Truck Travel times on freeway facilities and segments in Germany based on FCD from 2019

Abstract The paper describes the generation of truck travel times using Floating Car Data (FCD) for the year 2019 on German freeway facilities. FCD, collected by German Automobile Club (ADAC), include over 25 billion data points, which are assigned to a network model using the OpenSourceRoutingMachine (OSRM) map matching service. A k-means clustering algorithm classifies vehicles by type (e.g. car or truck) based on their velocity profiles. The Adaptive Smoothing Method (ASM) is applied for spatiotemporal interpolation, improving velocity estimates and deriving a continuous velocity function. The final method calculates travel times for predefined segments by adjusting velocities based on FCD-derived traffic conditions dynamically, updating every 10 minutes to reflect current traffic conditions. This approach provides detailed and reliable estimates of truck travel times, with a focus on accuracy through sufficient data penetration and aggregation intervals.