Feature Extraction and Selection for Traffic Monitoring System using Machine Learning
SP. Maniraj1, Abhishek Raj2, Nitin Saseendran3, Shashank Shekhar4, Rohit Haridas5
1Saseendran, Department of Computer Science and Engineering, SRM IST, Ramapuram, Chennai (Tamil Nadu), India.
2Rohit Haridas, Department of Computer Science and Engineering, SRM IST, Ramapuram Chennai (Tamil Nadu), India.
3Shashank Shekhar, Department of Computer Science and Engineering, SRM IST, Ramapuram Chennai (Tamil Nadu), India.
4SP. Maniraj, Asst Professor, Department of Computer Science and Engineering, SRM IST, Ramapuram Chennai (Tamil Nadu), India.
5Abhishek Raj, Department of Computer Science and Engineering, SRM IST, Ramapuram Chennai (Tamil Nadu), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 401-403 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3554048619/19©BEIESP
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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: Planning and Scheduling is Very Hard Problem in Real life. In Transportation System Such Problem occure a lot as traffic congestion. This paper presents feature extraction, feature selection using machine learning based classification to make to provide alternatives to the flow of traffic and also to prevent road accidents. The traffic observance tasks square measure performed by analyzing strength of radio radiation received by mobile devices from beacons that square measure placed on opposite sides of a road. This approach is appropriate for crowd sourcing applications aimed toward reducing time period, congestion, and emissions. blessings of the introduced technique were incontestible throughout experimental analysis in real traffic conditions.
Keyword: Congestion, Incontestable.
Scope of the Article: Machine Learning