Pedestrian Safety Prediction Methodology (2008) / Chapter Skim
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29 CHAPTER 3. PEDESTRIAN SAFETY DATABASES This chapter describes the pedestrian safety databases assembled for use in this research from the City of Toronto, the City of Charlotte, and two metropolitan areas in Minnesota.
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30 ADT expansion methodology was further refined, checked, and used in a 2005 FHWA study by Zegeer et al.
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31 • not the terminal of a freeway ramp • no one-way intersection legs • no turn restrictions • not equipped with automated enforcement For each site, the research team collected data on the intersection characteristics, signal data, vehicle and pedestrian volumes, and vehicle-pedestrian crashes. Intersection Characteristics Geometric and physical characteristics of an intersection can affect pedestrian safety.
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32 Since cities typically do not collect pedestrian volumes on a 24-hr basis, the only available counts were a large sample of pedestrian volume counts taken in Seattle, Washington, which were summarized by hour of the day and also by area type (e.g., CBD, fringe, and residential)
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33 • number of commercial land parcels within 300 m (1,000 ft) of the intersection ROADWAY SEGMENTS IN MINNESOTA A database of vehicle-pedestrian collisions for roadway segments in Minnesota was available from the research performed in Phases I and II of this project (25)
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TABLE 11. Site Characteristics Data Obtained for Roadway Segments on Urban and Suburban Arterials in Minnesota (25)
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TABLE 11. Site Characteristics Data Obtained for Roadway Segments on Urban and Suburban Arterials in Minnesota (25)
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36 TABLE 12. Number of Intersections, Number of Vehicle-Pedestrian Collisions, and Exposure Measures for Signalized Intersections in Toronto and Charlotte.
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37 TABLE 13. Descriptive Statistics for Key Data Elements at Signalized Intersections in Toronto and Charlotte.
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38 TABLE 14. Number and Length of Roadway Segments, Number of Vehicle-Pedestrian Collisions, and Exposure Measures for Minnesota Database.
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