Abstract
This study aims at investigating the relationship between driver injury severity level and driver, vehicle, roadway, and environmental factors on the basis of support vector machine (SVM) model. The multi layer perceptron (MLP) artificial neural network model formed the benchmark for evaluating the performance of SVM model. Historical crash data of the Wisconsin State from 1994 to 2009 were used as the data source. The best SVM model provided an overall classification accuracy of 63.4% and 58.6% for the training group datasets and testing group datasets, respectively. By comparing the performance of SVM model with those of MLP models, SVM model demonstrated satisfactory predicting accuracy with less datasets over-fitting, therefore, SVM model is capable of predicting driver injury severity levels in freeway rear-end crash.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
