Causal Conditional Hidden Markov Model for Multimodal Traffic Prediction
Authors: Yu Zhao, Pan Deng, 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 show that CCHMM can effectively disentangle causal representations of concepts of interest and identify causality, and accurately predict multimodal traffic flow. |
| 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. |