ST Data

Fine-Grained Urban Flow Inference

The ubiquitous deployment of monitoring devices in urban flow monitoring systems induces a significant cost for maintenance and operation. A technique is required to reduce the number of deployed devices, while preventing the degeneration of data …

Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks

Being able to predict the crowd flows in each and every part of a city, especially in irregular regions, is strategically important for traffic control, risk assessment, and public safety. However, it is very challenging because of interactions and …

AutoST: Efficient Neural Architecture Search forSpatio-Temporal Prediction

Spatio-temporal (ST) prediction (e.g. crowd flow prediction) is of great importance in a wide range of smart city applications from urban planning, intelligent transportation and public safety. How to automatically construct a general neural network …

Spatio-Temporal Meta Learning for Urban Traffic Prediction

Predicting urban traffic is of great importance to intelligent transportation systems and public safety, yet is very challenging in three aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between …

Unsupervised Learning of Disentangled Location Embeddings

Learning semantically coherent location embeddings can benefit downstream applications such as human mobility prediction. However, the conflation of geographic and semantic attributes of a location can harm such coherence, especially when semantic …

Revisiting Convolutional Neural Networks for Urban Flow Analytics

Convolutional Neural Networks (CNNs) have been widely adopted in raster-based urban flow analytics by virtue of their capability in capturing nearby spatial context. By revisiting CNN-based methods for different analytics tasks, we expose two common …

Predicting Urban Water Quality with Ubiquitous Data

Urban water quality is of great importance to our daily lives. Prediction of urban water quality help control water pollution and protect human health. However, predicting the urban water quality is a challenging task since the water quality varies …

Dynamic Public Resource Allocation based on Human Mobility Prediction

The objective of public resource allocation, e.g., the deployment of billboards, surveillance cameras, base stations, trash bins, is to serve more people. However, due to the dynamics of human mobility patterns, people are distributed unevenly on …

Learning to Generate Maps from Trajectories

Accurate and updated road network data is vital in many urban applications, such as car-sharing, and logistics. The traditional approach to identifying the road network, i.e., field survey, requires a significant amount of time and effort. With the …

UrbanFM: Inferring Fine-Grained Urban Flows

Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-lasting and enormous cost for maintenance and operation. This suggests …