Swarm-Enabled AI for Smart Mobility and Sustainable Transport

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 13 | 01 | Page :
    By

    Maitri Mohanty,

  • Rajlaxmi Biswal,

  • Madal Sameeksha,

  • Tanushree Patra,

  • Subham Bisoi,

  1. Assistant Professor, Department of Computer Science and Engineering, Gandhi Institute of Excellent Technocrats, Ghangapatana, Bhubaneswar, Odisha, India
  2. Student, Department of Computer Science and Engineering, Gandhi Institute of Excellent Technocrats, Ghangapatana, Bhubaneswar, Odisha, India
  3. Student, Department of Computer Science and Engineering, Gandhi Institute of Excellent Technocrats, Ghangapatana, Bhubaneswar, Odisha, India
  4. Student, Department of Computer Science and Engineering, Gandhi Institute of Excellent Technocrats, Ghangapatana, Bhubaneswar, Odisha, India
  5. Student, Department of Computer Science and Engineering, Gandhi Institute of Excellent Technocrats, Ghangapatana, Bhubaneswar, Odisha, India

Abstract

The significant issues facing the modern urban infrastructure are the management of road traffic problems, such as severe traffic congestion, the detection of unsafe driving behavior, and road safety. The traditional ground-based surveillance systems will be helpful, but they will reach their limits in large and dynamic environments. It is because of predetermined perspectives, blindness, and the inability to scale. To designate these problems, the present study proposes a traffic monitoring swarm system utilizing a UAV (unmanned aerial vehicle). This system provides continuous aerial multi-angle surveillance, real-time behavior monitoring, and early identification of anomalies. The swarm applies a decentralized spread-control to maintain an optimal spacing between the swarm members and to avoid overlapping observation space. It identifies and monitors traffic objects such as cars, pedestrians, and two-wheelers on a frame-by-frame basis. They are analyzed based on their movements to do behavioral modeling. This system can detect abnormal driving like sudden braking, driving in the reverse direction, near crashes, and improper lane changes. Verified exceptions are reported to a central traffic control platform, which launches dynamic control actions like timing the signal, regulating localized congestion, and operator alerting. The suggested framework is checked through field experiments and simulations that are carried out in a heterogeneous urban environment. The effectiveness of the suggested method can be observed in the positive outcomes, which present the correct detection, multi-entity tracking, and anomaly classification with significant increases in flow parameters, even in the presence of communication delays, occlusion, and data association difficulties.

Keywords: Aerial surveillance, anomaly detection, intelligent transportation systems, road safety, smart mobility, traffic behavior analysis, trajectory tracking, UAV swarm

How to cite this article:
Maitri Mohanty, Rajlaxmi Biswal, Madal Sameeksha, Tanushree Patra, Subham Bisoi. Swarm-Enabled AI for Smart Mobility and Sustainable Transport. Journal of Advancements in Robotics. 2026; 13(01):-.
How to cite this URL:
Maitri Mohanty, Rajlaxmi Biswal, Madal Sameeksha, Tanushree Patra, Subham Bisoi. Swarm-Enabled AI for Smart Mobility and Sustainable Transport. Journal of Advancements in Robotics. 2026; 13(01):-. Available from: https://journals.stmjournals.com/joarb/article=2026/view=237861


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Ahead of Print Subscription Original Research
Volume 13
01
Received 27/01/2026
Accepted 30/01/2026
Published 06/03/2026
Publication Time 38 Days
90

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