Abstract
The eco-toll estimation problem quantifies the expected environmental cost (e.g., energy consumption, exhaust emissions) for a vehicle to travel along a path. This problem is important for societal applications such as eco-routing, which aims to find paths with the lowest exhaust emissions or energy need. The challenges of this problem are threefold: (1) the dependence of a vehicle's eco-toll on its physical parameters; (2) the lack of access to data with eco-toll information; and (3) the influence of contextual information (i.e. the connections of adjacent segments in the path) on the eco-toll of road segments. Prior work on eco-toll estimation has mostly relied on pure data-driven approaches and has high estimation errors given the limited training data. To address these limitations, we propose a novel Eco-toll estimation Physics-informed Neural Network framework (Eco-PiNN) using three novel ideas, namely, (1) a physics-informed decoder that integrates the physical laws governing vehicle dynamics into the network, (2) an attention-based contextual information encoder, and (3) a physics-informed regularization to reduce overfitting. Experiments on real-world heavy-duty truck data show that the proposed method can greatly improve the accuracy of eco-toll estimation compared with state-of-the-art methods.
Original language | English (US) |
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Title of host publication | 2023 SIAM International Conference on Data Mining, SDM 2023 |
Publisher | Society for Industrial and Applied Mathematics Publications |
Pages | 838-846 |
Number of pages | 9 |
ISBN (Electronic) | 9781611977653 |
State | Published - 2023 |
Event | 2023 SIAM International Conference on Data Mining, SDM 2023 - Minneapolis, United States Duration: Apr 27 2023 → Apr 29 2023 |
Publication series
Name | 2023 SIAM International Conference on Data Mining, SDM 2023 |
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Conference
Conference | 2023 SIAM International Conference on Data Mining, SDM 2023 |
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Country/Territory | United States |
City | Minneapolis |
Period | 4/27/23 → 4/29/23 |
Bibliographical note
Publisher Copyright:Copyright © 2023 by SIAM.
Keywords
- eco-toll estimation
- physics-informed machine learning
- spatiotemporal data mining