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<p>This study presents a comprehensive evaluation of the real challenges related to the calibration of Network Macroscopic Fundamental Diagrams (NMFDs), with a focus on two key aspects: observability and stability. Observability addresses the estimation of NMFDs using multimodal traffic data, including loop detectors (LDD), floating car data (FCD), and public transport data (PTD), and examines how data quality, spatial coverage, and aggregation intervals influence NMFD characteristics. Stability explores the temporal consistency of NMFDs through longitudinal analysis, investigating their sensitivity to loading and unloading phases, weekday versus weekend dynamics, and recurring demand patterns. Using case studies in Athens and Lyon demonstrates that fusing data sources improves the accuracy of multimodal NMFD estimation, while separating network loading/unloading phases and different days enables reliable NMFD estimation. Building on these insights, we propose a structured NMFD calibration pipeline that integrates bias correction, data fusion, and temporal clustering. This framework supports reproducible, data-driven NMFD estimation and offers practical guidance for urban traffic monitoring and multimodal network management.</p>

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Challenges in calibrating multimodal network macroscopic fundamental diagrams: a review and definition of data fusion pipeline

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月29日

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  • Nandan Maiti,
  • Manon Seppecher,
  • Ludovic Leclercq

摘要

This study presents a comprehensive evaluation of the real challenges related to the calibration of Network Macroscopic Fundamental Diagrams (NMFDs), with a focus on two key aspects: observability and stability. Observability addresses the estimation of NMFDs using multimodal traffic data, including loop detectors (LDD), floating car data (FCD), and public transport data (PTD), and examines how data quality, spatial coverage, and aggregation intervals influence NMFD characteristics. Stability explores the temporal consistency of NMFDs through longitudinal analysis, investigating their sensitivity to loading and unloading phases, weekday versus weekend dynamics, and recurring demand patterns. Using case studies in Athens and Lyon demonstrates that fusing data sources improves the accuracy of multimodal NMFD estimation, while separating network loading/unloading phases and different days enables reliable NMFD estimation. Building on these insights, we propose a structured NMFD calibration pipeline that integrates bias correction, data fusion, and temporal clustering. This framework supports reproducible, data-driven NMFD estimation and offers practical guidance for urban traffic monitoring and multimodal network management.