Vehicle Trip Reconstruction Using Non-Aggregated, Timestamped, Loop-Detector Event Data

The 9th International IEEE Conference On Models And Technologies For Intelligent Transportation Systems (MT-ITS 2025), Luxembourg, Luxembourg, September 08-10, 2025

Loop-detectors count amongst the most adopted road sensing infrastructure. An emerging use-case for this type of sensor data are digital twin models of cities and highways, that allow for real-time, simulation-based support in safetycritical decision-making of intelligent transportation systems. While previous work used aggregated, counting data from loopdetectors for simulation calibration, the use of non-aggregated, timestamped, event data was overlooked. This study proposes a method to reconstruct single trips from traces that vehicles leave passing a sensor and road network, which goes far beyond mere estimate probabilistic models of origins and destinations. A case study of the arterial network Schorndorfer Strasse in Esslingen am Neckar (Germany) with 34 considered loop detectors and 96 traffic lights, demonstrates the feasibility of the approach. An extensive benchmark with other methods shows, that simulations generated by the method achieve similar levels of accuracy on a macroscopic and mesoscopic level, while significantly improving accuracy on a microscopic level of up to 40%. https://github.com/DerKevinRiehl/mtits_trip_reconstruction/