A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control

Khanam, Zeba; Achari, Vejey; Boukhennoufa, Issam; Jindal, Anish; Singh, Amit Kumar

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%.

Citation

Khanam, Z., Achari, V., Boukhennoufa, I., Jindal, A., & Singh, A. K. (2024). A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control. . https://doi.org/10.4230/OASIcs.NG-RES.2024.2

• Conference Name: Workshop on Next Generation Real-Time Embedded Systems (NG-RES)
• Conference Location: Munich, Germany
• Start Date: Jan 17, 2024
• Acceptance Date: Jan 11, 2024
• Online Publication Date: Mar 4, 2024
• Publication Date: Mar 4, 2024
• Deposit Date: Apr 27, 2024
• Publicly Available Date: Apr 29, 2024
• Publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
• Volume: 117
• Series Title: Open Access Series in Informatics (OASIcs)
• DOI: https://doi.org/10.4230/OASIcs.NG-RES.2024.2
• Keywords: Vehicle Detection; Deep Neural Network; Traffic Control; Edge Computing; Emergency Vehicle Detection; Sliding Window
• Public URL: https://durham-repository.worktribe.com/output/2407528