Advanced Technologies and Artificial Intelligence for Sustainable and Intelligent Transportation Systems

A special issue of Inventions (ISSN 2411-5134). This special issue belongs to the section "Inventions and Innovation in Electrical Engineering/Energy/Communications".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 7241

Special Issue Editors


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Guest Editor

Special Issue Information

Dear Colleagues,

Intelligent transportation systems (ITS) have been important elements in today’s era. Apart from the basic requirement of safety, affordability, and accessibility, a leading vision, namely, sustainability is desired. In the literature, we have witnessed the successful research and development of intelligent transportation systems. However, with the ever-growing transport network, it may require enhancement and upgrade of existing systems with advanced technologies and artificial intelligence. The vision is specified in many proposals, such as United Nations Sustainable Development Goals and European Commission Mobility and Transport. This Special Issue is intended to report high-quality research on recent advances in technologies and artificial intelligence in transportation systems, more specifically to the state-of-the-art theories, methodologies, and systems for the design, development, deployment, and innovative use of those convergence technologies for providing insights into the theoretical and technological advancement in transportation science and engineering. Real-world and pilot case studies are also welcome. The topics of interest include but are not limited to the following:

  • Large-scale data collection, storage, and processing for ITS
  • Deep learning for ITS
  • Transfer learning for ITS
  • Big data technologies for ITS
  • Autonomous and semi-autonomous vehicles for ITS
  • Edge, fog, and cloud computing for ITS
  • Cybersecurity for ITS
  • Video, image, and signal processing techniques for ITS.

Dr. Kwok Tai Chui
Prof. Dr. Brij B. Gupta
Guest Editors

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Keywords

  • artificial intelligence
  • big data
  • computational intelligence
  • deep learning
  • intelligent transportation systems
  • internet of things

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Published Papers (4 papers)

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Research

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25 pages, 3047 KiB  
Article
Hierarchical Dynamic Spatio-Temporal Graph Convolutional Networks with Self-Supervised Learning for Traffic Flow Forecasting
by Siwei Wei, Yanan Song, Donghua Liu, Sichen Shen, Rong Gao and Chunzhi Wang
Inventions 2024, 9(5), 102; https://doi.org/10.3390/inventions9050102 - 20 Sep 2024
Viewed by 785
Abstract
It is crucial for both traffic management organisations and individual commuters to be able to forecast traffic flows accurately. Graph neural networks made great strides in this field owing to their exceptional capacity to capture spatial correlations. However, existing approaches predominantly focus on [...] Read more.
It is crucial for both traffic management organisations and individual commuters to be able to forecast traffic flows accurately. Graph neural networks made great strides in this field owing to their exceptional capacity to capture spatial correlations. However, existing approaches predominantly focus on local geographic correlations, ignoring cross-region interdependencies in a global context, which is insufficient to extract comprehensive semantic relationships, thereby limiting prediction accuracy. Additionally, most GCN-based models rely on pre-defined graphs and unchanging adjacency matrices to reflect the spatial relationships among node features, neglecting the dynamics of spatio-temporal features and leading to challenges in capturing the complexity and dynamic spatial dependencies in traffic data. To tackle these issues, this paper puts forward a fresh approach: a new self-supervised dynamic spatio-temporal graph convolutional network (SDSC) for traffic flow forecasting. The proposed SDSC model is a hierarchically structured graph–neural architecture that is intended to augment the representation of dynamic traffic patterns through a self-supervised learning paradigm. Specifically, a dynamic graph is created using a combination of temporal, spatial, and traffic data; then, a regional graph is constructed based on geographic correlation using clustering to capture cross-regional interdependencies. In the feature learning module, spatio-temporal correlations in traffic data are subjected to recursive extraction using dynamic graph convolution facilitated by Recurrent Neural Networks (RNNs). Furthermore, self-supervised learning is embedded within the network training process as an auxiliary task, with the objective of enhancing the prediction task by optimising the mutual information of the learned features across the two graph networks. The superior performance of the proposed SDSC model in comparison with SOTA approaches was confirmed by comprehensive experiments conducted on real road datasets, PeMSD4 and PeMSD8. These findings validate the efficacy of dynamic graph modelling and self-supervision tasks in improving the precision of traffic flow prediction. Full article
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22 pages, 5609 KiB  
Article
Road Anomaly Detection with Unknown Scenes Using DifferNet-Based Automatic Labeling Segmentation
by Phuc Thanh-Thien Nguyen, Toan-Khoa Nguyen, Dai-Dong Nguyen, Shun-Feng Su and Chung-Hsien Kuo
Inventions 2024, 9(4), 69; https://doi.org/10.3390/inventions9040069 - 28 Jun 2024
Viewed by 1073
Abstract
Obstacle avoidance is essential for the effective operation of autonomous mobile robots, enabling them to detect and navigate around obstacles in their environment. While deep learning provides significant benefits for autonomous navigation, it typically requires large, accurately labeled datasets, making the data’s preparation [...] Read more.
Obstacle avoidance is essential for the effective operation of autonomous mobile robots, enabling them to detect and navigate around obstacles in their environment. While deep learning provides significant benefits for autonomous navigation, it typically requires large, accurately labeled datasets, making the data’s preparation and processing time-consuming and labor-intensive. To address this challenge, this study introduces a transfer learning (TL)-based automatic labeling segmentation (ALS) framework. This framework utilizes a pretrained attention-based network, DifferNet, to efficiently perform semantic segmentation tasks on new, unlabeled datasets. DifferNet leverages prior knowledge from the Cityscapes dataset to identify high-entropy areas as road obstacles by analyzing differences between the input and resynthesized images. The resulting road anomaly map was refined using depth information to produce a robust drivable area and map of road anomalies. Several off-the-shelf RGB-D semantic segmentation neural networks were trained using pseudo-labels generated by the ALS framework, with validation conducted on the GMRPD dataset. Experimental results demonstrated that the proposed ALS framework achieved mean precision, mean recall, and mean intersection over union (IoU) rates of 80.31%, 84.42%, and 71.99%, respectively. The ALS framework, through the use of transfer learning and the DifferNet network, offers an efficient solution for semantic segmentation of new, unlabeled datasets, underscoring its potential for improving obstacle avoidance in autonomous mobile robots. Full article
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30 pages, 12148 KiB  
Article
Investigations on Using Intelligent Learning Techniques for Anomaly Detection and Diagnosis in Sensors Signals in Li-Ion Battery—Case Study
by Nicolae Tudoroiu, Mohammed Zaheeruddin, Roxana-Elena Tudoroiu, Mihai Sorin Radu and Hana Chammas
Inventions 2023, 8(3), 74; https://doi.org/10.3390/inventions8030074 - 22 May 2023
Cited by 8 | Viewed by 2162
Abstract
This research paper aims to design and implement an intelligent least short time memory (LSTM) deep learning classification technique to detect possible anomalies in measurements dataset within a particular Li-ion battery type. For the state of charge (SOC) and battery faults estimation, a [...] Read more.
This research paper aims to design and implement an intelligent least short time memory (LSTM) deep learning classification technique to detect possible anomalies in measurements dataset within a particular Li-ion battery type. For the state of charge (SOC) and battery faults estimation, a Joint State and Parameter Extended Kalman Filter (JEKF) estimator is developed. The SOC accuracy performance is excellent, with less than 0.5% error during steady-state, compared to the 2% error reported in the literature. For the design and implementation of JEKF SOC and parameter estimation is chosen a preset Li-ion battery Simulink Simscape generic model. It is also helpful to generate the healthy and faulty measurement dataset to design and implement the proposed intelligent LSTM classifier deep learning technique. The generic Li-ion battery model is wisely selected for the “proof concept” purpose, model validation, and algorithms’ robustness, accuracy, and effectiveness. Compared to the traditional EKF fault diagnosis and isolation (FDI), a model-based estimation strategy, the proposed classification LSTM technique is an intelligent data-driven-based deep learning algorithm of high accuracy (around 80%) and loss performance close to zero. Therefore, this feature makes data collection of dataset measurements directly from Li-ion battery sensors possible, which is beneficial for generating online fault scenarios. Additionally, the LSTM deep learning technique can remarkably classify all detected anomalies with high accuracy, independent of battery model accuracy, uncertainties, and unmodeled dynamics. Also, high-performance accuracy root mean square error (RMSE) of 0.0588 (voltage fault), approximately 5.5×107 (healthy) and 8.87 × 106 (current fault) for deep learning shallow neural network (DLSNN) reveals an obvious superiority of both compared to the traditional FDI estimation strategies. Full article
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Review

