Intersection Crash Prediction Methods for the Highway Safety Manual (2021) / Chapter Skim
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16 Summary The first edition of the HSM included safety performance functions (SPFs) for roadway segments and intersections.
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17 Rural Three-Leg Intersections with Signal Control โข Three-leg signalized intersections on rural two-lane highways - Total crashes โข Three-leg signalized intersections on rural multilane highways - Total crashes - FI crashes Intersections on High-Speed Urban and Suburban Arterials โข Three-leg stop-controlled intersections on high-speed urban and suburban arterials - Multiple-vehicle (MV) total crashes - MV FI crashes - MV PDO crashes - Single-vehicle (SV)
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18 Five-Leg Intersections with Signal Control โข Five-leg signalized intersections on urban and suburban arterials - MV total crashes - MV FI crashes - MV PDO crashes - SV total crashes - SV FI crashes - SV PDO crashes Three-Leg Intersections Where the Through Movement Makes a Turning Maneuver at the Intersection โข Three-leg turning intersections on rural two-lane highways - Total crashes โข Three-leg turning intersections on urban and suburban arterials - MV total crashes - MV FI crashes - MV PDO crashes - SV total crashes - SV PDO crashes Crossroad Ramp Terminals at Single-Point Diamond Interchanges โข Crossroad ramp terminals at single-point diamond interchanges - FI crashes - PDO crashes Crossroad Ramp Terminals at Tight Diamond Interchanges โข Crossroad ramp terminals at tight diamond interchanges - FI crashes - PDO crashes In addition to formulating new SPFs, development of SDFs for the new intersection configurations and traffic control types was explored for potential use in combination with the SPFs to estimate crash severity as a function of geometric design elements and traffic control features. However, due to challenges and inconsistencies in developing and interpreting the SDFs, it is recommended for the second edition of the HSM that crash severity for the new intersection configurations and traffic control types be addressed in a manner consistent with existing methods in Chapters 10, 11, and 12 of the first edition of the HSM, without use of SDFs.
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19 Project 17-38 were updated to incorporate the new intersection crash prediction models developed as part of this research including intersections with all-way stop control, three-leg intersections with signal control on rural highways, intersections on high-speed urban and suburban arterials, five-leg intersections, and three-leg intersections where the through movement makes a turning maneuver at the intersection; and an unlocked version of the Enhanced Interchange Safety Analysis Tool (ISATe) that includes a module for ramp terminals and excludes modules for freeways and ramps was modified to incorporate the new crash prediction models for ramp terminals at single-point diamond interchanges and tight diamond interchanges.
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20 Chapter 1. Introduction 1.1 Background In May 2010, the American Association of State Highway and Transportation Officials (AASHTO)
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21 time of its development. It contains SPFs for all crash severities combined, with tabulated severity distributions in the form of proportions available to separate the total crash frequency predictions into crash frequencies for individual crash severity levels.
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22 elements and traffic control features considered by engineers and planners during the project development process. Roundabouts are not addressed in this research, as new crash prediction models were recently developed for possible inclusion in the second edition of the HSM as part of a separate study (NCHRP Project 17-70, Development of Roundabout Crash Prediction Models and Methods)
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23 existing models. Based on the results of the literature review and survey, the research team identified and prioritized the types of intersection configurations and traffic control types not currently addressed in the HSM for further consideration in this research.
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24 terminals at single-point diamond interchanges to address crossroad ramp terminals at tight diamond interchanges. Similar to Phase II, the research team updated existing spreadsheet tools to include the new crash prediction models developed in Phase III of this research, conducted sensitivity analyses to check that the results made sense, updated/revised the crash prediction models as necessary, developed recommended text for consideration in the second edition of the HSM, and prepared portions of this report that document Phase III of the research.
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25 Appendix A -- Draft Text for the Second Edition of the HSM HSM Chapter 10 -- Predictive Method for Rural Two-Lane, Two-Way Roads HSM Chapter 11 -- Predictive Method for Rural Multilane Highways HSM Chapter 12 -- Predictive Method for Urban and Suburban Arterials HSM Chapter 19 -- Predictive Method for Ramps
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26 Chapter 2. Literature Review and Survey of Practice This section summarizes literature relevant to the objectives of this research and results of a survey of transportation agencies intended to gain knowledge about their experience with the current HSM intersection predictive methods and assess their needs and priorities as they relate to additional (new)
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27 Figure 1. HSM definitions of segments and intersections (AASHTO, 2010)
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28 Intersection SPFs generally take one of the two forms shown in Equation 2 and Equation 3.
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29 HSM Chapter 11 -- Predictive Methods for Intersections on Rural Multilane Highways Chapter 11 of the HSM includes SPFs for the following intersection configurations and traffic control types on rural multilane highways: โข Three-leg intersections with minor road stop control on rural, four-lane divided or undivided highways (3ST) โข Four-leg intersections with minor road stop control on rural, four-lane divided or undivided highways (4ST)
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30 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)
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31 Where: Nbimv(FI) = predicted average crash frequency of MV, FI crashes of an intersection for base conditions (crashes/year)
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33 and cover crossroad ramp terminals with anywhere from two to six crossroad through lanes (total of both travel directions)
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34 Figure 2. Ramp terminal configurations (AASHTO, 2014)
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35 Figure 2. Ramp terminal configurations (AASHTO, 2014)
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36 One-way, stop-controlled crossroad ramp terminal SPFs generally take the form shown in Equation 15.
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37 PaS,x,at,A = probability of an incapacitating injury crash (given that a fatal or injury crash occurred) for all ramp terminal sites (aS)
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38 HSM Predictive Method Calibration The intersection predictive methods in the HSM contain calibration factors to adjust predictions of the HSM models, developed with data from selected jurisdictions and for specific time periods, to be applicable to other jurisdictions and time periods. Equation 1, for example, presents the calibration factor in general form as Ci, with i representing a specific site type.
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39 Where: Pp,aS,ac,at,KAB = predicted probability of a severe crash (i.e., K, A, or B) for all collision types (at)
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40 where: Nexpected = expected average crash frequency obtained by combining the predicted average crash frequency (Npredicted) with the observed crash frequency (Nobserved)
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41 (2010) , the Federal Highway Administration (FHWA)
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42 without those characteristics, commonly referred to as "base conditions" in the HSM predictive model context. For any given characteristic or treatment, CMF values greater than one indicate that the characteristic or treatment is expected to increase the number of crashes compared to the base conditions, while values lower than one indicate that the treatment is expected to decrease the number of crashes.
