Abstract
This study focuses on the development and evaluation of a smart, sustainable transportation tracker that calculates carbon emissions across various transport modes, including walking, cycling, driving, and public transit, while suggesting routes to reduce emissions. The application integrates with the Google Directions API to provide real-time traffic data and presents users with detailed information on route distance, duration, carbon emissions, and alternative trans-port options. Rigorous testing, including both black box and white box methods, ensures functionality and user-friendliness. The project aims to influence com-muter behaviour towards more sustainable practices, contributing to the reduction of urban carbon footprints. Supporting the UK's net-zero emissions goal by 2050, the project highlights the role of environmental data in daily technologies and raises individual awareness of carbon footprints. This work lays the foundation for further research and development in smart, sustainable urban transportation.
| Original language | English |
|---|---|
| Title of host publication | Advances on Intelligent Computing and Data Science II |
| Publisher | Springer |
| Pages | 161-172 |
| Number of pages | 12 |
| ISBN (Electronic) | 9783031913518 |
| ISBN (Print) | 9783031913501 |
| DOIs | |
| Publication status | Published (VoR) - 22 Jul 2025 |
Fingerprint
Dive into the research topics of 'Smart Carbon Emission Tracker: A Data-Driven Approach for Greener Multi-modal Transportation'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver