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Smartphone-Based CO2e Emission Estimation Using Transportation Mode Classification
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DOI：10.1109/ACCESS.2023.3281307.png)
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摘要
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As a first step towards decreasing greenhouse gas emissions originating from transportation, it is critical that we create efficient systems for monitoring individual travel patterns and the associated carbon footprints. To this end, this paper presents a CO(2)e emission estimator that combines transportation mode classification with mode-specific emissions data. In addition to assessing the accuracy of the final emission estimation, we also categorize error sources and discuss their relative importance. Finally, we provide recommendations for designers of future carbon footprint estimators. Experimental results support the notion that transportation mode classifiers used for carbon footprint estimation should be evaluated based on their ability to identify carbon emitting transportation modes, while giving lower priority to recognition of various stationary activities and low-emission transportation modes. Additionally, it is demonstrated that errors in the estimated traveled distance have a low impact on the overall emissions error compared to errors in the transportation mode classification or in the assumed emissions per traveled distance for a specific mode.
Keyword:
Transportation
Estimation
Sensors
Carbon footprint
Smart phones
Sensor phenomena and characterization
Global Positioning System
Transportation mode classification
carbon footprint estimation
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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