Intersection Crash Prediction Methods for the Highway Safety Manual (2021)

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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. The HSM Part C chapters in the first edition of the HSM include crash prediction models for minor approach stop control and signalized intersections; however, the intersections used in the modeling process were generally not located on high-speed facilities. For the development of crash prediction models in this effort, intersections were only selected if they were located on arterials with a speed limit of at least 50 mph. Crash prediction models are recommended for the following intersection types for the second edition of the HSM: • Three-leg intersections with minor road stop control (3ST) on high-speed urban and suburban arterials • Three-leg intersections with signal control (3SG) on high-speed urban and suburban arterials • Four-leg intersections with minor road stop control (4ST) on high-speed urban and suburban arterials • Four-leg intersections with signal control (4SG) on high-speed urban and suburban arterials Section 5.1 describes the site selection and data collection process for developing the crash prediction models for intersections on high-speed urban and suburban arterials. Section 5.2 presents descriptive statistics of the databases used for model development. Section 5.3 presents the statistical analysis and SPFs developed for intersections on high-speed urban and suburban arterials. Section 5.4 presents the CMFs recommended for use with the SPFs. Section 5.5 presents the results of an analysis to develop SDFs for use with the SPFs for intersections on high-speed urban and suburban arterials, and Section 5.6 summarizes the recommendations for incorporating new crash prediction models for intersections located on high-speed urban and suburban arterials in the second edition of the HSM. 5.1 Site Selection and Data Collection A list of potential intersections for model development was derived from HSIS or Safety Analyst databases in four states: • California (CA) • Illinois (IL) • Minnesota (MN) • Washington (WA)

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. Three types of data were collected for each intersection during detailed data collection: site characteristic, crash, and traffic volume data. Google Earth® was used to collect detailed site characteristics of the intersections. To reduce potential errors during data collection and to streamline data entry, a data collection tool was created using Visual Basic for Applications, very similar to the tool shown in Figure 4. Table 33 lists all the intersection attributes collected (and respective definitions and permitted values) for intersections on high-speed arterials on urban and suburban arterials using the data collection tool. The data collection tool dynamically changed as certain intersection configuration data were entered because some data elements were only applicable to signalized intersections or minor approach stop-controlled intersections. Once all necessary data were entered into the data collection tool and saved for a given intersection, the data collection tool was used to validate the inputs for that particular intersection consistent with the range and/or permitted values for the respective variables/parameters. Table 33. Site characteristic variables collected for intersections on high-speed urban and suburban arterials Variable/Parameter Definition Range or Permitted Values General Intersection Attributes Intersection configuration (i.e., number of legs and type of traffic control) Indicates the number of legs and type of traffic control 3ST, 4ST, 3SG, 4SG Area type (urban/rural) Indicates whether the intersection is in a rural or urban area Urban Presence of intersection lighting Indicates if overhead lighting is present at the intersection proper Yes, no Presence of flashing beacons Indicates if overhead flashing beacons are present at the intersection proper Yes, no (only applicable to stop- controlled intersections) Approach Specific Attributes Route name or number Specify the route name or number of the approach Location at intersection Side/quadrant of the intersection the approach is located N, S, E, W, NE, NW, SE, SW

101 Table 33. Site characteristic variables collected for intersections on high-speed urban and suburban arterials (Continued) Variable/Parameter Definition Range or Permitted Values Number of through lanes This includes dedicated through lanes and any lanes with shared movements. On the minor approach of a 3-leg intersection, if there is only one lane, then it should be classified as a through lane 0, 1, 2, 3 Presence/number of left-turn lanes The number of lanes in which only a left-turn movement can be made 0, 1, 2, 3 Left-turn channelization Type of left-turn channelization used on the intersection approach Raised or depressed island, painted, none Presence/number of right-turn lanes The number of lanes in which only a right-turn movement can be made 0, 1, 2, 3 Right-turn channelization Type of right-turn channelization used on the intersection approach Raised or depressed island, painted, none Median width Measured from outside of outer most through lane of approaching lanes to outside of lane in opposing direction Values in feet Median type Type of median separating opposing directions of travel Raised, depressed, flush, barrier, TWLTL Permit right-turn-on-red Indicates if turning right on red is permitted on the intersection approach Yes, no, not applicable (only applicable to signalized intersections) Presence of transverse rumble strips Indicates the presence of transverse rumble strips on the intersection approach Yes, no, unknown Presence/type of supplementary pavement markings Indicates the presence and type of supplementary pavement markings on the intersection approach Yes, no, unknown If yes and intersection is signalized, type of marking: “Signal Ahead”, other. If yes and intersection is stop- controlled, type of marking: “Stop Ahead”, other. Presence of stop ahead warning signs Indicates the presence of stop ahead warning signs on the intersection approach Yes, no, unknown (only applicable to stop-controlled intersections) Presence of signal ahead warning signs Indicates the presence of signal ahead warning signs on the intersection approach Yes, no, unknown (only applicable to signalized intersections) Presence of advance warning flashers Indicates the presence of advance warning flashers on the intersection approach Yes, no, unknown Horizontal alignment of intersection approach Indicates whether the approaching roadway, within 250 ft of the intersection, is a tangent or curved section of roadway Tangent, curve Horizontal curve radius Indicates the radius of the curve on the intersection approach if a curve is present within 250 ft of the intersection 2,000-ft Maximum Range: 25-2000 ft Posted speed limit Posted speed limit on the intersection approach (mph) 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, unknown Presence of crosswalk Indicates the presence of a crosswalk perpendicular to the intersection approach Yes, no, unknown Presence of bike lane Indicates the presence of a marked bike lane parallel to the intersection approach Yes, no, unknown Presence of railroad crossing Indicates the presence of a railroad crossing on the intersection approach within 250 ft of the intersection Yes, no, unknown Skew Indicates the difference between 90 degrees and the actual angle between the approach and the major road 0-90 degrees (only applicable to minor road approaches at stop- controlled intersections)

