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

Chapter: Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections

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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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Suggested Citation:"Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections." National Academies of Sciences, Engineering, and Medicine. 2021. Intersection Crash Prediction Methods for the Highway Safety Manual. Washington, DC: The National Academies Press. doi: 10.17226/26153.
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136 Chapter 6. Development of Models for Use in HSM Crash Prediction Methods: Five-Leg Intersections This section describes the development of crash predictive methods for five-leg intersections and presents the final models recommended for incorporation in the second edition of the HSM. None of the HSM Part C chapters in the first edition of the HSM include crash prediction models for five-leg intersections. A five-leg intersection is a junction of five roadway segments that intersect at a common paved area. Five-leg intersections can be stop-controlled or signal- controlled. Data collection and analysis focused on five-leg intersections with signal control on urban and suburban arterials (5SG) due to limitations in sample sizes and data availability for other area and traffic control types. 6.1 Site Selection and Data Collection A list of potential five-leg intersections for model development was derived using databases obtained from state DOTs, HSIS, or Safety Analyst. The five-leg intersections ultimately selected for model development were in four states: • Ohio (OH) • Illinois (IL) • Massachusetts (MA) • Minnesota (MN) Intersections in Michigan and California were also initially considered, but were not included in the databases. Data obtained from Michigan had limited information on approach-level traffic volumes for the five approach legs. HSIS data from California generally contained AADTs for major- and minor- approaches that were part of the state highway system but did not contain traffic volume information for the fifth leg, which was usually a local road. Each potential intersection was visually investigated using Google Earth® to verify the intersection had five approaches and to remove from consideration intersections where there were noticeable changes in geometry, traffic control, or access points in close proximity to the intersection during the study period. Lists of potential five-leg intersections and their coordinates were provided by Ohio, Illinois, and Massachusetts DOTs. Potential intersections in Minnesota were obtained from HSIS. Intersections from HSIS were located using the state’s linear referencing system (LRS), and it was challenging to position them accurately on a map for further processing. The Network Explorer for Traffic Analysis (NeXTA) and Minnesota’s roadway network shapefile were ultimately used as an alternative to identify potential intersections as nodes with five links. With this approach, nodes and links were created from the roadway network using the network segment endpoints to identify common intersection nodes. Five-leg intersections were then identified as node locations with five or more links connected. Finally, the coordinates of selected nodes were imported into ArcGIS to generate a KML file, which was then used for visual verifications in Google Earth®.

137 Visual investigation was essential to correct misclassifications of four-leg and six-leg intersections as five-leg intersections in the initial dataset. This was in part due to link-node roadway representations that showed five links approaching a given node. Figure 48 shows an example of a location identified as a five-leg intersection in the initial list, but later identified as a four-leg intersection during visual verification and not included in model development. Figure 48. Sample intersection excluded from the five-leg analysis after visual inspection (Source: Massachusetts DOT / ArcMap) As shown in Table 55, the initial set of 446 potential five-leg intersections in Ohio, Illinois, Massachusetts, and Minnesota resulted in a total of 177 verified five-leg intersections: 93 signalized in urban and suburban locations, 57 stop-controlled in urban and suburban locations, and 27 stop-controlled in rural locations. Difficulties in practically obtaining detailed traffic volume and crash data for five-leg intersections with stop control prevented further consideration of such locations for model development. Data collection continued for the 93 remaining five-leg intersections with signal control in urban and suburban areas. Table 55. Potential and verified five-leg intersections State Potential Intersections Verified Intersections Rural Urban and Suburban Stop-Controlled Stop-Controlled Signal Control OH 183 22 13 39 MA 18 0 0 18 IL 107 a 2 4 25 MN 138 3 40 11 Total 446 27 57 93 a The list of 86 potential five-leg intersections provided by Illinois DOT was expanded to 107 intersections during visual inspection.

