The Role of Artificial Intelligence in Transportation

Artificial Intelligence (AI) is increasingly shaping the transportation sector, driven by advancements in computing power, data availability, and algorithms. AI can be understood through key concepts like machine learning, deep learning, and neural networks, which enable tasks such as data analysis, decision-making, and language processing. Within transportation, AI applications are diverse, including automated driving, traffic management, predictive maintenance, and cybersecurity. Use cases can also encompass optimizing traffic flow through adaptive signal control, enhancing safety via real-time incident detection, and improving public transit efficiency with dynamic routing. However, AI's integration into transportation presents both opportunities and challenges. It offers potential safety improvements by predicting accidents and enabling faster responses, while also improving efficiency by reducing traffic congestion and optimizing logistics. Yet, concerns remain over technical risks, ethical considerations, and the energy demands of AI systems, which may offset some environmental benefits. The economic impact can include job displacement and the creation of new AI-related roles, with implications for workforce development. This brief discusses AI applications in transportation and potential policy needed to address data privacy, cybersecurity, and equity to guide AI's responsible deployment.