AI-driven collaboration in transport – Trust and decision support

Research Project, 2027 – 2028

This project investigates how AI-supported decision systems reshape collaboration, trust, and information sharing among firms in the transport sector. Collaboration, such as sharing loads, infrastructure, batteries, charging, driver resources, data, and predictive information, is essential for achieving climate-neutral and efficient transport systems. Yet trust barriers, information asymmetries, and fears of opportunism continue to hinder the data sharing and cross-organizational coordination required for zero-emission goals.

At the same time, transport firms are rapidly adopting AI systems that filter information, generate recommendations, detect anomalies, and support routing, risk assessment, and operational planning. This shift from human-only judgement to hybrid human-AI decisionmaking changes how firms interact, evaluate each other, and coordinate shared activities. AI can increase trust by offering transparent signals, uncertainty indicators, and consistent logic, but can also erode it when recommendations are inexplicable, biased, or overly confident. This postdoc project examines how trust in AI, trust in suppliers/partners, and trust in shared transport systems interact. It aims to deliver a practical and theoretically grounded trust calibration framework for AI-supported collaboration in transport, enabling firms to understand when AI insights are reliable, what should be verified, and how to design governance structures that support responsible collaboration. By grounding the research in real transport and logistics contexts, the project provides insights to generate knowledge on AI-enabled sustainable transport systems.