138 Table 56 lists the intersection attributes collected (and respective definitions and permitted values) for five-leg intersections. Table 56. Site characteristic variables collected for five-leg intersections Variable 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 5ST, 5SG Area type Indicates whether the intersection is in a rural or urban area Rural, urban Presence of intersection lighting Indicates if overhead lighting is present at the intersection proper Yes, no Approach Specific Attributes Route name or number Specifies 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 Presence of left-turn lanes The number of approaches with one or more left-turn lanes 0,1,2,3,4,5 Left-turn protected only Number of approaches with protected only left-turn operations 0,1,2,3,4,5 Left-turn permitted only Number of approaches with permitted only left-turn operations 0,1,2,3,4,5 Left-turn protected and permitted Number of approaches with protected and permitted left-turn operations 0,1,2,3,4,5 Two-way no left-turn Number of approaches with two-way operation and no left turns 0,1,2,3,4,5 Presence of right-turn lane Number of approaches with one or more right-turn lanes 0,1,2,3,4,5 No turn on red Number of approaches with no turn on red 0,1,2,3,4,5 Two-way no turn restrictions Number of approaches with two-way operation and no turn restrictions 0,1,2,3,4,5 One-way Number of approaches with one-way operation (traffic approaching intersection) 0,1,2,3,4,5 One-way receiving Number of approaches with one-way operation (receiving traffic from intersection) 0,1,2,3,4,5 Red light camera Indicates presence of red light cameras Yes, no In general, the goal of data collection was to obtain the most recent four to six years of crash and traffic volume data for each site for model development. After gathering all available information, a continuous five-year period from 2009 to 2013 was common to all four states and was therefore selected for model development. All data (i.e., site characteristics, crash, and traffic volume) were assembled into one database for the purposes of model development. Traffic volumes for the 57 urban, five-leg signalized intersections in Ohio and Massachusetts were readily available from the respective five-leg intersection databases provided to the research team by each state. Additional sources were required to complete traffic volume data collection for intersections in Illinois and Minnesota. Crash data were obtained directly from the state DOTs. All verified intersections with available traffic volumes also had available crash data except for five intersections in Massachusetts.

139 Therefore, the total number of intersections available for model development was 76, including 39 intersections from Ohio, 13 from Illinois, 13 from Massachusetts, and 11 from Minnesota. Definitions of intersection and intersection-related crashes from existing HSM intersection predictive methods were used for this study. 6.2 Descriptive Statistics of Database A total of 76 five-leg intersections with signal control on urban and suburban arterials were available for development of crash prediction models. The data collections sites were located in four states—Illinois, Massachusetts, Minnesota, and Ohio. 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 Table 57 summarizes the number of five-leg intersections with respect to lighting and red light camera presence, as well as selected operational characteristics by approach. Traffic volume and crash data were available for the years 2009 through 2013. Table 58 shows the summary statistics for traffic volumes at all 76 study sites used for model development, including the study period (date range), number of sites and site-years, and basic traffic volume statistics by state. Table 57. Number of intersections with attributes present by approach Approach Attribute Variable Number of Approaches with Attribute Present Total 0 1 2 3 4 5 Two-way no turn restrictions 1 0 3 8 14 50 76 Two-way no left-turn 62 8 5 0 0 1 76 One-way approaching 73 3 0 0 0 0 76 One-way receiving 61 12 3 0 0 0 76 Left-turn protected only 14 40 7 11 3 1 76 Left-turn permitted only 15 17 14 8 22 0 76 Left-turn protected and permitted 40 14 12 4 6 0 76 No turn on red 14 8 12 12 14 16 76 Presence of left-turn lane 14 11 24 8 15 4 76 Presence of right-turn lane 43 24 8 1 0 0 76 Intersection Attribute Variable Present Not Present Total Intersection lighting 71 5 76 Red light camera 0 76 76

140 Table 58. Major-, minor-, and fifth-road AADT statistics at urban, five-leg signalized intersections State Date Range Number of Sites Number of Site- Years Major Road AADT (veh/day) Minor Road AADT (veh/day) Fifth-Road AADT (veh/day) Min Max Mean Median Min Max Mean Median Min Max Mean Median OH 2009-2013 39 195 3,020 23,506 13,596 13,470 454 17,445 6,405 4,298 251 16,448 3,854 1,925 MA 2009-2013 13 65 5,425 28,208 13,576 13,679 2,782 14,489 6,608 6,704 2,479 15,421 6,252 3,325 IL 2009-2013 13 65 6,270 25,525 18,904 18,700 800 21,865 10,916 10,400 2,210 24,340 9,542 8,140 MN 2009-2013 11 55 7,270 29,630 15,503 15,330 2,190 10,650 5,358 4,870 247 11,493 5,412 5,230 All states 2009- 2013 76 380 3,020 29,630 14,776 14,276 454 21,865 7,060 6,162 247 24,340 5,463 3,319

