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 | Conference PDF | Archive PDF | Plain Text | 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. |