Aloupogianni, Eleni and Doctor, Faiyaz and Karyotis, Charalampos and Maniak, Tomasz and Tang, Raymond and Iqbal, Rahat (2024) An AI-based Digital Twin Framework for Intelligent Traffic Management in Singapore. In: 4th International Conference on Electrical, Computer and Energy Technologies (ICECET 2024), 2024-07-25 - 2024-07-27, Sydney, Australia. (In Press)
Aloupogianni, Eleni and Doctor, Faiyaz and Karyotis, Charalampos and Maniak, Tomasz and Tang, Raymond and Iqbal, Rahat (2024) An AI-based Digital Twin Framework for Intelligent Traffic Management in Singapore. In: 4th International Conference on Electrical, Computer and Energy Technologies (ICECET 2024), 2024-07-25 - 2024-07-27, Sydney, Australia. (In Press)
Aloupogianni, Eleni and Doctor, Faiyaz and Karyotis, Charalampos and Maniak, Tomasz and Tang, Raymond and Iqbal, Rahat (2024) An AI-based Digital Twin Framework for Intelligent Traffic Management in Singapore. In: 4th International Conference on Electrical, Computer and Energy Technologies (ICECET 2024), 2024-07-25 - 2024-07-27, Sydney, Australia. (In Press)
Abstract
Urban centers worldwide grapple with the intricate challenge of traffic congestion, necessitating sophisticated solutions grounded in real-time data analytics. This paper presents a cutting-edge Digital Twin (DT) framework tailored for urban traffic management, with a focus on the context of Singapore’s technologically advanced landscape. By seamlessly integrating live weather data and on-road camera information, the proposed framework offers insights into traffic dynamics, enabling adaptive decision-making. Leveraging a modular architecture and advanced artificial intelligence (AI) algorithms, the framework aims to optimize traffic flow, mitigate accidents, and ensure resilient commuting experiences, even amidst adverse weather conditions. Evaluation of individual components showcases promising performance metrics, albeit contingent upon data availability and user engagement. Future research endeavors will explore scalability, user centric design enhancements, and the longitudinal efficacy of the proposed framework, positioning it as a novel solution for urban traffic management.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | Published proceedings: _not provided_ |
Uncontrolled Keywords: | intelligent transportation system; artificial intelligence; digital twin; image analysis; traffic management |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 03 Oct 2024 12:03 |
Last Modified: | 09 Nov 2024 05:53 |
URI: | http://repository.essex.ac.uk/id/eprint/38711 |
Available files
Filename: 202407_icecet24_sydney_digital_twin_sg_camera_ready.pdf
Licence: Creative Commons: Attribution 4.0