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From page 201...
... 201   This section of the report documents the development of crash prediction methods for pedestrian and bicycle crashes for potential incorporation into the HSM based on the crash prediction models used by the U.S. Road Assessment Program (usRAP)
From page 202...
... 202 Pedestrian and Bicycle Safety Performance Functions For application in HSM2, the RAP models have been configured separately for two-lane roads, rural multilane highways, and urban and suburban arterials. 4.1.2 Use of Roadway Segment Terminology That Refers Specifically to the Left and Right Sides of the Road The RAP models were developed for worldwide application and, therefore, were developed for application in countries where vehicles are driven on either the left or right side of the road.
From page 203...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 203   in this way is equivalent to the following RAP procedure for getting four separate estimates of roadway-segment-related and intersection-related crash estimates.
From page 204...
... 204 Pedestrian and Bicycle Safety Performance Functions 4.1.4 Use of Explicit Rather Than Assumed Values for Side-Road Attributes The RAP models are constrained to use only the data included in the RAP core dataset that serves as input to RAP's software tool, ViDA. The only two variables in the core dataset that explicitly apply to the side road or minor road at an intersection for predicting pedestrian and bicycle crashes are: • Intersecting road volume (the AADT range for the side road)
From page 205...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 205   4.1.6 Explicit Estimation of Fatality Counts The RAP models estimate values for pedestrian or bicyclist fatalities separately for each crash type within each 327-ft (100-m) roadway segment and then sum the estimates across all crash types considered in the procedure to obtain an estimate of total pedestrian or bicyclist fatalities for the road segment.
From page 206...
... 206 Pedestrian and Bicycle Safety Performance Functions values for the midpoint of a range of values. This will avoid the possibility of large step changes in model predictions for small changes in input values, such as AADTs.
From page 207...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 207   separately for the prediction of pedestrian and bicycle crashes. This is equivalent to the calibration of HSM models to a single state as has been done for the HSM models and is planned for the HSM2 models.
From page 208...
... 208 Pedestrian and Bicycle Safety Performance Functions Characteristic Roadway Segment Type R2U RMU RMD U2U UMU UMD Three-Leg Intersection with Minor-Road Stop Control Major-road number of lanes to be crossed 2 4 4 2 4 4 Minor-road number of lanes to be crossed 2 2 2 2 2 2 Major-road AADT (veh/day) 4,980 8,550 15,760 10,470 16,060 27,920 Minor-road AADT (veh/day)
From page 209...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 209   Characteristic Roadway Segment Type R2U RMU RMD U2U UMU UMD Proportion of minor-road approaches with marked crossings 0.05 0.05 0.05 0.50 0.50 0.50 Pedestrian crossing facility type Unsignalized marked crossing without refuge or no marked crossing Proportion of major-road approaches with exclusive left-turn lanes 0.88 1.00 0.87 0.83 0.93 0.89 Proportion of minor-road approaches with exclusive left-turn lanes 0.37 0.06 0.63 0.40 0.40 0.54 Four-Leg Intersection with Minor-Road Stop Control Major-road number of lanes to be crossed 2 4 4 2 4 4 Minor-road number of lanes to be crossed 2 2 2 2 2 2 Major-road AADT (veh/day) 5,410 6,820 12,360 11,170 11,170 25,600 Minor-road AADT (veh/day)
From page 210...
... 210 Pedestrian and Bicycle Safety Performance Functions It should be noted that no facility-type factor was developed for roundabouts. In addition, the original RAP models have several adjustment factors to address roundabouts; however, the adjustment factors for roundabouts were based on a limited amount of research and were not considered appropriate for inclusion in the HSM.
From page 211...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 211   As with other HSM models, the modified pedestrian and bicycle crash prediction models, including the facility-type factors discussed above, should be calibrated by user agencies for each facility type of interest. The crash prediction models developed in this research include a variable that can be used to represent the value of the calibration factor(s)
From page 212...
... 212 Pedestrian and Bicycle Safety Performance Functions 4.2.1.2 Pedestrian Crash Prediction Model for Pedestrian Movements Along the Left Side of the Road On an undivided highway, the left side of the road refers to the roadway and roadside in the secondary or decreasing milepost direction of travel on the roadway being evaluated. On a divided highway, the left side of the road refers to the roadway and median roadside in the direction of travel opposite to the primary direction of travel.