141 Crash Counts All 76 intersections experienced crashes during the study period. The average number of single- and MV crashes per intersection over the 5-year study period was 35.2 crashes, and the average number of nonmotorized (i.e., vehicle-pedestrian plus vehicle-bicycle) crashes per intersection over the 5-year study period was 2.3 crashes. 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 59 shows all crashes combined, single- and MV crashes, and pedestrian and bicycle crash counts by crash severity and time of day for each state over the entire 5-year study period. Crash counts are aggregated by collision type and manner of collision across all states in Table 60.

142 Table 59. All crashes combined, single- and MV, and pedestrian and bicycle crash counts by crash severity—urban, five-leg signalized intersections State Date Range Number of Sites Number of Site- Years Time of Day All Crashes Combined SV Crashes Multiple-Vehicle Crashes Pedestrian Crashes Bicycle Crashes Total FI PDO Total FI PDO Total FI PDO FI FI OH 2009-2013 39 195 All 1,434 428 1,006 37 10 27 1,351 372 979 27 19 Night 322 109 213 15 4 11 294 92 202 8 5 MA 2009-2013 13 65 All 327 99 228 21 5 16 278 66 212 15 13 Night 88 30 58 7 4 3 72 17 55 7 2 IL 2009-2013 13 65 All 867 265 602 33 11 22 745 165 580 42 47 Night 222 71 151 11 7 4 190 43 147 12 9 MN 2009-2013 11 55 All 222 61 161 11 3 8 197 44 153 5 9 Night 50 13 37 6 1 5 40 8 32 0 4 All states 2009- 2013 76 380 All 2,850 853 1,997 102 29 73 2,571 647 1,924 89 88 Night 682 223 459 39 16 23 596 160 436 27 20

143 Table 60. Crash counts by collision type and manner of collision and crash severity at urban, five-leg signalized intersections Collision Type Total FI PDO Single-Vehicle Crashes Collision with parked vehicle 4 0 4 Collision with animal 0 0 0 Collision with fixed object 24 9 15 Collision with other object 2 0 2 Other SV collision 70 18 52 Noncollision 2 2 0 All SV crashesa 102 29 73 Multiple-Vehicle Crashes Rear-end collision 1,104 275 829 Head-on collision 88 42 46 Angle collision 665 208 457 Sideswipe collision 357 32 325 Other multiple-vehicle collisions 357 90 267 Total MV crashesa 2,571 647 1,924 Total Crashesa 2,673 676 1,997 a Note crash counts do not include pedestrian and bicycle crashes 6.3 Safety Performance Functions—Model Development Intersection SPFs were developed in the forms illustrated by Equations 50 through 52: 𝑁 = 𝑒𝑥𝑝 𝑎 + 𝑏 × ln 𝐴𝐴𝐷𝑇 + 𝑐 × ln(𝐴𝐴𝐷𝑇 ) + 𝑑 × ln 𝐴𝐴𝐷𝑇 (Eq. 50) 𝑁 = 𝑒𝑥𝑝 𝑎 + 𝑏 × ln 𝐴𝐴𝐷𝑇 + 𝑒 × ln 𝐴𝐴𝐷𝑇 (Eq. 51) 𝑁 = 𝑒𝑥𝑝 𝑎 + 𝑓 × ln(𝐴𝐴𝐷𝑇 ) (Eq. 52) 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) AADTfif = AADT on the fifth leg (veh/day) AADTmin+fif = sum of AADTmin and AADTfif (veh/day) AADTtotal = sum of AADTmaj, AADTmin, and AADTfif (veh/day) a, b, c, d, e, and f = estimated regression coefficients For five-leg signalized intersections on urban and suburban arterials, the SPFs were developed in a manner consistent with the methodology used in Chapter 12 of the HSM for predicting intersections crashes in urban and suburban areas. This methodology is illustrated in Equation 4 and Equation 5. 𝑁 = 𝑁 + 𝑁 + 𝑁 × 𝐶 (Eq. 4)

