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From page 283... ... 283 A P P E N D I X G : P E R F O R M A N C E R E L A T I O N S H I P D E V E L O P M E N T Performance Relationship Development Introduction This appendix describes the process used to develop performance relationships for two access management (AM) techniques. Read the entire page →
From page 284... ... 284  An operations-based performance relationship for bicycle travel;  An operations-based performance relationship for transit travel; and  An operations-based performance relationship for truck travel. These relationships describe the association between operational performance and right-turn lane presence. Read the entire page →
From page 285... ... 285 As shown in Table 159, average bicycle delay does not appear to be significantly influenced by the following factors: right-turn deceleration lane length, right-turn-on-red operation, traffic volume, rightturn percentage, bicycle volume, truck percentage, bus stop frequency, or bus dwell time. Table 159. Read the entire page →
From page 286... ... 286 Statistical Methods Regression analysis was used to develop predictive relationships between the site characteristics and performance measures. The best fit was based on minimization of the squared residual values. Read the entire page →
From page 287... ... 287 Table 161. Predictive model calibration statistics, bicycle operations model – right-turn deceleration. Read the entire page →
From page 288... ... 288 a. Signal cycle length (seconds) Read the entire page →
From page 289... ... 289 Table 163. Predictive model calibration statistics, transit operations model – right-turn deceleration. Read the entire page →
From page 290... ... 290 Figure 22. Predicted vs. Read the entire page →
From page 291... ... 291 a. Right-turn deceleration lane length (feet) Read the entire page →
From page 292... ... 292 Truck Operations Relationship A linear regression model was developed to describe the relationship between the average truck delay and various factors describing signalized intersection design, traffic demand, or operation. Average truck delay includes the delay to through and right-turning trucks at the intersection. Read the entire page →
From page 293... ... 293 A comparison between the observed average truck delay and the predicted delay is shown in Figure 24. The fitted pattern suggests predicted truck delays are clustered at four stratifications that may be caused by unobserved factors (e.g., truck arrival pattern) Read the entire page →
From page 294... ... 294 a. Right-turn deceleration lane length (feet) Read the entire page →
From page 295... ... 295 Safety Relationships based on Simulated Conflict Data This section describes the research undertaken to develop relationships for predicting the effect of right-turn deceleration lane presence on the safety of transit vehicles and trucks. Simulated conflict data were used to develop these relationships. Read the entire page →
From page 296... ... 296 Table 166. Simulated conflicts and observed crashes for hourly examination – right-turn deceleration. Read the entire page →
From page 297... ... 297 Equation 8 EAADC ∑ ∑ VolAADT 261 ∑ ∑ VolAADT 104 365 where EAADC = equivalent average annual daily conflict frequency, conflicts per day; is the number of simulated conflicts in a weekday hour ; is the number of simulated conflicts in a weekend hour ; is the traffic volume in a weekday hour ; and is the traffic volume in a weekend hour . A total of nine hours (four weekday hours and five weekend hours) Read the entire page →
From page 298... ... 298 Figure 27. Relationship between daily conflict frequency and average annual crash frequency – right-turn deceleration. Read the entire page →
From page 299... ... 299 Table 168. Factors affecting a change in safety performance – right-turn deceleration. Read the entire page →
From page 300... ... 300 independent variables at a confidence level of 95%. The candidate independent variables included a range of scenario factors (e.g., signal cycle, access level, traffic volume, bicycle volume, and truck percentage) Read the entire page →
From page 301... ... 301 Table 170. Predictive model calibration statistics, transit vehicle safety model – right-turn deceleration. Read the entire page →
From page 302... ... 302 Figure 28. Predicted vs. Read the entire page →
From page 303... ... 303 a. Right-turn deceleration lane length (feet) Read the entire page →
From page 304... ... 304 A crash modification function (CMFbus) based on transit-related conflict data was derived from Equation 9. Read the entire page →
From page 305... ... 305 Table 171. Statistics for variables in the truck-related safety model – right-turn deceleration. Read the entire page →
From page 306... ... 306 where y equals the truck-related conflict frequency, conflicts per hour; and all other variables are defined in Table 172. This equation should not be used with variable values outside the ranges provided in Table 171. Read the entire page →