From page 213...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 213   Severityalongright–ped = crash severity factor for pedestrian crashes involving pedestrian movements along the right side of the road for a specific roadway segment, MVTSFalongright–ped = motor vehicle traffic speed factor for pedestrian crashes involving pedestrian movements along the right side of the road for a specific roadway segment, MVTFFalongright–ped = motor vehicle traffic flow factor for pedestrian crashes involving pedestrian movements along the right side of the road for a specific roadway segment, PFFalongright = pedestrian flow factor for pedestrian crashes involving pedestrian movements along the right side of the road for a specific roadway segment, and L = length (mi) of a specific roadway segment.
From page 214...
... 214 Pedestrian and Bicycle Safety Performance Functions 4.2.1.5 Facility-Type Factor for Pedestrian Crashes on Roadway Segments The values of the facility-type factor (FTpedr) developed in this research for pedestrian crashes on roadway segments are presented in Table 129 by HSM Part C chapter and roadway type.
From page 215...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 215   4.2.1.8 Crash Likelihood Factor for Pedestrian Crashes Related to Pedestrian Movements Along the Right Side of the Road The crash likelihood factor for pedestrian crashes related to pedestrian movements along the right side of the road within a roadway segment is determined as follows: (4-6) .Likelihood AF AF AF AF AF AF AF AF AF AF 0 075ped LA ped LA ped LA ped LA ped LA ped LA ped LA ped LA ped LA ped LA ped alongright 1 2 6 7 8 9 10 11 12 13 # # # # # # # # # # =- - - - - - - - - where: AFLA1–ped = crash likelihood adjustment factor for pedestrian crashes, accounting for sidewalk or paved shoulder provision along a specific roadway segment; AFLA2–ped = crash likelihood adjustment factor for pedestrian crashes, accounting for presence of warning signs in school zones along a specific roadway segment; AFLA6–ped = crash likelihood adjustment factor for pedestrian crashes, accounting for lane width along a specific roadway segment; AFLA7–ped = crash likelihood adjustment factor for pedestrian crashes, accounting for horizontal curvature along a specific roadway segment; AFLA8–ped = crash likelihood adjustment factor for pedestrian crashes, accounting for advance visibility of a curve along a specific roadway segment; AFLA9–ped = crash likelihood adjustment factor for pedestrian crashes, accounting for percent grade along a specific roadway segment; AFLA10–ped = crash likelihood adjustment factor for pedestrian crashes, accounting for presence and condition of delineation along a specific roadway segment; AFLA11–ped = crash likelihood adjustment factor for pedestrian crashes, accounting for shoulder rumble strips along a specific roadway segment; AFLA12–ped = crash likelihood adjustment factor for pedestrian crashes, accounting for vehicle parking along a specific roadway segment; and AFLA13–ped = crash likelihood adjustment factor for pedestrian crashes, accounting for street lighting along a specific roadway segment.
From page 216...
... 216 Pedestrian and Bicycle Safety Performance Functions AFLM5–ped = crash likelihood adjustment factor for pedestrian crashes, accounting for pedestrian fencing at a specific midblock location; AFLM12–ped = crash likelihood adjustment factor for pedestrian crashes, accounting for vehicle parking at a specific midblock location; AFLM13–ped = crash likelihood adjustment factor for pedestrian crashes, accounting for street lighting at a specific midblock location; AFLM14–ped = crash likelihood adjustment factor for pedestrian crashes, accounting for number of traffic lanes to be crossed at a specific midblock location; and AFLM15–ped = crash likelihood adjustment factor for pedestrian crashes, accounting for median type at a specific midblock location. The procedures for determining the values of these adjustment factors are presented in Section 4.2.1.16.
From page 217...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 217   where: AFSM3–ped = crash severity adjustment factor for pedestrian crashes, accounting for pedestrian crossing facility type at a specific midblock location. The values for the adjustment factor, AFSM3–ped, are presented in Section 4.2.1.17.
From page 218...
