Abstract
Traffic congestion auto identification is a complicated problem. Many identification methods have been developed. SVM is taken as one of the most efficient traffic congestion identification methods. But the training computation cost of SVM is expensive. General SVM is difficult to be used in practical applications because that traffic congestion identification is a real-time task. Parallel SVM can improve the training speed markedly. It is possible to apply PSVM to practical applications. In this paper, PSVM is adopted to identify traffic congestion. Through example analysis, the training speed is improved without decreasing the traffic congestion identification precision. It illustrates that PSVM is suitable to be applied in practice.
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.