From page 307... ... 307 a. Right-turn deceleration lane length (feet) Read the entire page →
From page 308... ... 308 A crash modification function (CMFtruck) based on truck-related conflict data was derived from Equation 11. Read the entire page →
From page 309... ... 309 methods used to develop the relationships, and the procedures for using relationships in a practical application. Operations Relationships Based on Simulation Data This section describes the research undertaken to develop relationships for predicting the operation of bicycles and trucks on urban and suburban streets. Read the entire page →
From page 310... ... 310 A detailed description of the data elements in the database is provided in Appendix F Examination of Operations Data The results of the simulation runs are summarized in this section. Read the entire page →
From page 311... ... 311 Statistical Methods Based on the examination of operations data, linear regression models were developed to explore the relationship between operations performance measures and independent variables (scenario factors) Read the entire page →
From page 312... ... 312 Table 175. Predictive model calibration statistics, bicycle operations model, TWLTL – TWLTL vs. Read the entire page →
From page 313... ... 313 Segment length (miles) Figure 35. Read the entire page →
From page 314... ... 314 Table 177. Predictive model calibration statistics, bicycle operations model, non-traversable median – TWLTL vs. Read the entire page →
From page 315... ... 315 increase in cycle length corresponds to lower bicycle speed. The regression analysis indicated that the influence of traffic volume on bicycle speed is not significant. Read the entire page →
From page 316... ... 316 Table 178. Statistics for variables in the truck operations model, TWLTL – TWLTL vs. Read the entire page →
From page 317... ... 317 Figure 38. Predicted vs. Read the entire page →
From page 318... ... 318 a. Access density (access points per mile) Read the entire page →
From page 319... ... 319 Truck Operations Relationship – NTM A linear regression model was developed to describe the relationship between truck speed and various factors describing street design, traffic demand, or operation. The model discussed in this section predicts speed associated with a non-traversable median design. Read the entire page →
From page 320... ... 320 This equation is based on a truck free-flow speed of 40 miles per hour. The use of this equation for other free-flow speeds will require multiplying the result (i.e., y) Read the entire page →
From page 321... ... 321 a. Access density (access points per mile) Read the entire page →
From page 322... ... 322 Safety Relationships Based on Crash Data This section describes the research undertaken to develop relationships for predicting the safety of truck and transit vehicles on urban and suburban streets. A cross-sectional database was assembled to evaluate the safety effects of the TWLTL and NTM on transit vehicles and trucks. Read the entire page →
From page 323... ... 323 Examination of Crash Data As a precursor to model development, the database was examined graphically to identify the possible association between specific site characteristics and crash rate. The insights obtained from this examination were used to (1) Read the entire page →
From page 324... ... 324 Median Width The findings from the examination of non-traversable median width are shown in Figure 43. Figure 43a shows the relationship for transit-related crashes and Figure 43b shows the relationship for truck-related crashes. Read the entire page →
From page 325... ... 325 a. Transit-related crashes. Read the entire page →
From page 326... ... 326 Partial-Access Driveway Presence This examination investigated whether the prohibition of some driveway turn movements had an effect on transit or truck safety. A driveway for which one or more movements were prohibited is considered a "partial-access driveway." A driveway with access to all turn movements is considered a "full-access driveway." The count of commercial and office driveways was used for this examination. Read the entire page →
From page 327... ... 327 a. Transit-related crashes. Read the entire page →
From page 328... ... 328 Nm = predicted average crash frequency for a road segment with median type m (m = TM: traversable median; NTM: non-traversable median) , crashes/yr; Ls = length of segment, miles; NSPF,m = predicted crash frequency for base conditions on a segment with median type m, crashes/mile/year; CMFi = crash modification factor for traffic characteristic, geometric element, or traffic control feature i (applicable to all median types) Read the entire page →
From page 329... ... 329 Equation 21 where V[X] = crash frequency variance for a group of similar locations, crashes2; N = predicted average crash frequency, crashes/yr; X = reported crash count for y years, crashes; y = time interval during which X crashes were reported (i.e., evaluation period) Read the entire page →
From page 330... ... 330 distributions when computed in the following manner (Kvalseth, 1985) Read the entire page →
From page 331... ... 331 With Equation 27 , , 1000 1000 Equation 28 , / 46 where Ny,TM = predicted average number of crashes for y years for a road segment with a traversable median, crashes; y = time interval for reported crashes (i.e., evaluation period) , yr; Ls = length of segment, miles; NSPF,TM = predicted crash frequency for base conditions on a segment with a traversable median; crashes/mile/yr; CMFWlsb = crash modification factor for lane width and shoulder width; CMFDcox,TM = crash modification factor for commercial driveways, office driveways, and public street approaches on a segment with a traversable median; AADT = overall annual average daily traffic (AADT) Read the entire page →
From page 332... ... 332 Ny,NTM = predicted average number of crashes for y years for a road segment with a non-traversable median, crashes; NSPF,NTM = predicted crash frequency for base conditions on a segment with a non-traversable median; crashes/mile/yr; CMFWm,NTM = crash modification factor for median width on a segment with a traversable median; Wm,NTM = median width (measured from near edges of traveled way in both directions) , ft; CMFDcox,NTM = crash modification factor for commercial driveways, office driveways, and public street approaches on a segment with a non-traversable median; and all other variables as previously defined. Read the entire page →
From page 333... ... 333  Number of through lanes; and  Presence of a mid-segment crosswalk. This finding does not rule out the possibility that these factors have an influence on segment safety. Read the entire page →
From page 334... ... 334 The t-statistics listed in the last column of Table 182 indicate a test of the hypothesis that the coefficient value is equal to 0.0. Those t-statistics with an absolute value that is larger than 2.0 indicate that the hypothesis can be rejected with the probability of error in this conclusion being less than 0.05. Read the entire page →
From page 335... ... 335 Figure 48. Predicted transit-related crash frequency, TM segments – TWLTL vs. Read the entire page →
From page 336... ... 336 Each data point shown in Figure 49 represents the average predicted and average reported crash frequency for a group of 10 segments (i.e., 10 sites) Read the entire page →
From page 337... ... 337 Figure 50. Predicted transit-related crash frequency, NTM segments – TWLTL vs. Read the entire page →
From page 338... ... 338 Estimated CMFs Several CMFs were calibrated in conjunction with the SPFs. All of them were calibrated using total crash data (i.e., all severities) Read the entire page →
From page 339... ... 339 A review of the research literature indicates that the correlation between pavement width and transitrelated crash frequency has been investigated by McCummings and Chimba (2013) Read the entire page →
From page 340... ... 340 Access Point CMF. The access point CMF is described using the following two equations. Read the entire page →
From page 341... ... 341 (e.g., transit vehicles) or facility types. Read the entire page →
From page 342... ... 342 AADTtk = AADT for trucks, veh/d; nF,com = number of commercial driveways on the segment (two-way total; including only driveways with full access) , driveways; nF,off = number of office driveways on the segment (two-way total; including only driveways with full access) Read the entire page →
From page 343... ... 343 approach density for partial-access access points on TM segments. The values of "5" and "18" in Equation 47 and Equation 48, respectively, have similar definitions for NTM segments. Read the entire page →
From page 344... ... 344 observations and a wider range in variable values) to detect using regression, or (2) Read the entire page →
From page 345... ... Estimated SPFs This section describes the fit statistics and inverse dispersion parameter for each component model. Model for Traversable Median. Read the entire page →
From page 346... ... 346 The fit of the calibrated model is shown in Figure 56. This figure compares the predicted and reported crash frequency in the calibration database. Read the entire page →
From page 347... ... 347 Table 187. Predictive model calibration statistics, truck-related crashes, NTM segments. Read the entire page →
From page 348... ... 348 Figure 58. Predicted vs. Read the entire page →
From page 349... ... 349 Figure 59. Lane width CMF, truck-related crashes, both median types – TWLTL vs. Read the entire page →
From page 350... ... 350 Figure 60. Shoulder width CMF, truck-related crashes, both median types – TWLTL vs. Read the entire page →
From page 351... ... 351 Figure 61. Median width CMF, truck-related crashes, non-traversable median type – TWLTL vs. Read the entire page →
