摘要：The implementation of valid freight transport simulation models requires an extensive and detailed validation phase for understanding the feasibility of the outputs and the capacity of the structure of the proposed models in representing the real-world data. Traditional methods involve the use of surveys in order to describe the behaviour of stakeholders and to gather some aspects of the modal choices. Recent studies integrate this approach with Big Data as Floating Car Data to obtain better statistical information of the details at different levels. The current research involves the unexplored field of the validation of freight transport simulation models using a data-driven approach based on a large database of over 292 million Floating Car Data (FCD) signals generated by 29,298 commercial vehicles during the month of October 2019. The paper proposes an FCD processing methodology to identify freight vehicles using Ro-Ro/Ro-Pax services, and presents the results of an in-depth tracking analysis for combined transport and road transport. The validation phase permits the evaluation of the simulation tool results with real choices of heavy vehicles, referring also to the statistical information on travel times and the achievement of additional information through an in-depth analysis of tracking single vehicles.
