Hybrid Sensor-based traffic monitoring and managing urban areas in hyderabad city
Hybrid Sensor-based traffic monitoring and managing urban areas in hyderabad city
- Author(s): J. Moses C 1 and K. M. V. V. Prasad 1
- DOI: 10.1049/icp.2021.0903
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- Author(s): J. Moses C 1 and K. M. V. V. Prasad 1
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View affiliations
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Affiliations:
1:
Department of ECE , Centre for Signal Processing and Communication Systems Sreyas Institute of Engineering and Technology , Hyderabad , India
Source:
3rd Smart Cities Symposium (SCS 2020),
2021
p.
119 – 123
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Affiliations:
1:
Department of ECE , Centre for Signal Processing and Communication Systems Sreyas Institute of Engineering and Technology , Hyderabad , India
- Conference: 3rd Smart Cities Symposium (SCS 2020)
- DOI: 10.1049/icp.2021.0903
- ISBN: 978-1-83953-522-2
- Location: Online Conference
- Conference date: 21-23 September 2020
- Format: PDF
Traffic control and management is an important factor in related with development of urban. A smart city comprises of smart health care, smart environment, smart home and smart road transportation system. For all these modernizations both artificial intelligence (AI) and machine learning (ML) take major parts in smart operation. By using hybrid sensors, the traffic light systems can be triggered depending on the measured volume of traffic, including temporary deactivation of the traffic lights when roads are quiet. The traffic data collected with the hybrid technology is then used to analyse and control traffic volumes. The radar technology for traffic monitoring applications include traffic light controls, speed enforcement, traffic monitoring and traffic classification. Therefore, this proposal suggests exploring design and prototype model and utilize the model for appropriate road traffic monitoring and management system. The objectives of the proposed system are to reduce traffic congestion, to count traffic flow, to classify the traffic, to provide green environment by reducing air-pollution generated by vehicles, to provide safety for stakeholders (pedestrians and driver and passengers) and to forecast traffic flow.
Inspec keywords: road traffic control; intelligent transportation systems; learning (artificial intelligence); traffic engineering computing; smart cities
Subjects: Machine learning (artificial intelligence); Traffic engineering computing; Road-traffic system control