## 3 Results
### Analysis of AI and Smart Technology Applications Across Industry Sectors
#### 3.2.1 Integration of AI and Smart Technologies in Safety Management for the Construction Sector
For instance, the design of the PIPSC/CMF framework integrates indoor GIS with Bluetooth and RFID-based positioning technologies to develop comprehensive fire detection systems and command platforms. Additionally, AI-powered disaster response platforms combine historical disaster data, intelligent CCTV, sensors, satellite information, and social network data to support real-time emergency management. Smart street lighting systems, designed with GSM, GPS, cameras, LED lighting, and emergency buttons, are prototyped using Arduino and ATMEGA 32 microprocessors to enhance urban safety. These systems not only provide illumination but also integrate crime statistics analysis and case studies to optimize safety measures. Furthermore, the implementation of safety tags equipped with accelerometer sensors detects workers' immobilization, while command unit terminals use two-way wireless communication to issue escape commands during emergencies. Centralized control servers are developed to manage resources efficiently across multiple organizations. AI governance frameworks ensure the responsible use of AI systems in smart cities, protecting public safety while adhering to regulatory standards. Data mining and statistical analysis techniques are utilized to analyze fire occurrence patterns and causes, while Big Data Analysis (BD Analysis) identifies temporal and spatial fire hotspots and simulates fire scenarios to optimize firefighting and evacuation strategies. These applications demonstrate how AI and smart technologies address critical safety challenges in the disaster and public safety sector, leading to improved operational efficiency and enhanced safety outcomes. Table 4 summarizes AI and smart technology research methods for safety management factors in the disaster and public safety sector. By explicitly incorporating AI-related methodologies into safety management, the disaster and public safety sector can achieve significant improvements in safety protocols, emergency response effectiveness, and overall public safety. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Safety Management Factor** & **AI and Smart Technology Research Methods** & **Research Number** \\ \hline
**Efficiency and Cost Reduction** & - Designed smart street lighting system prototypes using cost-effective microprocessors. & [34; 35; 36] \\  & - Integrated IoT and knowledge management (KM) with sustainable development (SD) principles to form robust safety frameworks. & \\  & - Developed AI-based resource optimization systems for efficient safety management. & \\ \hline
**Quantitative Metrics-based Evaluation** & - Established quantitative metrics through Big Data Analysis for evaluating fire safety systems. & [27; 28; 35] \\  & - Utilized digital twin simulations to analyze safety scenarios and optimize response strategies. & \\  & - Applied quantitative metrics in BIM digital twin models for real-time safety assessments. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._3.2.4. Integration of AI and Smart Technologies in Safety Management for the Transport and Logistics Sector  
In the transport and logistics sector, key safety management factors such as traffic accident prevention and collision avoidance, railway safety and worker protection, maritime and industrial site safety management, and public transport safety management are being significantly enhanced through the integration of Artificial Intelligence (AI) and smart technologies. AI-based predictive models and machine learning algorithms analyze real-time traffic data, vehicle behavior, and environmental conditions to predict and prevent traffic accidents. These technologies enable proactive traffic management and collision avoidance systems in vehicles, reducing collision risks and improving overall road safety. For railway safety, AI algorithms process data from IoT sensors to detect equipment anomalies and predict maintenance needs, ensuring system reliability and preventing accidents caused by equipment failure.