Search Results for author: Zhan Zhao

Found 12 papers, 2 papers with code

Exploring Large Language Models for Human Mobility Prediction under Public Events

no code implementations29 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.


Deep trip generation with graph neural networks for bike sharing system expansion

no code implementations20 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.

Graph Neural Network regression

Adapting Node-Place Model to Predict and Monitor COVID-19 Footprints and Transmission Risks

no code implementations31 Dec 2022 Jiali Zhou, Mingzhi Zhou, Jiangping Zhou, Zhan Zhao

This article adapts this model to investigate whether and how node, place, and mobility would be associated with the transmission risks and presences of the local COVID-19 cases in a city.

Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction using Domain-Adversarial Graph Neural Networks

no code implementations16 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.

Graph Neural Network

HGARN: Hierarchical Graph Attention Recurrent Network for Human Mobility Prediction

1 code implementation14 Oct 2022 Yihong Tang, Junlin He, Zhan Zhao

To address these issues, we present Hierarchical Graph Attention Recurrent Network (HGARN) for human mobility prediction.

Decoder Graph Attention

Joint Demand Prediction for Multimodal Systems: A Multi-task Multi-relational Spatiotemporal Graph Neural Network Approach

no code implementations15 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.

Graph Neural Network Management

Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns

no code implementations17 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.

Imputation Traffic Data Imputation

NetTraj: A Network-based Vehicle Trajectory Prediction Model with Directional Representation and Spatiotemporal Attention Mechanisms

no code implementations21 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.

Decoder Graph Attention +2

Individual Mobility Prediction: An Interpretable Activity-based Hidden Markov Approach

no code implementations11 Jan 2021 Baichuan Mo, Zhan Zhao, Haris N. Koutsopoulos, Jinhua Zhao

Individual mobility is driven by demand for activities with diverse spatiotemporal patterns, but existing methods for mobility prediction often overlook the underlying activity patterns.

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