Smart Carbon Emission Tracker: A Data-Driven Approach for Greener Multi-modal Transportation
Rahman, Habibur and Javidroozi, Vahid (2025) Smart Carbon Emission Tracker: A Data-Driven Approach for Greener Multi-modal Transportation. In: 4th International Conference of Advanced Computing and Informatics (ICACIN 2024), 16th-17th December 2024, Birmingham City University.
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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 transport options. Rigorous testing, including both black box and white box methods, ensures functionality and user-friendliness. The project aims to influence commuter 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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Identification Number: | 10.1007/978-3-031-91351-8_15 |
| Dates: | Date Event 1 July 2025 Accepted 22 July 2025 Published |
| Uncontrolled Keywords: | Carbon Emissions, Sustainable Transportation, Python, Carbon Footprint, Smart Transportation, Environmental Impact, Smart City |
| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science |
| Divisions: | Architecture, Built Environment, Computing and Engineering > Computer Science |
| Depositing User: | Gemma Tonks |
| Date Deposited: | 30 Apr 2026 16:48 |
| Last Modified: | 30 Apr 2026 16:48 |
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/17017 |
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