144 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) 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 five-leg signalized intersections on 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

145 All of the vehicle-pedestrian and vehicle-bicycle crashes predicted with Equations 9 and 12 are assumed to be FI crashes (none as PDO). All SPFs were developed using a NB regression model based on all sites combined. 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. A significance level of 0.2 was used to assess the individual, estimated regression parameters. During model development, several intersection characteristics were initially tested in the models to develop CMFs for use with the SPFs. However, the intersection characteristics showed no consistent or statistically significant relationships to expected crash frequency. Therefore, no CMFs for use with the SPFs were developed. Additionally, existing CMFs for other intersection forms (e.g., four-leg signalized intersections) were not adapted to five-leg signalized intersections due to the different operational characteristics inherent to a five-leg intersection. Therefore, AADT-only models were developed with no base conditions for five-leg intersections with signal control on urban and suburban arterials. STATA 13 was used for all modeling. The final SPFs for five-leg intersections with signal control on urban and suburban arterials are provided in the following tables: • Table 61: MV total, FI, and PDO crashes using Equation 50 • Table 62: MV total, FI, and PDO crashes using Equation 51 • Table 63: MV total, FI, and PDO crashes using Equation 52 • Table 64: SV total, FI, and PDO crashes using Equation 50 • Table 65: SV total, FI, and PDO crashes using Equation 51 • Table 66: SV total, FI, and PDO crashes using Equation 52 Each table shows the estimated model coefficients and overdispersion parameter (estimate), their standard errors, and associated p-values (or significance level) for each severity level. Figures 49-54 graphically present the SPFs shown in Tables 61-66 for various major-, minor-, and fifth-approach AADTs. SPFs for vehicle-pedestrian and vehicle-bicycle crashes at five-leg intersections with signal control on urban and suburban arterials could not be developed as pedestrian and bicycle volumes were not available.

146 Table 61. SPF coefficients for five-leg intersections with signal control on urban and suburban arterials-MV crashes (AADTs separate for major-, minor-, and fifth-roads) Crash Severity Parameter Estimate Standard Error Pr > F Significance Level MULTIPLE-VEHICLE CRASHES Total Crashes Intercept -11.23 1.81 -- -- ln(AADTmaj) 0.87 0.21 0.00 Significant at 99% level ln(AADTmin) 0.36 0.10 0.00 Significant at 99% level ln(AADTfif) 0.19 0.08 0.02 Significant at 95% level Overdispersion 0.46 0.08 -- -- FI Crashes Intercept -15.00 2.64 -- -- ln(AADTmaj) 1.30 0.30 0.00 Significant at 99% level ln(AADTmin) 0.27 0.13 0.04 Significant at 95% level ln(AADTfif) 0.08 0.10 0.44 Not significant Overdispersion 0.64 0.13 -- -- PDO Crashes Intercept -10.92 1.83 -- -- ln(AADTmaj) 0.75 0.21 0.00 Significant at 99% level ln(AADTmin) 0.39 0.10 0.00 Significant at 99% level ln(AADTfif) 0.23 0.09 0.01 Significant at 99% level Overdispersion 0.48 0.09 -- -- No base conditions Table 62. SPF coefficients for five-leg intersections with signal control on urban and suburban arterials-MV crashes (AADTs combined for minor- and fifth-roads) Crash Severity Parameter Estimate Standard Error Pr > F Significance Level MULTIPLE-VEHICLE CRASHES Total Crashes Intercept -11.42 1.82 -- -- ln(AADTmaj) 0.85 0.22 0.00 Significant at 99% level ln(AADTmin+fif) 0.55 0.13 0.00 Significant at 99% level Overdispersion 0.47 0.08 -- -- FI Crashes Intercept -15.22 2.63 -- -- ln(AADTmaj) 1.24 0.31 0.00 Significant at 99% level ln(AADTmin+fif) 0.40 0.17 0.02 Significant at 95% level Overdispersion 0.63 0.13 -- -- PDO Crashes Intercept -11.07 1.85 -- -- ln(AADTmaj) 0.73 0.22 0.00 Significant at 99% level ln(AADTmin+fif) 0.60 0.14 0.00 Significant at 99% level Overdispersion 0.50 0.09 -- -- No base conditions Table 63. SPF coefficients for five-leg intersections with signal control on urban and suburban arterials-MV crashes (AADTs combined for major-, minor-, and fifth-roads) Crash Severity Parameter Estimate Standard Error Pr > F Significance Level MULTIPLE-VEHICLE CRASHES Total Crashes Intercept -12.83 1.82 -- -- ln(AADTtotal) 1.44 0.18 0.00 Significant at 99% level Overdispersion 0.47 0.08 --- -- FI Crashes Intercept -14.96 2.57 -- -- ln(AADTtotal) 1.51 0.25 0.00 Significant at 99% level Overdispersion 0.65 0.13 -- -- PDO Crashes Intercept -12.87 1.84 -- -- ln(AADTtotal) 1.41 0.18 0.00 Significant at 99% level Overdispersion 0.49 0.09 -- -- No base conditions

