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AuthorKhan, Hameed
AuthorKushwah, Kamal K.
AuthorMaurya, Muni Raj
AuthorSingh, Saurabh
AuthorJha, Prashant
AuthorMahobia, Sujeet K.
AuthorSoni, Sanjay
AuthorSahu, Subham
AuthorSadasivuni, Kishor Kumar
Available date2023-05-17T10:29:20Z
Publication Date2022-01-16
Publication NameComputing
Identifierhttp://dx.doi.org/10.1007/s00607-021-01038-1
CitationKhan, H., Kushwah, K. K., Maurya, M. R., Singh, S., Jha, P., Mahobia, S. K., ... & Sadasivuni, K. K. (2022). Machine learning driven intelligent and self adaptive system for traffic management in smart cities. Computing, 1-15.
ISSN0010-485X
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85123207227&origin=inward
URIhttp://hdl.handle.net/10576/42854
AbstractTraffic congestion is becoming a serious problem with the large number of vehicle on the roads. In the traditional traffic control system, the timing of the green light is adjusted regardless of the average traffic rate at the junction. Many strategies have been introduced to solve and improve vehicle management. However, in order to handle road traffic issues, an intelligent traffic management solution is required. This article represents a self adaptive real-time traffic light control algorithm based on the traffic flow. We present a machine learning approach coupled with image processing to manage the traffic clearance at the signal junction. The proposed system utilizes single image processing via neural network and You Only Look Once (YOLOv3) framework to establish traffic clearance at the signal. We employed YOLO architectures because it is accurate in terms of mean average precision (mAP), interaction over union (IOU) values and fast in object detection tasks as well. It runs significantly faster than other detection methods with comparable performance. The average processing time of single image was estimated to be 1.3 s. Further based on the input from YOLO we estimated the ‘on’ time period green light for effective traffic clearance. Several real time parameters like number of vehicles (two wheelers, four wheelers), road width and junction crossing time are considered to estimate the ‘on’time of green light. Moreover, we used the real traffic images to test the performance and trained the system with different dataset. Our experiments investigation reveals that the predicted vehicle counts were well matched with the actual vehicle count and proposed method apprehended an average accuracy of 81.1%. The reported strategy is self adaptive, highly accurate, fast and has the potential to be implemented in the traffic clearance at the junctions.
Languageen
PublisherSpringer Nature
SubjectImage processing
Machine learning YOLO
Open CV
Traffic management
TitleMachine learning driven intelligent and self adaptive system for traffic management in smart cities
TypeArticle
Pagination1-15
Issue Number5
Volume Number104
ESSN1436-5057
dc.accessType Abstract Only


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