کاربرد مدل های رگرسیون پرصفر در مدل سازی تصادفات بزرگراه های درون شهری

We handle in this paper, the research that have been performed to recognize the factors that affect
crash frequency and severity in urban highways and use crash data of Mashhad urban highways as a
case study. Statistical models that have been used in this research include Poisson, Negative binomial,
Zero-inflated poisson and Zero-inflated negative binomial regression models. Traffic flow related
variables and road geometric related variables have been used as independent variables of models. We
are interested in this study, to inspect the efficiency of Zero-inflated models against simple Poisson
and Negative binomial regression models in modeling accidents on urban highways. Special task that
have been done in this research, is separation of total traffic volume into passenger cars, heavy
vehicles and light non-passenger vehicles volume. Through this special, Researcher intend to have an
especial look at the role of traffic volume in accident occurrence to see precisely, which part of traffic
have effective role or more effective role in crash occurrence.
Accident data are two-level data, the first level is road segments i.e. highway is divided into several
segments. The segmentation is based on total traffic volume i.e. each segment has a constant volume.
The second level is daily hours; peak hour traffic considered as the first sublevel, day non-peak hour
traffic the second and night non-peak hour traffic as the third sublevel. SAS 9.1 software has been
used to fulfill statistical computations. It turns up, after statistical analyses, which factors affect crash
occurrence and which do not have much effect. Comparisons between obtained results and other
researchers’ results have been made then. The main object of researcher is to assess the efficiency of
Zero-inflated models against Poisson and Negative binomial regression models in modeling urban
highways crashes. This aim is followed by, with evaluating goodness of fit and making comparison
between models.
The Results of study show that the presence and number of access roads and horizontal curves on
highway segments increase the likelihood of accidents, both no injury and more severe. Also
increment of speed and number of lanes increase the likelihood of no injury accidents, but not more
severity ones. The conclusions also demonstrate that the volume of passenger cars and light nonpassenger
car vehicles increase the likelihood of no injury accidents, but heavy vehicles volume does
not have much effect on occurrence of no injury accidents, also light vehicles increase the likelihood
of more severe accidents, but passenger cars and heavy vehicles volume does not have much effect on
occurrence of severe accidents. Finally, the results of research indicate that Zero-inflated negative
binomial regression model is best fitting the modeling of accidents, whether no injury or more severe
and consequently, the efficiency of zero-inflated models in modeling accidents on urban highways is
approved.