no code implementations • 29 Nov 2023 • Yuebing Liang, Yichao Liu, Xiaohan Wang, Zhan Zhao
Accurate human mobility prediction for public events is thus crucial for event planning as well as traffic or crowd management.
no code implementations • 20 Mar 2023 • Yuebing Liang, Fangyi Ding, Guan Huang, Zhan Zhao
For station-based BSSs, this means planning new stations based on existing ones over time, which requires prediction of the number of trips generated by these new stations across the whole system.
no code implementations • 16 Nov 2022 • Yuebing Liang, Guan Huang, Zhan Zhao
Existing methods for bike sharing demand prediction are mostly based on its own historical demand variation, essentially regarding it as a closed system and neglecting the interaction between different transportation modes.
1 code implementation • 18 Jun 2022 • Zhan Zhao, Yuebing Liang
Route choice modeling is a fundamental task in transportation planning and demand forecasting.
no code implementations • 18 Mar 2022 • Yuebing Liang, Guan Huang, Zhan Zhao
Bike sharing is an increasingly popular part of urban transportation systems.
no code implementations • 15 Dec 2021 • Yuebing Liang, Guan Huang, Zhan Zhao
Despite some recent efforts, existing approaches to multimodal demand prediction are generally not flexible enough to account for multiplex networks with diverse spatial units and heterogeneous spatiotemporal correlations across different modes.
no code implementations • 17 Sep 2021 • Yuebing Liang, Zhan Zhao, Lijun Sun
The results show that our proposed model outperforms existing deep learning models in all kinds of missing scenarios and the graph structure estimation technique contributes to the model performance.
no code implementations • 21 Jun 2021 • Yuebing Liang, Zhan Zhao
None of them is ideal, as the cell-based representation ignores the road network structures and the other two are less efficient in analyzing city-scale road networks.