J
Jinlei Zhang
Researcher at Beijing Jiaotong University
Publications - 25
Citations - 323
Jinlei Zhang is an academic researcher from Beijing Jiaotong University. The author has contributed to research in topics: Computer science & Urban rail transit. The author has an hindex of 6, co-authored 11 publications receiving 83 citations.
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Journal ArticleDOI
Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit
TL;DR: A comparison of the prediction precisions obtained for time granularities of 10, 15, and 30 min indicates that prediction precision increases with increasing time granularity, and this study can provide subway operators with insight into short-term passenger flow forecasting by leveraging deep learning models.
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Multi-graph convolutional network for short-term passenger flow forecasting in urban rail transit
TL;DR: This study can provide critical insights for subway operators to optimise urban rail transit operations and propose a deep-learning architecture called Conv-GCN that combines a graph convolutional network (GCN) and a three-dimensional Convolutional neural network (3D CNN) that yields the best performance.
Journal ArticleDOI
Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method
TL;DR: A channel-wise attentive split–convolutional neural network (CAS-CNN) is proposed that contributes to the development of short-term OD flow prediction, and it also lays the foundations of real-time URT operation and management.
Journal ArticleDOI
Cluster-Based LSTM Network for Short-Term Passenger Flow Forecasting in Urban Rail Transit
Jinlei Zhang,Feng Chen,Qing Shen +2 more
TL;DR: Results show that the prediction based on subway station clusters can not only avoid the complication of developing numerous models for each of the hundreds of stations, but also improve the prediction performance, which make it possible to predict short-term passenger flow on a network scale using limited dataset.
Journal ArticleDOI
Short-Term Origin-Destination Forecasting in Urban Rail Transit Based on Attraction Degree
TL;DR: This study introduces the ODAD indicator and five ODAD levels to describe the attraction between OD pairs and uses the mature long short-term memory (LSTM) network model to examine the effects of the introduction of ODAD.