Deep Learning for Spatio-Temporal Data

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Advances in location-acquisition and wireless communication technologies have led to wider availability of spatio-temporal (ST) data. Deep neural networks (DNNs) have been successfully applied to various problems, such as computer vision, speech recognition, natural language understanding. Being different from these domains, ST data has unique spatial properties (i.e. geographical hierarchy and distance) and temporal properties (i.e. closeness, period and trend). It is very challenging to capture all of these ST properties simultaneously.

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Junbo Zhang
Senior Researcher

My research interests include deep learning, data mining, AI, big data analytics, and urban computing.

Publications

Being able to predict the crowd flows in each and every part of a city, especially in irregular regions, is strategically important for …

Predicting flows (e.g. the traffic of vehicles, crowds and bikes), consisting of the in-out traffic at a node and transitions between …

As urban crimes (e.g., burglary and robbery) negatively impact our everyday life and must be addressed in a timely manner, predict- ing …

Numerous sensors have been deployed in different geospatial locations to continuously and cooperatively monitor the surrounding …

Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected …

Estimating the travel time of any path (denoted by a sequence of connected road segments) in a city is of great importance to traffic …

We proposed a deep-learning-based approach, called ST-ResNet, to collectively forecast the inflow and outflow of crowds in each and …

Advances in location-acquisition and wireless communication technologies have led to wider availability of spatio-temporal (ST) data, …