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43 intersection SPFs for different levels of severity were independently estimated. For any given collision type (e.g., MV)
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44 Where: Xjr = a row of observed characteristics (e.g., driver, vehicle, roadway, environment) associated with crash r that have an impact on injury severity outcome j ฮฒj = a vector of parameters to be estimated that quantify how the characteristics in Xjr impact injury severity outcome j ฮตjr = a disturbance term that accounts for unobserved and unknown characteristics of crash r that impact injury severity outcome j There are as many such linear functions as there are possible injury severity outcomes.
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45 Where j in this case represents all possible injury severity outcomes except for the base outcome. In NCHRP Project 17-45, for example, possible injury (C)
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46 value distributed, resulting in the model structure shown in Equations 32-34 (as outlined in Washington et al., 2010 and Savolainen et al., 2011)
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48 vehicle-bicycle. Handling collision types within HSM predictive methods remains a topic of ongoing research.
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49 โข Three-leg intersections with all-way stop control on rural highways - Software makes use of SPF for four-leg intersections with all-way stop control on rural highways for three-leg intersections with all-way stop control on rural highways โข Three-leg intersections with signal control on rural highways - Software makes use of SPF for four-leg intersections with signal control on rural highways for three-leg intersections with signal control on rural highways โข Four-leg intersections with all-way stop control on rural highways โข Three-leg intersections with all-way stop control on urban streets - Software makes use of SPF for four-leg intersections with all-way stop control on rural highways for three-leg intersections with all-way stop control on urban streets โข Four-leg intersections with all-way stop control on urban streets - Software makes use of SPF for four-leg intersections with all-way stop control on rural highways for four-leg intersections with all-way stop control on urban streets The Pennsylvania DOT developed SPFs (Donnell et al., 2014) for rural two-lane highway segments and intersections.
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50 โข Install dynamic signal warning flashers โข Discontinue late night flash operations at signalized intersections โข Construct bypass lanes In NCHRP Project 17-59, researchers developed CMFs for intersection sight distance at unsignalized intersections (i.e., intersections with minor road stop control)
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51 382 treated sites (302 four-leg, 80 three-leg intersections) and 367 untreated sites (319 four-leg, 48 three-leg intersections)
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52 Very often 11.8% (4) Regularly 26.5% (9)
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53 โข Has your agency developed its own SPFs for use with HSM Part C?
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54 โข Are there any other intersection configurations not listed in the previous question that you believe should receive high priority for inclusion in the HSM? - U-turn intersections, also referred to as J-turn intersections, were mentioned the most by survey respondents.
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55 โข What types of data does your agency have available for intersections that might be useful for development of intersection crash prediction models? (Please check all that apply.)
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56 SPFs for intersections are equations that relate the expected intersection crash frequency (possibly by type and/or severity) for some defined time period to characteristics of the intersection.
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57 Chapter 3. Development of Models for Use in HSM Crash Prediction Methods: Intersections with All-Way Stop Control This section of the report describes the development of crash prediction models for all-way stopcontrolled intersections and presents the final models recommended for incorporation in the second edition of the HSM.
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58 Each intersection that was initially deemed appropriate for inclusion in model development was given a unique identification code and included in 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.
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59 intersection attributes collected, their definitions, and permitted values for all-way stopcontrolled 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.
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60 During detailed data collection, to the extent possible, the research team reviewed historical aerial images to determine if a site had recently been reconstructed or improved to determine which years of data should be used in model development. Crash and traffic volume data were obtained for California and Minnesota using HSIS databases.
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61 Crashes Of the 405 intersections included in the database, 70 (17%) experienced no crashes over the entire study period; their breakdown by area type and intersection type is as follows: โข Rural three-leg intersections: 5 out of 12 โข Rural four-leg intersections: 39 out of 199 โข Urban three-leg intersections: 3 out of 33 โข Urban four-leg intersections: 23 out of 161 Intersection crashes were defined as those crashes that occurred within 250 ft of the intersection and were classified as "at intersection" or "intersection-related", consistent with recommended practice in the HSM for assigning crashes to an intersection.
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62 Table 4. Major- and minor road AADTs and total AADT statistics by area type at all-way stop-controlled intersections Major Road AADT (veh/day)
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63 Table 5. All crashes combined and single- and MV crash counts by intersection type and crash severity -- rural all-way stop-controlled intersections State Date Range Number of Sites Number of Site-Years Time of Day All Crashes Combined SV Crashes a MV Crashes Total FI PDO Total FI PDO Total FI PDO RURAL THREE-LEG INTERSECTIONS IL 2008-2012 8 40 All 34 9 25 8 4 4 26 5 21 Night 11 4 7 6 3 3 5 1 4 OH 2009-2013 4 20 All 9 5 4 3 2 1 6 3 3 Night 5 3 2 3 2 1 2 1 1 All states 2008-2013 12 60 All 43 14 29 11 6 5 32 8 24 Night 16 7 9 9 5 4 7 2 5 RURAL FOUR-LEG INTERSECTIONS CA 2006-2011 29 174 All 252 77 175 26 4 22 226 73 153 Night 49 12 37 7 0 7 42 12 30 IL 2008-2012 87 435 All 405 99 306 42 14 28 363 85 278 Night 86 22 64 12 5 7 74 17 57 MN 2007-2011 17 85 All 55 22 33 12 6 6 43 16 27 Night 13 7 6 5 1 4 8 6 2 OH 2009-2013 66 327 All 279 75 204 48 13 35 231 62 169 Night 83 19 64 26 7 19 57 12 45 All states 2006-2013 199 1,021 All 991 273 718 128 37 91 863 236 627 Night 231 60 171 50 13 37 181 47 134 a Total and FI SV crashes include pedestrian and bicycle crashes.
