[HTML][HTML] Forecasting: theory and practice

F Petropoulos, D Apiletti, V Assimakopoulos… - International Journal of …, 2022 - Elsevier
Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

[PDF][PDF] Towards industrial revolution 5.0 and explainable artificial intelligence: Challenges and opportunities

I Taj, N Zaman - International Journal of Computing and …, 2022 - pdfs.semanticscholar.org
Technological growth is changing our everyday living, making it smarter and more
convenient day by day; Smart society 5.0, Healthcare 5.0, Agriculture 5.0 are only a few …

[HTML][HTML] A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks

S Reza, MC Ferreira, JJM Machado… - Expert Systems with …, 2022 - Elsevier
Traffic flow forecasting is an essential component of an intelligent transportation system to
mitigate congestion. Recurrent neural networks, particularly gated recurrent units and long …

Deep learning for spatio-temporal data mining: A survey

S Wang, J Cao, SY Philip - IEEE transactions on knowledge …, 2020 - ieeexplore.ieee.org
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …

Short-term traffic flow prediction for urban road sections based on time series analysis and LSTM_BILSTM method

C Ma, G Dai, J Zhou - IEEE Transactions on Intelligent …, 2021 - ieeexplore.ieee.org
The real-time performance and accuracy of traffic flow prediction directly affect the efficiency
of traffic flow guidance systems, and traffic flow prediction is a hotspot in the field of …

Big data analytics in intelligent transportation systems: A survey

L Zhu, FR Yu, Y Wang, B Ning… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Big data is becoming a research focus in intelligent transportation systems (ITS), which can
be seen in many projects around the world. Intelligent transportation systems will produce a …

Machine learning-based traffic prediction models for intelligent transportation systems

A Boukerche, J Wang - Computer Networks, 2020 - Elsevier
Abstract Intelligent Transportation Systems (ITS) have attracted an increasing amount of
attention in recent years. Thanks to the fast development of vehicular computing hardware …

Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting

Z Cui, K Henrickson, R Ke… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due
to the time-varying traffic patterns and the complicated spatial dependencies on road …

LSTM-based traffic flow prediction with missing data

Y Tian, K Zhang, J Li, X Lin, B Yang - Neurocomputing, 2018 - Elsevier
Traffic flow prediction plays a key role in intelligent transportation systems. However, since
traffic sensors are typically manually controlled, traffic flow data with varying length, irregular …

Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting

H Peng, H Wang, B Du, MZA Bhuiyan, H Ma, J Liu… - Information …, 2020 - Elsevier
Accurate and real-time traffic passenger flows forecasting at transportation hubs, such as
subway/bus stations, is a practical application and of great significance for urban traffic …