147 Table 64. SPF coefficients for five-leg intersections with signal control on urban and suburban arterials-SV crashes (AADTs separate for major-, minor-, and fifth-roads) Crash Severity Parameter Estimate Standard Error Pr > F Significance Level SV CRASHES Total Crashes Intercept -11.23 3.08 -- -- ln(AADTmaj) 0.70 0.35 0.05 Significant at 95% level ln(AADTmin) 0.09 0.15 0.54 Not significant ln(AADTfif) 0.28 0.14 0.05 Significant at 95% level Overdispersion 0.36 0.21 -- -- FI Crashes Intercept -15.54 4.89 0.00 -- ln(AADTmaj) 0.54 0.59 0.36 Not significant ln(AADTmin) 0.62 0.30 0.04 Significant at 95% level ln(AADTfif) 0.28 0.23 0.21 Not significant Overdispersion 0.19 0.39 -- -- PDO Crashes Intercept -10.13 3.27 0.00 -- ln(AADTmaj) 0.68 0.37 0.07 Significant at 90% level ln(AADTmin) -0.09 0.15 0.54 Not significant ln(AADTfif) 0.32 0.15 0.04 Significant at 95% level Overdispersion 0.19 0.22 -- -- No base conditions Table 65. SPF coefficients for five-leg intersections with signal control on urban and suburban arterials-SV crashes (AADTs combined for minor- and fifth-roads) Crash Severity Parameter Estimate Standard Error Pr > F Significance Level SV CRASHES Total Crashes Intercept -12.01 3.06 -- -- ln(AADTmaj) 0.56 0.36 0.12 Significant at 85% level ln(AADTmin+fif) 0.56 0.23 0.01 Significant at 99% level Overdispersion 0.34 0.21 -- -- FI Crashes Intercept -17.13 4.95 0.00 -- ln(AADTmaj) 0.21 0.60 0.73 Not significant ln(AADTmin+fif) 1.32 0.45 0.00 Significant at 99% level Overdispersion 0.14 0.35 -- -- PDO Crashes Intercept -11.14 3.40 -- -- ln(AADTmaj) 0.65 0.40 0.11 Significant at 85% level ln(AADTmin+fif) 0.34 0.24 0.15 Significant at 85% level Overdispersion 0.28 0.24 -- -- No base conditions Table 66. SPF coefficients for five-leg intersections with signal control on urban and suburban arterials-SV crashes (AADTs combined for major-, minor-, and fifth-roads) Crash Severity Parameter Estimate Standard Error Pr > F Significance Level SV CRASHES Total Crashes Intercept -13.94 3.10 -- -- ln(AADTtotal) 1.23 0.30 0.00 Significant at 99% level Overdispersion 0.34 0.20 -- -- FI Crashes Intercept -20.72 5.20 -- -- ln(AADTtotal) 1.76 0.50 0.00 Significant at 99% level Overdispersion 0.15 0.36 -- -- PDO Crashes Intercept -12.25 3.36 -- -- ln(AADTtotal) 1.03 0.33 0.00 Significant at 99% level Overdispersion 0.27 0.23 -- -- No base conditions