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64 Table 6. All crashes combined, single- and MV, and pedestrian and bicycle crash counts by intersection type and crash severity -- urban all-way stopcontrolled 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 URBAN THREE-LEG INTERSECTIONS CA 2006-2011 5 30 All 18 9 9 10 6 4 8 3 5 0 0 Night 7 4 3 4 2 2 3 2 1 0 0 IL 2008-2012 17 85 All 83 21 62 8 2 6 71 15 56 3 1 Night 26 3 23 5 0 5 20 2 18 1 0 OH 2009-2013 11 55 All 78 16 62 22 5 17 55 10 45 0 1 Night 24 4 20 12 3 9 12 1 11 0 0 All States 2006-2013 33 170 All 179 46 133 40 13 27 134 28 106 3 2 Night 57 11 46 21 5 16 35 5 30 1 0 URBAN FOUR-LEG INTERSECTIONS CA 2006-2011 13 78 All 89 34 55 14 3 11 73 29 44 2 0 Night 16 5 11 7 1 6 9 4 5 0 0 IL 2008-2012 60 300 All 608 132 476 20 2 18 566 108 458 14 8 Night 127 33 94 5 0 5 118 29 89 4 0 MN 2007-2011 28 138 All 115 36 79 14 4 10 97 28 69 1 3 Night 22 5 17 7 2 5 15 3 12 0 0 NV 2007-2011 26 130 All 180 67 113 28 9 19 152 58 94 0 0 Night 67 30 37 16 6 10 51 24 27 0 0 OH 2009-2013 34 170 All 140 24 116 14 3 11 124 19 105 0 2 Night 36 7 29 4 1 3 32 6 26 0 0 All States 2006-2013 161 816 All 1132 293 839 90 21 69 1012 242 770 17 13 Night 268 80 188 39 10 29 225 66 159 4 0
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65 Table 7. Crash counts by collision type and manner of collision and crash severity at rural all-way stopcontrolled intersections Rural Three-Leg Intersections Rural Four-Leg Intersections Total FI PDO Total FI PDO SINGLE-VEHICLE CRASHES Collision with animal 0 0 0 5 2 3 Collision with bicycle 0 0 0 4 4 0 Collision with pedestrian 0 0 0 1 1 0 Overturned 0 0 0 6 5 1 Other SV collision 11 6 5 112 25 87 Total SV crashes 11 6 5 128 37 91 MULTIPLE-VEHICLE CRASHES Angle collision 11 3 8 453 136 317 Head-on collision 0 0 0 14 4 10 Rear-end collision 18 5 13 289 81 208 Sideswipe collision 2 0 2 61 7 54 Other MV collision 1 0 1 46 8 38 Total MV crashes 32 8 24 863 236 627 Total Crashes 43 14 29 991 273 718 Table 8.
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66 Where: Nspf int = predicted average crash frequency for an intersection with base conditions (crashes/year) AADTmaj = AADT on the major road (veh/day)
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67 Based on these distributions, the following final modeling approach was chosen: โข Rural three-leg intersections: Because of the small number of intersections, SPFs were developed using both lighted and unlighted intersections combined; total crashes at lighted intersections were adjusted in reverse using the CMF for lighting based on the work by Elvik and Vaa (2004) and shown in Equation 10-24 in Chapter 10 in the HSM (i.e., divide rather than multiply the crashes by the CMF)
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68 Table 9. SPF coefficients for intersections with all-way stop control on rural two-lane highways Intersection Type Parameter Estimate Standard Error Pr > F Significance Level TOTAL CRASHESa Three-Leg Intercept -9.05 3.28 -- -- ln(AADTtotal)
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69 Figure 6. Graphical representation of the SPF for total crashes at rural four-leg, all-way stop-controlled intersections Table 10.
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70 Table 11. Distributions for collision type and manner of collision and crash severity at rural all-way stopcontrolled intersections Collision Type Percentage of Total Crashes by Collision Type Rural Three-Leg All-Way Stop-Controlled Intersections Rural Four-Leg All-Way Stop-Controlled Intersections Total FI PDO Total FI PDO Single-Vehicle Crashes Collision with animal 0.0 0.0 0.0 0.5 0.7 0.4 Collision with bicycle 0.0 0.0 0.0 0.4 1.5 0.0 Collision with pedestrian 0.0 0.0 0.0 0.1 0.4 0.0 Overturned 0.0 0.0 0.0 0.6 1.8 0.1 Other SV collision 25.6 42.9 17.2 11.3 9.2 12.1 Total SV crashes 25.6 42.9 17.2 12.9 13.6 12.7 Multiple-Vehicle Crashes Angle collision 25.6 21.4 27.6 45.7 49.8 44.2 Head-on collision 0.0 0.0 0.0 1.4 1.5 1.4 Rear-end collision 41.9 35.7 44.8 29.2 29.7 29.0 Sideswipe collision 4.7 0.0 6.9 6.2 2.6 7.5 Other MV collision 2.3 0.0 3.4 4.6 2.9 5.3 Total MV crashes 74.4 57.1 82.8 87.1 86.4 87.3 Total Crashes 100.0 100.0 100.0 100.0 100.0 100.0 Table 12 shows the coefficients and associated statistics of the final SPFs for urban all-way stopcontrolled intersections.
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71 Figure 7. Graphical representation of the SPF for FI crashes at urban and suburban three-leg, all-way stopcontrolled intersections Figure 8.
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72 Figure 9. Graphical representation of the SPF for FI crashes at urban and suburban four-leg, all-way stopcontrolled intersections Figure 10.
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73 Table 13 (similar to Table 11 for rural intersections) provides percentages of total crashes by collision type and severity level for urban all-way stop-controlled intersections.
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75 which seems reasonable as the traffic control at all-way stop-controlled intersections provides more direction in terms of defining the right of way through the intersection for drivers so it is reasonable to expect fewer crashes at all-way stop-controlled intersections than intersections with minor road stop control, given the same traffic volumes. Although not presented herein, a similar trend was found in terms of the 3aST SPFs predicting fewer crashes than the 3ST SPFs in the HSM as the major road AADT increases for intersections on urban and suburban arterials.
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76 Figure 12. Comparison of new crash prediction model to existing model in HSM: 4aST vs 4ST on urban and suburban arterials (FI crashes)
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77 Thus, the only CMF recommended for use with the final SPFs for all-way stop-controlled intersections is the CMF for intersection lighting based on the work by Elvik and Vaa (2004)
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78 No traffic or geometric variables showed consistent and statistically significant effects in the SDFs for rural three-leg, rural four-leg, urban three-leg, or urban four-leg all-way stop-controlled intersections. Therefore, distributions for rural all-way stop-controlled intersections in Table 10 and SPFs by severity for urban all-way stop-controlled intersections in Table 12 are recommended for addressing severity at all-way stop-controlled intersections.
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79 Chapter 4. Development of Models for Use in HSM Crash Prediction Methods: Three-Leg Intersections with Signal Control on Rural Highways This section of the report describes the development of crash prediction models for three-leg intersections with signal control on rural highways and presents the final models recommended for incorporation in the second edition of the HSM.
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80 โข The traffic control at the intersection was something other than signal control. โข The number of intersection legs was not three.
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81 Table 17. Site characteristic variables collected for three-leg intersections with signal control on rural highways (Continued)
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82 4.2 Descriptive Statistics of Database A total of 161 sites -- 89 on two-lane and 72 on multilane highways -- were available for development of crash prediction models. The data collections sites were located in eight states: California, Florida, Illinois, Kentucky, Michigan, New Hampshire, Ohio, and Washington.
