Spatio-Temporal Neural Structural Causal Models for Bike Flow Prediction
Authors: Pan Deng, Yu Zhao, Junting Liu, Xiaofeng Jia, Mulan Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world datasets demonstrate the superior performance of our model, especially its resistance to fluctuations caused by the external environment. The source code and data will be released.In this section, we evaluate the effectiveness of STNSCM by experiments conducted on real-world datasets1. |
| Researcher Affiliation | Collaboration | Pan Deng1, Yu Zhao1*, Junting Liu1, Xiaofeng Jia2, Mulan Wang1 1 Beihang University, Beijing, 100191, China. 2 Beijing Big Data Centre, Beijing, 100024, China. {pandeng, iyzhao, liujunting, wangmulan}@buaa.edu.cn, jiaxf@jxj.beijing.gov.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | The source code and data will be released.1https://github.com/Eternity ZY/STNSCM |
| Open Datasets | Yes | Datasets: We collect two real-world datasets, NYC-Bike and BJ-Bike, each dataset contains the corresponding weather and time information. The source code and data will be released.1https://github.com/Eternity ZY/STNSCM |
| Dataset Splits | Yes | We select the first 60% of data as the training set, 20% as the validation set, and 20% as the test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. Details about hyper-parameters or other training settings are not provided. |