A Count Data Model with Endogenous Covariates: Formulation and Application to Roadway Crash Frequency at Intersections
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This paper proposes an estimation approach for count data models with endogenous covariates. The maximum approximate composite marginal likelihood inference approach is used to estimate model parameters. The modeling framework is applied to predict crash frequency at urban intersections in Irving, Texas. The sample is drawn from the Texas Department of Transportation (TxDOT) crash incident files for the year 2008. The results highlight the importance of accommodating endogeneity effects in count models. In addition, the results reveal the increased propensity for crashes at intersections with flashing lights, intersections with crest approaches, and intersections that are on frontage roads.
At the time of publication Chandra R. Bhat, Kathryn Born, and Raghuprasad Sidharthan were at the University of Texas at Austin, and Prerna C. Bhat was at Harvard University.