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83 Table 19. Major- and minor road AADT statistics by roadway classification for rural three-leg intersections with signal control State Date Range Number of Sites Number of Site-Years Major Road AADT (veh/day)
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84 Table 21. Crash counts by collision type and manner of collision and crash severity at three-leg intersections with signal control on rural two-lane highways Collision Type Totala FI PDO SINGLE-VEHICLE CRASHES Collision with animal 16 0 16 Collision with bicycle 3 2 1 Collision with pedestrian 0 0 0 Overturned 16 13 3 Ran off road 1 0 1 Other SV crash 137 35 90 Total SV crashes 173 50 111 MULTIPLE-VEHICLE CRASHES Angle collision 171 74 75 Head-on collision 24 16 8 Rear-end collision 408 120 220 Sideswipe collision 43 7 22 Other MV collision 68 15 39 Total MV crashes 714 232 364 Total Crashes 887 282 475 a Crash records did not indicate severity level for a number of crashes for some intersections in Florida and Kentucky; FI and PDO crashes will not add up to total crashes in some cases.
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85 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.
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86 โข Installation of left-turn lanes: use CMF2i shown in Table 30 (same as Tables 10-13 and 11-22 in the HSM with the addition of a CMF for three-leg intersections with signal control) โข Installation of right-turn lanes: use CMF3i shown in Table 31 (same as Tables 10-14 and 11-23 in the HSM with the addition of a CMF for three-leg intersections with signal control)
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87 Figure 13. Graphical representation of the SPF for total crashes at three-leg intersections with signal control on rural two-lane highways Figure 14.
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88 Figure 15. Graphical representation of the SPF for PDO crashes at three-leg intersections with signal control on rural two-lane highways The final SPF models for crashes at three-leg intersections with signal control on rural multilane highways are shown in Table 25, separately for each crash severity.
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89 Figure 16. Graphical representation of the SPF for total crashes at three-leg intersections with signal control on rural multilane highways Figure 17.
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90 Figure 18. Graphical representation of the SPF for PDO crashes at three-leg intersections with signal control on rural multilane highways Similar to Tables 10-5 and 10-6 in the HSM, respectively, Tables 26 and 27 provide percentages for crash severity levels and collision types and manner of collision, respectively, for three-leg intersections with signal control on rural two-lane highways.
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91 Table 27. Distributions for collision type and manner of collision and crash severity at three-leg intersections with signal control on rural two-lane highways Collision Type Percentage of Total Crashes Total FI PDO SINGLE-VEHICLE CRASHES Collision with animal 1.8 0.0 3.4 Collision with bicycle 0.3 0.7 0.2 Collision with pedestrian 0.0 0.0 0.0 Overturned 1.8 4.6 0.6 Ran off road 0.1 0.0 0.2 Other SV crash 15.4 12.4 18.9 Total SV crashes 19.4 17.7 23.3 MULTIPLE-VEHICLE CRASHES Angle collision 19.3 26.2 15.8 Head-on collision 2.7 5.7 1.7 Rear-end collision 46.0 42.6 46.3 Sideswipe collision 4.8 2.5 4.6 Other MV collision 7.7 5.3 8.2 Total MV crashes 80.5 82.3 76.6 Total Crashes 100.0 100.0 100.0 Similar to Table 11-9 in the HSM, Table 28 provides percentages to break down total, FI (both with and without level C injuries)
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92 Figure 19 illustrates a comparison of the predicted average crash frequency for total crashes based on the 3SG model for rural two-lane roads (Table 24) to the predicted average crash frequency based on the 3SG model in Chapter 12 of the HSM.
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93 Figure 20. Comparison of new crash prediction model to existing model in HSM: 3SG for rural multilane highways vs 3SG for urban and suburban arterials (total crashes)
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94 โข 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 Chapters 10 and 11 of the first edition of the HSM โข The CMFs for providing a left-turn lane on one or two intersection approaches at a rural three-leg signalized intersection based on the work by Harwood et al.
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95 decision to remove or change the lighting CMFs is made, this should be done consistently for all facility types as part of the development of the HSM second edition. Intersection Approaches with Left-Turn Lanes CMF With the CMF for providing a left-turn lane on one or two intersection approaches at a rural three-leg signalized intersection based on the work by Harwood et al.
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96 4.5 Severity Distribution Functions The development of SDFs was explored for three-leg intersections with signal control on rural two-lane and multilane highways 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)
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97 PK|KAB,3SG,at = probability of a fatal crash given that the crash has a severity of either fatal, incapacitating injury, or non-incapacitating injury for three-leg signalized intersections (3SG) based on all collision types (at)
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98 The final predictive model for estimating total crashes at three-leg intersections with signal control on rural two-lane highways presented in Table 24 and the final predictive models for estimating total and FI crashes at three-leg intersections with signal control on multilane highways presented in Table 25 are recommended for inclusion in the second edition of the HSM, consistent with existing methods in HSM Chapters 10 and 11. Logical interpretations do exist for the SDFs reported in Section 4.5.
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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.
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100 Each intersection in the list was initially screened using Google Earthยฎ 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: โข The traffic control at the intersection was something other than signal control or minor approach stop control โข The speed limit on the major road was less than 50 mph โข The intersection was in a rural area โข A private driveway was located in close proximity to the intersection โข One or more of the approaches to the intersection was a private/commercial access โข Google Street Viewยฎ was not available to identify leg specific attributes โข One or more of the intersection legs was a one-way street Each intersection that was initially deemed appropriate for inclusion in model development was given a unique identification code and included on a refined database for detailed data collection.
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101 Table 33. Site characteristic variables collected for intersections on high-speed urban and suburban arterials (Continued)
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102 During detailed data collection, to the extent possible, the research team reviewed historical aerial images to determine if a site had recently been under construction or recent improvements were made to the site to determine the appropriate years of data for use in model development. Table 34 lists the crash and traffic volume data sources for the four states included in the study.
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103 Crash Counts Of the 504 intersections included in the study, only 18 (3.6%) experienced no crashes over the entire 5-year study period; the breakdown by area type and intersection type is as follows: โข Three-leg intersections with stop control: 10 out of 121 โข Three-leg intersections with signal control: 1 out of 50 โข Four-leg intersections with stop control: 6 out of 125 โข Four-leg intersections signal control: 1 out of 208 Intersection crashes were defined as those crashes that occurred within 250 ft of the intersection and were classified as at intersection or intersection-related, consistent with recommended practice in the HSM for assigning crashes to an intersection.
