As a representative of shared mobility, bike sharing has become a green and convenient
way to travel in cities in recent years. Bike usage prediction becomes more important for …

Accurate traffic flow prediction is valuable for satisfying citizens' travel needs and alleviating
urban traffic pressure. However, it is highly challenging due to the complexity of the urban …

Traffic flow prediction is increasingly vital for the administration of metropolitan areas. Many
research on spatio-temporal networks have been explored but the impacts of both spatial …

With the development of the Internet of Things (IoT) and 5G technologies, IoT devices
deployed on roads are able to collect a large amount of traffic data at any time. Road …

Traffic forecasting is a critical task to extract values from cyber-physical infrastructures, which
is the backbone of smart transportation. However owing to external contexts, the dynamics at …

Public security is a concept that, in western societies, is not given enough importance since
the crime rates are at socially acceptable levels. Nowadays, with the help of artificial …

Existing convolution recurrent neural networks (ConvRNNs)-based memory cells majorly
take advantage of gated structures and attention mechanisms to extract discontinuous latent …

Previous traffic flow prediction studies have utilized spatio-temporal neural networks
combined with the multi-task learning framework to seek complementary information for …

Traffic flow prediction is crucial in smart cities and traffic management, yet it presents
challenges due to intricate spatial-temporal dependencies and external factors. Most …

The silicon content of hot metal, which is one of the important indexes characterizing the
blast furnace temperature, reflects the blast furnace condition, iron output, and energy …