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View all- Kumar RPrakash ASingh RKumar P(2024)Machine learning-based prediction of hazards fine PM2.5 concentrations: a case study of Delhi, IndiaDiscover Geoscience10.1007/s44288-024-00043-z2:1Online publication date: 18-Jul-2024
Real-time Route Planning to Reduce Pedestrian Pollution Exposure in Urban Settings
Article No.: 01, Pages 1 - 10
Abstract
PM2.5 refers to fine particulate matter less than 2.5 micrometers in diameter. PM2.5 is a common air pollutant. It is capable of entering the respiratory system, and is associated with a variety of health issues such as asthma and other diseases. Pedestrians are at risk of exposure to traffic-related PM2.5 due in part to increased numbers of vehicles in city settings and their associated exhaust fumes - a key contributor to PM2.5. In this paper, we present a framework to minimise PM2.5 exposure for pedestrians by helping them avoid areas with high PM2.5 concentration levels. Specifically we predict the concentration levels through an XGBoost model and background concentration levels from official air quality monitoring stations around Melbourne. We factor in real-time, portable, air quality monitoring devices, weather conditions and real-time traffic flow information. The coefficient of determination (R2), root mean squared error (RMSE) and the mean average error (MAE) for the XGBoost model achieves 0.71, 1.98 and 1.1 respectively. The Dijkstra algorithm is then applied to generate the minimum PM2.5 exposure of routes with alternative routes suggested trading off distance and PM2.5 exposure. Compared with the shortest route, experiments show that PM2.5 exposure can be decreased by 11 - 15% with only a marginal increase in route length.
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December 2023
187 pages
ISBN:9798400704734
DOI:10.1145/3632366
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Published: 03 April 2024
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View all- Kumar RPrakash ASingh RKumar P(2024)Machine learning-based prediction of hazards fine PM2.5 concentrations: a case study of Delhi, IndiaDiscover Geoscience10.1007/s44288-024-00043-z2:1Online publication date: 18-Jul-2024
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