@article{(Open Science Index):https://publications.waset.org/pdf/3237,
	  title     = {Crash Severity Modeling in Urban Highways Using Backward Regression Method},
	  author    = {F. Rezaie Moghaddam and  T. Rezaie Moghaddam and  M. Pasbani Khiavi and  M. Ali Ghorbani},
	  country	= {},
	  institution	= {},
	  abstract     = {Identifying and classifying intersections according to
severity is very important for implementation of safety related
counter measures and effective models are needed to compare and
assess the severity. Highway safety organizations have considered
intersection safety among their priorities. In spite of significant
advances in highways safety, the large numbers of crashes with high
severities still occur in the highways. Investigation of influential
factors on crashes enables engineers to carry out calculations in order
to reduce crash severity. Previous studies lacked a model capable of
simultaneous illustration of the influence of human factors, road,
vehicle, weather conditions and traffic features including traffic
volume and flow speed on the crash severity. Thus, this paper is
aimed at developing the models to illustrate the simultaneous
influence of these variables on the crash severity in urban highways.
The models represented in this study have been developed using
binary Logit Models. SPSS software has been used to calibrate the
models. It must be mentioned that backward regression method in
SPSS was used to identify the significant variables in the model.
Consider to obtained results it can be concluded that the main
factor in increasing of crash severity in urban highways are driver
age, movement with reverse gear, technical defect of the vehicle,
vehicle collision with motorcycle and bicycle, bridge, frontal impact
collisions, frontal-lateral collisions and multi-vehicle crashes in
urban highways which always increase the crash severity in urban
highways.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {3},
	  number    = {12},
	  year      = {2009},
	  pages     = {2779 - 2784},
	  ee        = {https://publications.waset.org/pdf/3237},
	  url   	= {https://publications.waset.org/vol/36},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 36, 2009},
	}