Graph neural network for traffic forecasting: A survey
Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …
learning models, including convolution neural networks and recurrent neural networks, have …
Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges
As the globally increasing population drives rapid urbanization in various parts of the world,
there is a great need to deliberate on the future of the cities worth living. In particular, as …
there is a great need to deliberate on the future of the cities worth living. In particular, as …
A survey on artificial intelligence assurance
FA Batarseh, L Freeman, CH Huang - Journal of Big Data, 2021 - Springer
Artificial Intelligence (AI) algorithms are increasingly providing decision making and
operational support across multiple domains. AI includes a wide (and growing) library of …
operational support across multiple domains. AI includes a wide (and growing) library of …
Deep learning for road traffic forecasting: Does it make a difference?
Deep Learning methods have been proven to be flexible to model complex phenomena.
This has also been the case of Intelligent Transportation Systems, in which several areas …
This has also been the case of Intelligent Transportation Systems, in which several areas …
[HTML][HTML] An alliance of humans and machines for machine learning: Hybrid intelligent systems and their design principles
J Ostheimer, S Chowdhury, S Iqbal - Technology in Society, 2021 - Elsevier
With the growing number of applications of artificial intelligence such as autonomous cars or
smart industrial equipment, the inaccuracy of utilized machine learning algorithms could …
smart industrial equipment, the inaccuracy of utilized machine learning algorithms could …
Graph Markov network for traffic forecasting with missing data
Traffic forecasting is a classical task for traffic management and it plays an important role in
intelligent transportation systems. However, since traffic data are mostly collected by traffic …
intelligent transportation systems. However, since traffic data are mostly collected by traffic …
Interpretable deep learning LSTM model for intelligent economic decision-making
For sustainable economic growth, information about economic activities and prospects is
critical to decision-makers such as governments, central banks, and financial markets …
critical to decision-makers such as governments, central banks, and financial markets …
Human-in-loop: A review of smart manufacturing deployments
The recent increase in computational capability has led to an unprecedented increase in the
range of new applications where machine learning can be used in real time …
range of new applications where machine learning can be used in real time …
Inferring intercity freeway truck volume from the perspective of the potential destination city attractiveness
Accurately inferring the spatiotemporal distribution of freeway traffic volume is one of the
bottleneck problems for intelligent management of ground transportation. Although the …
bottleneck problems for intelligent management of ground transportation. Although the …
Efficient and explainable ship selection planning in port state control
Port state control is the safeguard of maritime transport achieved by inspecting foreign
visiting ships and supervising them to rectify the non-compliances detected. One key issue …
visiting ships and supervising them to rectify the non-compliances detected. One key issue …