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Data Science for Transportation publishes high-quality original research and reviews in a wide range of topics related to Data Science for Transportation. This includes classical approaches when data sources are used to unravel underlying physical mechanisms leading to general laws and new modelling frameworks. It also includes new data-driven approaches when AI plays a central role.
Deep LearningMachine LearningIntelligent Transportation SystemsTraffic PredictionTrajectory DataShort-term TrafficTraffic Flow TheoryInterpretable Machine LearningTransportation DataTravel BehaviorSignalized IntersectionsTransportation PlanningTraffic Volume PredictionTraffic DynamicsProbe DataTransportation SystemsDEEP LEARNING APPROACHBig DataDriving BehaviorTraffic Congestion
vol.7 (2025)
vol.6 (2024)
vol.5 (2023)
Fredriksson, JoelKarlström, Anders