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@article{ART002934480},
author={Jeong-In Park},
title={Deep Learning Based Emergency Response Traffic Signal Control System},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2023},
volume={28},
number={2},
pages={121-129},
doi={10.9708/jksci.2023.28.02.121}

TY - JOUR
AU - Jeong-In Park
TI - Deep Learning Based Emergency Response Traffic Signal Control System
JO - Journal of The Korea Society of Computer and Information
PY - 2023
VL - 28
IS - 2
PB - The Korean Society Of Computer And Information
SP - 121
EP - 129
SN - 1598-849X
AB - In this paper, we developed a traffic signal control system for emergency situations that can minimize loss of property and life by actively controlling traffic signals in a certain section in response to emergency situations. When the emergency vehicle terminal transmits an emergency signal including identification information and GPS information, the surrounding image is obtained from the camera, and the object is analyzed based on deep learning to output object information having information such as the location, type, and size of the object. After generating information tracking this object and detecting the signal system, the signal system is switched to emergency mode to identify and track the emergency vehicle based on the received GPS information, and to transmit emergency control signals based on the emergency vehicle's traveling route. It is a system that can be transmitted to a signal controller. This system prevents the emergency vehicle from being blocked by an emergency control signal that is applied first according to an emergency signal, thereby minimizing loss of life and property due to traffic obstacles.
KW - Emergency Vehicle;GPS;Object Response;Signal Control;Vehicle Tracking
DO - 10.9708/jksci.2023.28.02.121
ER -

Jeong-In Park. (2023). Deep Learning Based Emergency Response Traffic Signal Control System. Journal of The Korea Society of Computer and Information, 28(2), 121-129.

Jeong-In Park. 2023, "Deep Learning Based Emergency Response Traffic Signal Control System", Journal of The Korea Society of Computer and Information, vol.28, no.2 pp.121-129. Available from: doi:10.9708/jksci.2023.28.02.121

Jeong-In Park "Deep Learning Based Emergency Response Traffic Signal Control System" Journal of The Korea Society of Computer and Information 28.2 pp.121-129 (2023) : 121.

Jeong-In Park. Deep Learning Based Emergency Response Traffic Signal Control System. 2023; 28(2), 121-129. Available from: doi:10.9708/jksci.2023.28.02.121

Jeong-In Park. "Deep Learning Based Emergency Response Traffic Signal Control System" Journal of The Korea Society of Computer and Information 28, no.2 (2023) : 121-129.doi: 10.9708/jksci.2023.28.02.121

Jeong-In Park. Deep Learning Based Emergency Response Traffic Signal Control System. Journal of The Korea Society of Computer and Information, 28(2), 121-129. doi: 10.9708/jksci.2023.28.02.121

Jeong-In Park. Deep Learning Based Emergency Response Traffic Signal Control System. Journal of The Korea Society of Computer and Information. 2023; 28(2) 121-129. doi: 10.9708/jksci.2023.28.02.121

Jeong-In Park. Deep Learning Based Emergency Response Traffic Signal Control System. 2023; 28(2), 121-129. Available from: doi:10.9708/jksci.2023.28.02.121

Jeong-In Park. "Deep Learning Based Emergency Response Traffic Signal Control System" Journal of The Korea Society of Computer and Information 28, no.2 (2023) : 121-129.doi: 10.9708/jksci.2023.28.02.121