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Link Flow Correction For Inconsistent Traffic Flow Data Via l1-Minimization

Penghang Yin Zhe Sun Wenlong Jin Jack Xin

Analysis of PDEs mathscidoc:1912.43919

arXiv preprint arXiv:1704.02052, 2017.4
A computational method, based on l1-norm minimization, is proposed for the problem traffic flow correction. Without extra information, the problem is generally ill-posed when a large portion of the link sensors are unhealthy. It is possible, however, to correct the corruptions accurately if there are only a few bad sensors which are located at certain links by the proposed method. We mathematically quantify these links that are robust to miscounts and relate them to the geometric structure of the traffic network. In a more realistic setting, besides the unhealthy link sensors, if small measure noise are present at the other sensors, our method guarantees to give an estimated traffic flow fairly close to the ground-truth. Both toy and real-world examples are provided to demonstrate the effectiveness of the proposed method.
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@inproceedings{penghang2017link,
  title={Link Flow Correction For Inconsistent Traffic Flow Data Via l1-Minimization},
  author={Penghang Yin, Zhe Sun, Wenlong Jin, and Jack Xin},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224210642689254483},
  booktitle={arXiv preprint arXiv:1704.02052},
  year={2017},
}
Penghang Yin, Zhe Sun, Wenlong Jin, and Jack Xin. Link Flow Correction For Inconsistent Traffic Flow Data Via l1-Minimization. 2017. In arXiv preprint arXiv:1704.02052. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191224210642689254483.
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