首页|Study Findings from University of Paris Saclay Update Knowledge in Machine Learn ing (Scaling traffic variables from sensors sample to the entire city at high sp atiotemporal resolution with machine learning: applications to the Paris megacit y)
Study Findings from University of Paris Saclay Update Knowledge in Machine Learn ing (Scaling traffic variables from sensors sample to the entire city at high sp atiotemporal resolution with machine learning: applications to the Paris megacit y)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on artificial intelligenc e is the subject of a new report. According to news reporting out of Gif-sur-Yve tte, France, by NewsRx editors, research stated, “Road transportation accounts f or up to 35% of carbon dioxide and 49% of nitrogen o xides emissions in the Paris region.” Funders for this research include Grantham Foundation For The Protection of The Environment. The news reporters obtained a quote from the research from University of Paris S aclay: “However, estimates of city traffic patterns are often incomplete and of coarse spatio-temporal resolution, even where extensive networks of sensors exis t. This study uses a machine learning approach to analyze data from 2086 magneti c road sensors across Paris, generating a detailed dataset of hourly traffic flo w and road occupancy covering 6846 road segments from 2018 to 2022. Our model ca ptures flow and occupancy with a symmetric mean absolute percentage error of 37% and 54% respectively, providing high-resolution insights into traf fic patterns. These insights allow for the creation of a comprehensive map of ho urly transportation patterns in Paris, offering a robust framework for assessing traffic variables for each significant road link in the city. The model’s abili ty to incorporate an emission factor based on the mean speed of the vehicle flee t, derived from flow and occupancy data, holds promise for developing a detailed CO _2 and pollutant inventory.”
University of Paris SaclayGif-sur-Yvet teFranceEuropeCyborgsEmerging TechnologiesMachine Learning
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