Explainable Origin-Destination Crowd Flow Interpolation via Variational Multi-Modal Recurrent Graph Auto-Encoder

Authors: Qiang Zhou, Xinjiang Lu, Jingjing Gu, Zhe Zheng, Bo Jin, Jingbo Zhou

AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Extensive experiments on two real-world datasets validate that VMR-GAE outperforms the state-of-the-art baselines. Also, an exploratory empirical study shows that the proposed explainer can generate meaningful spatiotemporal explanations.
Researcher Affiliation Collaboration 1Nanjing University of Aeronautics and Astronautics, Nanjing, China 2Baidu Research, Beijing, China 3Dalian University of Technology, Dalian, China
Pseudocode No The paper describes algorithms in prose (e.g., efficient explanation generation algorithm) but does not provide structured pseudocode or clearly labeled algorithm blocks.
Open Source Code No The paper does not include an unambiguous statement that the authors are releasing their code for the described work, nor does it provide a direct link to a source-code repository.
Open Datasets Yes NYC dataset was generated from NYC taxicab data published by NYC TLC1. 1https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page
Dataset Splits Yes Then, we perform a random 7:1:2 division to create the training, validation, and test sets.
Hardware Specification No The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments.
Software Dependencies No The paper discusses the models and architectures used but does not provide specific ancillary software details, such as library names with version numbers (e.g., Python, PyTorch, TensorFlow versions).
Experiment Setup Yes The unit time step is 1h in Beijing and 2h in NYC datasets. We choose the number of input snapshots T as 6 and set the diffusion step to be 2 in this work.