Density-Based Traffic Control System for Emergency Vehicles using ArtificialIntelligence
Priyanka Abhang1, Vinit Agrharkar2, Shriya Akella3, Siddhant Bhagat4, Shrishti Kaushik5, Piyush Mishra6, YS Rao7
1Priyanka Abhang*, Student, Department of Electronics and Telecommunications, S.P.I.T, Mumbai, India.
2Vinit Agrharkar, Student, Department of Electronics and Telecommunications, S.P.I.T, Mumbai, India.
3Shriya Akella, Student, Department of Electronics and Telecommunications, S.P.I.T, Mumbai, India.
4Siddhant Bhagat, Student, Department of Electronics and Telecommunications, S.P.I.T, Mumbai, India.
5Shrishti Kaushik, Student, Department of Electronics and Telecommunications, S.P.I.T, Mumbai, India.
6Piyush Mishra, is currently a final year student in pursuing Electronics and Telecommunications Engineering at S.P.I.T, Mumbai. His research interests include Data Science, Machine Learning and IOT.
7Dr. Y.Srinivasa Rao, Vice-Principal, Department of Electronics and Telecommunications Engineering Sardar Patel Institute of Technology
Manuscript received on June 08, 2020. | Revised Manuscript received on June 25, 2020. | Manuscript published on June 30, 2020. | PP: 1217-1221 | Volume-9 Issue-5, June 2020. | Retrieval Number: E1228069520/2020©BEIESP | DOI: 10.35940/ijeat.E1228.069520
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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 (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Fire brigade officers, health care personnel, police are often delayed due to traffic congestion, across major cities in India. Considering the predicament, Artificial Intelligence has the potential to enable us to solve such problems by adopting a number of unique perspectives and approaches, especially in this domain. The solution developed by us enables an emergency vehicle driver to select the route to reach the destination as quickly as possible. As cameras are deployed at most of the traffic signals today, especially in cities where traffic congestion is a major pain point, Video Analytics can be used for calculating vehicle count, which will be streamed and updated continually. We create effective algorithms to alter the time of the traffic signals based on this real-time vehicle count, the distance of the vehicle from the signal, the bearing angle made by the vehicle with the signal and also by making sure that the traffic congestion doesn’t increase exponentially and multiple emergency vehicles do not put the system in a deadlock. The loss of life due to accidents and the delay in getting the required treatment must be avoided. The designed system will automatically control traffic light intervals based on vehicle density. This solution will allow an emergency vehicle to reach its destination during emergencies, plying on the best possible route, in the most decongested traffic conditions, which will be facilitated by specifically developed algorithms. To save human life from accidents and unnecessary delays due to traffic congestion, is the main aim of our system.
Keywords: Anti-collision algorithm, Computer Vision, Real-time location monitoring, Video Analytics