Automated Injury Detection and Alert Systems in Public Transportation Integrating IoT with Convolutional Neural Networks

Innovative methods to improve public transportation safety and response mechanisms have emerged due to Internet of Things (IoT) and artificial intelligence (AI) technologies. IoT devices and Convolutional Neural Networks (CNNs), a class of deep learning algorithms analyzing visual imagery, are used to create an innovative Automated Injury Detection and Alert System (AIDAS) for public transportation. The proposed AIDAS architecture employs IoT sensors and cameras in buses and trains to monitor and evaluate passengers’ physical health in real time. These sensors can detect unexpected impacts, odd motions, and distressing noises. A strong CNN model trained to detect passenger injury or bodily damage receives live images from the onboard cameras. An automatic alarm process is activated immediately upon injury detection. The closest emergency response teams and transportation system operators get complete event reports, including time, position (by GPS coordinates), and injury type. A quick reaction to accidents with this real-time alarm system might save lives and reduce public transportation injuries. AIDAS’ use of IoT and CNN technology is innovative in public transportation safety. The technology significantly improved emergency response times and reduced unattended injuries in public transport networks. The system’s excellent damage detection accuracy is due to CNNs’ advanced image processing and pattern recognition skills and IoT devices’ sensory inputs. It also addresses privacy issues and the necessity for strong data encryption and proposes solutions. Finally, it analyzes how AIDAS affects public transportation safety and future research and development in this critical field.