... 218 Pedestrian and Bicycle Safety Performance Functions where: MVTFFpedr = motor vehicle traffic flow factor for pedestrian crashes on a specific roadway segment, AADT = annual average daily volume (veh/day) for motor vehicles for both directions of travel combined on a specific roadway segment or intersection approach, and Nlanes = number of travel lanes for through traffic in both directions of travel combined on a specific roadway segment or intersection approach.
From page 219...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 219   Figure 49. Graph of motor vehicle traffic flow factor as a function of AADT for motor vehicles and number of lanes.
From page 220...
... 220 Pedestrian and Bicycle Safety Performance Functions If the pedestrian flow along a segment or across a given midblock crossing is zero, then zero pedestrian crashes will be predicted by the model. In this case, the pedestrian flow factor can be set to zero, and no further analysis is needed for crashes related to that pedestrian movement.
From page 221...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 221   barrier present. To compensate for this change in the values of AFLA1–ped, a constant factor equal to 1/13.33 = 0.075 has been inserted into Equations 4-5 and 4-6.
From page 222...
... 222 Pedestrian and Bicycle Safety Performance Functions to a pedestrian facility created by a public agency) provides a benefit to pedestrians between that of a sidewalk and a paved shoulder.
From page 223...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 223   AFLM4–ped 2 Advance Visibility of a Pedestrian Crossing Advance visibility of a pedestrian crossing represents an assessment of the ability of approaching drivers to see a pedestrian crossing on the roadway ahead under daytime conditions. Advance visibility of a pedestrian crossing considers pavement markings in advance of and at the crossing, advance signing, flashing beacons, and sight distance to the crossing.
From page 224...
... 224 Pedestrian and Bicycle Safety Performance Functions AFLM5–ped 2 Pedestrian Fencing Pedestrian fencing can reduce pedestrian crashes by denying pedestrians access to the roadway for crossing movements except at specific crossing facilities. Pedestrian fencing may consist of a conventional fence or other feature, such as a landscaping barrier, that prevents pedestrians from entering the traveled way, except at designated points.
From page 225...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 225   for horizontal curvature, for pedestrian movements along the road are shown in Table 139. The horizontal curvature categories are defined by advisory speed ranges and corresponding ranges of horizontal curve radius.
From page 226...
... 226 Pedestrian and Bicycle Safety Performance Functions AFLA10–ped 2 Presence and Condition of Delineation Presence and condition of delineation involves the placement of pavement markings, delineators on the roadside or the roadway surface, or other devices purposely placed by a highway agency to help guide drivers along the roadway. Motor vehicles are more likely to run off the road where delineation has lost its reflectivity (e.g., is weathered or faded)
From page 227...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 227   The rationale for the adjustment factor values is presented in iRAP Road Attribute Risk Factors: Shoulder Rumble Strips (iRAP 2013p)
From page 228...
... 228 Pedestrian and Bicycle Safety Performance Functions Clearinghouse (www.cmfclearinghouse.org)
From page 229...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 229   4.2.1.17 Crash Severity Adjustment Factors for Pedestrian Crashes on Roadway Segments This section presents the values of the crash severity factors used in Equations 4-8 through 4-10. AFSA1–ped 2 Sidewalk and Paved Shoulder Provision The crash severity adjustment factors for pedestrian crashes along the road, accounting for sidewalk and paved shoulder provision (AFSA1–ped)
From page 230...
... 230 Pedestrian and Bicycle Safety Performance Functions Npedr can be divided into individual crash severity levels as follows, where the crash severity level is defined by the most severe pedestrian injury in the crash: (4-12) P#N=N pedr pedr Kpedr K- (4-13)
From page 231...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 231   4.2.1.19 Comparison of Predictive Method for Pedestrian Crashes on Roadway Segments with Existing HSM Models Following development of the pedestrian roadway segment model, compatibility testing of the new model was conducted to check the reasonableness of the results across facility types. To gain a better understanding of the potential use of the model within the HSM, output results from the new model were compared to output from existing models in HSM Part C
From page 232...
... 232 Pedestrian and Bicycle Safety Performance Functions Figure 51. Comparison of predicted average total pedestrian crashes per year from new pedestrian roadway segment model and existing HSM Part C, Chapter 10 model for rural two-lane undivided roads.