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. The goal was to obtain the most recent four to six years of crash and traffic volume data for each site for model development. All of the data (i.e., site characteristics, crash, and traffic volume) were assembled into one database for the purposes of model development. Table 34. Traffic volume and crash data sources for intersections on high-speed urban and suburban arterials State Traffic Volume Data Source Crash Data Source California HSIS HSIS Illinois Safety Analyst Safety Analyst Minnesota HSIS State agency Washington HSIS and Safety Analyst HSIS 5.2 Descriptive Statistics of Database A total of 504 sites—121 three-leg stop-controlled, 50 three-leg signalized, 125 four-leg stop- controlled, and 208 four-leg signalized intersections—were available for development of crash prediction models. The data collections sites were located in four states—California, Illinois, Minnesota, and Washington. To remain consistent with the standards for development of the intersection predictive models in the first edition of the HSM, the goal of this research was to develop crash prediction models with a minimum of 200 site-years of data, and preferably 450 site-years of data or more. Traffic Volumes and Site Characteristics Traffic volume and crash data were available for a 5-year period. Table 35 shows the breakdown of all sites by area type and intersection type. Study period (date range), number of sites and site- years, and basic traffic volume statistics are shown by state in each category and across all states within a category. Of the intersection characteristics collected in Google Earth® (see Table 33), many showed no or very little variability (i.e., most intersections were predominantly of one type for a specific variable) across sites within a category and were thus excluded from modeling. The remaining variables (percent of “Yes” by type of traffic control indicated in parentheses) of potential interest in modeling were: • presence of intersection lighting: (stop-controlled: 59%; signalized: 97%) • presence of left-turn lanes on major road (stop-controlled: 67%; signalized: 96%) • presence of right-turn lanes on major road (stop-controlled: 48%; signalized: 93%) The use of these site characteristics is discussed later in the SPF model development section.

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. Table 36 (three-leg intersections) and Table 37 (four-leg intersections) show all crashes combined, single- and MV, and pedestrian and bicycle crash counts over the study period for each state by intersection type. Crash counts are also tallied by collision type and manner of collision across all states, separately by intersection type, in Table 38 (three-leg intersections) and Table 39 (four-leg intersections).

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) Minor Road AADT (veh/day) Min Max Mean Median Min Max Mean Median URBAN THREE-LEG STOP-CONTROLLED INTERSECTIONS (3ST) CA 2007-2011 47 229 2,631 58,494 18,077 16,289 21 5,901 622 291 IL 2008-2012 27 121 1,450 18,200 6,625 4,750 93 7,900 1,516 950 MN 2010-2014 47 217 7,764 39,000 15,261 13,820 105 11,335 1,844 1,152 All States 2007-2014 121 567 1,450 58,494 14,428 12,225 21 11,335 1,296 675 URBAN THREE-LEG SIGNALIZED INTERSECTIONS (3SG) CA 2007-2011 18 78 7,058 55,000 22,500 16,500 40 29,800 3,672 860 IL 2008-2012 9 44 4,200 10,780 8,231 8,500 200 6,800 3,050 2,350 MN 2010-2014 16 79 9,670 59,000 23,119 18,999 409 10,925 4,784 4,137 WA 2007-2011 7 35 18,089 28,952 21,832 20,487 1,875 16,000 7,529 6,863 All States 2007-2014 50 236 4,200 59,000 20,036 16,395 40 29,800 4,456 2,860 URBAN FOUR-LEG STOP-CONTROLLED INTERSECTIONS (4ST) CA 2007-2011 50 236 2,765 30,525 12,575 12,354 20 9,100 1,037 391 IL 2008-2012 31 134 1,350 12,900 5,713 4,600 12 3,750 1,146 1,040 MN 2010-2014 44 206 7,000 47,200 15,864 12,940 170 11,282 1,996 1,466 All States 2007-2014 125 576 1,350 47,200 12,023 10,491 12 11,282 1,402 950 URBAN FOUR-LEG SIGNALIZED INTERSECTIONS (4SG) CA 2007-2011 47 215 6,269 51,600 21,502 19,341 141 15,200 4,050 2,295 IL 2008-2012 62 296 4,100 22,221 10,422 9,350 800 15,300 4,383 3,750 MN 2010-2014 89 425 3,104 59,800 25,117 21,600 202 24,028 7,521 6,420 WA 2007-2011 10 50 10,841 36,802 23,694 24,325 708 30,029 11,225 9,176 All States 2007-2014 208 986 3,104 59,800 19,851 17,410 141 30,029 5,979 4,994

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) CA 2007-2011 47 229 All 212 84 125 44 14 30 165 70 95 3 0 Night 59 16 42 22 3 19 36 13 23 1 0 IL 2008-2012 27 121 All 185 67 118 10 1 9 175 66 109 0 0 Night 24 6 18 5 1 4 19 5 14 0 0 MN 2010-2014 47 217 All 312 114 198 102 28 74 210 86 124 0 0 Night 88 27 61 47 13 34 41 14 27 0 0 All States 2007-2014 121 567 All 709 265 441 156 43 113 550 222 328 3 0 Night 171 49 121 74 17 57 96 32 64 1 0 URBAN THREE-LEG SIGNALIZED INTERSECTIONS (3SG) CA 2007-2011 18 78 All 205 93 111 29 8 21 175 85 90 1 0 Night 51 23 27 15 4 11 35 19 16 1 0 IL 2008-2012 9 44 All 166 54 111 13 6 7 152 48 104 0 1 Night 36 17 19 7 4 3 29 13 16 0 0 MN 2010-2014 16 79 All 364 109 255 44 8 36 320 101 219 0 0 Night 71 23 48 17 2 15 54 21 33 0 0 WA 2007-2011 7 35 All 138 49 88 16 7 9 121 42 79 1 0 Night 38 16 22 10 5 5 28 11 17 0 0 All States 2007-2014 50 236 All 873 305 565 102 29 73 768 276 492 2 1 Night 196 79 116 49 15 34 146 64 82 1 0

