Crash-severity modeling

SUMMARY Thispaperconsidersmixed,orrandomcoeAcients,multinomiallogit (MMNL)modelsfordiscreteresponse, andestablishesthefollowingresults.Undermildregularityconditions,anydiscretechoicemodelderivedfrom random utility maximization has choice probabilities that can be approximated as closely as one pleases by a MMNLmodel.PracticalestimationofaparametricmixingfamilycanbecarriedoutbyMaximumSimulated LikelihoodEstimationorMethodofSimulatedMoments,andeasilycomputedinstrumentsareprovidedthat make the latter procedure fairly eAcient. The adequacy of a mixing specification can be tested simply as an omittedvariabletestwithappropriatelydefinedartificialvariables.Anapplicationtoaproblemofdemandfor alternativevehiclesshowsthatMMNL provides aflexible and computationally practical approach todiscrete response analysis. Copyright # 2000 John Wiley & Sons, Ltd.

/pdf/mixed-mnl-models-for-discrete-response-2n67bd3vmx.pdf

Mixed mnl models for discrete response

SUMMARY A general class of regression models for ordinal data is developed and discussed. These models utilize the ordinal nature of the data by describing various modes of stochastic ordering and this eliminates the need for assigning scores or otherwise assuming cardinality instead of ordinality. Two models in particular, the proportional odds and the proportional hazards models are likely to be most useful in practice because of the simplicity of their interpretation. These linear models are shown to be multivariate extensions of generalized linear models. Extensions to non-linear models are discussed and it is shown that even here the method of iteratively reweighted least squares converges to the maximum likelihood estimate, a property which greatly simplifies the necessary computation. Applications are discussed with the aid of examples.

https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.2517-6161.1980.tb01109.x

Regression Models for Ordinal Data

/pdf/specification-tests-for-the-multinomial-logit-model-fwjnd5ax49.pdf

Specification tests for the multinomial logit model

Econometric Models of Probabilistic Choice

This article describes the gologit2 program for generalized ordered logit models. gologit2 is inspired by Vincent Fu's gologit routine (Stata Technical Bulletin Reprints 8: 160–164) and is backward...

/pdf/generalized-ordered-logit-partial-proportional-odds-models-1kcdou8ptt.pdf

Crash-severity modeling

References

Mixed mnl models for discrete response

Regression Models for Ordinal Data

Specification tests for the multinomial logit model

Econometric Models of Probabilistic Choice

Generalized ordered logit/partial proportional odds models for ordinal dependent variables

Related Papers (5)

Crash severity modeling in transportation systems

Understanding the Relationship Between Crash Severity, Change in Velocity, and Driver’s Reaction

A Deep Learning Approach to Predict Severity Levels of Work Zone Crashes

Work zone crash analysis and modeling to identify factors associated with crash severity and frequency

A joint model of crash type and severity for two-vehicle crashes