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http://dx.doi.org/10.12652/Ksce.2010.30.4D.351

Development of Hazard-Level Forecasting Model using Combined Method of Genetic Algorithm and Artificial Neural Network at Signalized Intersections  

Kim, Joong-Hyo (도로교통공단 교통과학연구원)
Shin, Jae-Man (한국도로공사 도로교통연구원)
Park, Je-Jin (한국도로공사 도로교통연구원)
Ha, Tae-Jun (전남대학교 토목공학과)
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.30, no.4D, 2010 , pp. 351-360 More about this Journal
Abstract
In 2010, the number of registered vehicles reached almost at 17.48 millions in Korea. This dramatic increase of vehicles influenced to increase the number of traffic accidents which is one of the serious social problems and also to soar the personal and economic losses in Korea. Through this research, an enhanced intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network will be developed in order to obtain the important data for developing the countermeasures of traffic accidents and eventually to reduce the traffic accidents in Korea. Firstly, this research has investigated the influencing factors of road geometric features on the traffic volume of each approaching for the intersections where traffic accidents and congestions frequently take place and, a linear regression model of traffic accidents and traffic conflicts were developed by examining the relationship between traffic accidents and traffic conflicts through the statistical significance tests. Secondly, this research also developed an intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network through applying the intersection traffic volume, the road geometric features and the specific variables of traffic conflicts. Lastly, this research found out that the developed model is better than the existed forecasting models in terms of the reliability and accuracy by comparing the actual number of traffic accidents and the predicted number of accidents from the developed model. In conclusion, it is expect that the cost/effectiveness of any traffic safety improvement projects can be maximized if this developed intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network use practically at field in the future.
Keywords
signalized intersection; traffic accident; traffic conflict; linear regression analysis; artificial neural network; genetic algorithm & artificial neural network;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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