254 citations · 421 across the 11 of their papers we have counts for
11 papers
Meta-learning Based Short-Term Passenger Flow Prediction for Newly-Operated Urban Rail Transit Stations
Kuo Han, Jinlei Zhang, Chunqi Zhu +3
Accurate short-term passenger flow prediction in urban rail transit stations has great benefits for reasonably allocating resources, easing congestion, and reducing operational ris…
ST-former for short-term passenger flow prediction during COVID-19 in urban rail transit system
Shuxin Zhang, Jinlei Zhang, Lixing Yang +2
Accurate passenger flow prediction of urban rail transit is essential for improving the performance of intelligent transportation systems, especially during the epidemic. How to dy…
Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction
Guangyin Jin, Fuxian Li, Jinlei Zhang +2
Accurate traffic prediction is a challenging task in intelligent transportation systems because of the complex spatio-temporal dependencies in transportation networks. Many existin…
Short-term passenger flow prediction for multi-traffic modes: A Transformer and residual network based multi-task learning method
Yongjie Yang, Jinlei Zhang, Lixing Yang +2
With the prevailing of mobility as a service (MaaS), it becomes increasingly important to manage multi-traffic modes simultaneously and cooperatively. As an important component of…
Spatial-Temporal Attention Fusion Network for short-term passenger flow prediction on holidays in urban rail transit systems
Shuxin Zhang, Jinlei Zhang, Lixing Yang +2
The short term passenger flow prediction of the urban rail transit system is of great significance for traffic operation and management. The emerging deep learning-based models pro…
An end-to-end predict-then-optimize clustering method for intelligent assignment problems in express systems
Jinlei Zhang, Ergang Shan, Lixia Wu +3
Express systems play important roles in modern major cities. Couriers serving for the express system pick up packages in certain areas of interest (AOI) during a specific time. How…
STG-GAN: A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems
Jinlei Zhang, Hua Li, Lixing Yang +3
Short-term passenger flow prediction is an important but challenging task for better managing urban rail transit (URT) systems. Some emerging deep learning models provide good insi…
Network-wide link travel time and station waiting time estimation using automatic fare collection data: A computational graph approach
Jinlei Zhang, Feng Chen, Lixing Yang +3
Urban rail transit (URT) system plays a dominating role in many megacities like Beijing and Hong Kong. Due to its important role and complex nature, it is always in great need for…
Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method
Jinlei Zhang, Hongshu Che, Feng Chen +2
Short-term origin-destination (OD) flow prediction in urban rail transit (URT) plays a crucial role in smart and real-time URT operation and management. Different from other short-…
Multi-graph convolutional network for short-term passenger flow forecasting in urban rail transit
Jinlei Zhang, Feng Chen, Yinan Guo +1
Short-term passenger flow forecasting is a crucial task for urban rail transit operations. Emerging deep-learning technologies have become effective methods used to overcome this p…
Deep-learning Architecture for Short-term Passenger Flow Forecasting in Urban Rail Transit
Jinlei Zhang, Feng Chen, Zhiyong Cui +2
Short-term passenger flow forecasting is an essential component in urban rail transit operation. Emerging deep learning models provide good insight into improving prediction precis…