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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 …

Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi*, Yu Zheng

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

When Will You Arrive Estimating Travel Time Based on Deep Neural Networks

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

Dong Wang*, Junbo Zhang, Wei Cao, Jian Li, Yu Zheng

Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

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

Junbo Zhang, Yu Zheng, Dekang Qi

Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

ST-MVL: Filling missing values in geo-sensory time series data

Many sensors have been deployed in the physical world, generating massive geo-tagged time series data. In reality, we usually lose …

Xiuwen Yi*, Yu Zheng, Junbo Zhang, Tianrui Li

ST-MVL: Filling missing values in geo-sensory time series data

DNN-Based Prediction Model for Spatio-Temporal Data

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

Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, Xiuwen Yi*

Supervised Deep Learning with Auxiliary Networks

Deep learning well demonstrates its potential in learning latent feature representations. Recent years have witnessed an increasing …

Junbo Zhang, Guangjian Tian, Yadong Mu, Wei Fan