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Authors: Meghana Kshirsagar 1 ; Tanishq More 2 ; Rutuja Lahoti 2 ; Shreya Adgaonkar 3 ; Shruti Jain 3 and Conor Ryan 1

Affiliations: 1 Biocomputing and Developmental Systems Research Group, University of Limerick, Ireland ; 2 Department of Information Technology, Government College of Engineering, Aurangabad, India ; 3 Department of Computer Science, Government College of Engineering, Aurangabad, India

Keyword(s): Deep Learning, Ensemble Learning, Object Detection, Routing Algorithm, Xai, Explainable Ai, Transfer Learning, Energy Efficiency.

Abstract: Rapid growth in vehicular congestion increases the challenges of traffic management concerning pollution and infrastructure. Efficient traffic governance can have a significant impact on a country’s economy. To alleviate these challenges, we propose an intelligent integrated traffic management system that manages congestion through cost pricing models to achieve smooth traffic flow. We propose a novel rerouting algorithm and ensemble architecture for vehicle detection and classification, tested on live traffic captured in several Indian cities. The ensemble architectures are designed on a combination of existing pre-trained models. Choice of the ensembles is based on accuracy, model interpretability, and energy efficiency. We show that the second-best ensemble produced operates with significantly less energy and better explainability than our best performer and is still within 3% accuracy of the best performer. Based on predefined road priorities, these ensemble models provide traffi c and individual vehicle counts, further fed to our proposed rerouting algorithm as input. The rerouting algorithm then recommends alternative routes and estimated journey time to the user. The paper also presents the results obtained by testing the models on real-time traffic videos from Aurangabad (India) on a GPU/CPU cluster consisting of machines incorporating different GPU hardware. (More)

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Paper citation in several formats:
Kshirsagar, M.; More, T.; Lahoti, R.; Adgaonkar, S.; Jain, S. and Ryan, C. (2022). Rethinking Traffic Management with Congestion Pricing and Vehicular Routing for Sustainable and Clean Transport. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0; ISSN 2184-433X, pages 420-427. DOI: 10.5220/0010830300003116

@conference{icaart22,
author={Meghana Kshirsagar. and Tanishq More. and Rutuja Lahoti. and Shreya Adgaonkar. and Shruti Jain. and Conor Ryan.},
title={Rethinking Traffic Management with Congestion Pricing and Vehicular Routing for Sustainable and Clean Transport},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={420-427},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010830300003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Rethinking Traffic Management with Congestion Pricing and Vehicular Routing for Sustainable and Clean Transport
SN - 978-989-758-547-0
IS - 2184-433X
AU - Kshirsagar, M.
AU - More, T.
AU - Lahoti, R.
AU - Adgaonkar, S.
AU - Jain, S.
AU - Ryan, C.
PY - 2022
SP - 420
EP - 427
DO - 10.5220/0010830300003116