چكيده به لاتين
There are active and inactive approaches in accident analysis. In the researches that have been carried out by an active approach, incident points are observed before the accidents are happened. Inactive approach involves modelings that deal with crash statistics after the utilization of the roads and includes an evaluation of parameters affecting the number and severity of accidents.
In this study, two intelligent models were used to predict the severity of an accident among drivers over 55 years of age, who had an obstacle collision; artificial neural networks and genetic algorithms. In this age group of drivers, there is a decrease in the accuracy and understanding of the environment and a longer response time. Thus, it is necessary to form intelligent models to predict the severity and the importance of these crashes as well as the amount of effective parameters and the importance of them, in this category of accidents.
In this research, the database of California Department of Transportation was used to obtain the accidents data needed for modeling. After data preparation, a random data removal technique was used to improve the performance of intelligent prediction models. Afterwards, the data were normalized to begin the modeling, and were used to train the network and verification of the model.
At the end of this research, while designing and selecting the most optimal model, based on the accuracy of each model, the importance of each effective parameter is determined in each artificial neural network model and genetic algorithm. Finally, the results are presented in the following tables and charts. After the formation of both models, the neural network model which is based on training, turns out to be extensively more accurate than intelligent models with optimal functions of the genetic algorithm.