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104 Table 35. Major- and minor road AADT statistics by intersection type on high-speed urban and suburban arterials State Date Range Number of Sites Number of Site-Years Major Road AADT (veh/day)
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105 Table 36. All crashes combined, single- and MV, and pedestrian and bicycle crash counts by crash severity and intersection type for three-leg intersections on high-speed urban and suburban arterials State Date Range Number of Sites Number of Site Years Time of Day All Crashes Combined Single- Vehicle Crashes Multiple- Vehicle Crashes Pedestrian Crashes Bicycle Crashes Total FI PDO Total FI PDO Total FI PDO FI FI URBAN THREE-LEG STOP-CONTROLLED INTERSECTIONS (3ST)
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106 Table 37. All crashes combined, single- and MV, and pedestrian and bicycle crash counts by crash severity and intersection type for four-leg intersections on high-speed urban and suburban arterials State Date Range Number of Sites Number of Site Years Time of Day All Crashes Combined Single- Vehicle Crashes Multiple- Vehicle Crashes Pedestrian Crashes Bicycle Crashes Total FI PDO Total FI PDO Total FI PDO FI FI URBAN FOUR-LEG STOP-CONTROLLED INTERSECTIONS (4ST)
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From page 107... ...
107 Table 38. Crash counts by collision type and manner of collision, crash severity, and intersection type at three-leg intersections on high-speed urban and suburban arterials Collision Type Three-Leg Stop-Controlled Intersections (3ST)
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From page 108... ...
108 5.3 Safety Performance Functions -- Model Development SPFs of the form shown in Equation 2 were developed separately for three- and four-leg intersections, for multiple- and SV crashes.
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From page 109... ...
109 Where: Nbimv = predicted average crash frequency of MV crashes of an intersection for base conditions (crashes/year) Nbisv = predicted average crash frequency of SV crashes of an intersection for base conditions (crashes/year)
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From page 110... ...
110 โข Intersections on two-lane highways: 58 with right-turn lane on one approach; 31 with none (35% with none) โข Intersections on multilane highways: 48 with right-turn lane on one approach; 24 with none (33% with none)
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From page 111... ...
111 โข Table 44: SV total crashes โข Table 45: SV FI crashes โข Table 46: SV PDO crashes Each table shows the model coefficients and overdispersion parameter (estimate) , their standard error, and associated p-values (or significance level)
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From page 112... ...
112 Figure 21. Graphical representation of the SPF for MV total crashes at three-leg stop-controlled intersections on high-speed urban and suburban arterials Figure 22.
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From page 113... ...
113 Figure 23. Graphical representation of the SPF for MV total crashes at four-leg stop-controlled intersections on high-speed urban and suburban arterials Figure 24.
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From page 114... ...
114 Table 42. SPF coefficients for intersections on high-speed urban and suburban arterials -- MV FI crashes Intersection Type Parameter Estimate Standard Error Pr > F Significance Level?
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From page 115... ...
115 Figure 26. Graphical representation of the SPF for MV FI crashes at three-leg signalized intersections on high-speed urban and suburban arterials Figure 27.
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From page 116... ...
116 Figure 28. Graphical representation of the SPF for MV FI crashes at four-leg signalized intersections on highspeed urban and suburban arterials Table 43.
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From page 117... ...
117 Figure 29. Graphical representation of the SPF for MV PDO crashes at three-leg stop-controlled intersections on high-speed urban and suburban arterials Figure 30.
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From page 118... ...
118 Figure 31. Graphical representation of the SPF for MV PDO crashes at four-leg stop-controlled intersections on high-speed urban and suburban arterials Figure 32.
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From page 119... ...
119 Table 44. SPF coefficients for intersections on high-speed urban and suburban arterials -- SV total crashes Intersection Type Parameter Estimate Standard Error Pr > F Significance Level?
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From page 120... ...
120 Figure 34. Graphical representation of the SPF for SV total crashes at three-leg signalized intersections on high-speed urban and suburban arterials Figure 35.
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From page 121... ...
121 Figure 36. Graphical representation of the SPF for SV total crashes at four-leg signalized intersections on high-speed urban and suburban arterials Table 45.
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From page 122... ...
122 Figure 37. Graphical representation of the SPF for SV FI crashes at three-leg stop-controlled Intersections on high-speed urban and suburban arterials Figure 38.
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From page 123... ...
123 Figure 39. Graphical representation of the SPF for SV FI crashes at four-leg stop-controlled intersections on high-speed urban and suburban arterials Figure 40.
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From page 124... ...
124 Table 46. SPF coefficients for intersections on high-speed urban and suburban arterials -- SV PDO crashes Intersection Type Parameter Estimate Standard Error Pr > F Significance Level?
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From page 125... ...
125 Figure 42. Graphical representation of the SPF for SV PDO crashes at three-leg signalized intersections on high-speed urban and suburban arterials Figure 43.
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From page 126... ...
126 Figure 44. Graphical representation of the SPF for SV PDO crashes at four-leg signalized intersections on high-speed urban and suburban arterials Similar to Tables 12-11 (MV crashes)
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From page 127... ...
127 Table 48. Distribution of SV crashes for intersections on high-speed urban and suburban arterials Manner of Collision Percentage of SV Crashes Three-Leg Stop-Controlled Intersections (3ST)
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From page 128... ...
128 Following the development of the crash prediction models for intersections on high-speed urban and suburban arterials, compatibility testing of the new models to confirm that the new models provide reasonable results over a broad range of input conditions and that the new models integrate seamlessly with existing intersection crash prediction models in the first edition of the HSM was conducted. The graphical representations of the crash prediction models in Figures 21-44 provide some sense of the reasonableness of the new models for intersections on high-speed urban and suburban arterials.
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From page 129... ...
129 Figure 45. Comparison of new crash prediction model to existing model in HSM: 3ST for MV crashes for urban and suburban high-speed arterials vs 3ST for MV crashes from HSM Chapter 12 (total crashes)
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From page 130... ...
130 Figure 47. Comparison of new crash prediction model to existing model in HSM: 4SG for MV crashes for urban and suburban high-speed arterials vs 4SG for multiple vehicle crashes from HSM Chapter 12 (FI crashes)
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From page 131... ...
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: โข The CMF for intersection lighting based on the work by Elvik and Vaa (2004) , which is identified for use with the intersection crash prediction models in Chapter 12 of the first edition of the HSM.
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From page 132... ...
132 Intersection Approaches with Left-Turn Lanes CMF With the CMFs for providing a left-turn lane on one or more intersection approaches at an intersection on a high-speed urban and suburban arterial based on the work by Harwood et al.
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From page 133... ...
133 used to explore SDFs for intersections on high-speed urban and suburban arterials consisted of the same crashes and intersections as the databases used to estimate the SPFs, but restructured so that the basic observation unit (i.e., database row) is a crash instead of an intersection.
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From page 134... ...
134 The basic model form for the systematic components of crash severity likelihood at 4-leg intersections on high-speed urban and suburban arterials is illustrated by Equation 49.