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) CA 2007-2011 50 236 All 250 101 145 28 6 22 218 95 123 4 0 Night 68 28 38 11 2 9 55 26 29 2 0 IL 2008-2012 31 134 All 276 109 167 20 6 14 256 103 153 0 0 Night 52 19 33 9 1 8 43 18 25 0 0 MN 2010-2014 44 206 All 464 190 274 105 34 71 359 156 203 0 0 Night 111 36 75 55 13 42 56 23 33 0 0 All States 2007-2014 125 576 All 990 400 586 153 46 107 833 354 479 4 0 Night 231 83 146 75 16 59 154 67 87 2 0 URBAN FOUR-LEG SIGNALIZED INTERSECTIONS (4SG) CA 2007-2011 47 215 All 1,022 414 600 93 28 65 921 386 535 8 0 Night 259 109 145 43 13 30 211 96 115 5 0 IL 2008-2012 62 296 All 2,292 661 1,621 112 25 87 2,170 636 1,534 7 3 Night 513 153 356 63 9 54 446 144 302 3 1 MN 2010-2014 89 425 All 3,050 980 2,070 241 91 150 2,809 889 1,920 0 0 Night 601 209 392 95 30 65 506 179 327 0 0 WA 2007-2011 10 50 All 383 131 247 31 6 25 347 125 222 3 2 Night 86 32 51 13 2 11 70 30 40 3 0 All States 2007-2014 208 986 All 6,747 2,186 4,538 477 150 327 6,247 2,036 4,211 18 5 Night 1,459 503 944 214 54 160 1,233 449 784 11 1

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) Three-Leg Signalized Intersections (3SG) Total FI PDO Total FI PDO SINGLE-VEHICLE CRASHES Collision with parked vehicle 0 0 0 0 0 0 Collision with animal 1 0 1 0 0 0 Collision with fixed object 37 8 29 46 16 30 Collision with other object 0 0 0 0 0 0 Other SV collision 106 29 77 48 9 39 Noncollision 12 6 6 8 4 4 Total SV crashesa 156 43 113 102 29 73 MULTIPLE-VEHICLE CRASHES Rear-end collision 239 80 159 494 175 319 Head-on collision 12 5 7 10 8 2 Angle collision 209 105 104 145 64 81 Sideswipe collision 54 17 37 66 10 56 Other MV collisions 36 15 21 53 19 34 Total MV crashesa 550 222 328 768 276 492 Total Crashesa 706 265 441 870 305 565 a Note crash counts do not include pedestrian and bicycle crashes. Table 39. Crash counts by collision type and manner of collision, crash severity, and intersection type at four-leg intersections on high-speed urban and suburban arterials Collision Type Four-Leg Stop-Controlled Intersections (4ST) Four-Leg Signalized Intersections (4SG) Total FI PDO Total FI PDO SINGLE-VEHICLE CRASHES Collision with parked vehicle 0 0 0 0 0 0 Collision with animal 2 0 2 1 0 1 Collision with fixed object 31 5 26 174 36 138 Collision with other object 2 0 2 2 0 2 Other SV collision 109 34 75 263 93 170 Noncollision 9 7 2 37 21 16 Total SV crashesa 153 46 107 477 150 327 MULTIPLE-VEHICLE CRASHES Rear-end collision 210 59 151 3,906 1,141 2,765 Head-on collision 27 18 9 95 57 38 Angle collision 435 231 204 1,359 617 742 Sideswipe collision 78 10 68 449 53 396 Other MV collisions 83 36 47 438 168 270 Total MV crashesa 833 354 479 6,247 2,036 4,211 Total Crashesa 986 400 586 6,724 2,186 4,538 a Note crash counts do not include pedestrian and bicycle crashes.

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. 𝑵𝒔𝒑𝒇 𝒊𝒏𝒕 = 𝒆𝒙𝒑 𝒂 + 𝒃 × 𝐥𝐧 𝑨𝑨𝑫𝑻𝒎𝒂𝒋 + 𝒄 × 𝐥𝐧(𝑨𝑨𝑫𝑻𝒎𝒊𝒏) (Eq. 2) Where: Nspf int = predicted average crash frequency for an intersection with base conditions (crashes/year) AADTmaj = AADT on the major road (veh/day) AADTmin = AADT on the minor road (veh/day) a, b, and c = estimated regression coefficients For intersections on high-speed urban and suburban arterials, the SPFs were developed consistent with the methodology in Chapter 12 of the HSM for predicting intersections crashes in urban and suburban areas as illustrated in Equation 4 and Equation 5. 𝑁 = 𝐶 × 𝑁 + 𝑁 + 𝑁 (Eq. 4) 𝑁 = 𝑁 × 𝐶𝑀𝐹 × 𝐶𝑀𝐹 × … × 𝐶𝑀𝐹 (Eq. 5) Where: Npredicted int = predicted average crash frequency for an individual intersection for the selected year (crashes/year) Nbi = predicted average crash frequency of an intersection (excluding vehicle-pedestrian and vehicle-bicycle crashes) (crashes/year) Npedi = predicted average crash frequency of vehicle-pedestrian crashes of an intersection (crashes/year) Nbikei = predicted average crash frequency of vehicle-bicycle crashes of an intersection (crashes/year) Nspf int = predicted total average crash frequency of intersection-related crashes for base conditions (excluding vehicle-pedestrian and vehicle-bicycle collisions) (crashes/year) CMF1i…CMFyi = crash modification factors specific to intersection type i and specific geometric design and traffic control features y Ci = calibration factor to adjust the SPF for intersection type i to local conditions The SPF portion of Nbi, Nspf int, is the sum of two more disaggregate predictions by collision type, as shown in Equation 6. 𝑁 = 𝑁 + 𝑁 (Eq. 6)

