Accurate short-term passenger flow prediction in urban rail transit is critical in ensuring the
stable operation of urban rail systems. However, accurate passenger flow prediction still …

Mobility trajectory data is of great significance for mobility pattern study, urban computing,
and city science. Self-driving, traffic prediction, environment estimation, and many other …

Transportation demand forecasting is a critical precondition of optimal online transportation
dispatch, which will greatly reduce drivers' wasted mileage and customers' waiting time …

Sequential recommendation which aims to predict a user's next interaction based on his/her
previous behaviors, has attracted great attention. Recent studies mainly employ deep …

In recent years, many spatial-temporal graph convolutional network (STGCN) models are
proposed to deal with the spatial-temporal network data forecasting problem. These STGCN …

Wi-Fi-on devices such as smartphones search for network availability by periodically
broadcasting probe requests which encapsulate MAC addresses as device identifiers. To …

Urban vibrancy describes the prosperity, diversity, and accessibility of urban areas, which is
vital to a city's socio-economic development and sustainability. While many efforts have …

Air pollution seriously affects public health, while effective air quality prediction remains a
challenging problem since the complex spatial-temporal couplings exist in multi-area …

Great efforts have been devoted to solving the crowd counting problem based on vision or
other fine-grained measurements. Popular vision and WiFi channel state information based …

Knowledge Graph (KG) reasoning has been an interesting topic in recent decades. Most
current researches focus on predicting the missing facts for incomplete KG. Nevertheless …