Abstract
Urban transportation is an essential part of modern urban development. With the rapid development of China's economy, the number of private vehicles is increasing year by year, and traffic congestion is becoming more and more frequent. However, as existing map software determines the congestion judgment method based on the number of navigation software installed in users' vehicles or cell phones, there is a defect of inaccurate prediction results. This paper compares and analyzes the object detection methods based on deep learning by studying and analyzing the current mainstream object detection frameworks and finally selects YOLOv3 as the object detection tool. We combine the driving recorder video and GPS navigation tracking to judge the congestion situation in real-time. It feeds the congestion status to the map, effectively making up for the shortcomings of mapping software in judging congestion, facilitating car owners and traffic management departments to make more good travel planning and traffic diversions, and improving traffic efficiency.
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.
