Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)

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Adaptive Artificial Intelligence Enabled Public Engagement Models for Future Autonomous Transportation Networks Smart Mobility and Predictive Traffic Optimization

Authors
M. Srinivasulu1, *, K. H. Preethi2, H. Pradeep3, D. Prasad4, S. R. Arun Raj5, M. Mahima6
1Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, 500043, Telangana, India
2Assistant Professor, Department of Industrial Engineering and Management, Bangalore Institute of Technology, Bangalore, 560004, Karnataka, India
3Assistant professor, Department of Mechanical Engineering, BGSIT, Faculty of Engineering Management and Technology, Adichunchanagiri University, BGNAGARA, 571448, Nagamangala (Tq), Mandya (Dis), Karnataka, India
4Assistant Professor, Department of EEE, Sona College of Technology, Salem, 636005, Tamil Nadu, India
5Assistant Professor, Department of Electronics & Communication Engineering, University BDT College of Engineering, Davanagere, 577004, Karnataka, India
6Assistant Professor, Department of IT, New Prince Shri Bhavani College of Engineering and Technology, Chennai, 600073, Tamil Nadu, India
*Corresponding author. Email: srinivasulu.m@mlrit.ac.in
Corresponding Author
M. Srinivasulu
Available Online 23 May 2025.
DOI
10.2991/978-94-6463-718-2_4How to use a DOI?
Keywords
Adaptive AI; Public Engagement; Autonomous Transportation Networks; Smart Mobility; Predictive Traffic Optimization; Reinforcement Learning; AI-Driven Traffic Control
Abstract

The rapid evolution of autonomous transportation networks and smart mobility solutions has necessitated the integration of adaptive artificial intelligence (AI) models to enhance public engagement, optimize traffic flow, and ensure predictive traffic management. This research proposes a human-centric AI framework that leverages real-time adaptive learning, reinforcement-based decision-making, and scalable AI-driven public engagement models to transform urban mobility. The study introduces privacy-preserving federated AI models, bias-free decision-making algorithms, and cooperative AI traffic control systems to address challenges in mixed-traffic environments. By integrating real-time user feedback, demand-responsive transport systems, and explainable AI (XAI) governance, the proposed approach fosters public trust and ensures inclusive, ethical, and efficient smart transportation ecosystems. Furthermore, this research tackles emerging challenges such as the curse of rarity in autonomous vehicle (AV) decision-making, low-connectivity environments for smart traffic optimization, and scalable generative AI for vehicular networks. The findings highlight the importance of AI-driven public engagement in developing resilient, self-optimizing, and future-proof smart transportation systems, paving the way for next-generation predictive mobility solutions.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
Series
Advances in Computer Science Research
Publication Date
23 May 2025
ISBN
978-94-6463-718-2
ISSN
2352-538X
DOI
10.2991/978-94-6463-718-2_4How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - M. Srinivasulu
AU  - K. H. Preethi
AU  - H. Pradeep
AU  - D. Prasad
AU  - S. R. Arun Raj
AU  - M. Mahima
PY  - 2025
DA  - 2025/05/23
TI  - Adaptive Artificial Intelligence Enabled Public Engagement Models for Future Autonomous Transportation Networks Smart Mobility and Predictive Traffic Optimization
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 25
EP  - 36
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_4
DO  - 10.2991/978-94-6463-718-2_4
ID  - Srinivasulu2025
ER  -