most citedSTG-GAN: A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems

4 citations · 11 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2022

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…

cs.LG2022

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…

cs.LG2022★ 4 cited

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…

cs.LG2022★ 2 cited

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…

cs.LG2022

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…

cs.LG2022★ 4 cited

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…

cs.LG2021★ 1 cited

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…