Methodological approach to analyze vehicle data in electric road systems: case study – ELISA, the eHighway project

Evaluation of data-driven engineering solutions depends on efficient analysis and IT knowledge at expert level. Recent technologies and their pilot applications in transportation engineering generate big data such as vehicle data. To handle and analyze vehicle data, solutions, and new methodologies are necessary for field engineers to interpret the results reliably. ELISA, the eHighway pilot project in Germany, is an engineering solution to achieve zero emission goal by road freight transport. Five overhead contact line trucks (O-trucks) are operating along five kilometers of the eHighway test track in each direction. In this study, we present a difference-based run detection methodology to analyze vehicle data obtained through five O-trucks. Dataloggers on the O-trucks transmit more than 100 parameters for each 100 ms. We propose that difference between following records for a parameter is an efficient way of analyzing and detecting runs on a highway section. The proposed difference-based approach and run detection algorithm performed well to investigate 650 GB data from O-trucks in the ELISA project. Operational and energy consumption analyses by using this data process provide reliable and reasonable results. We shared numerical and visual analysis samples to give insight for further evaluation and monitoring of the large-scale eHighway implementations.