Social Physics Informed Diffusion Model for Crowd Simulation
Authors: Hongyi Chen, Jingtao Ding, Yong Li, Yue Wang, Xiao-Ping Zhang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on two real-world datasets demonstrate the superior performance of SPDiff in terms of macroscopic and microscopic evaluation metrics. Code and appendix are available at https://github.com/tsinghua-fib-lab/SPDiff. |
| Researcher Affiliation | Academia | Hongyi Chen1,3, Jingtao Ding2,*, Yong Li2, Yue Wang2, Xiao-Ping Zhang1, 1Shenzhen Key Laboratory of Ubiquitous Data Enabling, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China 2Department of Electronic Engineering, Tsinghua University, China 3Department of Strategic and Advanced Interdisciplinary Research, Peng Cheng Laboratory, Shenzhen, China chenhy23@mails.tsinghua.edu.cn, dingjt15@tsinghua.org.cn, xpzhang@ieee.org |
| Pseudocode | Yes | The details of the MRT in the form of pseudo-codes are provided in Appendix A in Algorithm 1. ... The pseudo-code is provided in Appendix A. |
| Open Source Code | Yes | Code and appendix are available at https://github.com/tsinghua-fib-lab/SPDiff. |
| Open Datasets | Yes | We conduct crowd simulation evaluation experiments of the model on two open-source datasets: the GC and the UCY datasets. |
| Dataset Splits | No | The paper states: 'We temporally split the datasets into training and testing sets, with a training-to-testing ratio of 4:1 for the GC dataset and 3:1 for the UCY dataset.' It does not explicitly mention a validation set or a three-way split including validation data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models, or memory specifications. |
| Software Dependencies | No | The paper does not specify the version numbers for any software dependencies or libraries used in the experiments (e.g., Python version, PyTorch version, CUDA version). |
| Experiment Setup | No | The paper discusses the datasets, baseline methods, and evaluation metrics. However, it does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |