Urban Flow

Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction

CIKM 2019, Applied Research Track (acceptance rate: 21.8%)

UrbanFM: Inferring Fine-Grained Urban Flows

KDD 2019, ADS Track (acceptance rate: 20.7%)

DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction

ACL 2019 (acceptance rate: 25.7%)

Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning

Predicting flows (e.g. the traffic of vehicles, crowds and bikes), consisting of the in-out traffic at a node and transitions between different nodes, in a spatio-temporal network plays an important role in transportation systems. However, this is a …