Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
When Transfer Learning Meets Cross-City Urban Flow Prediction: Spatio-Temporal Adaptation Matters
Authors: Ziquan Fang, Dongen Wu, Lu Pan, Lu Chen, Yunjun Gao
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on five real datasets show STAN substantially outperforms state-of-the-art methods. |
| Researcher Affiliation | Academia | 1College of Computer Science, Zhejiang University, Hangzhou, China, 2College of Computer Science, Zhejiang University of Technology, Hangzhou, China |
| Pseudocode | No | The paper describes the model architecture and mathematical formulations but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | Yes | We adopt five popular open-source urban crowd datasets, namely NYCTaxi, NYCBike, CHIBike, BJTaxi, and Chengdu, which are commonly used in related studies. Table 1 summarizes the statistics of the datasets. |
| Dataset Splits | No | The paper specifies train and test splits (e.g., '9 months of data [...] for training and the rest for testing'), but it does not explicitly mention a separate validation split for hyperparameter tuning or early stopping. |
| Hardware Specification | Yes | STAN is implemented with Pytorch framework on GTX-3090 24G GPU. |
| Software Dependencies | No | The paper mentions 'Pytorch framework' but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Parameter Settings. [...] Then, we select 3 days of historical data where each day contains 9 time intervals. Further, we compute inflow/outflow of each region based on Eq. 1 and then normalize the flow data into [0, 1]. In terms of SAAM, the convolution kernel size is set to 3x3, and the domain discriminator contains two fully-connected layers with sizes are 64 and 1. In terms of TAAM, the length of LSTM and hidden feature size are set to 9 and 128. In terms of PM, two connected layers sizes are 256 and 512. Then, we set β and γ as 0.1. As for model training, the epoch size, dropout, and learning rate are set to 32, 0.5, and 1e-6. Besides, after 50 epochs, we reduce the learning rate to 0.9 times the original after every five epochs. |