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) Separate model structures are used to estimate the yearly number of vehicle-pedestrian (Npedi) and vehicle-bicycle (Nbikei) crashes at stop-controlled and signalized intersections on high-speed urban and suburban arterials. The average number of annual vehicle-pedestrian and vehicle- bicycle crashes are estimated with Equations 9 and 12, respectively. 𝑁 = 𝑁 × 𝑓 (Eq. 9) Where: fpedi = pedestrian crash adjustment factor for intersection type i 𝑁 = 𝑁 × 𝑓 (Eq. 12) Where: fbikei = bicycle crash adjustment factor for intersection type i All of the vehicle-pedestrian and vehicle-bicycle crashes predicted with Equations 9 and 12 are assumed to be FI crashes (none as PDO). Based on a review of the number of states, sites, site-years, and crashes for the database assembled, data for all sites were used for model development to maximize the sample size rather than using a portion of the data for model development and a portion for model validation. All SPFs were developed using a NB regression model based on all sites combined within a given area type and intersection type. In all models, state was included as a random blocking effect, with sites nested within their respective state. A significance level of 0.20 for inclusion in a model was selected for an individual parameter. This was based on previous models included in the first edition of the HSM (Harwood et al., 2007). PROC GLIMMIX of SAS 9.3 was used for all modeling (SAS). Models were developed for total, FI, and PDO crashes, separately for multiple- and SV crashes. In general, the base conditions for intersection models in Chapter 12 of the HSM are the absence of intersection lighting and that of left- and right-turn lanes. A very small number of intersections in the database for model development met all three requirements (three intersections on two- lane and one on multilane highways). The distribution of intersections by the three characteristics is as follows: • Intersections on two-lane highways: 72 lighted; 17 unlighted (19% unlighted) • Intersections on multilane highways: 53 lighted; 19 unlighted (26% unlighted)

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) • Intersections on two-lane highways: 79 with left-turn lane on one approach; 10 with none (11% with none) • Intersections on multilane highways: 66 with left-turn lane on one approach; 1 with left- turn lane on two approaches; 5 with none (7% with none) To include all intersections in the models, crashes at intersections that did not meet base conditions for these three characteristics were first adjusted using the following CMFs in reverse (i.e., divide rather than multiply the crashes by the product of the CMFs): • Lighting: use CMFi, shown in Equation 12-36 in the HSM and the proportion of total crashes for unlighted intersections that occurred at night in the current database, shown in Table 40 (similar to Table 12-27 in the HSM); this CMF was applied to total, FI, and PDO crashes before modeling • Installation of left-turn lanes: use CMFi shown in Table 52 (see Table 12-24 in the HSM) • Installation of right-turn lanes: use CMFi shown in Table 53 (see Table 12-26 in the HSM) Table 40. Nighttime crash counts and proportions for unlighted intersections on high-speed urban and suburban arterials used for modeling Intersection Type Number of Sitesa Number of Nighttime Crashes Total Crashes Proportion of Crashes that Occurred at Night (pni) Three-Leg Stop-Controlled Intersections (3ST) 53 75 258 0.291 Three-Leg Signalized Intersections (3SG) 1 13 63 0.206 Four-Leg Stop-Controlled Intersections (4ST) 47 91 356 0.256 Four-Leg Signalized Intersections (4SG) 6 39 159 0.245 Three- and Four-Leg Signalized Intersections (3SG and 4SG) 7 52 222 0.234 a Number of unlighted intersections only. The final SPF models for crashes at intersections on high-speed urban and suburban arterials are shown, separately for each intersection type, in the following order: • Table 41: MV total crashes • Table 42: MV FI crashes • Table 43: MV PDO crashes

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) for each severity level. Figures 21-44 graphically present the SPFs shown in Tables 41-46 for various major- and minor road AADTs. Both major- and minor road AADT coefficients are significant at the 80-percent level or better in all MV crash models. However, for the SV crash models, major- and minor road AADT coefficients were not always significant at the 80-percent level or better. For completeness, these models are provided and are considered the most reasonable models for estimating SV crashes at intersections on high-speed urban and suburban arterials. Table 41. SPF coefficients for intersections on high-speed urban and suburban arterials—MV total crashes Intersection Type Parameter Estimate Standard Error Pr > F Significance Level MULTIPLE-VEHICLE TOTAL CRASHES Three-Leg Stop-Controlled Intersections (3ST) Intercept -8.26 2.56 -- -- ln(AADTmaj) 0.58 0.25 0.03 Significant at 95% level ln(AADTmin) 0.49 0.11 <.01 Significant at 99% level Overdispersion 0.85 0.18 -- -- Three-Leg Signalized Intersections (3SG) Intercept -4.41 1.31 . ln(AADTmaj) 0.43 0.14 <.01 Significant at 99% level ln(AADTmin) 0.19 0.06 <.01 Significant at 99% level Overdispersion 0.21 0.06 -- -- Four-Leg Stop-Controlled Intersections (4ST) Intercept -5.78 2.09 -- -- ln(AADTmaj) 0.48 0.21 0.02 Significant at 95% level ln(AADTmin) 0.36 0.10 <.01 Significant at 99% level Overdispersion 0.91 0.17 -- -- Four-Leg Signalized Intersections (4SG) Intercept -9.65 0.92 -- -- ln(AADTmaj) 0.98 0.08 <.01 Significant at 99% level ln(AADTmin) 0.28 0.05 <.01 Significant at 99% level Overdispersion 0.31 0.03 -- -- Base Condition: Absence of intersection lighting; for stop control intersections, absence of turn lanes on non-stop control approaches; for signal control intersections, absence of turn lanes on all intersection approaches

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. Graphical representation of the SPF for MV total crashes at three-leg signalized intersections on high-speed urban and suburban arterials

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. Graphical representation of the SPF for MV total crashes at four-leg signalized intersections on high-speed urban and suburban arterials

