Predicting The Mode Of Transportation Using GPS Data, For Vehicular Carbon Footprint Determination
Journal Title: International Journal of engineering Research and Applications - Year 2018, Vol 8, Issue 10
Abstract
Greenhouse gas emissions by vehicles is damaging the environment. In order to take remedial measures at individual level, one must first know the full scale of damage being done. This study suggests that using GPS data from smartphones, the travel mode used by individuals can be classified into motorized and non-motorized. It can assist in correctly estimating the vehicular carbon footprint by each individual. In this study, simplest features (travel duration, travel distance and average velocity), derived from GPS data, were used to train and test two popular algorithms i.e. Support Vector Machine and Random Forest. Results show that Random Forest provides a prediction accuracy of 90%, outperforming Support Vector Machine.
Authors and Affiliations
Muhammed Awais Shafique, Eiji Hato
Keywords
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- EP ID EP402011
- DOI 10.9790/9622-0810037072.
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