AIGC-Driven Human-Machine Intelligence in Intelligent Transportation Systems (ITS): Technologies, Applications, Challenges, and Future Directions

This paper explores the integration of Artificial Intelligence Generated Content (AIGC) with human-machine intelligence (HMI) to enhance the functionality of Intelligent Transportation Systems (ITS). Adaptive decision-making mechanisms are crucial as transportation networks become increasingly complex, generating vast real-time data from vehicles, infrastructure, and users. AIGC plays a transformative role in optimizing traffic flow through dynamic routing and real-time traffic management. At the same time, human intelligence ensures that these systems remain responsive to ever-changing real-world conditions. In terms of safety, AIGC is employed to simulate complex driving scenarios for autonomous vehicle training and detect traffic anomalies, with human oversight providing contextual decisionmaking in unexpected or ambiguous situations. For sustainability, AIGC develops data-driven strategies to reduce emissions and energy consumption, while human expertise ensures alignment with ethical and environmental goals. This synergy between AIGC and human intelligence is vital for refining real-time decision-making, ensuring both accuracy and adaptability across diverse ITS scenarios. This paper offers a comprehensive literature review on core and supporting AIGC technologies and their applications in key ITS domains. Case studies and initiatives from industry leaders demonstrate practical implementations of AIGC-driven HMI collaboration in ITS. We also address challenges such as compatibility with legacy systems, data privacy, model bias, and scalability. The paper concludes by outlining future research directions, emphasizing the need for scalable, interpretable, and ethically compliant AIGC models. By prioritizing humanmachine intelligence, this work advances the safe, efficient, and sustainable deployment of AIGC-driven HMI solutions in modern transportation networks.