Intersection Crash Prediction Methods for the Highway Safety Manual (2021) / Chapter Skim
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From page 99... ...
99 Chapter 5. Development of Models for Use in HSM Crash Prediction Methods: Intersections on High-Speed Urban and Suburban Arterials This section of the report describes the development of crash prediction models for intersections on high-speed urban and suburban arterials and presents the final models recommended for incorporation in the second edition of the HSM.
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100 Each intersection in the list was initially screened using Google Earth® to determine if the site was suitable for inclusion in model development. Several reasons a site could be deemed inappropriate for use in model development were: • The traffic control at the intersection was something other than signal control or minor approach stop control • The speed limit on the major road was less than 50 mph • The intersection was in a rural area • A private driveway was located in close proximity to the intersection • One or more of the approaches to the intersection was a private/commercial access • Google Street View® was not available to identify leg specific attributes • One or more of the intersection legs was a one-way street Each intersection that was initially deemed appropriate for inclusion in model development was given a unique identification code and included on a refined database for detailed data collection.
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101 Table 33. Site characteristic variables collected for intersections on high-speed urban and suburban arterials (Continued)
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102 During detailed data collection, to the extent possible, the research team reviewed historical aerial images to determine if a site had recently been under construction or recent improvements were made to the site to determine the appropriate years of data for use in model development. Table 34 lists the crash and traffic volume data sources for the four states included in the study.
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103 Crash Counts Of the 504 intersections included in the study, only 18 (3.6%) experienced no crashes over the entire 5-year study period; the breakdown by area type and intersection type is as follows: • Three-leg intersections with stop control: 10 out of 121 • Three-leg intersections with signal control: 1 out of 50 • Four-leg intersections with stop control: 6 out of 125 • Four-leg intersections signal control: 1 out of 208 Intersection crashes were defined as those crashes that occurred within 250 ft of the intersection and were classified as at intersection or intersection-related, consistent with recommended practice in the HSM for assigning crashes to an intersection.
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104 Table 35. Major- and minor road AADT statistics by intersection type on high-speed urban and suburban arterials State Date Range Number of Sites Number of Site-Years Major Road AADT (veh/day)
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105 Table 36. All crashes combined, single- and MV, and pedestrian and bicycle crash counts by crash severity and intersection type for three-leg intersections on high-speed urban and suburban arterials State Date Range Number of Sites Number of Site Years Time of Day All Crashes Combined Single- Vehicle Crashes Multiple- Vehicle Crashes Pedestrian Crashes Bicycle Crashes Total FI PDO Total FI PDO Total FI PDO FI FI URBAN THREE-LEG STOP-CONTROLLED INTERSECTIONS (3ST)
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106 Table 37. All crashes combined, single- and MV, and pedestrian and bicycle crash counts by crash severity and intersection type for four-leg intersections on high-speed urban and suburban arterials State Date Range Number of Sites Number of Site Years Time of Day All Crashes Combined Single- Vehicle Crashes Multiple- Vehicle Crashes Pedestrian Crashes Bicycle Crashes Total FI PDO Total FI PDO Total FI PDO FI FI URBAN FOUR-LEG STOP-CONTROLLED INTERSECTIONS (4ST)
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107 Table 38. Crash counts by collision type and manner of collision, crash severity, and intersection type at three-leg intersections on high-speed urban and suburban arterials Collision Type Three-Leg Stop-Controlled Intersections (3ST)
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108 5.3 Safety Performance Functions -- Model Development SPFs of the form shown in Equation 2 were developed separately for three- and four-leg intersections, for multiple- and SV crashes.
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109 Where: Nbimv = predicted average crash frequency of MV crashes of an intersection for base conditions (crashes/year) Nbisv = predicted average crash frequency of SV crashes of an intersection for base conditions (crashes/year)
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110 • Intersections on two-lane highways: 58 with right-turn lane on one approach; 31 with none (35% with none) • Intersections on multilane highways: 48 with right-turn lane on one approach; 24 with none (33% with none)
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111 • Table 44: SV total crashes • Table 45: SV FI crashes • Table 46: SV PDO crashes Each table shows the model coefficients and overdispersion parameter (estimate) , their standard error, and associated p-values (or significance level)
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112 Figure 21. Graphical representation of the SPF for MV total crashes at three-leg stop-controlled intersections on high-speed urban and suburban arterials Figure 22.
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113 Figure 23. Graphical representation of the SPF for MV total crashes at four-leg stop-controlled intersections on high-speed urban and suburban arterials Figure 24.
