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AI-Enhanced Sensor Technologies for Traffic Safety and Intelligent Transportation Systems
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AI-Enhanced Sensor Technologies for Traffic Safety and Intelligent Transportation Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3146

Special Issue Editor

Dr. Tamer M. Nadeem

E-Mail Website
Guest Editor
Department of Computer Science, Virginia Commonwealth University, 401 W. Main Street, Room ERB 2330, Richmond, VA 23284, USA
Interests: smart wireless systems; mobile and edge computing; AI for networks and systems; software-defined networks; network security and privacy; Internet-of-things and smart city systems; vehicular networks; intelligent transportation systems; and location determination systems.
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid growth of urban populations and the increasing demand for efficient transportation networks have brought traffic safety and intelligent transportation systems (ITSs) to the forefront of modern infrastructure development. As cities strive to become smarter and more sustainable, advanced sensor technologies play a critical role in enhancing traffic safety, managing congestion, and improving the overall efficiency of transportation.

Traffic safety is a paramount concern in urban planning, with the aim of reducing accidents, fatalities, and injuries on the road. Advanced sensors provide real-time data that support a wide range of applications, including traffic monitoring, vehicle-to-infrastructure communication, autonomous driving, and the protection of pedestrians. By integrating sensor technologies with advancements in artificial intelligence (AI) and machine learning (ML), we can develop robust systems that significantly enhance the safety and efficiency of traffic.

AI and ML have the potential to transform ITSs by enabling the development of predictive analytics, real-time decision-making, and adaptive control mechanisms. These technologies can analyze vast amounts of data from sensors to identify patterns, predict traffic conditions, and implement proactive measures to mitigate risks and optimize traffic flow.

This Special Issue, entitled “AI-Enhanced Sensor Technologies for Traffic Safety and Intelligent Transportation Systems”, seeks to gather cutting-edge research and practical insights into the development, deployment, and evaluation of AI-driven sensor-based systems that have been designed to enhance traffic safety. We welcome submissions that explore novel sensor technologies, AI/ML algorithms, system designs, and applications focused on enhancing road safety and promoting intelligent transportation solutions.

The scope of this Special Issue includes, but is not limited to, the following:

  • Advanced traffic monitoring and control systems;
  • Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication;
  • Autonomous and connected vehicle technologies;
  • Pedestrian and cyclist safety systems;
  • Intelligent traffic signal control and optimization;
  • Real-time traffic data collection and analytics;
  • Edge computing applications in transportation;
  • AI and machine learning for traffic prediction and management;
  • Sensor fusion and data integration for ITS;
  • Environmental sensing for transportation applications;
  • Cybersecurity and privacy in ITS;
  • Energy-efficient sensor networks for transportation;
  • Testbeds and experimental evaluations of ITS solutions;
  • Smart parking systems and management;
  • IoT applications for public transportation;
  • Human factors and user interfaces in intelligent transportation systems.

We invite researchers and practitioners from academia, industry, and government to contribute their original research and review articles to this Special Issue. By sharing innovative approaches and practical solutions, we aim to advance the field of traffic safety and intelligent transportation systems, ultimately creating safer and more efficient urban environments.

Dr. Tamer M. Nadeem
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • traffic monitoring
  • vehicle-to-vehicle
  • V2V
  • vehicle-to-infrastructure
  • V2I
  • autonomous and connected vehicles
  • pedestrian and cyclist safety
  • intelligent traffic
  • traffic prediction
  • smart parking
  • intelligent transportation systems

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Published Papers (2 papers)

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Research

16 pages, 4344 KiB  
Article
Multi-Scale Spatio-Temporal Attention Networks for Network-Scale Traffic Learning and Forecasting
by Cong Wu, Hui Ding, Zhongwang Fu and Ning Sun
Sensors 2024, 24(17), 5543; https://doi.org/10.3390/s24175543 - 27 Aug 2024
Cited by 1 | Viewed by 2023
Abstract
Accurate and timely forecasting of traffic on local road networks is crucial for deploying effective dynamic traffic control, advanced route planning, and navigation services. This task is particularly challenging due to complex spatio-temporal dependencies arising from non-Euclidean spatial relations in road networks and [...] Read more.
Accurate and timely forecasting of traffic on local road networks is crucial for deploying effective dynamic traffic control, advanced route planning, and navigation services. This task is particularly challenging due to complex spatio-temporal dependencies arising from non-Euclidean spatial relations in road networks and non-linear temporal dynamics influenced by changing road conditions. This paper introduces the spatio-temporal network embedding (STNE) model, a novel deep learning framework tailored for learning and forecasting graph-structured traffic data over extended input sequences. Unlike traditional convolutional neural networks (CNNs), the model employs graph convolutional networks (GCNs) to capture the spatial characteristics of local road network topologies. Moreover, the segmentation of very long input traffic data into multiple sub-sequences, based on significant temporal properties such as closeness, periodicity, and trend, is performed. Multi-dimensional long short-term memory neural networks (MDLSTM) are utilized to flexibly access multi-dimensional context. Experimental results demonstrate that the STNE model surpasses state-of-the-art traffic forecasting benchmarks on two large-scale real-world traffic datasets. Full article
(This article belongs to the Special Issue AI-Enhanced Sensor Technologies for Traffic Safety and Intelligent Transportation Systems)
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28 pages, 12387 KiB  
Article
Research on a Train Safety Driving Method Based on Fusion of an Incremental Clustering Algorithm and Lightweight Shared Convolution
by Hongping Wang, Xin Liu, Linsen Song, Yiwen Zhang, Xin Rong and Yitian Wang
Sensors 2024, 24(15), 4951; https://doi.org/10.3390/s24154951 - 30 Jul 2024
Viewed by 754
Abstract
This paper addresses the challenge of detecting unknown or unforeseen obstacles in railway track transportation, proposing an innovative detection strategy that integrates an incremental clustering algorithm with lightweight segmentation techniques. In the detection phase, the paper innovatively employs the incremental clustering algorithm as [...] Read more.
This paper addresses the challenge of detecting unknown or unforeseen obstacles in railway track transportation, proposing an innovative detection strategy that integrates an incremental clustering algorithm with lightweight segmentation techniques. In the detection phase, the paper innovatively employs the incremental clustering algorithm as a core method, combined with dilation and erosion theories, to expand the boundaries of point cloud clusters, merging adjacent point cloud elements into unified clusters. This method effectively identifies and connects spatially adjacent point cloud clusters while efficiently eliminating noise from target object point clouds, thereby achieving more precise recognition of unknown obstacles on the track. Furthermore, the effective integration of this algorithm with lightweight shared convolutional semantic segmentation algorithms enables accurate localization of obstacles. Experimental results using two combined public datasets demonstrate that the obstacle detection average recall rate of the proposed method reaches 90.3%, significantly enhancing system reliability. These findings indicate that the proposed detection strategy effectively improves the accuracy and real-time performance of obstacle recognition, thereby presenting important practical application value for ensuring the safe operation of railway tracks. Full article
(This article belongs to the Special Issue AI-Enhanced Sensor Technologies for Traffic Safety and Intelligent Transportation Systems)
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