到二〇三〇年 智能综合立体交通网全面推进 人工智能让交通运输更“聪明”（政策解读）

Core Viewpoint - The integration of artificial intelligence (AI) with transportation is transforming production and lifestyle, enhancing precision, reliability, and efficiency in traffic management and infrastructure development [1][2]. Group 1: Implementation and Goals - The Ministry of Transport and six other departments issued implementation opinions on "AI + Transportation," outlining 16 specific tasks across four areas, aiming for a fully advanced intelligent transportation network by 2030, with key technologies being self-controlled and at the forefront globally [1][2]. - The focus is on application technology breakthroughs, innovation in smart products, and the construction of a comprehensive transportation model to support the intelligent transformation of the industry [2][3]. Group 2: Efficiency Improvements - The integration of AI is expected to enhance the efficiency of demonstration corridors by approximately 20% and improve emergency response efficiency by around 30% through advanced traffic management models [3][4]. - In 20 demonstration areas for digital transformation, the total mileage of demonstration corridors exceeds 60,000 kilometers, covering major components of the national comprehensive transportation network [3][4]. Group 3: Application Scenarios - The implementation opinions cover seven key areas for intelligent applications, including combined auxiliary driving, smart railways, and intelligent shipping, providing ample testing grounds for new technologies and products [4][5]. - The development of automated ports and intelligent navigation systems is accelerating, with 52 automated terminals established and applications in over ten domestic and international ports [4][5]. Group 4: Infrastructure Support - The successful integration of AI in transportation relies on robust infrastructure, with specific deployments in computing power, data, and network capabilities to support the industry [7][8]. - The establishment of a comprehensive transportation big data center is underway to enhance data sharing and the creation of high-quality datasets, which are essential for AI model training and application [7][8].