Prospects and challenges of Metaverse application in data‐driven intelligent transportation systems

JN Njoku, CI Nwakanma, GC Amaizu… - IET Intelligent Transport …, 2023 - Wiley Online Library
The Metaverse is a concept used to refer to a virtual world that exists in parallel to the
physical world. It has grown from a conceptual level to having real applications in virtual …

A survey on deep learning and its applications

S Dong, P Wang, K Abbas - Computer Science Review, 2021 - Elsevier
Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …

Smart transportation: an overview of technologies and applications

D Oladimeji, K Gupta, NA Kose, K Gundogan, L Ge… - Sensors, 2023 - mdpi.com
As technology continues to evolve, our society is becoming enriched with more intelligent
devices that help us perform our daily activities more efficiently and effectively. One of the …

Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting

S Guo, Y Lin, H Wan, X Li… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Accurate traffic forecasting is critical in improving safety, stability, and efficiency of intelligent
transportation systems. Despite years of studies, accurate traffic prediction still faces the …

Data-driven fault diagnosis for traction systems in high-speed trains: A survey, challenges, and perspectives

H Chen, B Jiang, SX Ding… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Recently, to ensure the reliability and safety of high-speed trains, detection and diagnosis of
faults (FDD) in traction systems have become an active issue in the transportation area over …

A vision and framework for the high altitude platform station (HAPS) networks of the future

GK Kurt, MG Khoshkholgh, S Alfattani… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
A High Altitude Platform Station (HAPS) is a network node that operates in the stratosphere
at an of altitude around 20 km and is instrumental for providing communication services …

Attention based spatial-temporal graph convolutional networks for traffic flow forecasting

S Guo, Y Lin, N Feng, C Song, H Wan - Proceedings of the AAAI …, 2019 - ojs.aaai.org
Forecasting the traffic flows is a critical issue for researchers and practitioners in the field of
transportation. However, it is very challenging since the traffic flows usually show high …

Triboelectric nanogenerator based self-powered sensor for artificial intelligence

Y Zhou, M Shen, X Cui, Y Shao, L Li, Y Zhang - Nano Energy, 2021 - Elsevier
Triboelectric nanogenerator based sensor has excellent material compatibility, low cost, and
flexibility, which is a unique candidate technology for artificial intelligence. Triboelectric …

Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis

S Kaffash, AT Nguyen, J Zhu - International journal of production economics, 2021 - Elsevier
The volume and availability of data in the Intelligent Transportation System (ITS) result in the
need for data-driven approaches. Big Data algorithms are applied to further enhance the …

A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction

H Zheng, F Lin, X Feng, Y Chen - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Accurate short-time traffic flow prediction has gained gradually increasing importance for
traffic plan and management with the deployment of intelligent transportation systems (ITSs) …