title: The significance of endogeneity problems in crash models: an examination of left-turn lanes in intersection crash models.
abstract: Crash prediction models are used for a variety of purposes including forecasting the expected future performance of various transportation system segments with similar traits. The influence of intersection features on safety have been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes compared to other segments in the transportation system. The effects of left-turn lanes at intersections in particular have seen mixed results in the literature. Some researchers have found that left-turn lanes are beneficial to safety while others have reported detrimental effects on safety. This inconsistency is not surprising given that the installation of left-turn lanes is often endogenous, that is, influenced by crash counts and/or traffic volumes. Endogeneity creates problems in econometric and statistical models and is likely to account for the inconsistencies reported in the literature. This paper reports on a limited-information maximum likelihood (LIML) estimation approach to compensate for endogeneity between left-turn lane presence and angle crashes. The effects of endogeneity are mitigated using the approach, revealing the unbiased effect of left-turn lanes on crash frequency for a dataset of Georgia intersections. The research shows that without accounting for endogeneity, left-turn lanes 'appear' to contribute to crashes; however, when endogeneity is accounted for in the model, left-turn lanes reduce angle crash frequencies as expected by engineering judgment. Other endogenous variables may lurk in crash models as well, suggesting that the method may be used to correct simultaneity problems with other variables and in other transportation modeling contexts.
authors.affiliation: Department of Civil Engineering and Engineering Mechanics, University of Arizona, Tucson, AZ 85712-0072, USA. dokkang@u.arizona.edu
authors.forename: Do-Gyeong
authors.initials: DG
authors.lastname: Kim
authors.forename: Simon
authors.initials: S
authors.lastname: Washington
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D012449:Safety