Patterns of injuries and motor-vehicle traffic accidents in Athens

2006 ◽  
Vol 13 (3) ◽  
pp. 190-193 ◽  
Author(s):  
E. Pikoulis ◽  
V. Filias ◽  
N. Pikoulis ◽  
P. Daskalakis ◽  
E. D. Avgerinos ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Motor Vehicle ◽  
Vehicle Traffic

Risk factors of fatality in motor vehicle traffic accidents

1994 ◽  
Vol 26 (3) ◽  
pp. 391-397 ◽  
Author(s):  
Akira Shibata ◽  
Katsuhiro Fukuda
Keyword(s):  
Risk Factors ◽  
Traffic Accidents ◽  
Motor Vehicle ◽  
Vehicle Traffic

Hospital-based emergency department visits in children with motor vehicle traffic accidents: estimates from the nationwide emergency department sample

2014 ◽  
Vol 11 (3) ◽  
pp. 261-266 ◽  
Author(s):  
Veerajalandhar Allareddy ◽  
Ingrid M. Anderson ◽  
Min Kyeong Lee ◽  
Veerasathpurush Allareddy ◽  
Sankeerth Rampa ◽  
...  
Keyword(s):  
Emergency Department ◽  
Traffic Accidents ◽  
Motor Vehicle ◽  
Emergency Department Visits ◽  
Vehicle Traffic

Prediction Method of Motor Vehicle Traffic Accident Based on Support Vector Machine

2014 ◽  
Vol 631-632 ◽  
pp. 284-287
Author(s):  
Bo Yang ◽  
Li Na Zhang
Keyword(s):  
Social Influence ◽  
Index System ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Motor Vehicle ◽  
Rapid Development ◽  
Prediction Method ◽  
Living Standard ◽  
Support Vector ◽  
Vehicle Traffic

With the rapid development of economy and the improvement of people's living standard, there are more and more vehicles in China, with the increase of traffic accidents. In this paper, by analyzing the factors of social influence on motor vehicle traffic accident, we establish the index system, that is corresponding relationship of motor vehicle traffic accident and factors of social influence, According to this index system, design of motor vehicle traffic accident prediction method based on SVM. Based on the statistical data of social factors and motor vehicle traffic accident in 1985-2012 in china, to train the SVM model, at the same time, the kernel function and parameters of SVM used were setting and compared. The experimental results show that, the accuracy of the use of the RBF function is 97.2%, predicted by using time 95ms, with higher accuracy and faster computing speed.

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