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A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation

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Hao LiuAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 4346 - 4356

https://doi.org/10.1145/3580305.3599767

Published: 04 August 2023 Publication History

Abstract

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.

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Cited By

Zhang WHan JXu ZNi HLiu HXiong HBaeza-Yates RBonchi F(2024)Urban Foundation Models: A SurveyProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671453(6633-6643)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671453

Index Terms

A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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cover image ACM Conferences

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

Copyright © 2023 ACM.

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Publication History

Published: 04 August 2023

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Guangzhou Science and Technology Plan Guangzhou-HKUST(GZ) Joint Project
National Natural Science Foundation of China
Foshan HKUST Projects

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KDD '23

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KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 6 - 10, 2023

CA, Long Beach, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Zhang WHan JXu ZNi HLiu HXiong HBaeza-Yates RBonchi F(2024)Urban Foundation Models: A SurveyProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671453(6633-6643)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671453

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