Scaling traffic variables from sensors sample to the entire city at high spatiotemporal resolution with machine learning: applications to the Paris megacity

Abstract

Road transportation accounts for up to 35% of carbon dioxide and 49% of nitrogen oxides emissions in the Paris region. However, estimates of city traffic patterns are often incomplete and of coarse spatio-temporal resolution, even where extensive networks of sensors exist. This study uses a machine learning approach to analyze data from 2086 magnetic road sensors across Paris, generating a detailed dataset of hourly traffic flow and road occupancy covering 6846 road segments from 2018 to 2022. Our model captures flow and occupancy with a symmetric mean absolute percentage error of 37% and 54% respectively, providing high-resolution insights into traffic patterns. These insights allow for the creation of a comprehensive map of hourly transportation patterns in Paris, offering a robust framework for assessing traffic variables for each significant road link in the city. The model’s ability to incorporate an emission factor based on the mean speed of the vehicle fleet, derived from flow and occupancy data, holds promise for developing a detailed CO _2 and pollutant inventory. This methodology is not limited to Paris; it can be applied to other urban centers with similar data availability, highlighting its potential as a versatile tool for sustainable urban monitoring.

Keywords

• road transportation
• traffic monitoring
• carbon dioxide emissions
• COVID-19
• bottom-up approach
• machine learning