Reliability of Crash Prediction Models: A Guide for Quantifying and Improving the Reliability of Model Results (2021) / Chapter Skim
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From page 47... ...
47 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. One question a practitioner may face is whether a CPM with more variables is more reliable than a simpler CPM.
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From page 48... ...
48 Reliability of Crash Prediction Models: A Guide for Quantifying and Improving the Reliability of Model Results as an independent variable in the CPM even though the CPM was developed from a dataset containing 10% curved segments and 90% tangent segments. If the CPM were applied to a group of segments that have 60% horizontal curves, bias would almost certainly exist given that expected crash frequencies are higher on curved segments than on tangents.
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From page 49... ...
Quantifying the Reliability of CPM Estimates for How the Number of Variables in Crash Prediction Models Affects Reliability 49 The procedure also provides a related rating of reliability that could contribute to an overall rating system of CPM reliability. The procedure combines the steps for both Scenario 3, Case A, and Scenario 3, Case B
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From page 50... ...
50 Reliability of Crash Prediction Models: A Guide for Quantifying and Improving the Reliability of Model Results It is recommended to use The Calibrator (https://safety.fhwa.dot.gov/rsdp/toolbox- content.aspx? toolid=150)
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From page 51... ...
Quantifying the Reliability of CPM Estimates for How the Number of Variables in Crash Prediction Models Affects Reliability 51 outlined in the HSM (Chapter 3, Chapter 4, Part C, Appendix A)
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From page 52... ...
52 Reliability of Crash Prediction Models: A Guide for Quantifying and Improving the Reliability of Model Results Measure Reliability Rating High Medium Low Critically Low Spearman's correlation coefficient (Rho) 0.90 to 1.00 0.70 to 0.89 0.40 to 0.69 <0.40 % False positives in top 30 sites <10% 11% to 25% 26% to 40% >40% % False positives in top 50 sites <7.5% 7.6% to 20% 21% to 40% >40% % False positives in top 100 sites <5% 6% to 15% 15% to 40% >40% Table 14.
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From page 53... ...
Quantifying the Reliability of CPM Estimates for How the Number of Variables in Crash Prediction Models Affects Reliability 53 is evaluated for each GOF measure for design applications or evaluation of countermeasures. Using Table 14, each CPM is evaluated for each GOF measure for network screening applications.
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From page 54... ...
54 Reliability of Crash Prediction Models: A Guide for Quantifying and Improving the Reliability of Model Results 2. Each of the measures estimates in sub-step 1 was divided by the respective value for the full CPM.
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From page 55... ...
Quantifying the Reliability of CPM Estimates for How the Number of Variables in Crash Prediction Models Affects Reliability 55 Step 6B. Determine Spearman's Correlation Coefficient and Compare the Rankings For each ranked list, Spearman's correlation coefficient (Rho)
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From page 56... ...
56 Reliability of Crash Prediction Models: A Guide for Quantifying and Improving the Reliability of Model Results The results indicate that among the alternate CPMs evaluated, CPM 1 and CPM 2 should not be applied as their reliability ratings are Critically Low for the predicted crash values GOF measures and Low for the network screening GOF measures. CPM 3 and CPM 4 are rated Medium for predicted crash values GOF measures and network screening GOF measures, while CPM 5 is rated High for both sets of measures.
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