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Predicting the future supply and demand of a transport network are challenging and important problems in real-time traffic management systems that are essential to enhance the decision-making process for deploying adequate traffic strategies under different conditions (e.g., road works, accidents). In the context of the TANGENT H2020 project, simulation-based and data-driven methodologies are developed focusing on the real-time demand and supply prediction problems. This paper focuses on the development and integration of the demand and supply models as well as incident detection methods into traffic simulation environments for network-wide traffic predictions. The role of each component of the framework and their interoperability is explained in the paper, using as testbed the network of Athens, Greece.

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Predicting Demand and Supply in a Real-Time Traffic Management Framework

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Athina Tympakianaki,
  • Mohammadmahdi Rahimiasl,
  • Charis Chalkiadakis,
  • Monica Dominguez,
  • Ynte Vanderhoydonc,
  • Jordi Casas,
  • Eleni I. Vlahogianni,
  • Siegfried Mercelis

摘要

Predicting the future supply and demand of a transport network are challenging and important problems in real-time traffic management systems that are essential to enhance the decision-making process for deploying adequate traffic strategies under different conditions (e.g., road works, accidents). In the context of the TANGENT H2020 project, simulation-based and data-driven methodologies are developed focusing on the real-time demand and supply prediction problems. This paper focuses on the development and integration of the demand and supply models as well as incident detection methods into traffic simulation environments for network-wide traffic predictions. The role of each component of the framework and their interoperability is explained in the paper, using as testbed the network of Athens, Greece.