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From page 135... ...
135 โข Four-leg intersections with signal control (4SG) on high-speed urban and suburban arterials The final models presented in Tables 41-46 are recommended for inclusion in the second edition of the HSM.
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From page 136... ...
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.
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From page 137... ...
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.
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From page 138... ...
138 Table 56 lists the intersection attributes collected (and respective definitions and permitted values) for five-leg intersections.
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From page 139... ...
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.
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From page 140... ...
140 Table 58. Major-, minor-, and fifth-road AADT statistics at urban, five-leg signalized intersections State Date Range Number of Sites Number of SiteYears Major Road AADT (veh/day)
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From page 141... ...
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)
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From page 142... ...
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 SiteYears 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 20092013 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
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From page 143... ...
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: ๐ = ๐๐ฅ๐ ๐ + ๐ ร ln ๐ด๐ด๐ท๐ + ๐ ร ln(๐ด๐ด๐ท๐ )
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From page 145... ...
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)
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From page 146... ...
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)
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From page 147... ...
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)
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From page 148... ...
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)
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From page 149... ...
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)
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From page 150... ...
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)
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From page 151... ...
151 and bicycle crashes, respectively, for five-leg intersections with signal control on urban and suburban arterials. Table 67.
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From page 152... ...
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)
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From page 153... ...
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.
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From page 154... ...
154 โข CMFs developed as part of this research based on a cross-sectional study design and regression modeling; โข CMFs already incorporated into the first edition of the HSM and applicable to urban fiveleg signalized intersections; and โข High-quality CMFs applicable to urban five-leg signalized intersections developed using defensible study designs (e.g., observational before-after evaluation studies using SPFs โ 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.
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From page 155... ...
155 The final models recommended for inclusion in the second edition of the HSM include: โข The model for MV total crashes in Table 61 โข The model for MV FI crashes in Table 62 โข The model for MV PDO crashes inTable 61 โข The model for SV total crashes in Table 66 โข The model for SV FI crashes in Table 66 โข 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.
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From page 156... ...
156 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 This section describes the development of crash prediction models for three-leg intersections, where the through movement makes a turning maneuver at the intersection, and presents the final models recommended for incorporation in the second edition of the HSM.
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From page 157... ...
157 โข The traffic control at the intersection was something other than stop control or stop except right turn โข The number of intersection legs was not three โข A private driveway was located at the intersection โข One or more of the approaches to the intersection was a private/commercial access โข 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 in 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.
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From page 158... ...
158 Table 71. Site characteristic variables collected for three-leg intersections where the through movements make turning maneuvers at the intersections (Continued)
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From page 159... ...
159 Curve length and radius were also of potential interest for model development. The use of some of these site characteristics is discussed later in the SPF model development section (Section 7.3)
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From page 160... ...
160 Table 72. Major- and minor road AADT and total entering volume statistics for three-leg intersections where the through movements make turning maneuvers at the intersections Major Road AADT (veh/day)
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From page 161... ...
161 Crash Counts 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. Of the 211 intersections used in model development, 87 intersections (41.2%)
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From page 162... ...
162 Table 74. Crash counts by crash severity for urban three-leg intersections where the through movements make turning maneuvers at the 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 KY 2014-2018 19 95 All 49 5 44 15 4 11 34 1 33 0 0 Night 11 3 8 7 3 4 4 0 4 0 0 OH 2008-2017 7 70 All 67 21 46 34 11 23 33 10 23 0 0 Night 17 4 13 15 4 11 2 0 2 0 0 PA 2013-2017 21 105 All 61 26 35 29 11 18 32 15 17 2 0 Night 32 13 19 19 8 11 13 5 8 2 0 All States 2008-2018 47 270 All 177 52 125 78 26 52 99 26 73 2 0 Night 60 20 40 41 15 26 19 5 14 2 0
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From page 163... ...
163 Crash counts are tallied by collision type and manner of collision across all states in Table 75 for rural three-leg turning intersections and in Table 76 for urban three-leg turning intersections. Table 75.
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From page 164... ...
164 Where: Nspf int = predicted average crash frequency for an intersection with base conditions (crashes/year) AADTmaj = AADT on the major road (veh/day)
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From page 165... ...
165 The SPF portion of Nbi, Nspf int, is the sum of two more disaggregate predictions by collision type, as shown in Equation 6.
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From page 166... ...
166 For the rural three-leg turning intersection model, presence of intersection lighting was found to be the only intersection characteristic that was statistically significant. Since there was an abundance of site-years, it was decided to exclude all intersections with lighting and derive a model based only on unlighted intersections.
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From page 167... ...
167 Figure 57. Graphical representation of the SPF for total crashes at three-leg turning intersections on rural two-lane roadways (based on model for total crashes in Equation 53)
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From page 168... ...
168 Table 79. Distributions for collision type and manner of collision and crash severity at three-leg turning intersections on rural two-lane roadways Collision Type Percentage of Total Crashes by Collision Type Total FI PDO Single-Vehicle Crashes Collision with animal 7.1 0.0 11.2 Collision with bicycle 0.0 0.0 0.0 Collision with pedestrian 0.0 0.0 0.0 Overturned 3.8 6.9 2.1 Ran off road 57.1 61.1 54.9 Other SV crash 3.9 3.8 3.9 Total SV crashes 71.9 71.8 72.1 Multiple-Vehicle Crashes Angle collision 18.1 19.8 17.2 Head-on collision 2.8 3.8 2.1 Rear-end collision 2.2 1.5 2.6 Sideswipe collision 3.9 2.3 4.7 Other MV collision 1.1 0.8 1.3 Total MV crashes 28.1 28.2 27.9 Total crashes 100.0 100.0 100.0 Table 80 shows the coefficients and associated statistics of the final SPFs for urban three-leg turning intersections.
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From page 169... ...
169 Figure 58. Graphical representation of the SPF for total MV crashes at three-leg turning intersections on urban and suburban arterials (based on model for total crashes in Equation 53)
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From page 170... ...
170 Figure 60. Graphical representation of the SPF for PDO multiple-vehicle crashes at three-leg turning intersections on urban and suburban arterials (based on model for total crashes in Equation 53)
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From page 171... ...
171 Figure 62. Graphical representation of the SPF for FI SV crashes at three-leg turning intersections on urban and suburban arterials (based on model for total crashes in Equation 53)
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From page 172... ...
172 Table 81 (similar to Table 79 for rural intersections) provides percentages of total crashes by collision type and severity level for urban three-leg turning intersections.
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From page 173... ...
