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Personalized Long-distance Fuel-efficient Route Recommendation Through Historical Trajectories Mining

Published: 15 February 2022 Publication History

Abstract

Finding fuel-efficient routes for drivers has increasingly important value in terms of saving energy, protecting the environment and saving expenses. Previous studies basically adopt simple fuel consumption calculation or prediction methods to recommend the fuel-efficient routes within a city, which have two major limitations. First, the effect of drivers' driving behavior preferences (e.g. acceleration, frequency of clutch use, etc.) on fuel consumption is not fully studied and utilized. Second, existing methods mainly focus on short-distance route recommendation. Due to the difference in the road network structure and route composition, it is not effective to directly apply the route recommendation methods designed for short-distance travel within a city on the scenario of long-distance travel among cities. In this paper, we propose a novel model PLd-FeRR for the Personalized Long-distance Fuel-efficient Route Recommendation. Specifically, we first identify the features reflecting the user's driving behavior preference based on the user's historical driving trajectory, and then extract the potential factors that can affect long-distance fuel consumption. As transformer can effectively capture the temporal features for long sequence data, the extracted personalized driving preference features and long-distance fuel consumption features are input into a transformer-based fuel consumption prediction model. Next, the prediction model is combined with a genetic algorithm to further improve the performance of recommending fuel-efficient routes. Extensive evaluations are conducted on the large real-world dataset, and the results show the effectiveness of our proposal.

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  • (2024)ST-ECP: A Novel Spatial-Temporal Framework for Energy Consumption Prediction of Vehicle TrajectoryProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679807(2807-2816)Online publication date: 21-Oct-2024
  • (2024)Deep learning for cross-domain data fusion in urban computingInformation Fusion10.1016/j.inffus.2024.102606113:COnline publication date: 21-Nov-2024
  • (2024)A survey of route recommendations: Methods, applications, and opportunitiesInformation Fusion10.1016/j.inffus.2024.102413108(102413)Online publication date: Aug-2024
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cover image ACM Conferences
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
February 2022
1690 pages
ISBN:9781450391320
DOI:10.1145/3488560
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 15 February 2022

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Author Tags

  1. genetic algorithm
  2. route recommendation
  3. spatiotemporal data
  4. trajectory data mining

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View all
  • (2024)ST-ECP: A Novel Spatial-Temporal Framework for Energy Consumption Prediction of Vehicle TrajectoryProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679807(2807-2816)Online publication date: 21-Oct-2024
  • (2024)Deep learning for cross-domain data fusion in urban computingInformation Fusion10.1016/j.inffus.2024.102606113:COnline publication date: 21-Nov-2024
  • (2024)A survey of route recommendations: Methods, applications, and opportunitiesInformation Fusion10.1016/j.inffus.2024.102413108(102413)Online publication date: Aug-2024
  • (2024)Vehicle-Based Evolutionary Travel Time Estimation with Deep Meta LearningArtificial Neural Networks and Machine Learning – ICANN 202410.1007/978-3-031-72356-8_17(246-262)Online publication date: 17-Sep-2024
  • (2023)Spatiotemporal Data Mining Problems and MethodsAnalytics10.3390/analytics20200272:2(485-508)Online publication date: 14-Jun-2023
  • (2023)A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption EstimationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599767(4346-4356)Online publication date: 6-Aug-2023
  • (2023)Range Restricted Route Recommendation Based on Spatial KeywordProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570434(213-221)Online publication date: 27-Feb-2023
  • (2023)Empowering A* Algorithm With Neuralized Variational Heuristics for Fastest Route RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.326908435:10(10011-10023)Online publication date: 1-Oct-2023

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