From page 233...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 233   Figure 53. Comparison of predicted average total pedestrian crashes per year from new pedestrian roadway segment model and existing HSM Part C, Chapter 11 model for rural four-lane undivided roads (4U)
From page 234...
... 234 Pedestrian and Bicycle Safety Performance Functions Figure 56. Comparison of predicted average total pedestrian crashes per year from pedestrian roadway segment model and existing HSM Part C, Chapter 11 model for rural four-lane divided roads (4D)
From page 235...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 235   total crashes (i.e., all crash types combined) from the existing HSM Part C, Chapter 11 model for rural multilane divided roads.
From page 236...
... 236 Pedestrian and Bicycle Safety Performance Functions where: Likelihoodintcrossing–ped–j = crash likelihood factor for pedestrian crashes involving pedestrian movements crossing a specific intersection leg j, Severityintcrossing–ped–j = crash severity factor for pedestrian crashes involving pedestrian movements crossing a specific intersection leg j, MVTSFintcrossing–ped–j = motor vehicle traffic speed factor for pedestrian crashes involving pedestrian movements crossing a specific intersection leg j, MVTFFintcrossing–ped–j = motor vehicle traffic flow factor for pedestrian crashes involving pedestrian movements crossing a specific intersection leg j, and PFFintcrossing–j = pedestrian flow factor for pedestrian crashes involving pedestrian movements crossing a specific intersection leg j. The terms in Equation 4-17 are discussed in Sections 4.2.2.5 through 4.2.2.9.
From page 237...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 237   where: AFLI2–ped = crash likelihood adjustment factor for pedestrian crashes on a specific intersection leg, accounting for school zone warning; AFLI3–ped = crash likelihood adjustment factor for pedestrian crashes on a specific intersection leg, accounting for pedestrian crossing facility type; AFLI4–ped = crash likelihood adjustment factor for pedestrian crashes on a specific intersection leg, accounting for advance visibility of a pedestrian crossing; AFLI5–ped = crash likelihood adjustment factor for pedestrian crashes on a specific intersection leg, accounting for pedestrian fencing; AFLI12–ped = crash likelihood adjustment factor for pedestrian crashes on a specific intersection leg, accounting for vehicle parking; AFLI13–ped = crash likelihood adjustment factor for pedestrian crashes on a specific intersection leg, accounting for street lighting; AFLI14–ped = crash likelihood adjustment factor for pedestrian crashes on a specific intersection leg, accounting for number of lanes to be crossed; AFLI15–ped = crash likelihood adjustment factor for pedestrian crashes for a specific intersection leg, accounting for median type; and AFLI16–ped = crash likelihood adjustment factor for pedestrian crashes for a specific intersection leg, accounting for intersection type. The procedures for determining the values of these adjustment factors are presented in Section 4.2.2.10.
From page 238...
... 238 Pedestrian and Bicycle Safety Performance Functions 4.2.2.8 Motor Vehicle Traffic Flow Factors for Pedestrian Crashes Involving Pedestrians Crossing an Intersection Leg The value of the motor vehicle traffic flow factor used to evaluate pedestrian movements crossing an intersection leg, MVTFFintcrossing–ped–j is determined in the same manner as MVTFFalongleft–ped and MVTFFalongright–ped for the roadway segment containing the intersection leg, using Equation 4-11. Thus, at intersections, the value of MVTFFintcrossing–ped–j for major-road approaches should be set equal to the value of MVTFFalongleft–ped or MVTFFalongright–ped based on the AADT for the road segment that contains the major-road approach.
From page 239...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 239   AFLI4–ped 2 Advance Visibility of a Pedestrian Crossing Advance visibility of a pedestrian crossing represents an assessment of the ability of approaching drivers to see a pedestrian crossing at the intersection ahead. Selecting the appropriate adjustment factor for advance visibility of a pedestrian crossing involves an assessment made from a street-level photograph or a field visit as to whether the pedestrian crossing is visible to and likely to be seen by a driver approaching the pedestrian crossing.
From page 240...
