With the continuous and vigorous development of China's road transportation industry, people's daily travel needs have increased sharply. As an important link connecting cities and rural areas and promoting economic exchanges, passenger vehicles have become increasingly prominent in terms of safety and efficient management during their operation, and have become a focus of attention in various sectors of society. To address this challenge, this article innovatively proposes a big data-driven Internet of Things (IoT) passenger safety monitoring and early warning system. This system deeply integrates IoT technology and deep learning (DL) algorithms, and achieves comprehensive and accurate monitoring of passenger safety by real-time collection and analysis of multi-dimensional information such as operational data of passenger vehicles, driver behavior data, and road environment data. The experimental results show that the system can timely detect and warn potential safety hazards, effectively reduce the incidence of traffic accidents, and improve the quality and efficiency of passenger transportation services. In addition, the system also has strong data analysis capabilities, which can provide scientific decision support for management departments, help optimize passenger route planning, and improve resource utilization efficiency.
Design of IoT Passenger Transport Safety Monitoring and Early Warning System Driven by Big Data
26.02.2025
229424 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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