Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Causal Conditional Hidden Markov Model for Multimodal Traffic Prediction
Authors: Yu Zhao, Pan Deng, Junting Liu, Xiaofeng Jia, Mulan Wang
AAAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 16,835 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,037 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world datasets show that CCHMM can effectively disentangle causal representations of concepts of interest and identify causality, and accurately predict multimodal traf๏ฌc ๏ฌow. |
| Researcher Affiliation | Collaboration | 1 Beihang University, Beijing, 100191, China. 2 Beijing Big Data Centre, Beijing, 100024, China. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1https://github.com/Eternity ZY/CCHMM |
| Open Datasets | No | The paper describes the XC-Trans and XC-Speed datasets but does not provide specific access information (link, DOI, repository) for these processed datasets. |
| Dataset Splits | Yes | 60% of the data is used for training, 20% is used for validating and the rest is used for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software dependencies used in the experiments. |
| Experiment Setup | No | The paper describes the learning strategy and loss functions but does not specify concrete experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or optimizer settings. |