... 240 Pedestrian and Bicycle Safety Performance Functions manner as the adjustment factor applicable to pedestrian crossing movements at midblock crossings, AFLM14–ped. For pedestrian crossing movements at intersections, both through and auxiliary lanes should be considered.
From page 241...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 241   divided into individual crash severity levels as follows, where the crash severity level is defined by the most severe pedestrian injury in the crash: (4-20)
From page 242...
... 242 Pedestrian and Bicycle Safety Performance Functions 4.2.2.13 Comparison of Predictive Method for Pedestrian Crashes at Intersections with Existing HSM Models Following development of the pedestrian intersection model, compatibility testing of the new model was conducted to check the reasonableness of the results across intersection types. To gain a better understanding of the potential use of the model within the HSM, output results from the new model were compared to output from existing models in HSM Part C.
From page 243...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 243   Figure 58. Comparison of predicted average total pedestrian crashes per year from the new pedestrian intersection model and existing HSM Part C, Chapter 10 model for three-leg stop control intersections (3ST)
From page 244...
... 244 Pedestrian and Bicycle Safety Performance Functions Figure 59. Comparison of predicted average total pedestrian crashes per year from the new pedestrian intersection model and existing HSM Part C, Chapter 10 model for four-leg signal control intersections (4SG)
From page 245...
... Figure 61. Comparison of pedestrian crashes per year from the new pedestrian intersection model (4SG)
From page 246...
... 246 Pedestrian and Bicycle Safety Performance Functions intersection legs are shown from the existing HSM Part C, Chapter 12 model: 300 ped/day and 1,000 ped/day. To provide more perspective, Figure 62 provides the same comparison but also includes estimates for total crashes (i.e., multiple-vehicle + single-vehicle + pedestrian + bicycle crashes)
From page 247...
... Figure 62. Comparison of predicted average pedestrian crashes per year from pedestrian intersection model and existing HSM Part C, Chapter 12 model for four-leg signal control intersections (4SG)
From page 248...
... 248 Pedestrian and Bicycle Safety Performance Functions where: Likelihoodbiker = crash likelihood factor for bicycle crashes involving bicycle movements along the road for a specific roadway segment, Severitybiker = crash severity factor for bicycle crashes involving bicycle movements along the road for a specific roadway segment, MVTSFbiker = motor vehicle traffic speed factor for bicycle crashes involving bicycle movements along the road for a specific roadway segment, MVTFFbiker = motor vehicle traffic flow factor for bicycle crashes involving bicycle movements along the road for a specific roadway segment, BFFr = bicycle flow factor for bicycle crashes involving bicycle movements along the road for a specific roadway segment, and L = length (mi) of a specific roadway segment.
From page 249...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 249   AFLA9–bike = crash likelihood adjustment factor for bicycle crashes along a specific roadway segment, accounting for percent grade; AFLA14–bike = crash likelihood adjustment factor for bicycle crashes along a specific roadway segment, accounting for presence and condition of delineation; AFLA15–bike = crash likelihood adjustment factor for bicycle crashes along a specific roadway segment, accounting for shoulder rumble strips; AFLA16–bike = crash likelihood adjustment factor for bicycle crashes along a specific roadway segment, accounting for vehicle parking; and AFLA18–bike = crash likelihood adjustment factor for bicycle crashes along a specific roadway segment, accounting for street lighting. The values of these adjustment factors are presented in Section 4.3.1.10.
From page 250...
... 250 Pedestrian and Bicycle Safety Performance Functions collisions as the speed of motor vehicle traffic, which is typically substantially higher than the speed of bicyclists, increases. The mean speed of motor vehicle traffic along the specific roadway segment of interest should be used to determine the value of MVTSFbiker.
From page 251...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 251   The procedure uses categories for the traffic flow on the roadway segment being analyzed that represent ranges of AADT per lane. The AADT ranges and midpoints applicable to MVTFFbiker are shown in Table 157.
From page 252...
... 252 Pedestrian and Bicycle Safety Performance Functions Figure 65. Graph of bicycle flow factor for bicycle crashes along the road as a function of bicycle peak-hour flow.
