Spatiooral prediction of roadside pm2.5based on sparse mobile sensing and traffic information

Kakarla, A. and Munagala, V.S.K.R. and Ishizaka, T. and Fukuda, A. and Jana, S. (2021) Spatiooral prediction of roadside pm2.5based on sparse mobile sensing and traffic information. In: 27th National Conference on Communications, NCC 2021, 27 July 2021 through 30 July 2021, Virtual, Kanpur.

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While real-time management of urban mobility has become common in modern cities, it is now imperative to attempt such management subject to a sustainable emission target. To achieve this, one would require emission estimates at spatiotemporal resolutions that are significantly higher than the usual. In this paper, we consider roadside concentration of PM2.5, and make predictions at high spatiooral resolution based on location, time and traffic levels. Specifically, we optimized various machine learning models, including ones involving bagging and boosting, and found Extreme Gradient Boosting (XGBoost, XGB) to be superior. Moreover, the tuned and optimized XGB utilizing traffic information achieved significant gain in terms of multiple performance measures over a reference method ignoring such information, indicating the usefulness of the latter in predicting PM2.5 concentration. © 2021 IEEE.

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IITH Creators:
IITH CreatorsORCiD
Jana, Soumya
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Low-cost sensor, Machine learning, Road-side PM2.5, Sparse mobile sensing, Spatiooral prediction, XGBoost
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: Mrs Haseena VKKM
Date Deposited: 26 Apr 2022 06:30
Last Modified: 26 Apr 2022 06:30
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