Turkish Journal of Computer and Mathematics Education
Journal license

Journal

Turkish Journal of Computer and Mathematics Education


Volume
& Issue

Volume 14, Issue 3


Published
on


Pages

1217-1225


DOI

Article

A Multi-Rivulet Feature Synthesis Tactic for Traffic Prediction


Authors

Shiva Bhavani K* Affiliation:
Department of IoT, Malla Reddy Engineering College for Women, Hyderabad
, M. Bhanu Affiliation:
Department of IoT, Malla Reddy Engineering College for Women, Hyderabad
, S. Akshitha Affiliation:
Department of IoT, Malla Reddy Engineering College for Women, Hyderabad
, P. Swejal Affiliation:
Department of IoT, Malla Reddy Engineering College for Women, Hyderabad
and Ch. Divya Affiliation:
Department of IoT, Malla Reddy Engineering College for Women, Hyderabad


Abstract

As the problem of urban traffic congestion intensifies, there is a pressing need for the introduction of advanced technology and equipment to improve the state-of-the-art of traffic control. The current methods used such as timers or human control are proved to be inferior to alleviate this crisis. In this paper, a system to control the traffic by measuring the realtime vehicle density using canny edge detection with digital image processing is proposed. This imposing traffic control system offers significant improvement in response time, vehicle management, automation, reliability and overall efficiency over the existing systems. Besides that, the complete technique from image acquisition to edge detection and finally green signal allotment using four sample images of different traffic conditions is illustrated with proper schematics and the final results are verified by hardware implementation.


Keywords


Citation

K, S. B., Bhanu, M., Akshitha, S., Swejal, P., & Divya, C. (2023). A multi-rivulet feature synthesis tactic for traffic prediction. Turkish Journal of Computer and Mathematics Education, 14(3), 1217–1225.

Published by: Engineering Journals

Engineering Journals Logo