Urban Flow

Sep 15, 2015

Using a diversity of big data and deep learning techniques to predict citywide crowd/traffic flows throughout a city.

Urban Flow

Publications

Fine-grained Urban Flow Prediction

Yuxuan Liang, Kun Ouyang, Junkai Sun, Yiwei Wang, Junbo Zhang, Yu Zheng, David S.Rosenblum, Roger Zimmermann

Fine-grained Urban Flow Prediction

Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks

IEEE TKDE, 2020

Junkai Sun, Junbo Zhang#, Qiaofei Li, Xiuwen Yi, Yuxuan Liang, Yu Zheng

Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks

Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics

Yuxuan Liang, Kun Ouyang, Yiwei Wang, Ye Liu, Junbo Zhang, Yu Zheng, David S. Rosenblum

Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics

Urban flow prediction from spatiotemporal data using machine learning: A survey

Information Fusion

Peng Xie, Tianrui Li, Jia Liu, Shengdong Du, Xin Yang, Junbo Zhang

Urban flow prediction from spatiotemporal data using machine learning: A survey

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

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

Zheyi Pan*, Zhaoyuan Wang, Weifeng Wang, Yong Yu, Junbo Zhang#, Yu Zheng

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

CityTraffic: Modeling Citywide Traffic via Neural Memorization and Generalization Approach

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

Xiuwen Yi, Zhewen Duan, Ting Li, Junbo Zhang#, Tianrui Li, Yu Zheng

CityTraffic: Modeling Citywide Traffic via Neural Memorization and Generalization Approach

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

[ORAL] KDD 2019, Research Track (acceptance rate: 14.2%)

Zheyi Pan*, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, Junbo Zhang

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

UrbanFM: Inferring Fine-Grained Urban Flows

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

Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S. Rosenblum, Yu Zheng

UrbanFM: Inferring Fine-Grained Urban Flows

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 …

Junbo Zhang#, Yu Zheng, Junkai Sun, Dekang Qi

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

Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks

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

Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks

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