Deep Learning

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 …

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 …