173 Where: fpedi = pedestrian crash adjustment factor for intersection type i Table 82. Pedestrian crash adjustment factor for three-leg turning intersections on urban and suburban arterials Intersection Type Pedestrian Crash Adjustment Factor (fpedi)
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From page 174... ...
174 potentially more time to assess gaps once vehicles on the uncontrolled approach(es) are seen.
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From page 175... ...
175 Figure 65. Comparison of new crash prediction model to existing model in HSM: 3STT vs 3ST on urban and suburban arterials (MV-total crashes)
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From page 176... ...
176 Figure 66. Comparison of new crash prediction model to existing model in HSM: 3STT vs 3ST on urban and suburban arterials (MV FI crashes)
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From page 177... ...
177 Figure 67. Comparison of new crash prediction model to existing model in HSM: 3STT vs 3ST on urban and suburban arterials (SV-total crashes)
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From page 178... ...
178 Figure 68. Comparison of new crash prediction model to existing model in HSM: 3STT vs 3ST on urban and suburban arterials (SV FI crashes)
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From page 179... ...
179 identified for potential use with the crash prediction models for rural three-leg turning intersections was 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 Chapters 10, 11, and 12 of the first edition of the HSM.
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From page 180... ...
180 Table 84. CMF coefficients for curve CMF at three-leg turning intersections on urban and suburban arterials SPF to which the CMF applies Coefficients used in Equation 55 A b MV-Total -0.014 0.017 MV FI -0.014 0.019 MV PDO -0.017 0.020 SV-Total 0a 0.009 SV FI 0a 0.013 SV PDO 0a 0.008 a Curve radius was not found to be statistically significant in predicting SV crashes Tables 85-90 show computed curve CMF values for various crash types and severities by various levels of curve length and radius.
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From page 181... ...
181 Table 88. Curve CMF values for SV total crashes Curve Radius (ft)
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From page 182... ...
182 7.6 Summary of Recommended Models for Incorporation in the HSM In summary, several crash prediction models were developed for three-leg intersections where the through movements make turning maneuvers at the intersections for consideration in the second edition of the HSM, including models for: โข Three-leg turning intersections on rural two-lane roadways โข Three-leg turning intersections on urban and suburban arterials The final models recommended for inclusion in the second edition of the HSM are for total crashes on three-leg turning intersections on rural two-lane roadways (as shown in Table 77) and MV total, MV FI, MV PDO, SV total, and SV PDO crashes at three-leg turning intersections on urban and suburban arterials (as shown in Table 80)
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From page 183... ...
183 Chapter 8. Development of Models for Use in HSM Crash Prediction Methods: Crossroad Ramp Terminals at Single-Point Diamond Interchanges This section describes the development of crash prediction models for crossroad ramp terminals at single-point diamond interchanges (SPs)
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From page 184... ...
184 Table 91. Site characteristic variables collected for crossroad ramp terminals at single-point diamond interchanges (Continued)
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From page 185... ...
185 Crash data were obtained from state DOTs. The crash data generally included details about the crash location (geographic coordinates)
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From page 186... ...
186 extends 100 ft beyond the outermost ramp connections, capturing crashes associated with the right-turn movements and the left-turn lanes. Figure 69.
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From page 187... ...
187 All of the collected data (i.e., site characteristics, crashes, and traffic volumes) were assembled into one database for the purposes of model development.
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From page 188... ...
188 Interchange geometric characteristics were collected using Google Earthยฎ and Google Street Viewยฎ (Table 91)
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From page 189... ...
189 Table 93. All crashes combined, single- and MV, and pedestrian and bicycle crash counts by crash severity -- single-point diamond interchange crossroad ramp terminals State Date Range Number of Sites Number of SiteYears 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 AZ 2011-2015 28 140 All 4071 1079 2992 287 83 204 3723 941 2782 18 43 UT 2011-2015 21 99 All 2133 504 1629 53 15 38 2040 454 1586 16 24 All states 20112015 49 239 All 6,204 1,583 4,621 340 98 242 5,763 1,395 4,368 34 67 Table 94.
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From page 190... ...
190 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.
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From page 191... ...
191 ๐ = ๐๐ฅ๐ ๐ + ๐ ร ln(๐ด๐ด๐ท๐ )
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From page 192... ...
192 Figure 72. Graphical representation of the SPF for FI crashes at crossroad ramp terminals at single-point diamond interchanges Figure 73.
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From page 193... ...
193 Table 96. SPF coefficients for crossroad ramp terminals at single-point diamond interchanges (based on Equation 57)
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From page 194... ...
194 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 71-73 provide some sense of the reasonableness of the new models for crossroad ramp terminals at single-point diamond interchanges.
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From page 195... ...
195 8.4 Crash Modification Factors During the development of the crash prediction models for crossroad ramp terminals at single-point diamond interchanges, three potential sources of CMFs for use with the SPFs were considered: โข CMFs developed as part of this research based on a cross-sectional study design and regression modeling โข CMFs already incorporated into the first edition of the HSM and applicable to crossroad ramp terminals at single-point diamond interchanges โข High-quality CMFs applicable to crossroad ramp terminals at single-point diamond interchanges 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 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 that were adaptable to the predictive models for crossroad ramp terminals at single-point diamond interchanges.
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From page 196... ...
196 8.6 Summary of Recommended Models for Incorporation in the HSM In summary, several crash prediction models were developed for crossroad ramp terminals at single-point diamond interchanges for consideration in the second edition of the HSM. The final models for FI and PDO severity levels presented in Table 96 are recommended for inclusion in the second edition of the HSM, consistent with existing methods in HSM Chapter 19.
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From page 197... ...
197 Chapter 9. Development of Models for Use in HSM Crash Prediction Methods: Crossroad Ramp Terminals at Tight Diamond Interchanges This section describes the development of crash prediction models for crossroad ramp terminals at tight diamond interchanges (TDs)
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From page 198... ...
198 Table 99. Site characteristic variables collected for crossroad ramp terminals at tight diamond interchanges Variable Definition Range of Permitted Values General Intersection Attributes Intersection configuration (i.e., number of legs and type of traffic control)
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From page 199... ...
199 Traffic data collection activities primarily involved accessing publicly available traffic volumes and statistics. Crash data were obtained from state DOTs.
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From page 200... ...
200 Figure 74. Example of a tight diamond interchange with the ramp boundaries definition All of the collected data (i.e., site characteristics, crashes, and traffic volumes)
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From page 201... ...
201 Traffic Volumes and Site Characteristics Traffic volumes and crash data from years 2011 through 2015 were used for analysis. Table 100 provides summary statistics for traffic volumes at the study sites used for model development.
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From page 202... ...
202 The findings with respect to some of these site characteristics are discussed in Section 9.3 on SPF development. Crash Counts All 51 interchanges included in the study experienced crashes.
