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The IVORY<\/strong> doctoral candidates showcased their research at the 12th International Congress on Transportation Research (ICTR 2025)<\/strong>, held from October 16\u201318, 2025<\/strong>, in Thessaloniki, Greece<\/strong>.<\/p>\n The conference brought together academics, researchers, and professionals from across Europe to exchange ideas and innovations shaping the future of transportation systems, AI applications, and road safety. Several IVORY researchers presented their latest findings, demonstrating the project\u2019s interdisciplinary approach and its growing contribution to data-driven, AI-enabled road safety research.<\/p>\n \ud83d\udd39 Simone Paradiso (DC9): \u201cAggregating Telematics for Road Safety Analysis<\/em>\u201d<\/strong><\/p>\n Simone presented a refined method for aggregating telematics data onto road networks, introducing a structured framework that integrates statistical and machine learning techniques. This approach aims to improve accuracy beyond traditional aggregation methods. The work was conducted in collaboration with OSeven Telematics, Simone\u2019s industrial partner within the IVORY Network.<\/p>\n \ud83d\udc49\u00a0 The final paper can be seen here<\/a>.<\/p>\n \ud83d\udd39Aristotelis Tsoutsanis (DC7): \u201cIdentifying Dangerous Street Segments and Traffic Behavior in Athens Using Telematics, Crash and Traffic Data<\/em>\u201d<\/strong><\/p>\n Aristotelis presented his work in Session S2B, focusing on identifying hazardous road segments in Athens by combining telematics, crash, and traffic data. The analysis provides valuable insights into urban safety assessment and supports data-driven policy interventions.<\/p>\n \ud83d\udc49\u00a0 The final paper can be seen here<\/a>.<\/p>\n \ud83d\udd39Amirhossein Hassani (DC3) & Muhammad Shahid (DC11): \u201cEfficientNet-Swin Transformer for Automated iRAP Road Safety Attribute Extraction in Low- and Middle-Income Countries<\/em>\u201d<\/strong><\/p>\n Amirhossein and Muhammad presented a hybrid EfficientNet-Swin Transformer model for multi-task classification of seven iRAP road safety attributes using image data from Kenya. Their work tackles key challenges such as class imbalance and label inconsistency in LMIC datasets, with future research focusing on temporal context and synthetic data generation for rare classes.<\/p>\n
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