Black spots identification through a Bayesian Networks quantification of accident risk index
Mouskos, Kyriacos C.
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Traffic accidents constitute a major problem worldwide. One of the principal causes of traffic accidents is adverse driving behavior that is inherently influenced by traffic conditions and infrastructure among other parameters. Probabilistic models for the assessment of road accidents risk usually employs machine learning using historical data of accident records. The main drawback of these approaches is limited coverage of traffic data. This study illustrates a prototype approach that escapes from this problem, and highlights the need to enhance historical accident records with traffic information for improved road safety analysis. Traffic conditions estimation is achieved through Dynamic Traffic Assignment (DTA) simulation that utilizes temporal aspects of a transportation system. Accident risk quantification is achieved through a Bayesian Networks (BNs) model learned from the method’s enriched accidents dataset. The study illustrates the integration of BN with the DTA-based simulator, Visual Interactive Systems for Transport Algorithms (VISTAs), for the assessment of accident risk index (ARI), used to identify accident black spots on road networks.