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? MULTIPLE-VEHICLE FI CRASHES Three-Leg Stop-Controlled Intersections (3ST) Intercept -6.84 2.71 -- -- ln(AADTmaj) 0.40 0.27 0.14 Significant at 85% level ln(AADTmin) 0.38 0.12 <.01 Significant at 99% level Overdispersion 0.76 0.19 -- -- Three-Leg Signalized Intersections (3SG) Intercept -7.28 1.25 -- -- ln(AADTmaj) 0.64 0.13 <.01 Significant at 99% level ln(AADTmin) 0.17 0.06 <.01 Significant at 99% level Overdispersion 0.09 0.05 -- -- Four-Leg Stop-Controlled Intersections (4ST) Intercept -7.93 2.23 -- -- ln(AADTmaj) 0.55 0.22 0.01 Significant at 99% level ln(AADTmin) 0.45 0.11 <.01 Significant at 99% level Overdispersion 0.89 0.18 -- -- Four-Leg Signalized Intersections (4SG) Intercept -9.61 0.97 -- -- ln(AADTmaj) 0.86 0.09 <.01 Significant at 99% level ln(AADTmin) 0.29 0.05 <.01 Significant at 99% level Overdispersion 0.31 0.04 -- -- Base Condition: Absence of intersection lighting; for stop control intersections, absence of turn lanes on non-stop control approaches; for signal control intersections, absence of turn lanes on all intersection approaches Figure 25. Graphical representation of the SPF for MV FI crashes at three-leg stop-controlled intersections on high-speed urban and suburban arterials

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. Graphical representation of the SPF for MV FI crashes at four-leg stop-controlled intersections on high-speed urban and suburban arterials

116 Figure 28. Graphical representation of the SPF for MV FI crashes at four-leg signalized intersections on high- speed urban and suburban arterials Table 43. SPF coefficients for intersections on high-speed urban and suburban arterials—MV PDO crashes Intersection Type Parameter Estimate Standard Error Pr > F Significance Level? MULTIPLE-VEHICLE PDO CRASHES Three-Leg Stop-Controlled Intersections (3ST) Intercept -9.89 3.00 -- -- ln(AADTmaj) 0.65 0.29 0.03 Significant at 95% level ln(AADTmin) 0.56 0.14 <.01 Significant at 99% level Overdispersion 1.11 0.25 -- -- Three-Leg Signalized Intersections (3SG) Intercept -3.08 1.67 -- -- ln(AADTmaj) 0.25 0.18 0.17 Significant at 80% level ln(AADTmin) 0.19 0.08 0.02 Significant at 95% level Overdispersion 0.34 0.10 -- -- Four-Leg Stop-Controlled Intersections (4ST) Intercept -5.46 2.14 -- -- ln(AADTmaj) 0.42 0.21 0.05 Significant at 95% level ln(AADTmin) 0.32 0.10 <.01 Significant at 99% level Overdispersion 0.94 0.18 -- -- Four-Leg Signalized Intersections (4SG) Intercept -10.70 1.05 -- -- ln(AADTmaj) 1.04 0.10 <.01 Significant at 99% level ln(AADTmin) 0.29 0.05 <.01 Significant at 99% level Overdispersion 0.38 0.04 -- -- Base Condition: Absence of intersection lighting; for stop control intersections, absence of turn lanes on non-stop control approaches; for signal control intersections, absence of turn lanes on all intersection approaches

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. Graphical representation of the SPF for MV PDO crashes at three-leg signalized intersections on high-speed urban and suburban arterials

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. Graphical representation of the SPF for MV PDO crashes at four-leg signalized intersections on high-speed urban and suburban arterials

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? SV TOTAL CRASHES Three-Leg Stop-Controlled Intersections (3ST) Intercept -12.28 3.34 -- -- ln(AADTmaj) 0.92 0.32 <.01 Significant at 99% level ln(AADTmin) 0.36 0.13 <.01 Significant at 99% level Overdispersion 0.69 0.22 -- -- Three-Leg Signalized Intersections (3SG) Intercept -6.77 2.31 -- -- ln(AADTmaj) 0.60 0.24 0.02 Significant at 95% level ln(AADTmin) 0.04 0.11 0.73 Not significant Overdispersion 0.57 0.21 -- -- Four-Leg Stop-Controlled Intersections (4ST) Intercept -7.63 2.93 -- -- ln(AADTmaj) 0.44 0.28 0.13 Significant at 85% level ln(AADTmin) 0.39 0.15 0.01 Significant at 99% level Overdispersion 1.12 0.31 -- -- Four-Leg Signalized Intersections (4SG) Intercept -6.04 1.12 -- -- ln(AADTmaj) 0.52 0.11 <.01 Significant at 99% level ln(AADTmin) 0.10 0.07 0.13 Significant at 85% level Overdispersion 0.55 0.08 -- -- Base Condition: Absence of intersection lighting; for stop control intersections, absence of turn lanes on non-stop control approaches; for signal control intersections, absence of turn lanes on all intersection approaches Figure 33. Graphical representation of the SPF for SV total crashes at three-leg stop-controlled intersections on high-speed urban and suburban arterials

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. Graphical representation of the SPF for SV total crashes at four-leg stop-controlled intersections on high-speed urban and suburban arterials

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. SPF coefficients for intersections on high-speed urban and suburban arterials—SV FI crashes Intersection Type Parameter Estimate Standard Error Pr > F Significance Level? SV FI CRASHES Three-Leg Stop-Controlled Intersections (3ST) Intercept -14.00 6.64 -- -- ln(AADTmaj) 0.79 0.64 0.22 Not significant ln(AADTmin) 0.53 0.26 0.05 Significant at 95% level Overdispersion 2.10 0.70 -- -- Three-Leg Signalized Intersections (3SG) Intercept -7.41 3.69 -- -- ln(AADTmaj) 0.63 0.39 0.11 Significant at 85% level ln(AADTmin) -0.09 0.17 0.61 Not significant Overdispersion 1.04 0.57 -- -- Four-Leg Stop-Controlled Intersections (4ST) Intercept -13.96 4.85 -- -- ln(AADTmaj) 0.91 0.46 0.05 Significant at 95% level ln(AADTmin) 0.45 0.27 0.10 Significant at 90% level Overdispersion 1.64 0.56 -- -- Four-Leg Signalized Intersections (4SG) Intercept -9.89 1.88 -- -- ln(AADTmaj) 0.83 0.18 <.01 Significant at 99% level ln(AADTmin) 0.04 0.10 0.65 Not significant Overdispersion 0.98 0.19 -- -- Base Condition: Absence of intersection lighting; for stop control intersections, absence of turn lanes on non-stop control approaches; for signal control intersections, absence of turn lanes on all intersection approaches

