Abstract
This paper expounds about adaptive traffic signals system (ATSS) by applying artificial intelligence and machine learning models. In order to improve emergency response times and guarantee the safety of both the general public and emergency responders, effective traffic management is essential. Machine-learning models describe the traffic situation by capturing data using advanced camera and uses algorithm to changes the traffic lights to green signal for emergency vehicles (EV) dynamically in real time and effectively. The EV approach is determined using object detection camera, RFID and inbuilt GPS radar present in the EV. This paper also presents a real-time EV alert in the real-time map service providers alerting the drivers to give way for the EV in advance. The research's findings highlight the potential advantages of ATSS systems for EV in cities. The adaptive system exhibits a notable decrease in emergency response times, potentially enhancing public safety and reducing the severity of critical incidents. The report also emphasizes the significance of data-driven decision-making in traffic management and the potential for technology to change how emergency services are provided in crowded urban areas.
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Gowthamy, J., Senthilselvi, A., Ram, A., Rohit, R., Niranjan, S. (2024). Emergency Alert and Adaptive Traffic Signal System Using Machine Learning. In: Senjyu, T., So–In, C., Joshi, A. (eds) Smart Trends in Computing and Communications. SmartCom 2024 2024. Lecture Notes in Networks and Systems, vol 948. Springer, Singapore. https://doi.org/10.1007/978-981-97-1329-5_7
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DOI: https://doi.org/10.1007/978-981-97-1329-5_7
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