From page 253...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 253   AFLA1-bike 2 Bicycle Facilities and Paved Shoulder Provision The derivation of the crash likelihood adjustment factor for bicycle crashes along the road, accounting for bicycle facilities and paved shoulder provision (AFLA1–bike) , is illustrated in Table 159 and Table 160.
From page 254...
... 254 Pedestrian and Bicycle Safety Performance Functions the crash predictions for each type of bicycle facility is unchanged, a constant factor of 0.1 was incorporated in Equation 4-26. If the bicycle facility or shoulder types differ for the two directions of travel on an undivided road, average the two applicable adjustment factor values to determine the value of AFLA1–bike.
From page 255...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 255   curvature for bicycle movements along the road (AFLA7–bike) are shown in Table 162.
From page 256...
... 256 Pedestrian and Bicycle Safety Performance Functions The rationale for the adjustment factor values is presented in iRAP Road Attribute Risk Factors: Grade (iRAP 2013e)
From page 257...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 257   in the traveled way are more likely to be struck by motor vehicles than bicyclists riding on the roadside or shoulder. The crash likelihood adjustment factors for bicycle crashes along the road, accounting for vehicle parking (AFLR16–bike)
From page 258...
... 258 Pedestrian and Bicycle Safety Performance Functions If the bicycle facility or shoulder types differ for the two directions of travel on an undivided road, average the two applicable adjustment factor values to determine the value of AFSA1–bike. For divided roads, the procedures address all features for the roadways in each direction of travel separately.
From page 259...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 259   where: Nbiker = predicted number of bicycle crashes per year for all crash severity levels combined for a specific roadway segment, Nbiker–K = predicted number of fatal bicycle crashes per year for a specific roadway segment, Nbiker–A = predicted number of A-injury bicycle crashes per year for a specific roadway segment, Nbiker–B = predicted number of B-injury bicycle crashes per year for a specific roadway segment, Nbiker–C = predicted number of C-injury bicycle crashes per year for a specific roadway segment, Pbiker–K = proportion of fatal bicycle crashes for specific roadway segment facility types (see Table 170) , Pbiker–A = proportion of A-injury bicycle crashes for specific roadway segment facility types (see Table 170)
From page 260...
... 260 Pedestrian and Bicycle Safety Performance Functions crashes (i.e., multiple-vehicle + single-vehicle + pedestrian + bicycle crashes) from the existing HSM Part C, Chapter 10 model for rural two-lane undivided roads.
From page 261...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 261   Figure 68 provides a comparison of the predicted average total bicycle crash frequency for urban multilane undivided roads (e.g., four-lane undivided) from the bicycle roadway segment model presented in this section and the predicted average total bicycle crash frequency from the existing HSM Part C, Chapter 12 model for urban and suburban four-lane undivided arterials.
From page 262...
... 262 Pedestrian and Bicycle Safety Performance Functions comparison but also includes estimates for total crashes (i.e., multiple-vehicle + single-vehicle + pedestrian + bicycle crashes) from the existing HSM Part C model for urban and suburban fourlane undivided roads.
From page 263...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 263   Severitybikei–major = crash severity factor for bicycle crashes involving bicycle movements through a specific intersection on the major road, MVTSFbikei–major = motor vehicle traffic speed factor for bicycle crashes involving bicycle movements through a specific intersection on the major road, MVTFFbikei–major = motor vehicle traffic flow factor for bicycle crashes involving bicycle movements through a specific intersection on the major road, BFFmajor = bicycle flow factor for bicycle crashes involving bicycle movements through a specific intersection on the major road, Likelihoodbikei–minor = crash likelihood factor for bicycle crashes involving bicycle movements through a specific intersection on the minor road, Severitybikei–minor = crash severity factor for bicycle crashes involving bicycle movements through a specific intersection on the minor road, MVTSFbikei–minor = motor vehicle traffic speed factor for bicycle crashes involving bicycle movements through a specific intersection on the minor road, MVTFFbikei–minor = motor vehicle traffic flow factor for bicycle crashes involving bicycle movements through a specific intersection on the minor road, and BFFminor = bicycle flow factor for bicycle crashes involving bicycle movements through a specific intersection on the minor road. The terms in Equation 4-34 are discussed in Sections 4.3.2.5 through 4.3.2.9.
From page 264...
