Safety Prediction Methodology and Analysis Tool for Freeways and Interchanges (2021) / Chapter Skim
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Pages 319-348
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319 CHAPTER 8: SEVERITY DISTRIBUTION FUNCTIONS This chapter describes the activities undertaken to calibrate severity distribution functions (SDFs) for various components of the freeway system.
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320 C severity crashes. When under-reporting occurs, the ordered probability model yields biased and inconsistent coefficient estimates (Ye and Lord, 2011)
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321 The (multinomial) random parameters logit model (i.e., mixed logit model)
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322 Chapter 5. This approach is intended to minimize the frequency-severity indeterminacy problem described by Hauer (2006)
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323 BAK A VVV V A eee C eP +++ = 0.1 (322)
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324 Calibration Data The database assembled for calibration included crash severity level as the dependent variable. Geometric design features, traffic control features, and traffic characteristics were included as independent variables.
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326 TABLE 103. Parameter estimation for freeway SDF Variable Inferred Effect of...
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327 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 0.2 0.4 0.6 0.8 1 Proportion of Segment with Barrier D is tri bu tio n of C ra sh es b y Se ve rit y, % C B A K Equations 68 and 71 (in Chapter 5) , respectively.
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328 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 0.2 0.4 0.6 0.8 1 Proportion of AADT During High-Volume Hours D is tri bu tio n of C ra sh es b y Se ve rit y, % C B A K segment on an hourly basis. A more detailed explanation of this variable is provided in Chapter 5.
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329 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 0.2 0.4 0.6 0.8 1 Proportion of Segment with Rumble Strips D is tri bu tio n of C ra sh es b y Se ve rit y, % C B A K the segment length. For the outside shoulders, this proportion is computed by summing the length of roadway with rumble strips on the outside shoulder in both travel directions and dividing by twice the segment length.
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330 rumble strip deployment or other undefined factors. Smith and Ivan (2005)
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331 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 0.2 0.4 0.6 0.8 1 Proportion of Segment with Horizontal Curve D is tri bu tio n of C ra sh es b y Se ve rit y, % C B A K Figure 141. Freeway severity distribution based on the proportion of segment with horizontal curve.
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332 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 10 11 12 13 14 Lane Width, ft D is tri bu tio n of C ra sh es b y Se ve rit y, % C B A K Figure 142. Freeway severity distribution based on lane width.
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333 was very small and not statistically significant. This finding suggests that the state effect is very similar between Maine and Washington.
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334 In the third step, Equation 333 is used to estimate the local calibration factor using the probabilities computed in steps 1 and 2.
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335 As discussed in Appendix B, several of the geometry and lane use variables in the study state databases were of unknown accuracy. Also, several variables often had subtly different definitions among states.
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336 Calibration Data The database assembled for calibration included crash severity level as the dependent variable. Geometric design features, traffic control features, and traffic characteristics were included as independent variables.
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338 variable was important to the model and its trend was found to be consistent with previous research findings (even if the specific value was not known with a great deal of certainty as applied to this database)
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339 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 0.2 0.4 0.6 0.8 1 Proportion of Segment with Barrier D is tri bu tio n of C ra sh es b y Se ve rit y, % C B K+A Figure 143. Ramp severity distribution based on the proportion of segment with barrier.
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340 TABLE 108. Ramp severity distribution based on lanes, area type, and ramp type Variable Type Severity Level Distribution, percent Fatal (K)
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341 calibrate the SDFs. The second section provides an overview of the approach used to develop the SDF model form.
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342 Calibration Data The database assembled for calibration included crash severity level as a dependent variable. Geometric design features, traffic control features, and traffic characteristics were included as independent variables.
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343 TABLE 109. Summary statistics for crossroad ramp terminal SDF development Control Type Variable Type Mean Standard Deviation Minimum Maximum Crash Count Signalized Driveway + public street approach freq.
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344 TABLE 110. Parameter estimation for crossroad ramp terminal SDF Control Type Variable Inferred Effect of...
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345 ( ) ruralAK IV ×+−=+ 8907.01676.3 (347)
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346 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 1 2 3 4 Access Point Frequency D is tri bu tio n of C ra sh es b y Se ve rit y, % C B K+A Figure 144. Crossroad ramp terminal severity distribution based on access point frequency.
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347 It is rationalized that intersections without protected left-turn operation are more likely to have crashes between left-turn vehicles turning permissively through gaps in oncoming traffic. The collisions that result from permissive operation tend to involve oncoming traffic that is moving with high speed, relative to the vehicles involved in collisions associated with protectedonly operation.
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348 Predicted Probabilities for Unsignalized Crossroad Ramp Terminals Unsignalized ramp terminals are fairly common in rural areas. Of 301 ramp terminals in the database, about 65 percent are located in a rural area.
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