Journal article
Peeking inside the black-box: Explainable machine learning applied to household transportation energy consumption
Computers, environment and urban systems, v 88, 101647
Jul 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Sustainability policies to mitigate transportation energy impacts on the urban environment are urgently needed. Energy prediction models provide critical information to decision-makers who develop sustainability policies to reduce energy use and emissions. We present a transportation energy model (TEM) that uses Explainable Artificial Intelligence (XAI) methods to predict household transportation energy consumption in this study. The TEM model uses data-driven approaches for household transportation energy prediction. Machine learning techniques in artificial intelligence (AI) predictive modeling have become popular due to their ability to capture nonlinear and complex relationships. On the other hand, developing comprehensive understanding the inference mechanisms in AI models and ensuring trust in their predictions is challenging. This is because AI models are mostly of high complexity and low interpretability; in other words, they are black-box models. This study presents a case study of how model transparency and explanation can be generated using the Local Interpretable Model-Agnostic Explanation (LIME) to support advanced machine learning techniques in the transportation energy field. The methodology has been implemented based on the Household Travel Survey (HTS) data, which is used to train the artificial neural network with a relatively high degree of accuracy. The importance and effect (local explanation) of HTS inputs (such as household travel, demographics, and neighborhood data) on transportation energy consumption for specific traffic analysis zones (TAZs) are analyzed. The results are valuable to promote intelligent and user-friendly transportation energy planning models in urban regions across the world.
•A Methodology to address the tradeoff between model complexity and interpretability.•Explainable Artificial Intelligence methods used to develop a interpretable prediction model.•A local interpretable model-agnostic explanations provide trust and transparency.•Explaining model inference mechanisms of individual prediction provide interpretation.•Evaluation of variation of proportional impact of influential features are important.
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Details
- Title
- Peeking inside the black-box: Explainable machine learning applied to household transportation energy consumption
- Creators
- Shideh Shams Amiri - Drexel University, Philadelphia, USASam Mottahedi - Pennsylvania State UniversityEarl Rusty Lee - University of DelawareSimi Hoque - Drexel University
- Publication Details
- Computers, environment and urban systems, v 88, 101647
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:000661278600016
- Scopus ID
- 2-s2.0-85104303690
- Other Identifier
- 991019167685604721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Engineering, Environmental
- Environmental Studies
- Geography
- Operations Research & Management Science
- Regional & Urban Planning