148 Figure 49. Graphical representation of the SPF for MV total crashes at five-leg intersections with signal control on urban and suburban arterials (based on model for MV total crashes in Table 61) Figure 50. Graphical representation of the SPF for MV FI crashes at five-leg intersections with signal control on urban and suburban arterials (model for multiple- vehicle FI crashes in Table 62)

149 Figure 51. Graphical representation of the SPF for MV PDO crashes at five-leg intersections with signal control on urban and suburban arterials (based on model for MV PDO crashes in Table 61) Figure 52. Graphical representation of the SPF for SV total crashes at five-leg intersections with signal control on urban and suburban arterials (based on model for SV total crashes in Table 66)

150 Figure 53. Graphical representation of the SPF for SV FI crashes at five-leg intersections with signal control on urban and suburban arterials (based on model for SV FI crashes in Table 66) Figure 54. Graphical representation of the SPF for SV PDO crashes at five-leg intersections with signal control on urban and suburban arterials (based on model for SV PDO crashes in Table 66) Tables 67 (MV crashes) and 68 (SV crashes) provide percentages to break down FI and PDO crash frequencies into collision types for five-leg intersections with signal control on urban and suburban arterials. These percentages were calculated based on all multiple- and SV crash counts at all intersections in all states combined. Tables 69 and 70 provide the distribution of pedestrian

151 and bicycle crashes, respectively, for five-leg intersections with signal control on urban and suburban arterials. Table 67. Distribution of MV crashes for five-leg intersections with signal control on urban and suburban arterials Manner of Collision Percentage of Multiple-Vehicle Crashes Five-Leg Signalized Intersections (5SG) FI PDO Rear-end collision 42.5 43.1 Head-on collision 6.5 2.4 Angle collision 32.1 23.8 Sideswipe collision 4.9 16.9 Other MV collisions 13.9 13.9 Total MV crashes 100.0 100.0

152 Table 68. Distribution of SV crashes for five-leg intersections with signal control on urban and suburban arterials Manner of Collision Percentage of SV Crashes Five-Leg Signalized Intersections (5SG) FI PDO Collision with parked vehicle 0.0 5.5 Collision with animal 0.0 0.0 Collision with fixed object 31.0 20.5 Collision with other object 0.0 2.7 Other SV collision 62.1 71.2 Noncollision 6.9 0.0 Total SV crashes 100.0 100.0 Table 69. Distribution of pedestrian crash counts and percentage for five-leg intersections with signal control on urban and suburban arterials Intersection Type Number of Sites Number of Pedestrian Crashes Number of Total Crashes Percentage of Pedestrian Crashes Five-leg Signalized Intersections (5SG) 76 89 2850 3.1 Table 70. Distribution of bicycle crash counts and percentage for five-leg intersections with signal control on urban and suburban arterials Intersection Type Number of Sites Number of Bicycle Crashes Number of Total Crashes Percentage of Bicycle Crashes Five-leg Signalized Intersections (5SG) 76 88 2850 3.1 Following the development of the crash prediction models for five-leg intersections with signal control on urban and suburban arterials, the research team conducted 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. The graphical representations of the crash prediction models in Figures 49-54 provide some sense of the reasonableness of the new models for five-leg intersections with signal control on urban and suburban arterials. Nothing from these figures suggests that the models provide unreasonable results. In addition, several of the crash prediction models for five-leg intersections with signal control on urban and suburban arterials were compared to models for four-leg intersections with signal control on urban and suburban arterials in Chapter 12 of the HSM. Figure 55 illustrates a comparison of the predicted average crash frequency for MV total crashes based on the five-leg intersection with signal control on urban and suburban arterials model in Table 61 to the corresponding predicted average crash frequency based on the 4SG model in Chapter 12 of the HSM. The dashed lines in the figure represent the predicted average crash frequency for the 5SG model, and the solid lines represent the predicted average crash frequency for the 4SG model in the HSM. For the comparisons, the traffic volumes used for the minor road and fifth-road in the 5SG model were combined and used for the traffic volume of the minor road for the 4SG model. As Figure 55 illustrates, for very low minor- and fifth-road volumes, fewer MV crashes are predicted for five-leg signalized intersections compared to four-leg signalized intersections. This seems reasonable as the right of way is more clearly defined for

