README.md

Low-Rank Tensor Completion with Truncated Minimax-Concave Penalty (LRTC-TMCP)

This is the code repository for paper Spatiotemporal traffic data completion with truncated minimax-concave penalty which is published on Transportation Research Part C: Emerging Technologies.

Usage

1. Data preparation

We provide the Hangzhou metro passenger flow dataset as a concrete example to show how to use the code. The dataset is stored in the datasets folder.

• Hangzhou: This dataset provides the incoming passenger flow of 80 metro stations over 25 days (from January 1st to January 25th, 2019) with a 10-minute resolution in Hangzhou, China. The interval from 0:00 a.m. to 6:00 a.m. with no services is discarded, and we only consider the remaining 108-time intervals of a day. The data is the tensor of size 80 × 25 × 108 (80 × 2700 in the form of time series matrix). More information can refer to this page.

More datasets can be found in the datasets of this Github repository.

2. Run the code

The' requirements.txt' file contains the necessary packages for running our codes. You can run the following shell command to create a new environment named lrtc and install the packages.

conda create --name lrtc --file requirements.txt

We provide the code demo in Jupyter notebooks, which can be executed using the lrtc environment.

References

If you find this repo useful for your research, please consider citing the paper:

Cited as:

bibtex:

@article{CHEN2024104657,
    title = {Spatiotemporal traffic data completion with truncated minimax-concave penalty},
    journal = {Transportation Research Part C: Emerging Technologies},
    volume = {164},
    pages = {104657},
    year = {2024},
    issn = {0968-090X},
    doi = {https://doi.org/10.1016/j.trc.2024.104657},
    url = {https://www.sciencedirect.com/science/article/pii/S0968090X24001785},
    author = {Peng Chen and Fang Li and Deliang Wei and Changhong Lu},
    publisher={Elsevier}
}