Work

Quantifying and Forecasting Emission Reductions in Urban Mobility: An IoT-Driven Bike-Sharing Analysis

Year: 2025

Type: article

Abstract: The growing urgency to address urban air quality and climate change has intensified the need for sustainable mobility solutions that mitigate vehicular emissions. Bike-sharing systems (BSSs) represent a viable alternative; however, their precise environmental impact remains insufficiently explored. This study quantifies and forecasts reductions in CO2 and NOx emissions resulting from BSS usage in Madrid by integrating real-time IoT sensor data with an advanced predictive model. The proposed framework effectively captures nonlinear and seasonal mobility and emission patterns, achieving high predictive accuracy while demonstrating significant energy savings. These findings confirm the environmental benefits of BSSs and provide urban planners and policymakers with a robust tool to extend and replicate this analysis in other cities, fostering sustainable urban mobility and improved air quality.

Source: Sensors

DOI: 10.3390/s25072163

Authors: Manuel Uche-Soria, Bernardo Tabuenca, Gonzalo Halcón-Gibert, Yilsy Núñez-Guerrero

Institution: Universidad Politécnica de Madrid

Language: English

Topic: Urban Transport and Accessibility

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities, Climate action

Open Access status: gold

Estimated APC paid: $3,137