  Vehicle Energy Consumption (VEC) estimation aims to predict the total energy
required for a given trip before it starts, which is of great importance to
trip planning and transportation sustainability. Existing approaches mainly
focus on extracting statistically significant factors from typical trips to
improve the VEC estimation. However, the energy consumption of each vehicle may
diverge widely due to the personalized driving behavior under varying travel
contexts. To this end, this paper proposes a preference-aware meta-optimization
framework Meta-Pec for personalized vehicle energy consumption estimation.
Specifically, we first propose a spatiotemporal behavior learning module to
capture the latent driver preference hidden in historical trips. Moreover,
based on the memorization of driver preference, we devise a selection-based
driving behavior prediction module to infer driver-specific driving patterns on
a given route, which provides additional basis and supervision signals for VEC
estimation. Besides, a driver-specific meta-optimization scheme is proposed to
enable fast model adaption by learning and sharing transferable knowledge
globally. Extensive experiments on two real-world datasets show the superiority
of our proposed framework against ten numerical and data-driven machine
learning baselines. The source code is available at
https://github.com/usail-hkust/Meta-Pec.