... 264 Pedestrian and Bicycle Safety Performance Functions where: AFLI1–bike = crash likelihood adjustment factor for bicycle crashes through a specific intersection, accounting for bicycle facilities and paved shoulder provision; AFLI2–bike = crash likelihood adjustment factor for bicycle crashes through a specific intersection, accounting for bicycle path and pedestrian crossing type; AFLI3–bike = crash likelihood adjustment factor for bicycle crashes through a specific intersection, accounting for intersection type; AFLI4–bike = crash likelihood adjustment factor for bicycle crashes through a specific intersection, accounting for advance visibility of an intersection; AFLI5–bike = crash likelihood adjustment factor for bicycle crashes through a specific intersection, accounting for intersection channelization; and AFLI18–bike = crash likelihood adjustment factor for bicycle crashes through a specific intersection, accounting for street lighting. Equation 4-35 can be applied to determine either Likelihoodbikei–major or Likelihoodbikei–minor.
From page 265...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 265   flow factor for bicycle movements along a roadway segment shown in Table 157, except Table 172 is slightly adapted. For bicycle movements through an intersection along the major road, the side road is the minor road.
From page 266...
... 266 Pedestrian and Bicycle Safety Performance Functions In Table 173, the term "separated bicycle path" is used to indicate any bicycle facility that is separated from the motor vehicle travel lanes by a raised or depressed divider or by a traffic barrier. The term "dedicated bicycle lane on roadway" is used to indicate a bicycle facility that is flush with travel lanes used by motor vehicles; a dedicated bicycle lane may be either immediately adjacent to or separated by a flush, paved buffer from the travel lanes used by motor vehicles.
From page 267...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 267   The rationale for these adjustment factors combines information from iRAP Methodology Fact Sheet #10: Facilities for Bicycles (iRAP 2014a) and from iRAP Road Attribute Risk Factors: Pedestrian Crossing Facilities (iRAP 2014b)
From page 268...
... 268 Pedestrian and Bicycle Safety Performance Functions of AFLI4–bike for bicycle movements through an intersection along the minor road should be based on the ability of drivers to see the at-grade intersection along the minor road. The rationale for the adjustment factor values is presented in iRAP Road Attribute Risk Factors: Intersection Quality (iRAP 2013g)
From page 269...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 269   into individual crash severity levels as follows, where the crash severity level is defined by the most severe bicyclist injury in the crash: P#N=N K Ki i ibike bike bike- - (4-37) P#N=N i A i i Abike bike bike- - (4-38)
From page 270...
... 270 Pedestrian and Bicycle Safety Performance Functions comparisons, the new model and the existing HSM models were not calibrated using a single agency's data; and unlike the existing models in the HSM, bicycle exposure is accounted for in the new model so some differences are expected. Nonetheless, output results from the new model were compared to output from existing models in the HSM to gain a sense of the reasonableness of the new bicycle intersection model for application and potential incorporation in HSM2.
From page 271...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 271   Figure 71. Comparison of predicted average bicycle crashes per year from new bicycle intersection model and existing HSM Part C, Chapter 10 model for three-leg stop control intersections on rural two-lane undivided roads (including total crashes from HSM model)
From page 272...
... Figure 72. Comparison of predicted average bicycle crashes per year from new bicycle intersection model and existing HSM Part C, Chapter 12 model for four-leg signal control intersections on urban and suburban arterials.
From page 273...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 273   model presented in this section and the predicted average total bicycle crash frequency from the existing HSM Part C, Chapter 12 model for four-leg signal control intersections. The predicted average crash frequencies are for the base conditions for both models.
From page 274...
... Figure 73. Comparison of predicted average bicycle crashes per year from new bicycle intersection model and existing HSM Part C, Chapter 12 model for four-leg signal control intersections on urban and suburban arterials (including total crashes from HSM model)
From page 275...
... Development of Pedestrian and Bicycle Models Based on Road Assessment Program Methodology 275   Another notable way that the pedestrian and bicycle SPFs developed herein are different from existing HSM Part C models is that the exposure measures for pedestrians and bicycles (i.e., the pedestrian and bicycle flow factors) are based on peak-hour volumes.

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