The inaugural batch of typical application scenario projects integrating artificial intelligence into transportation has officially entered the construction phase, according to an announcement made at a special press conference held by the Ministry of Transport on August 20.
The comprehensive "AI + Transportation" innovation initiative has been fully launched, establishing three distinct categories of scenarios: application promotion, innovative demonstration, and breakthrough research. These categories are designed to closely track frontier technological developments, including embodied intelligence, and will introduce construction guidelines for typical application scenarios in successive phases.
Forty-one representative scenarios have been selected for the initial round of project creation, with a strong emphasis on public welfare priorities, industrial attributes, and practical implementation pathways. Xu Wenqiang, Director of the Science and Technology Department of the Ministry of Transport, outlined three core objectives of this innovation drive during the briefing.
The first objective is to translate macroeconomic policies into tangible outcomes, addressing the challenge of ineffective policy implementation and the absence of actionable mechanisms. The second is to dismantle barriers between technology and industry supply and demand, by actively opening up real, high-value transportation scenarios to AI enterprises while guiding the industry to embrace new technologies, thereby closing the gap between those who understand technology and those who understand business operations. The third objective is to leverage AI to strengthen weak links within the sector, promoting the large-scale adoption of mature technologies, the integrated demonstration of advanced solutions, and concentrated research efforts on core scenarios to cultivate a self-reliant and controllable AI ecosystem for transportation.
This initiative operates under an open and inclusive principle, without quotas or competitive evaluations. Projects meeting the criteria of technological maturity, authentic application scenarios, and industry-wide promotion value will be incorporated into the "AI + Transportation" innovation achievements repository. Support will be provided through policy guidance, standards development, and outcome dissemination to stimulate innovation enthusiasm across the entire industry.
Data serves as the fuel for artificial intelligence. In collaboration with the National Data Administration, the Ministry of Transport will concurrently implement a "Hundred Data Race" innovation action for building high-quality transportation datasets. This effort will coordinate both public and private data resources, aiming to develop a comprehensive data resource map with substantial scale and diverse modalities over the next three to five years, establishing a robust, efficient, and secure industry data system.
The innovation action seeks to promote integrated development through data connectivity, enhance safety through model transformation, drive digital upgrades through systemic restructuring, and facilitate green transformation through cost reduction and efficiency gains. It targets solutions to persistent issues such as data fragmentation and poor connectivity across different transport modes, shifting safety governance from reactive post-incident response to proactive pre-warning mechanisms.
Simultaneously, efforts will advance the development of comprehensive transportation large models, high-quality industry datasets, and specialized intelligent agents, applying artificial intelligence to transform the entire chain of transportation infrastructure construction, management, maintenance, operation, and service delivery.
The three scenario categories form a complete continuum from technology development and integrated demonstration to large-scale deployment, addressing past shortcomings where single-point demonstrations were prioritized over widespread adoption, and business applications overshadowed core technology research. The initiative adopts two leadership models, either scenario owners or technology enterprises taking the lead, ensuring that all stakeholders, including scenario holders, tech companies, and research institutions, can identify their roles and leverage their strengths without being constrained by a one-size-fits-all standard, thereby maximizing innovative vitality across the board.