – 96.3 Yes – 3.7 AreaType Suburban – 2,920 Urban – 209 Suburban – 93.3 Urban – 6.7 TraffDir One-Way – 105 Two-Way – 3,024 One-Way – 3.4 Two-Way – 96.6 Treatment Midblock_Int Intersection – 345 Midblock – 21 Intersection – 94.3 Midblock – 5.7 BusStop No – 274 Yes – 92 No – 74.9 Yes – 25.1 Lighting No – 4 Yes – 362 No – 1.1 Yes – 98.9 PHB Presence Yes – 359 No – 7 Yes – 98.1 No – 1.9 CrossingGuard No – 344 Yes – 22 No – 94.0 Yes – 6.0 SchoolCrossing No – 211 Yes – 155 No – 57.7 Yes – 42.3 Median No – 356 Yes – 10 No – 97.3 Yes – 2.7 AreaType Suburban – 312 Urban – 54 Suburban – 85.3 Urban – 14.7 TraffDir One-Way – 27 Two-Way – 339 One-Way – 7.4 Two-Way – 92.6 Table 4-11. Frequency tallies for PHB dataset.
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38 Development of Crash Modification Factors for Uncontrolled Pedestrian Crossing Treatments Table 4-12. Summary statistics for RRFB dataset.
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Data Analysis 39 2. Interaction terms were attempted to account for, for example, the effect of presence of treatment and crossing location, but these did not improve the model's ability to explain the variation in crashes between sites.
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40 Development of Crash Modification Factors for Uncontrolled Pedestrian Crossing Treatments Parameter estimates for all models are shown in Table 4-14. As noted above, the estimates for the city variable are not provided in the interest of brevity and because, in any case, they do not impact the estimated CMFs.
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Data Analysis 41 Advanced YIELD or STOP Markings and Signs The following model forms pertain to each crash type. The models predict the expected number of crashes per year.
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42 Development of Crash Modification Factors for Uncontrolled Pedestrian Crossing Treatments more crashes are expected for target and target injury crashes. For total crashes, the estimate is extremely close to 0, which would indicate no effect.
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Data Analysis 43 Parameter estimates for all models are shown in Table 4-18. The estimates for city are not provided in the interest of brevity, and they do not impact the estimated CMFs.
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44 Development of Crash Modification Factors for Uncontrolled Pedestrian Crossing Treatments Rectangular Rapid Flashing Beacons The following model forms pertain to each crash type. The models predict the expected number of crashes per year.
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Data Analysis 45 The parameter estimates for presence of RRFB are of low statistical significance. With the exception of the parameter estimate for area type in the total crash model and the estimates for advanced YIELD or STOP markings and signs, all parameters are statistically significant at the 5 percent level.
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46 Development of Crash Modification Factors for Uncontrolled Pedestrian Crossing Treatments Consolidation of Analysis Results As presented in Table 4-22, credible CMFs for pedestrian crashes and vehicle-involved crashes were estimated by either cross-sectional or before-after analysis, or both, for refuge islands, advanced YIELD or STOP markings and signs, PHB, and PHB + advanced YIELD or STOP markings and signs. The CMF for RRFB was based on a very limited sample, and hence should be used with caution.
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Data Analysis 47 summary statistics in Tables 4-6 to 4-13 to see how closely the site under consideration for one of the treatments resembles the sites used to develop the CMF. As mentioned earlier, CMFunctions were explored in order to determine the effectiveness of the treatments under study in relation to different levels of AADT, posted speed limit, area type, number of lanes, and other factors.
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