Traffic Signal Scheduling using Machine Learning
Onkar Nichat1, Suraj Kulkarni2, Aditya Mane3, Siddhesh Naik4, Shubham Bhandari5
1Mr. Onkar Nichat, Department of Computer Science and Engineering, Imperial College of Engineering and Research, Wagholi, Pune (Maharashtra), India.
2Mr. Suraj Kulkarni, Department of Computer Science and Engineering, Imperial College of Engineering and Research, Wagholi, Pune (Maharashtra), India.
3Mr. Aditya Shripad Mane, Department of Computer Engineering, JSPM’s Imperial College of Engineering and Research, Wagholi, Pune (Maharashtra), India.
4Mr. Siddhesh Naik, Department of Computer Engineering, JSPM’s Imperial College of Engineering and Research, Wagholi, Pune (Maharashtra), India.
5Mr. Shubham Bhandari, Assistant Professor, Jayawant Shikshan Prasarak Mandal’s Imperial college of engineering and research, Wagholi, Pune (Maharashtra), India.
Manuscript received on 20 February 2023 | Revised Manuscript received on 14 March 2023 | Manuscript Accepted on 15 March 2023 | Manuscript published on 30 March 2023 | PP: 111-116 | Volume-11 Issue-6, March 2023 | Retrieval Number: 100.1/ijrte.F74890311623 | DOI: 10.35940/ijrte.F7489.0311623
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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: Within the past few years the number of vehicles increased drastically and therefore the traffic of vehicles became a major issue in urban as well as in rural areas. Major traffic is happening in the area where many roads do intersect with each other. Our existing traffic signal is not real-time and it is run according to how it is programmed earlier irrespective of traffic. To avoid traffic, traffic signals should give the priority to the road that has the maximum density of vehicles. By doing this we can pass the maximum number of vehicles in a certain period of time. This type of signal acts according to the real-time situation, and take a decision smartly. Hence this system is also called a smart traffic light system. The purpose of this study is to get the traffic situation on the roads in real-time and acts accordingly. Using a web camera that should be mounted on the signals, we can get real-time footage of the roads and by using image processing methods, we can determine the densities of vehicles on each road. Signals which are programmed priorly or wrong signal scheduling was found to play the greatest role in causing vehicle traffic. This smart traffic signal scheduling system is definitely a better option in comparison with existing traffic signal scheduling as it is taking the decision according to the traffic situations.
Keywords: Image recognition, Machine Learning, Image Processing.
Scope of the Article: Machine Learning