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. Graphical representation of the SPF for SV FI crashes at three-leg signalized intersections on high-speed urban and suburban arterials

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. Graphical representation of the SPF for SV FI crashes at four-leg signalized intersections on high-speed urban and suburban arterials

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? SV PDO CRASHES Three-Leg Stop-Controlled Intersections (3ST) Intercept -12.07 3.58 -- -- ln(AADTmaj) 0.92 0.34 < .01 Significant at 99% level ln(AADTmin) 0.31 0.15 0.04 Significant at 95% level Overdispersion 0.75 0.25 -- -- Three-Leg Signalized Intersections (3SG) Intercept -7.54 2.69 -- -- ln(AADTmaj) 0.61 0.28 0.04 Significant at 95% level ln(AADTmin) 0.08 0.13 0.52 Not significant Overdispersion 0.74 0.27 -- -- Four-Leg Stop-Controlled Intersections (4ST) Intercept -6.15 3.11 -- -- ln(AADTmaj) 0.24 0.30 0.44 Not significant ln(AADTmin) 0.41 0.16 0.01 Significant at 99% level Overdispersion 1.40 0.38 -- -- Four-Leg Signalized Intersections (4SG) Intercept -5.10 1.35 -- -- ln(AADTmaj) 0.37 0.13 < .01 Significant at 99% level ln(AADTmin) 0.11 0.08 0.17 Significant at 80% level Overdispersion 0.84 0.12 -- -- Base Condition: Absence of intersection lighting; for stop control intersections, absence of turn lanes on non-stop control approaches; for signal control intersections, absence of turn lanes on all intersection approaches Figure 41. Graphical representation of the SPF for SV PDO crashes at three-leg stop-controlled intersections on high-speed urban and suburban arterials

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. Graphical representation of the SPF for SV PDO crashes at four-leg stop-controlled intersections on high-speed urban and suburban arterials

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) and 12-13 (SV crashes) in the HSM, Table 47 (MV crashes) and Table 48 (SV crashes) provide percentages of FI and PDO crash severities by collision types, separately for each intersection type. These percentages were calculated based on all multiple- and SV crash counts at all intersections for all states combined. Table 47. Distribution of MV crashes for intersections on high-speed urban and suburban arterials Manner of Collision Percentage of Multiple-Vehicle Crashes Three-Leg Stop-Controlled Intersections (3ST) Three-Leg Signalized Intersections (3SG) Four-Leg Stop-Controlled Intersections (4ST) Four-Leg Signalized Intersections (4SG) FI PDO FI PDO FI PDO FI PDO Rear-end collision 36.0 48.5 63.4 64.8 16.7 31.5 56.0 65.7 Head-on collision 2.3 2.1 2.9 0.4 5.1 1.9 2.8 0.9 Angle collision 47.3 31.7 23.2 16.5 65.3 42.6 30.3 17.6 Sideswipe collision 7.7 11.3 3.6 11.4 2.8 14.2 2.6 9.4 Other MV collisions 6.8 6.4 6.9 6.9 10.2 9.8 8.3 6.4 Total MV crashes 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

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) Three-Leg Signalized Intersections (3SG) Four-Leg Stop-Controlled Intersections (4ST) Four-Leg Signalized Intersections (4SG) FI PDO FI PDO FI PDO FI PDO Collision with parked vehicle 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Collision with animal 0.0 0.9 0.0 0.0 0.0 1.9 0.0 0.3 Collision with fixed object 18.6 25.7 55.2 41.1 10.9 24.3 24.0 42.2 Collision with other object 0.0 0.0 0.0 0.0 0.0 1.9 0.0 0.6 Other SV collision 67.4 68.1 31.0 53.4 73.9 70.1 62.0 52.0 Noncollision 14.0 5.3 13.8 5.5 15.2 1.9 14.0 4.9 Total SV crashes 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Table 49 provides the distribution of pedestrian crashes by total crashes for intersections on high- speed urban and suburban arterials. The proportion of pedestrian crashes is used to estimate the number of pedestrian crashes at intersections on high-speed urban and suburban arterials. Table 49. Distribution of pedestrian crash counts and percentages for intersections on high-speed urban and suburban arterials Intersection Type Number of Sites Number of Pedestrian Crashes Number of Total Crashes Percentage of Pedestrian Crashes Three-Leg Stop-Controlled Intersections (3ST) 121 3 706 0.42 Three-Leg Signalized Intersections (3SG) 50 2 870 0.23 Four-Leg Stop-Controlled Intersections (4ST) 125 4 986 0.41 Four-Leg Signalized Intersections (4SG) 208 18 6,724 0.27 Table 50 provides the distribution of bicycle crashes by total crashes for intersections on high- speed urban and suburban arterials. The proportion of bicycle crashes is used to estimate the number of bicycle crashes at intersections on high-speed urban and suburban arterials. Table 50. Distribution of bicycle crash counts and percentages for intersections on high-speed urban and suburban arterials Intersection Type Number of Sites Number of Bicycle Crashes Number of Total Crashes Percentage of Bicycle Crashes Three-Leg Stop-Controlled Intersections (3ST) 121 0 706 0.00 Three-Leg Signalized Intersections (3SG) 50 1 870 0.11 Four-Leg Stop-Controlled Intersections (4ST) 125 0 986 0.00 Four-Leg Signalized Intersections (4SG) 208 5 6,724 0.07

