Intelligent Traffic Management System
Vedant Singh1, Vyom Unadkat2, Pratik Kanani3

1Vedant Singh Information Technology student at Dwarkadas J. Sanghvi College of Engineering.
2Vyom Unadkat Information Technology from the University of Mumbai.
3Pratik Kanani. Pratik Doctoral studies from The MS university of Baroda.

Manuscript received on 13 August 2019. | Revised Manuscript received on 17 August 2019. | Manuscript published on 30 September 2019. | PP: 7592-7597 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6168098319/2019©BEIESP | DOI: 10.35940/ijrte.C6168.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Urbanization has presented opportunities of progress which has attracted people from rural areas to the cities thus leading to mass migration. This migration has been going on for decades all around the globe and has reached a point of saturation. The area of the city remains the same but the population density has increased multiple times. Commuting for work is a scene of chaos on the roads. Though there are modes of public transport, roadway is the major mode of commute and the load on roadways is ever increasing due to the rise in population. There is hardly any scope to expand the area of the roadways. The rise in the number of vehicles each year has saturated the capacity the roads were built to carry. This leads to congestion and long hours of traffic on a daily basis which tests the patience of citizens. This provokes the daily commuters to violate the traffic rules which may sometimes amount to grave accidents. Even on Highways, the empty roads entice drivers to experience the thrill of speed overlooking the fact that they are putting themselves at risk. There have been regulations imposed to reduce the chance of an accidents by implementing rules and levying heavy fines on traffic violations. Traffic cameras have been installed all around the city to monitor for traffic violations and get hold of violators. With the technological advancements to store and process large chunks of data efficiently using techniques like Deep Learning and Computer Vision, this paper proposes an automated system to detect Traffic Violations using YOLOv3 to detect and track vehicles and save a snapshot in case a violation is committed.
Keywords: Computer Vision, Deep Learning, Object Detection, Traffic Violation, YOLOv3.

Scope of the Article: Deep Learning