no code implementations • 12 Dec 2022 • Hui Wang, Jialin Liu, Feng Li, Hao Ji, Bin Jia, Ziyou Gao
Numerical cases of Beijing Metro Line 9 verify the efficiency and effectiveness of our proposed model, and results show that: (1) when occurring a disruption event during peak hours, the impact on the normal timetable is greater, and passengers in the direction with fewer train services are more affected; (2) if passengers stranded at the terminal stations of disruption area are not transported in time, they will rapidly increase at a speed of more than 300 passengers per minute; (3) compared with the fixed shortest path, using the response vehicles reduces the total travel time about 7%.
no code implementations • 14 Oct 2022 • Shuxin Zhang, Jinlei Zhang, Lixing Yang, Chengcheng Wang, Ziyou Gao
How to dynamically model the complex spatiotemporal dependencies of passenger flow is the main issue in achieving accurate passenger flow prediction during the epidemic.
no code implementations • 27 Feb 2022 • Yongjie Yang, Jinlei Zhang, Lixing Yang, Xiaohong Li, Ziyou Gao
As an important component of MaaS, short-term passenger flow prediction for multi-traffic modes has thus been brought into focus.
no code implementations • 27 Feb 2022 • Shuxin Zhang, Jinlei Zhang, Lixing Yang, Jiateng Yin, Ziyou Gao
The short term passenger flow prediction of the urban rail transit system is of great significance for traffic operation and management.
no code implementations • 18 Feb 2022 • Jinlei Zhang, Ergang Shan, Lixia Wu, Lixing Yang, Ziyou Gao, Haoyuan Hu
To solve these problems, we put forward an intelligent end-to-end predict-then-optimize clustering method to simultaneously predict the future pick-up requests of AOIs and assign AOIs to couriers by clustering.
no code implementations • 10 Feb 2022 • Jinlei Zhang, Hua Li, Lixing Yang, Guangyin Jin, Jianguo Qi, Ziyou Gao
To overcome these limitations, we propose a novel deep learning-based spatiotemporal graph generative adversarial network (STG-GAN) model with higher prediction accuracy, higher efficiency, and lower memory occupancy to predict short-term passenger flows of the URT network.
no code implementations • 19 Aug 2021 • Jinlei Zhang, Feng Chen, Lixing Yang, Wei Ma, Guangyin Jin, Ziyou Gao
This paper focuses on an essential and hard problem to estimate the network-wide link travel time and station waiting time using the automatic fare collection (AFC) data in the URT system, which is beneficial to better understand the system-wide real-time operation state.