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Invited Paper

DOI: 10.4230/OASIcs.NG-RES.2024.2

A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)

Authors: Zeba Khanam, Vejey Pradeep Suresh Achari, Issam Boukhennoufa, Anish Jindal, and Amit Kumar Singh

Published in: OASIcs, Volume 117, Fifth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2024)

Abstract

Traffic congestion is one of the growing urban problem with associated problems like fuel wastage, loss of lives, and slow productivity. The existing traffic system uses programming logic control (PLC) with round-robin scheduling algorithm. Recent works have proposed IoT-based frameworks that use traffic density of each lane to control traffic movement, but they suffer from low accuracy due to lack of emergency vehicle image datasets for training deep neural networks. In this paper, we propose a novel distributed IoT framework that is based on two observations. The first observation is major structural changes to road are rare. This observation is exploited by proposing a novel two stage vehicle detector that is able to achieve 77% vehicle detection accuracy on UA-DETRAC dataset. The second observation is emergency vehicle have distinct siren sound that is detected using a novel acoustic detection algorithm on an edge device. The proposed system is able to detect emergency vehicles with an average accuracy of 99.4%.

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Zeba Khanam, Vejey Pradeep Suresh Achari, Issam Boukhennoufa, Anish Jindal, and Amit Kumar Singh. A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper). In Fifth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2024). Open Access Series in Informatics (OASIcs), Volume 117, pp. 2:1-2:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{khanam_et_al:OASIcs.NG-RES.2024.2,
  author =	{Khanam, Zeba and Achari, Vejey Pradeep Suresh and Boukhennoufa, Issam and Jindal, Anish and Singh, Amit Kumar},
  title =	{{A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control}},
  booktitle =	{Fifth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2024)},
  pages =	{2:1--2:10},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-313-3},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{117},
  editor =	{Yomsi, Patrick Meumeu and Wildermann, Stefan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2024.2},
  URN =		{urn:nbn:de:0030-drops-197057},
  doi =		{10.4230/OASIcs.NG-RES.2024.2},
  annote =	{Keywords: Vehicle Detection, Deep Neural Network, Traffic Control, Edge Computing, Emergency Vehicle Detection, Sliding Window}
}

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A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)

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