From page 352... ... 352 Figure 62. Full access point CMF, truck-related crashes, both median types – TWLTL vs. Read the entire page →
From page 353... ... 353 It implies that a partial-access driveway has a smaller effect on truck safety than a full-access driveway. This safety benefit of partial-access driveways was initially noted in the discussion of Figure 46. Read the entire page →
From page 354... ... 354 Transit Safety Prediction Method This section describes the transit safety prediction method as sequence of steps that are completed to evaluate the influence on segment design on transit-related crash frequency. The associated crash prediction model predicts total crash frequency (i.e., all severities) Read the entire page →
From page 355... ... 355 This CMF is applicable to segments with a traversable or non-traversable median. The lane widths used to calibrate this CMF ranged from 9.5 to 12.7 feet. Read the entire page →
From page 356... ... 356 Step 3 – Compute Predicted Crash Frequency. The CMF and SPF values from the preceding steps are used in this step to compute the predicted total average crash frequency. Read the entire page →
From page 357... ... 357 Step 6 – Compute Predicted Crash Frequency by Crash Type and Severity (optional) Read the entire page →
From page 358... ... 358 Equation 74 , , , , , , where Np,TM,t,s = predicted average crash frequency for crash type t and severity category s for a road segment with a traversable median, crashes/yr; and Np,NTM,t = predicted average crash frequency for crash type t and severity category s for a road segment with a non-traversable median, crashes/yr. Transit Safety Prediction Method - Sensitivity Analysis This section describes the findings from a sensitivity analysis of selected factors that influence the transit-related predicted average crash frequency. Read the entire page →
From page 359... ... 359 20 percent of the access points on the traversable median segment would be consolidated. This assumption was intended to reflect the type of consolidation that often occurs when the median is converted from traversable to non-traversable. Read the entire page →
From page 360... ... 360 Columns 5 and 10 of Table 190 list the predicted overall traffic stream average crash frequency for the traversable and non-traversable median types, respectively. These estimates were obtained using the safety prediction methodology in Chapter 12 of the HSM (AASHTO, 2010) Read the entire page →
From page 361... ... 361 Truck Safety Prediction Method This section describes the truck safety prediction method as sequence of steps that are completed to evaluate the influence on segment design on truck-related crash frequency. The associated crash prediction model predicts total crash frequency (i.e., all severities) Read the entire page →
From page 362... ... 362 where CMFWsb = crash modification factor for shoulder width; Ws = paved outside shoulder width (average for both travel directions) , ft; and Wb = bike lane width (average for both travel directions) Read the entire page →
From page 363... ... 363 access point density used to calibrate the CMF for non-traversable medians ranged from 0 to 26 access points per mile. Partial Access Point CMF. Read the entire page →
From page 364... ... 364 Np,NTM = predicted average crash frequency for a road segment with a non-traversable median, crashes/yr; and C = local calibration factor. Step 4 – Apply Crash Severity Distribution (optional) Read the entire page →
From page 365... ... 365 Np,NTM,s = predicted average crash frequency for severity category s (s = KABC or PDO) for a road segment with a non-traversable median, crashes/yr; Ps,TM = proportion of crash severity s for a road segment with a traversable median; and Ps,NTM = proportion of crash severity s for a road segment with a non-traversable median. Read the entire page →
From page 366... ... 366 comparison is to illustrate how the method could be used to identify the conditions for which each median type is safer, from the perspective of truck-related crash frequency. In this regard, the method calculations were repeated for a range of access point densities to illustrate the sensitivity of the predictions to access point density. Read the entire page →
From page 367... ... 367 Columns 5 and 10 of Table 194 list the predicted overall traffic stream average crash frequency for the traversable and non-traversable median types, respectively. These estimates were obtained using the safety prediction methodology in Chapter 12 of the HSM (AASHTO, 2010) Read the entire page →
From page 368... ... 368 References Highway Safety Manual. Read the entire page →

From page 283...

... 283 A P P E N D I X G : P E R F O R M A N C E R E L A T I O N S H I P D E V E L O P M E N T Performance Relationship Development Introduction This appendix describes the process used to develop performance relationships for two access management (AM) techniques.