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<p>Efficient traffic analysis and management are crucial for supporting stakeholders involved in urban and suburban road network monitoring, anomaly detection, and strategic planning. The scale and complexity of traffic data make manual approaches infeasible, necessitating automated, interpretable solutions.This study introduces TrAnSIT, an AI-driven framework that combines real-time monitoring, predictive modelling, and anomaly detection tailored to traffic management. Designed with a strong emphasis on interpretability, TrAnSIT provides clear, explainable outputs, enabling stakeholders to derive insights from complex data.Through a series of use cases, TrAnSIT is shown to enhance traffic management by identifying emerging issues, focusing on high-priority areas, and providing data-driven strategies for optimizing traffic flow and improving safety. The framework’s outputs are interpretable and tailored to various user roles, supporting both technical and non-technical decision-makers.TrAnSIT empowers stakeholders with actionable intelligence for proactive and informed decision-making in traffic management, demonstrating the value of interpretable AI frameworks in complex, data-intensive environments.</p>

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TrAnSIT: an interpretable and explainable AI framework for urban–suburban traffic analysis and understanding

  • 期刊论文

  • 发表日期：

    2026年3月20日

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  • Stefano Ferilli,
  • Davide Di Pierro,
  • Domenico Redavid,
  • Eleonora Bernasconi

摘要

Efficient traffic analysis and management are crucial for supporting stakeholders involved in urban and suburban road network monitoring, anomaly detection, and strategic planning. The scale and complexity of traffic data make manual approaches infeasible, necessitating automated, interpretable solutions.This study introduces TrAnSIT, an AI-driven framework that combines real-time monitoring, predictive modelling, and anomaly detection tailored to traffic management. Designed with a strong emphasis on interpretability, TrAnSIT provides clear, explainable outputs, enabling stakeholders to derive insights from complex data.Through a series of use cases, TrAnSIT is shown to enhance traffic management by identifying emerging issues, focusing on high-priority areas, and providing data-driven strategies for optimizing traffic flow and improving safety. The framework’s outputs are interpretable and tailored to various user roles, supporting both technical and non-technical decision-makers.TrAnSIT empowers stakeholders with actionable intelligence for proactive and informed decision-making in traffic management, demonstrating the value of interpretable AI frameworks in complex, data-intensive environments.