Understanding and Communicating Reliability of Crash Prediction Models (2021) / Chapter Skim
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51 Chapter 5. Procedures for Quantifying the Reliability of Crash Prediction Model Estimates with a Focus on How the Number of Variables in CPM Affects Reliability Introduction In some instances, CPMs may include only traffic volumes as predictor variables, traffic volumes plus a limited number of geometric and traffic control variables, or, traffic volumes and a large number of geometric and traffic control variables.
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52 hand, there are variables with a relatively low impact on expected crashes and the inclusion of those variables would do little to reduce bias and increase reliability. For example, the shoulder type may have little impact on the frequency of total crashes for rural multilane roads.
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53 This procedure essentially answers two questions: 1. Which of multiple CPMs to apply, particularly when the number of variables varies between SPFs?
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54 where: f(k) = estimate of the dispersion parameter.
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55 Procedural Steps It is important to first mention that the focus of the procedure is not on developing a CPM but to help analysts determine which of multiple candidate CPMs to use, or, if not all variables are readily available for applying a CPM how much reliability is lost if those variables are not used in applying the CPM. The methodology to evaluate reliability issues related to the number of variables in a CPM makes use of the FHWA Calibrator Tool (https://safety.fhwa.dot.gov/rsdp/toolbox-content.aspx?
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56 guidance is for CPMs that will be used for Network Screening. The guidance classifies each measure for reliability as High, Medium, Low or Critically Low.
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57 Urbrur = 0 if rural environment; 1 if urban Surftype = 1 if asphalt; 0 if concrete Avgshldwid = average of left and right shoulder width in feet Lanewid = lanewidth in feet Terrain = -0.3181 if flat, 0.0000 if rolling, 0.3464 if mountainous overdispersion parameter, k = 0.7667 To remove variables from the CPM to compare its reliability when using fewer variables, the average value for that variable was used. For non-continuous variables such as terrain, the average value of that variable multiplied by its parameter estimate was used to in effect remove that variable from the CPM.
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58 Table 22. General GOF Statistics Cont'd.
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59 Table 25. Network Screening Evaluation for Example.
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