Artificial Intelligence and IoT Driven Algorithms for Intelligent Transportation Systems

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (30 January 2023) | Viewed by 2873

Special Issue Editor


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Guest Editor
Department of Computer Science, South Ural State University (National Research University), Chelyabinsk 454080, Russia
Interests: IoT; machine learning; industrial sensor; intelligent transportation; smart city; health informatics
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Special Issue Information

Dear Colleagues,

The Smart technologies and transport have become synonymous with each other in recent years, and there continues to be large growth in the Intelligent Transportation Systems (ITS) market. ITS utilize a range of technologies, with examples including passive and active sensors, communications systems, and data processing and analysis tools, all of which contribute to a greater understanding of road infrastructure for operators. The ITS sector is a leader in the utilization of advanced technologies including big data analytics, artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). The benefits of this smart transportation environment, are reduced carbon footprints, a decrease in accidents, enhanced mobility, reduced fuel consumption, and lower costs. This results in safer, smarter, cleaner, and more efficient roads that benefit drivers, pedestrians, and highway agencies.By incorporating intelligent traffic solutions into everyday operations, organizations can build a wealth of data from numerous sources, which can be used for various purposes including real-time tracking and alerting, as well as predictive analysis of what might happen in the future.This special issue aims to publish high-quality articles that represent cutting-edge research on the development of Artificial Intelligence and IoT-driven algorithms and models for Intelligent Transportation Systems.

Dr. Sachin Kumar
Guest Editor

Manuscript Submission Information

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Keywords

  • AI driven algorithms for air traffic management, road traffic management
  • AI and IOT driven algorithms for smart vehicles 
  • AI-IoT for logistics and supply chain management 
  • Optimization algorithm for ITS 
  • Object detection in ITS 
  • AI-IoT enabled solutions for safe and secure ITS 
  • AI and IoT based models for pothole detection on road 
  • AI-Driven IoT for Vehicle Safety and Driver Assistance 
  • AI and Deep Learning in ITS 
  • AI-IoT based pollution control for improved ITS

Published Papers (1 paper)

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Research

17 pages, 4401 KiB  
Article
Deep Learning Process and Application for the Detection of Dangerous Goods Passing through Motorway Tunnels
by George Sisias, Myrto Konstantinidou and Sotirios Kontogiannis
Algorithms 2022, 15(10), 370; https://doi.org/10.3390/a15100370 - 10 Oct 2022
Cited by 1 | Viewed by 2119
Abstract
Automated deep learning and data mining algorithms can provide accurate detection, frequency patterns, and predictions of dangerous goods passing through motorways and tunnels. This paper presents a post-processing image detection application and a three-stage deep learning detection algorithm that identifies and records dangerous [...] Read more.
Automated deep learning and data mining algorithms can provide accurate detection, frequency patterns, and predictions of dangerous goods passing through motorways and tunnels. This paper presents a post-processing image detection application and a three-stage deep learning detection algorithm that identifies and records dangerous goods’ passage through motorways and tunnels. This tool receives low-resolution input from toll camera images and offers timely information on vehicles carrying dangerous goods. According to the authors’ experimentation, the mean accuracy achieved by stage 2 of the proposed algorithm in identifying the ADR plates is close to 96% and 92% of both stages 1 and 2 of the algorithm. In addition, for the successful optical character recognition of the ADR numbers, the algorithm’s stage 3 mean accuracy is between 90 and 97%, and overall successful detection and Optical Character Recognition accuracy are close to 94%. Regarding execution time, the proposed algorithm can achieve real-time detection capabilities by processing one image in less than 2.69 s. Full article
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