  We present Vehicle Energy Dataset (VED), a novel large-scale dataset of fuel
and energy data collected from 383 personal cars in Ann Arbor, Michigan, USA.
This open dataset captures GPS trajectories of vehicles along with their
time-series data of fuel, energy, speed, and auxiliary power usage. A diverse
fleet consisting of 264 gasoline vehicles, 92 HEVs, and 27 PHEV/EVs drove in
real-world from Nov, 2017 to Nov, 2018, where the data were collected through
onboard OBD-II loggers. Driving scenarios range from highways to traffic-dense
downtown area in various driving conditions and seasons. In total, VED
accumulates approximately 374,000 miles. We discuss participant privacy
protection and develop a method to de-identify personally identifiable
information while preserving the quality of the data. After the
de-identification, we conducted case studies on the dataset to investigate the
impacts of factors known to affect fuel economy and identify energy-saving
opportunities that hybrid-electric vehicles and eco-driving techniques can
provide. The case studies are supplemented with a number of examples to
demonstrate how VED can be utilized for vehicle energy and behavior studies.
Potential research opportunities include data-driven vehicle energy consumption
modeling, driver behavior modeling, machine and deep learning, calibration of
traffic simulators, optimal route choice modeling, prediction of human driver
behaviors, and decision making of self-driving cars. We believe that VED can be
an instrumental asset to the development of future automotive technologies. The
dataset can be accessed at https://github.com/gsoh/VED.