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23 pages, 3215 KiB  
Review
Aircraft Innovation Trends Enabling Advanced Air Mobility
by Raj Bridgelall
Inventions 2024, 9(4), 84; https://doi.org/10.3390/inventions9040084 - 26 Jul 2024
Viewed by 1666
Abstract
This study presents a comprehensive exploration of vertical take-off and landing (VTOL) aircraft within advanced air mobility (AAM), examining the crucial challenges of integrating these innovative technologies into transportation systems. AAM promises transformational social change by enhancing transportation energy efficiency, safety, and operational [...] Read more.
This study presents a comprehensive exploration of vertical take-off and landing (VTOL) aircraft within advanced air mobility (AAM), examining the crucial challenges of integrating these innovative technologies into transportation systems. AAM promises transformational social change by enhancing transportation energy efficiency, safety, and operational effectiveness. This research utilizes a methodical approach that juxtaposes a systematic review of patents with an extensive analysis of the academic literature to map the innovation landscape of VTOL technology. This dual analysis reveals a dynamic progression in VTOL advancements, highlighting significant strides in aerodynamic optimization, propulsion technology, and control systems. The novelty of this study lies in its dual-method approach, combining patent analysis with the academic literature to provide a holistic view of VTOL technological evolution. The patent analysis reveals that companies have been most productive on innovations relating to VTOL aircraft transition efficiency, control enhancement, and energy management. The literature review identifies key trends such as the rise in electric propulsion technologies and the integration of AI-driven control mechanisms. These results provide new engineering knowledge that can guide future VTOL development and policy formulation. The original contributions include a detailed mapping of VTOL innovation trends, identification of key technological advancements, and a predictive lens into future directions. These findings offer a valuable resource for aerospace engineers, policymakers, and urban planners. This study contributes a detailed assessment of both theoretical foundations and practical applications, fostering a holistic view of the challenges and innovations shaping the future of AAM. By connecting research and practical development, this study serves as a critical tool for strategic decision making and policy formulation towards advancing the integration of VTOL aircraft into sustainable urban transportation networks. Full article
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