The Uses of Correlation Analysis Towards Artificial Intelligence in Public Transportation to Enhance Safety and Efficiency

Abstract

The application of AI-driven prediction models to improve transportation efficiency and safety is examined in this research. Artificial Intelligence (AI) enhances autonomous vehicle navigation, facilitates predictive maintenance, and optimizes traffic management by utilizing machine learning and real-time data. Important uses include preventing accidents, maintaining infrastructure, and optimizing traffic flow. The research underscores the noteworthy advantages and obstacles associated with the deployment of these technologies, stressing the necessity of sustained progress to promote more intelligent and durable transportation networks. By using the quantitative method, 375 respondents will focus on people who use public transportation in Klang Valley to answer the questionnaire. Statistical Package for Social Science (SPSS) will use to analyze the data. These results may serve as a reference to enhance the quality of the tourism sector in Malaysia.