Deep Learning

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 …

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 …

Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning

Predicting urban traffic (e.g., flow, speed) is of great importance to intelligent transportation systems and public safety, yet is very challenging as it is affected by two aspects: 1) complex spatio-temporal correlations of urban traffic, including …

GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction

Numerous sensors have been deployed in different geospatial locations to continuously and cooperatively monitor the surrounding environment, such as the air quality. These sensors generate multiple geo-sensory time series, with spatial correlations …

HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting

Spatio-temporal (ST) data, which represent multiple time series data corresponding to different spatial locations, are ubiquitous in real-world dynamic systems, such as air quality readings. Forecasting over ST data is of great importance but …