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From page 203... ...
203 9.3 Safety Performance Functions -- Model Development SPFs for the crossroad ramp terminal of a tight diamond interchange were developed using Equation 57: ๐ = ๐๐ฅ๐ ๐ + ๐ ร ln(๐ด๐ด๐ท๐ )
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From page 204... ...
204 Before finalizing the models in Table 103, multiple models were developed testing other variables, such as traffic control type for right turns, number of left-turn lanes, number of rightturn lanes, number of channelized right turns, distance between terminals, number of driveways, and number of intersections. However, none of the parameters associated with the tested variables were statistically significant in the models.
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From page 205... ...
205 Figure 76. Graphical representation of the SPF for FI crashes at crossroad ramp terminals at tight diamond interchanges Figure 77.
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From page 206... ...
206 Following the development of the crash prediction models for crossroad ramp terminals at tight diamond interchanges, 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 75-77 provide some sense of the reasonableness of the new models for crossroad ramp terminals at tight diamond interchanges.
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From page 207... ...
207 Figure 79. Comparison of crash prediction models for FI crashes at crossroad ramp terminals at tight diamond interchanges and single-point diamond interchanges Figure 80.
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From page 208... ...
208 Table 104 displays the distribution of crashes at tight diamond interchange crossroad ramp terminals by severity level. Table 105 displays the distribution of crashes at tight diamond interchange crossroad ramp terminals by collision type and manner of collision.
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From page 209... ...
209 9.5 Severity Distribution Functions Development of SDFs was explored for tight diamond interchange crossroad ramp terminals using methods outlined in Section 2.2.3 of this report. The database used to explore SDFs consisted of the same crashes and crossroad ramp terminals as the database used to estimate the SPFs but restructured so that the basic observation unit (i.e., database row)
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From page 210... ...
210 Chapter 10. Conclusions and Recommendations The following conclusions and recommendations have been developed in this research: 1.
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From page 211... ...
211 โข Four-leg stop-controlled intersections on high-speed urban and suburban arterials - MV total crashes - MV FI crashes - MV PDO crashes - SV total crashes - SV FI crashes - SV PDO crashes โข Four-leg signalized intersections on high-speed urban and suburban arterials - MV total crashes - MV FI crashes - MV PDO crashes - SV total crashes - SV FI crashes - SV PDO crashes Five-Leg Intersections with Signal Control โข Five-leg signalized intersections on urban and suburban arterials - MV total crashes - MV FI crashes - MV PDO crashes - SV total crashes - SV FI crashes - SV PDO crashes Three-Leg Intersections Where the Through Movement Makes a Turning Maneuver at the Intersection โข Three-leg turning intersections on rural two-lane highways - Total crashes โข Three-leg turning intersections on urban and suburban arterials - MV total crashes - MV FI crashes - MV PDO crashes - SV total crashes - SV PDO crashes Crossroad Ramp Terminals at Single-Point Diamond Interchanges โข Crossroad ramp terminals at single-point diamond interchanges - FI crashes - PDO crashes
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From page 212... ...
212 Crossroad Ramp Terminals at Tight Diamond Interchanges โข Crossroad ramp terminals at tight diamond interchanges - FI crashes - PDO crashes Recommended draft text for inclusion in the second edition of the HSM is presented in Appendix A that incorporates the new crash prediction models for the intersection configurations and traffic control types developed as part of this research.
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From page 213... ...
213 Chapter 11. References Al-Ghamdi, A
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From page 214... ...
214 Leisch, J.P., Freeway and Interchange Geometric Design Handbook, Institute of Transportation Engineers, Washington, D.C., 2005.
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From page 215... ...
215 Chapter 12. Abbreviations, Acronyms, Initialisms, and Symbols AASHTO American Association of State Highway and Transportation Officials ANSI American National Standards Institute CMF crash modification factor CURE cumulative residual DOT Department of Transportation EB Empirical Bayes FB Full Bayesian FHWA Federal Highway Administration HSIS Highway Safety Information System HSM Highway Safety Manual IHSDM Interactive Highway Safety Design Model IIA independence of irrelevant alternatives ISATe Enhanced Interchange Safety Analysis Tool ITE Institute of Transportation Engineers LRS linear reference system MNL multinomial logit MV multiple-vehicle NB negative binomial NeXTA Network Explorer for Traffic Analysis NCHRP National Cooperative Highway Research Program RTM regression-to-the-mean SDF severity distribution function SP single-point diamond interchange SPF safety performance function SV single-vehicle TD tight diamond interchange TWLTL two-way left-turn lane AZ Arizona CA California FL Florida IL Illinois KY Kentucky MA Massachusetts MI Michigan MN Minnesota MO Missouri NH New Hampshire NV Nevada OH Ohio PA Pennsylvania
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216 TN Tennessee UT Utah WA Washington ST stop control SG signal control 3ST three-leg intersections with minor road stop control 3STT three-leg intersection with minor road stop control where through movement makes turning maneuver 3SG three-leg intersections with signal control 3aST three-leg intersections with all-way stop control 4ST four-leg intersections with minor road stop control 4SG four-leg intersections with signal control 4aST four-leg intersections with all-way stop control 5SG five-leg intersections with signal control D3ex three-leg terminals with diagonal exit ramp D3en three-leg terminals with diagonal entrance ramp D4 four-leg terminals with diagonal ramps A4 four-leg terminals at four-quadrant parclo A B4 four-leg terminals at four-quadrant parclo B A2 three-leg terminals at two-quadrant parclo A B2 three-leg terminals at two-quadrant parclo B K fatal A incapacitating injury B non-incapacitating injury C possible injury O or PDO property-damage-only FI fatal-and-injury j refers to the severity levels predicted by the SDFs in Chapter 19 of the HSM and takes the value of either K, A, B, or C; for general discussion (e.g., Eq. 27 and beyond)
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217 AADTmin+fif sum of AADTmin and AADTfif (veh/day) AADTxrd AADT on the crossroad (veh/day)
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218 Nbimv(FI) predicted average crash frequency of MV, FI crashes of an intersection for base conditions (crashes/year)
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220 P4x,at,K probability of a fatal crash (given that a fatal or injury crash occurred) for 4-leg intersections (4x)
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221 ndw number of unsignalized driveways on the crossroad leg outside of the interchange and within 250 ft of the ramp terminal nps number of unsignalized public street approaches to the crossroad leg outside of the interchange and within 250 ft of the ramp terminal nmajLTL total number of left-turn lanes on both major road approaches (0, 1, or 2) nmajthru total number of through lanes on the major road nmajRTL total number of right-turn lanes on both major road approaches (0, 1, or 2)
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Key Terms
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