关键词: 智能交通系统；深度学习；教学应用

Abstract: With the rapid development of intelligent transportation systems worldwide, education on intelligent transportation systems, as a critical link in cultivating professional talents, faces challenges such as teaching content lagging behind technological advancements, weak practical components, and insufficient student innovation capabilities. Aiming at the practical challenges in intelligent transportation system education, this paper proposes teaching application strategies based on deep learning. Starting from three aspects, integration methods, specific applications, and teaching effectiveness evaluation, it focuses on exploring specific application methods of deep learning technology in core teaching content such as traffic flow prediction and traffic incident detection. The research adopts a mixed-method approach, combining quantitative data analysis with qualitative case studies to systematically evaluate the practical effectiveness of deep learning in intelligent transportation system education. Experimental results indicate that the introduction of deep learning technology significantly enhances students' technical application ability and problem-solving skills, while simultaneously improving the advanced nature and practicality of the teaching content.

Key words: intelligent transportation systems; deep learning; teaching application