Extended vehicle energy dataset (eVED): An enhanced large-scale dataset for vehicle energy consumption analysis

This work presents an extended version of the Vehicle Energy Dataset (VED), which is a openly released large-scale dataset of vehicle trip energy consumption records. Compared with its original version, the extended VED (eVED) dataset is enhanced with accurate vehicle trip GPS coordinates. Based on the accurate trip trajectories, we associate the VED trip records with external information that is essential in analyzing vehicle energy consumption e.g., road speed limit and intersections, from open-source map services. Particularly, we calibrate all the GPS trace records in the original VED data, upon which we associated the VED data with external attributes extracted from Geographic Information System (QGIS), the Overpass API, the Open Street Map API, and Google Maps API. The extracted attributes include 12,609,170 records of road elevation, 12,203,044 of speed limit, 12,281,719 of bi-directional speed limit, 584,551 of intersections, 429,638 of bus stop, 312,196 of crossings, 195,856 of traffic signals, 29,397 of stop signs, 5,848 of turning loops, 4,053 of railway crossings, 3,554 of turning circles, and 2,938 of motorway junctions. With the accurate GPS traces and enriched features of the vehicle trip records, the obtained eVED dataset can facilitate research on vehicle energy consumption and energy-efficient approaches, especially machine/deep learning approaches that are demanding on data volume and richness. Moreover, our software work of data calibration and enrichment can be reused to generate further vehicle trip datasets for specific user cases that empower vehicle behavior and traffic dynamic analyses. We anticipate that the eVED dataset and our data enrichment software can serve the academia and industry as an apparatus in developing future vehicle technologies.

Extended vehicle energy dataset (eVED): An enhanced large-scale dataset for vehicle energy consumption analysis

Shiliang Zhang, University of Oslo; Dyako Fatih, Fahmi Abdulqadir, Chalmers University of Technology; Tobias Schwarz, University of Gothenburg; Xuehui Ma, Xi'an University of Technology