Canadian Journal of Civil Engineering · 2016 · 23 citations · 29 references
Public HealthAutomotive EngineeringRisk AssessmentStatistical ModelingVehicle Safety EngineeringDiscrete Choice ModelsModel SelectionModel Selection For Categorical DataRoad SafetyTraffic SafetyTransportation SafetyAccident Severity ModelingCrash Injury SeverityCrash Severity AnalysisModel Structure SelectionParameter InterpretationPartial Proportional Odds ModelsSeverity AnalysisValidation Of Crash Models
Run-off-road (ROR) crashes account for a large proportion of fatalities and serious injuries to vehicle occupants, especially in rural areas. While performing crash severity analysis using discrete choice models (DCMs), researchers may be confused by the following questions: first, should an ordered or unordered model structure be used and secondly, which modeling level is more appropriate, basic or advanced? A model selection framework is developed considering the following factors: (1) model structure — ordered or unordered; (2) intrinsic deficiency of each model; (3) computational burdens; (4) complexity of parameter interpretation; and (5) model fitness. Historical ROR crash data were utilized to illustrate how to choose an appropriate DCM based on the proposed framework. Using statistical tests and comparison of evaluation and validation measurements, both the mixed logit model and the partial proportional odds model yield a reasonable performance. All factors that significantly affect the severity level of a single-vehicle ROR crash were identified as well.
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Specification Tests for the Multinomial Logit Model
Jerry A. Hausman, Daniel McFadden · Econometrica · 1984 · 2.5K citations · Full text