Traffic prediction using artificial intelligence: review of recent advances and emerging opportunities

M Shaygan, C Meese, W Li, XG Zhao… - … research part C: emerging …, 2022 - Elsevier
Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …

A comprehensive survey of the key technologies and challenges surrounding vehicular ad hoc networks

Z Xia, J Wu, L Wu, Y Chen, J Yang, PS Yu - ACM Transactions on …, 2021 - dl.acm.org
Vehicular ad hoc networks (VANETs) and the services they support are an essential part of
intelligent transportation. Through physical technologies, applications, protocols, and …

Privacy-preserving blockchain-based federated learning for traffic flow prediction

Y Qi, MS Hossain, J Nie, X Li - Future Generation Computer Systems, 2021 - Elsevier
As accurate and timely traffic flow information is extremely important for traffic management,
traffic flow prediction has become a vital component of intelligent transportation systems …

Deep learning for security in digital twins of cooperative intelligent transportation systems

Z Lv, Y Li, H Feng, H Lv - IEEE transactions on intelligent …, 2021 - ieeexplore.ieee.org
The purpose is to solve the security problems of the Cooperative Intelligent Transportation
System (CITS) Digital Twins (DTs) in the Deep Learning (DL) environment. The DL algorithm …

[HTML][HTML] A3t-gcn: Attention temporal graph convolutional network for traffic forecasting

J Bai, J Zhu, Y Song, L Zhao, Z Hou, R Du… - … International Journal of …, 2021 - mdpi.com
Accurate real-time traffic forecasting is a core technological problem against the
implementation of the intelligent transportation system. However, it remains challenging …

A constrained differential evolution algorithm to solve UAV path planning in disaster scenarios

X Yu, C Li, JF Zhou - Knowledge-Based Systems, 2020 - Elsevier
Disasters have caused significant losses to humans in the past decades. It is essential to
learn about the disaster situation so that rescue works can be conducted as soon as …

[HTML][HTML] Ensemble learning methods using the Hodrick–Prescott filter for fault forecasting in insulators of the electrical power grids

LO Seman, SF Stefenon, VC Mariani… - International Journal of …, 2023 - Elsevier
Electrical power grid insulators installed outdoors are exposed to environmental conditions,
such as the accumulation of contaminants on their surface. The contaminants increase the …

A novel hybrid model based on nonlinear weighted combination for short-term wind power forecasting

J Duan, P Wang, W Ma, S Fang, Z Hou - International Journal of Electrical …, 2022 - Elsevier
Wind power forecasting plays a vital role in enhancing the efficiency of power grid operation
and increasing the competitiveness of power market. In this paper, a novel hybrid …

[HTML][HTML] Weather impact on solar farm performance: a comparative analysis of machine learning techniques

A Gopi, P Sharma, K Sudhakar, WK Ngui… - Sustainability, 2022 - mdpi.com
Forecasting the performance and energy yield of photovoltaic (PV) farms is crucial for
establishing the economic sustainability of a newly installed system. The present study aims …

Unified spatial-temporal neighbor attention network for dynamic traffic prediction

W Long, Z Xiao, D Wang, H Jiang… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Traffic prediction plays an essential role in many real-world applications ranging from route
planning to vehicular communications. The goal of making accurate prediction is …