153 vehicles traveling through five-leg intersections due to the need for more signal phases. Then, as the minor- and fifth-road volumes increase, the predicted crashes for five-leg signalized intersections exceed the predicted crashes for four-leg signalized intersections. This seems reasonable as the signal phasing and operations for five-leg intersections with increasing minor- and fifth-road volumes would become more and more complex and would likely lead to more potential conflicts and a higher potential for crashes than similar volumes as four-leg signalized intersections. In summary, the models for five-leg intersections with signal control on 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. Figure 55. Comparison of new crash prediction model to existing model in HSM: 5SG for MV crashes for urban and suburban arterials vs 4SG for multiple-vehicle crashes from HSM Chapter 12 (total crashes) 6.4 Crash Modification Factors During the development of the crash prediction models for urban five-leg signalized intersections, three potential sources of CMFs for use with the SPFs were considered:

154 • 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 urban five- leg signalized intersections; and • High-quality CMFs applicable to urban five-leg signalized intersections 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. Based on the regression modeling as part of this research, no geometric features or traffic control devices were identified for CMF development. Also, 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, no CMFs were identified as applicable to urban five-leg signalized intersections and were considered of sufficient quality for use with the urban five-leg signalized intersection SPFs. Therefore, the SPFs for five-leg intersections with signal control on urban and suburban arterials were developed as AADT only models with no base conditions, and no CMFs are recommended for use with the SPFs provided in Section 6.4. 6.5 Severity Distribution Functions Development of SDFs was explored for five-leg intersections with signal control on 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 used to explore SDFs for five-leg intersections with signal control on urban and suburban arterials consisted of the same crashes and intersections as the database used to estimate the SPFs for five-leg intersections with signal control on urban and suburban arterials, 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 consistent and statistically significant effects in the SDFs for five-leg intersections with signal control on urban and suburban arterials. 6.6 Summary of Recommended Models for Incorporation in the HSM In summary, several crash prediction models were developed for five-leg intersections with signal control on urban and suburban arterials for consideration in the second edition of the HSM, including models where: • The fifth-road AADT was included separately as a predictor variable in the models • The minor road and fifth-road AADTs were combined together as a predictor variable in the models • The major road, minor road, and fifth-road AADTs were summed together as a predictor variable in the models

155 The final models recommended for inclusion in the second edition of the HSM include: • The model for MV total crashes in Table 61 • The model for MV FI crashes in Table 62 • The model for MV PDO crashes inTable 61 • The model for SV total crashes in Table 66 • The model for SV FI crashes in Table 66 • The model for SV PDO crashes in Table 66 Attempts to develop SDFs for five-leg intersections with signal control on urban and suburban arterials proved unsuccessful for the reasons explained in Section 4.6. Therefore, it is recommended for the second edition of the HSM that crash severity for five-leg intersections 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 five-leg intersections with signal control on urban and suburban arterials into Chapter 12 of the HSM.

Next: Chapter 7. Development of Models for Use in HSM Crash Prediction Methods: Three-Leg Intersections Where the Through Movements Make Turning Maneuvers at the Intersections »
Intersection Crash Prediction Methods for the Highway Safety Manual Get This Book
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The first edition of the Highway Safety Manual (HSM), in 2010, included Safety Performance Functions (SPFs) for roadway segments and intersections. However, not all intersection types are covered in the first edition of the HSM.

The TRB National Cooperative Highway Research Program's NCHRP Web-Only Document 297: Intersection Crash Prediction Methods for the Highway Safety Manual develops SPFs for new intersection configurations and traffic control types not covered in the first edition of the HSM, for consideration in the second edition of the HSM.

Supplemental to the Document is recommended draft text for the second edition if the HSM, a worksheet for Chapter 10, a worksheet for Chapter 11, a worksheet for Chapter 12, a worksheet for Chapter 19, and a presentation.

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