In recent years, numerous researches have been carried out with purpose of predicting motor vehicle crashes on transportation facilities as freeways and urban or rural highways. Accident process can be modeled successfully with assuming a dual-state data-generating process. Based on this assumption, road components like intersections or road segments have two states of perfectly safe and unsafe. Zero-inflated regression models are applied to model accidents usually in cases of preponderance of excess zero data in crash data. We handle in this research, the investigation into effective factors on frequency and severity of accidents on urban highways and use crash data of Mash had-Iran urban highways as a case study. We use in this study, the Poisson, Negative binomial, Zero-inflated Poisson and Zero-inflated Negative binomial regression models for modeling accidents, and traffic flow and road geometry related variables as in dependent variables of models. In addition to identifying effective factors on crash occurrence probability, we deal with comparison of models, evaluate and prove the efficiency of Zero-inflated regression models against traditional Poisson and Negative binomial models.