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Precise travel and traffic information are becoming increasingly important to road users. This is backed up by a growing number of users who are planning their trips before they start their journey. People primarily aim to avoid or minimize the travel delays by knowing alternative routes or changing the departure time beforehand. Furthermore, they are increasingly customizing their travels to maximize efficiency and convenience. From years of experience, and considering the Traffic telematics report [1], ASFINAG, as the national operator of Austrian motorways and highways, has recognized this need and has focused on bringing the accuracy of our traffic information to real traffic conditions. In this paper we present our hybrid approach that combines two methodologies to provide traffic information that is closer to the real traffic conditions, with a special focus on the traffic jams and delays. Our main goal is to improve the accuracy of the travel delays and traffic jams both in terms of time and location. The first methodology, which is a fusion of data from various data sources, is based on pure statistical model, whereas the second methodology is a machine learning (ML) model. This hybrid approach is not a comprehensive one but rather restricted to selected road sections.

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Hybrid Approach for Estimation of Traffic Hazards: Fusion of ML and Pure Statistical Model

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Natasa Mojic,
  • Vijay Mudunuri,
  • Radim Slovák,
  • Thomas Mariacher,
  • Peter Hrassnig

摘要

Precise travel and traffic information are becoming increasingly important to road users. This is backed up by a growing number of users who are planning their trips before they start their journey. People primarily aim to avoid or minimize the travel delays by knowing alternative routes or changing the departure time beforehand. Furthermore, they are increasingly customizing their travels to maximize efficiency and convenience. From years of experience, and considering the Traffic telematics report [1], ASFINAG, as the national operator of Austrian motorways and highways, has recognized this need and has focused on bringing the accuracy of our traffic information to real traffic conditions. In this paper we present our hybrid approach that combines two methodologies to provide traffic information that is closer to the real traffic conditions, with a special focus on the traffic jams and delays. Our main goal is to improve the accuracy of the travel delays and traffic jams both in terms of time and location. The first methodology, which is a fusion of data from various data sources, is based on pure statistical model, whereas the second methodology is a machine learning (ML) model. This hybrid approach is not a comprehensive one but rather restricted to selected road sections.