DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model
Authors: Yuanshao Zhu, Yongchao Ye, Shiyao Zhang, Xiangyu Zhao, James Yu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on two real-world trajectory datasets to show the superior performance of the proposed Diff Traj in GPS trajectory generation. |
| Researcher Affiliation | Academia | Yuanshao Zhu1,2, Yongchao Ye1, Shiyao Zhang1, Xiangyu Zhao2, , James J.Q. Yu3, 1 Southern University of Science and Technology 2 City University of Hong Kong 3University of York |
| Pseudocode | Yes | The training and sampling phase of the proposed framework is summarized in Algorithm 1 and Algorithm 2, respectively. |
| Open Source Code | Yes | The code for the implementation of Diff Traj is available for reproducibility2. 2https://github.com/Yasoz/Diff Traj |
| Open Datasets | Yes | These datasets can be downloaded at https://outreach.didichuxing.com/ |
| Dataset Splits | No | The paper mentions using real-world datasets and evaluating performance but does not specify the explicit training, validation, and test splits (e.g., percentages or counts) for the primary Diff Traj model's development. |
| Hardware Specification | Yes | All experiments are implemented in Py Torch and conducted on a single NVIDIA A100 40GB GPU. |
| Software Dependencies | No | The paper mentions that experiments are implemented in 'Py Torch' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For the proposed Diff Traj framework, we summarize the adopted hyperparameters in Table 3. ... Table 3: Hyperparameters setting for Diff Traj. Diffusion Steps 500, Skip steps 5, Guidance scale 3, β (linear) 0.0001, Batch size 1024, Sampling blocks 4, Resnet blocks 2, Input Length 200 |