The AI for ITS Program provides a framework for coordinated action to help realize the potential of AI. The program’s vision is to advance next-generation transportation systems and services by leveraging trustworthy, ethical AI and machine learning (ML) for safer, more efficient, and accessible movement of people and goods. The program’s mission is to identify, develop, implement, evaluate, and coordinate technology and policy research to advance the contextualization and integration of AI (including ML) into all aspects of the transportation system. The AI for ITS Program’s goals are to:
AI refers to processes that allow systems to augment routine human tasks or enable new capabilities that humans cannot perform. AI enables systems to: (1) sense and perceive the environment; (2) reason and analyze information; (3) learn from experience and adapt to new situations, potentially without human interaction; and (4) make decisions, communicate, and take actions.
Examples of AI include ML, natural language processing, and object recognition. ML is a broad subfield of AI in which computers learn from, discover patterns in, and make decisions based on data without human intervention. The ML field is broadly categorized into supervised, semi-supervised, unsupervised, and reinforcement learning.
In ITS, AI can be used to augment actions of field, handheld, and remote sensing devices; connected and automated vehicles; transportation management center operators; and transit and freight operators, decision-makers, and travelers. For example, AI can be used to identify objects and images; recognize speech and audio; process large amounts of data to recognize patterns, learn from experience, and adapt to new environments to predict traffic phenomena; provide situational awareness; assist drivers with maneuvering; recognize unsafe driving conditions in real-time; identify or isolate malfunctioning or misbehaving system entities; improve cybersecurity; operate infrastructure devices and vehicles; monitor pavement; and support decision-making. AI can be embedded in any system entity (vehicle, mobile device, roadside infrastructure, or management center) or be distributed among many entities in the system.
The AI for ITS Program has defined seven broad categories to provide a framework for exploring ITS applications leveraging AI. The following table describes the broad categories of AI-enabled applications in ITS.
|1.||Accessible Transportation||This category includes applications that make use of AI specifically for accessible transportation supporting independent travel for all travelers including people with disabilities and older adults.|
|2.||Asset Management and Roadway Construction and Maintenance||This category includes applications that make use of AI to address the strategic and systematic process of operating, maintaining, and improving physical assets. These applications focus on engineering and economic analysis based on quality information to identify a structured sequence of maintenance, preservation, repair, rehabilitation, and replacement actions that will achieve and sustain a desired state of good repair over the lifecycle of the assets at minimum feasible cost.|
|3.||Commercial Vehicle and Freight Operations||This category includes applications that make use of AI to address the management of the efficiency, safety, and operation of commercial vehicle fleets and the movement of freight.|
|4.||Emergency Management||This category includes applications that make use of AI to address public safety agency management of emergencies or incidents in the transportation network including those relating to hazardous materials that are transported through the transportation network.|
|5.||Transit Operations and Management||This category includes applications that make use of AI to address the management, operations, maintenance, and security of public transportation and mobility services to meet the demands of users and operate an efficient and integrated mobility system.|
|6.||Transportation Systems Management and Operations||This category includes applications that make use of AI to optimize the performance of a multimodal infrastructure through implementation of real-time and dynamic systems, services, and management strategies to preserve capacity; advance efficiency and productivity; and improve the security, safety, and reliability of our transportation system.|
|7.||Traveler Decision Support Tools||This category includes applications that make use of AI for the provision of static, dynamic, and other information about the transportation network such as route and mode travel times, transit status, mobility services, flight arrivals, weather conditions, pricing information, and incentive-based data.|
The AI for ITS Program, with input from modal partners and public, private, and academic sectors, has researched and documented high-value opportunities for leveraging AI for ITS needs.
For more information, please view the following fact sheet and webinars:
The U.S. DOT’s mission is to “ensure America has the safest, most efficient and modern transportation system in the world, …”. Therefore, although AI has been around for decades, the AI for ITS Program seeks to fully understand the potential challenges and risks of adopting AI in ITS. These include, but are not limited to, issues surrounding data, supporting technology, bias, security, privacy, ethics and equity, generalization, model drift, explainability, liability, talent/workforce availability, and stakeholder perception.
Please explore this site for a more detailed description of the program and progress. We will continue to upload relevant program information for public consumption as it becomes available. For inquiries regarding the program, please contact the USDOT Point of Contact below.
Chief Policy, Architecture, and Knowledge Transfer
Office of the Assistant Secretary for Research and Technology
ITS Joint Program Office