Pedestrian Safety Prediction Methodology (2008) / Chapter Skim
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39 CHAPTER 4. PEDESTRIAN SAFETY MODELING This chapter presents the results for pedestrian safety modeling conducted with the available databases.
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40 PedVol = sum of the daily pedestrian volumes crossing all intersection legs (pedestrians/day) a,b,c,d = regression coefficients Table 15 illustrates the results obtained when the eight candidate model functional forms were applied to the data for three- and four-leg signalized intersections in Toronto.
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41 • maximum number of traffic lanes crossed by a pedestrian in any crossing maneuver at the intersection (considering presence of refuge islands) • number of intersection legs with marked crosswalks • presence of a skewed intersection leg The only one of these independent variables that was statistically significant with an effect in the expected direction was the maximum number of lanes crossed by a pedestrian on any intersection leg (considering presence of refuge islands)
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42 TABLE 15. Models in Various Functional Forms for Vehicle-Pedestrian Collisions at Toronto Intersections.
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43 TABLE 16. Models for Vehicle-Pedestrian Collisions at Toronto Intersections.
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44 TABLE 17. Models for Vehicle-Pedestrian Collisions at Toronto Intersections Including Term for Number of Lanes Crossed by Pedestrians.
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45 The Toronto model for 4SG intersections shown in Table 17 could potentially be used in the HSM. The models for 3SG intersections in Table 17 would require further investigation because of the negative coefficient for total traffic volume.
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46 may result from multi-colinearity or correlation between the total traffic volume term (ADTtot) and the maximum number of lanes crossed term (nlanesx)
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47 TABLE 18. Models in Various Functional Forms for Vehicle-Pedestrian Collisions at Charlotte Intersections.
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48 TABLE 19. Models for Vehicle-Pedestrian Collisions at Charlotte Intersections.
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49 TABLE 20. Models for Vehicle-Pedestrian Collisions at Charlotte Intersections Including Term for Number of Lanes Crossed by Pedestrians.
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50 TABLE 21. Models for Vehicle-Pedestrian Collisions From Combined Data for Toronto and Charlotte Intersections.
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51 EFFECTS OF LAND USE AND DEMOGRAPHIC VARIABLES FOR SIGNALIZED INTERSECTIONS Basic Modeling With Data for Charlotte Intersections Data were available in Charlotte, but not in Toronto, for a range of land use and demographic variables including: • presence of bus stops within 300 m (1,000 ft) of the intersection • presence of schools (either public or private)
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52 probably due to limited sample sizes and low accident counts. The results of the analysis for 4SG intersections in Charlotte are presented in Table 22.
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53 TABLE 22. Effects of Land Use and Demographic Variables for Charlotte Intersections Added to the 4SG Model Shown in Table 21.
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54 Schools Near an Intersection An AMF for intersections near schools, based on the regression coefficients in Table 22, can be presented as follows: Presence of school within 300 m (1,000 ft) of the intersection AMF No school present 1.00 School present 1.35 To use this AMF, the base models shown in Equations (17)
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55 In applying this AMF, pci should be limited to the range from $9,000 to $85,000, which is the range of average per capita income in the data used to develop this relationship. This will result in a maximum range of AMFs from 0.17 to 1.62.
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56 ( 22 ) Similarly, the base model for 4SG intersections in final form is: ( 23 )
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57 Figure 1. Sensitivity analysis of pedestrian safety base models for 3SG intersections.
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58 Figure 2. Sensitivity analysis of pedestrian safety base models for 4SG intersections.
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