285 citations · 329 across the 5 of their papers we have counts for
5 papers
Incorporating Reachability Knowledge into a Multi-Spatial Graph Convolution Based Seq2Seq Model for Traffic Forecasting
Jiexia Ye, Furong Zheng, Juanjuan Zhao +2
Accurate traffic state prediction is the foundation of transportation control and guidance. It is very challenging due to the complex spatiotemporal dependencies in traffic data. E…
Completion and Augmentation based Spatiotemporal Deep Learning Approach for Short-Term Metro Origin-Destination Matrix Prediction under Limited Observable Data
Jiexia Ye, Juanjuan Zhao, Furong Zheng +1
Short-term OD flow (i.e. the number of passenger traveling between stations) prediction is crucial to traffic management in metro systems. Due to the delayed effect in latest compl…
How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
Jiexia Ye, Juanjuan Zhao, Kejiang Ye +1
In recent years, various deep learning architectures have been proposed to solve complex challenges (e.g. spatial dependency, temporal dependency) in traffic domain, which have ach…
Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction
Jiexia Ye, Juanjuan Zhao, Kejiang Ye +1
Stock price movement prediction is commonly accepted as a very challenging task due to the volatile nature of financial markets. Previous works typically predict the stock price ma…
Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems
Juanjuan Zhao, Fan Zhang, Lai Tu +5
Nowadays, metro systems play an important role in meeting the urban transportation demand in large cities. The understanding of passenger route choice is critical for public transi…