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..
FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction
Authors: Zhonghang Li, Lianghao Xia, Yong Xu, Chao Huang
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations demonstrate the effectiveness of our Flash ST across different spatio-temporal prediction tasks using diverse urban datasets. and We conduct extensive experiments on four distinct types of spatio-temporal data tasks to evaluate the effectiveness of our proposed framework. |
| Researcher Affiliation | Academia | Zhonghang Li 1 2 Lianghao Xia 2 Yong Xu 1 3 Chao Huang* 2 1South China University of Technology 2University of Hong Kong 3PAZHOU LAB. |
| Pseudocode | No | The paper describes its methodology using textual descriptions and mathematical equations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/HKUDS/Flash ST. |
| Open Datasets | Yes | In the pre-training phase, we use four datasets (PEMS03, PEMS04, PEMS07, and PEMS08 (Song et al., 2020)) as our training sets. [...] During the subsequent prompt-tuning phase, we concentrate on four distinct target datasets to fine-tune and evaluate our framework: PEMS07(M) (Yu et al., 2018), CA-D5 (Liu et al., 2023b), Cheng Du-DIDI (Lu et al., 2022), and NYC Citi Bike (Ye et al., 2021). |
| Dataset Splits | Yes | Each target dataset is partitioned into training, validation, and test sets in a ratio of 6:2:2. |
| Hardware Specification | No | The paper discusses training time and computational efficiency, but it does not provide specific details about the hardware used, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as programming languages, libraries, or frameworks with their version numbers. |
| Experiment Setup | Yes | We configure the model with 32 hidden units for d, dt, and dr, while employing a spatial and temporal encoder with 2 layers. The pre-training phase involves alternating training for 300 epochs on the four pre-training datasets. Subsequently, we conduct fine-tuning for 20 epochs during the prompt-tuning phase. In both the baseline and full fine-tuning setups, the maximum number of training epochs is limited to 100. Furthermore, a batch size of 64 is employed for both phases and all baselines. |