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. Nothing from these figures suggests that the models provide unreasonable results. In addition, the new models for intersections on high-speed urban and suburban arterials were compared to the corresponding models in Chapter 12 of the HSM. Figure 45 illustrates a comparison of the predicted average crash frequency for MV total crashes based on the 3ST model for urban and suburban high-speed arterials (Table 41) to the predicted average crash frequency based on the 3ST model in Chapter 12 of the HSM. The dashed lines in the figure represent the predicted average crash frequency for the new model (i.e., 3ST model for urban and suburban high-speed arterials), and the solid lines represent the predicted average crash frequency for the 3ST model in the HSM. Similarly, Figure 46 illustrates a comparison of the predicted average crash frequency for MV FI crashes based on the 4ST model for urban and suburban high-speed arterials (Table 42) to the predicted average crash frequency based on the 4ST model in Chapter 12 of the HSM, and Figure 47 illustrates a comparison of the predicted average crash frequency for MV FI crashes based on the 4SG model for urban and suburban high-speed arterials (Table 42) to the predicted average crash frequency based on the 4SG model in Chapter 12 of the HSM. As illustrated in Figures 45-47 and consistent with most of the other compatibility testing for intersections on high-speed urban and suburban arterials, the new crash prediction models for intersections on high-speed urban and suburban arterials predicted slightly higher crash frequencies for the same traffic conditions as the corresponding models in HSM Chapter 12. This seems reasonable as higher speeds will require quicker reaction times to avoid potential conflicts. Similarly, it is not surprising that more MV FI crashes are predicted on high- speed urban and suburban arterials compared to the predictions for lower speed urban and suburban arterials, given the correlation between vehicle speed and crash severity. In summary, the models for intersections on high-speed urban and suburban arterials appear to provide reasonable results over a broad range of input conditions and can be integrated seamlessly with existing intersection crash prediction models in the first edition of the HSM.

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) Figure 46. Comparison of new crash prediction model to existing model in HSM: 4ST for MV crashes for urban and suburban high-speed arterials vs 4ST for MV crashes from HSM Chapter 12 (FI crashes)

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) 5.4 Crash Modification Factors During the development of the crash prediction models for intersections on high-speed urban and suburban arterials, three potential sources of CMFs for use with the SPFs were considered: • CMFs developed as part of this research based on a cross-sectional study design and regression modeling • CMFs already incorporated into the first edition of the HSM and applicable to intersections on high-speed urban and suburban arterials • High-quality CMFs applicable to intersections on high-speed urban and suburban arterials developed using defensible study designs (e.g., observational before-after evaluation studies using SPFs—the EB method), as referenced in FHWA’s CMF Clearinghouse with four or five-star quality ratings or based on a review of relevant intersection safety literature

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. • The CMFs for providing a left-turn lane on one or more intersection approaches at an urban or suburban intersection based on the work by Harwood et al. (2002), which is identified for use with the intersection crash prediction models in Chapter 12 of the first edition of the HSM. • The CMFs for providing a right-turn lane on one or more intersection approaches at an urban or suburban intersection based on the work by Harwood et al. (2002), which is identified for use with the intersection crash prediction models in Chapter 12 of the first edition of the HSM. The CMFs recommended for use with the SPFs for intersections on high-speed urban and suburban arterials are presented below. Lighting CMF With the CMF for intersection lighting based on the work by Elvik and Vaa (2004), the base condition is the absence of intersection lighting. The CMF for lighted intersections is similar to the CMF in Equation 12-36 in the HSM and has the form: 𝐶𝑀𝐹 = 1 − 0.38 × 𝑝 (Eq. 39) Where: CMFi = crash modification factor for the effect of lighting on total crashes; and pni = proportion of total crashes for unlighted intersections that occur at night. This CMF applies to total intersection crashes (not including vehicle-pedestrian and vehicle- bicycle crashes). Table 51 (similar to Table 12-27 in the HSM) presents default values for the nighttime crash proportion, pni, by roadway type. Table 51. Nighttime crash proportions for unlighted intersections on high-speed urban and suburban arterials Intersection Type Proportion of Crashes that Occur at Night pni Three-Leg Stop-Controlled 0.291 Three-Leg Signalized 0.206 Four-Leg Stop-Controlled 0.256 Four-Leg Signalized 0.245

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. (2002), the base condition is the absence of left-turn lanes on intersection approaches. The CMFs for providing a left-turn lane on one or more intersection approaches are presented in Table 52. Table 52 is presented in the same format as Table 12-24 in the HSM Part C (AASHTO, 2010). These CMFs apply to all severity levels. Table 52. CMFi for installation of left-turn lanes on intersection approaches (Harwood et al., 2002; AASHTO, 2010) Intersection Type Intersection Traffic Control Number of Approaches with Left-Turn Lanesa One Approach Two Approaches Three Approaches Four Approaches Three-Leg Minor road stop controlb 0.67 0.45 - - Traffic signal 0.93 0.86 0.80 - Four-Leg Minor road stop controlb 0.73 0.53 - - Traffic signal 0.90 0.81 0.73 0.66 a Stop-controlled approaches are not considered in determining the number of approaches with left-turn lanes. b Stop signs present on minor road approaches only. Intersection Approaches with Right-Turn Lanes CMF With the CMFs for providing a right-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. (2002), the base condition is the absence of right-turn lanes on intersection approaches. The CMFs for providing a right-turn lane on one or more intersection approaches are presented in Table 53. Table 53 is presented in the same format as Table 12-26 in the HSM Part C (AASHTO, 2010). These CMFs apply to all severity levels. Table 53. CMFi for installation of right-turn lanes on intersection approaches (Harwood et al., 2002; AASHTO, 2010) Intersection Type Intersection Traffic Control Number of Approaches with Right-Turn Lanesa One Approach Two Approaches Three Approaches Four Approaches Three-leg Minor- road stop controlb 0.86 0.74 - - Traffic signal 0.96 0.92 - - Four-leg Minor road stop controlb 0.86 0.74 - - Traffic signal 0.96 0.92 0.88 0.85 a Stop-controlled approaches are not considered in determining the number of approaches with right-turn lanes. b Stop signs present on minor road approaches only. 5.5 Severity Distribution Functions The development of SDFs was explored for intersections on high-speed urban and suburban arterials using methods outlined in Section 2.2.3 of this report. SDFs were not used in the development of crash prediction methods in the first edition of the HSM but were subsequently used in the Supplement to the HSM for freeways and ramps (AASHTO, 2014). The database

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. No traffic or geometric variables showed statistically significant effects in the SDFs for three-leg intersections with stop control (3ST) or signal control (3SG) on high-speed urban and suburban arterials. For four-leg intersections with stop control (4ST) and signal control (4SG) on high- speed urban and suburban arterials, the SDF takes the following form: 𝑃 , , = ( ) ( ) × 𝑃 | , , (Eq. 45) 𝑃 , , = ( ) ( ) × 𝑃 | , , (Eq. 46) 𝑃 , , = (1 − 𝑃 , , − 𝑃 , , ) × 𝑃 | , , (Eq. 47) 𝑃 , , = (1 − 𝑃 , , − 𝑃 , , ) × 𝑃 | , , (Eq. 48) Where: P4x,at,K = probability of a fatal crash (given that a fatal or injury crash occurred) for 4-leg intersections (4x) based on all collision types (at) and control type x (x = ST: minor road stop control; SG: signal control); P4x,at,A = probability of an incapacitating injury crash (given that a fatal or injury crash occurred) for 4-leg intersections (4x) based on all collision types (at) and control type x (x = ST: minor road stop control; SG: signal control); P4x,at,B = probability of a non-incapacitating injury crash (given that a fatal or injury crash occurred) for 4-leg intersections (4x) based on all collision types (at) and control type x (x = ST: minor road stop control; SG: signal control); P4x,at,C = probability of a possible injury crash (given that a fatal or injury crash occurred) for 4-leg intersections (4x) based on all collision types (at) and control type x (x = ST: minor road stop control; SG: signal control); VKA = systematic component of crash severity likelihood for severity KA; PK|KA,4x,at = probability of a fatal crash given that the crash has a severity of either fatal or incapacitating injury for 4-leg intersections (4x) based on all collision types (at) and control type x (x = ST: minor road stop control; SG: signal control); and PA|KA,4x,at = probability of an incapacitating injury crash given that the crash has a severity of either fatal or incapacitating injury for 4-leg intersections (4x) based on all collision types (at) and control type x (x = ST: minor road stop control; SG: signal control).

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. 𝑉 = 𝑎 + 𝑏 × 0.001 × 𝐴𝐴𝐷𝑇 + (𝑐 × 0.001 × 𝐴𝐴𝐷𝑇 ) + 𝑑 × 𝑛 + 𝑒 × 𝑛 + 𝑓 × 𝑛 (Eq. 49) Where: 𝐴𝐴𝐷𝑇 = AADT on the major road (veh/day) 𝐴𝐴𝐷𝑇 = AADT on the minor road (veh/day) nmajLTL = total number of left-turn lanes on both major road approaches (0, 1, or 2) nmajRTL = total number of right-turn lanes on both major road approaches (0, 1, or 2) nmajthru = total number of through lanes on the major road a, b, c, d, e, and f = estimated SDF coefficients The SDF coefficients for 4-leg intersections on high-speed urban and suburban arterials are provided in Table 54. Table 54. SDF coefficients for four-leg intersections on high-speed urban and suburban arterials Control Type (x) Severity (z) Variable a b c d e f Minor road stop control (ST) Fatal or incapacitating injury (KA) VKA -1.932 -0.0741 0.000 0.000 -0.338 0.383 Signal control, (SG) Fatal or incapacitating injury (KA) VKA -1.971 -0.0598 -0.0373 -0.178 -0.182 0.479 For four-leg intersections with stop control on high-speed urban and suburban arterials, values of 0.18 and 0.82 are used for PK|KA and PA|KA, respectively. For four-leg intersections with signal control on high-speed urban and suburban arterials, values of 0.18 and 0.82 are also used for PK|KA and PA|KA, respectively. 5.6 Summary of Recommended Models for Incorporation in the HSM In summary, several crash prediction models were developed for three- and four-leg intersections with stop control and signal control on high-speed urban and suburban arterials for consideration in the second edition of the HSM, including models for: • Three-leg intersections with minor road stop control (3ST) on high-speed urban and suburban arterials • Three-leg intersections with signal control (3SG) on high-speed urban and suburban arterials • Four-leg intersections with minor road stop control (4ST) on high-speed urban and suburban arterials

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. As noted, several of the models for SV crashes include major- and minor road AADT coefficients that were not significant. These models are still considered the most reasonable models for estimating SV crashes at intersections on high-speed urban and suburban arterials. Having models with coefficients for major- and minor road AADTs that are not significant is not a major concern because SV crashes at intersections do not occur often and is not a crash type of interest that agencies often consider to remedy. MV crashes are the major concern at intersections, and all of the MV models include coefficients for major- and minor road AADTs that are statistically significant. In addition, the final models for intersections on high- speed urban and suburban arterials recommended for inclusion in the HSM are not intended to replace the existing models in the HSM for the corresponding intersection configurations and traffic control types. Rather it is recommended the second edition of the HSM state that the intersection SPFs not designated specifically for high-speed arterials can be used to predict crash frequencies at intersections located on high-speed arterials, but use of models that have been developed specifically for intersections located on high-speed arterials is recommended when analyzing intersections located on urban and suburban arterials with posted speed limits of 50 mph or greater. SDFs for intersections on high-speed urban and suburban arterials are reported in Section 5.5. However, for the reasons provided in Section 4.6, it is recommended for the second edition of the HSM that crash severity for intersections on high-speed urban and suburban arterials be addressed in a manner consistent with existing methods in Chapter 12 of the HSM, without use of SDFs. Appendix A presents recommended text for incorporating the final recommended models for intersections on high-speed urban and suburban arterials into Chapter 12 of the HSM.