Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation

• URL: http://arxiv.org/abs/2301.05739v1
• Date: Fri, 13 Jan 2023 19:34:18 GMT
• Title: Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation
• Authors: Yan Li (1), Mingzhou Yang (1), Matthew Eagon (1), Majid Farhadloo (1), Yiqun Xie (2), William F. Northrop (1), Shashi Shekhar (1) ((1) University of Minnesota, (2) University of Maryland)
• Abstract summary: We propose a novel Eco-toll estimation Physics-informed Neural Network framework (Eco-PiNN) The proposed method can greatly improve the accuracy of eco-toll estimation compared with state-of-the-art methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 three-fold: (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 of the vehicle engine 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.

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