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114 Table 42. SPF coefficients for intersections on high-speed urban and suburban arterials -- MV FI crashes Intersection Type Parameter Estimate Standard Error Pr > F Significance Level?
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115 Figure 26. Graphical representation of the SPF for MV FI crashes at three-leg signalized intersections on high-speed urban and suburban arterials Figure 27.
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116 Figure 28. Graphical representation of the SPF for MV FI crashes at four-leg signalized intersections on highspeed urban and suburban arterials Table 43.
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117 Figure 29. Graphical representation of the SPF for MV PDO crashes at three-leg stop-controlled intersections on high-speed urban and suburban arterials Figure 30.
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118 Figure 31. Graphical representation of the SPF for MV PDO crashes at four-leg stop-controlled intersections on high-speed urban and suburban arterials Figure 32.
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119 Table 44. SPF coefficients for intersections on high-speed urban and suburban arterials -- SV total crashes Intersection Type Parameter Estimate Standard Error Pr > F Significance Level?
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120 Figure 34. Graphical representation of the SPF for SV total crashes at three-leg signalized intersections on high-speed urban and suburban arterials Figure 35.
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121 Figure 36. Graphical representation of the SPF for SV total crashes at four-leg signalized intersections on high-speed urban and suburban arterials Table 45.
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122 Figure 37. Graphical representation of the SPF for SV FI crashes at three-leg stop-controlled Intersections on high-speed urban and suburban arterials Figure 38.
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123 Figure 39. Graphical representation of the SPF for SV FI crashes at four-leg stop-controlled intersections on high-speed urban and suburban arterials Figure 40.
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124 Table 46. SPF coefficients for intersections on high-speed urban and suburban arterials -- SV PDO crashes Intersection Type Parameter Estimate Standard Error Pr > F Significance Level?
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125 Figure 42. Graphical representation of the SPF for SV PDO crashes at three-leg signalized intersections on high-speed urban and suburban arterials Figure 43.
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126 Figure 44. Graphical representation of the SPF for SV PDO crashes at four-leg signalized intersections on high-speed urban and suburban arterials Similar to Tables 12-11 (MV crashes)
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127 Table 48. Distribution of SV crashes for intersections on high-speed urban and suburban arterials Manner of Collision Percentage of SV Crashes Three-Leg Stop-Controlled Intersections (3ST)
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128 Following the development of the crash prediction models for intersections on high-speed urban and suburban arterials, compatibility testing of the new models to confirm that the new models provide reasonable results over a broad range of input conditions and that the new models integrate seamlessly with existing intersection crash prediction models in the first edition of the HSM was conducted. The graphical representations of the crash prediction models in Figures 21-44 provide some sense of the reasonableness of the new models for intersections on high-speed urban and suburban arterials.
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129 Figure 45. Comparison of new crash prediction model to existing model in HSM: 3ST for MV crashes for urban and suburban high-speed arterials vs 3ST for MV crashes from HSM Chapter 12 (total crashes)
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130 Figure 47. Comparison of new crash prediction model to existing model in HSM: 4SG for MV crashes for urban and suburban high-speed arterials vs 4SG for multiple vehicle crashes from HSM Chapter 12 (FI crashes)
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131 After considering developing CMFs through regression modeling as part of this research and based on a review of the CMFs already incorporated in the first edition of the HSM and other potential high-quality CMFs developed using defensible study designs, three CMFs were identified for potential use with the crash prediction models for intersections on high-speed urban and suburban arterials, including: • The CMF for intersection lighting based on the work by Elvik and Vaa (2004) , which is identified for use with the intersection crash prediction models in Chapter 12 of the first edition of the HSM.
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132 Intersection Approaches with Left-Turn Lanes CMF With the CMFs for providing a left-turn lane on one or more intersection approaches at an intersection on a high-speed urban and suburban arterial based on the work by Harwood et al.
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133 used to explore SDFs for intersections on high-speed urban and suburban arterials consisted of the same crashes and intersections as the databases used to estimate the SPFs, but restructured so that the basic observation unit (i.e., database row) is a crash instead of an intersection.
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134 The basic model form for the systematic components of crash severity likelihood at 4-leg intersections on high-speed urban and suburban arterials is illustrated by Equation 49.
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135 • Four-leg intersections with signal control (4SG) on high-speed urban and suburban arterials The final models presented in Tables 41-46 are recommended for inclusion in the second edition of the HSM.
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