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Distributionally robust origin-destination demand estimation
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DOI：10.1016/j.trc.2024.104716.png)
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摘要
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Gaining a good understanding of the travel demands of a city or region is extremely important for many transportation applications. For stochastic origin-destination (OD) estimation problems, an accurate distribution assumption or observation of OD estimates or data is usually desired but not always available. In this paper, we establish a novel two-stage OD estimation framework based on distributionally robust optimization (DRO) and quasi-sparsity property of large-scale OD demand matrices. The proposed two-stage Distributionally Robust QuasiSparsity OD estimation (DR-QSOD) model does not require an accurate or complete distribution assumption of estimates/data. Numerical results demonstrate that DR-QSOD model outperforms stochastic QSOD model in estimating OD demands when the distribution assumption of data is biased. This paper also discusses two different approaches to solve the DR-QSOD model as well as compares their computational efficiency. In addition, DR-QSOD model is shown to keep relatively high quasi-sparsity consistency, which also brings lots of meaningful practical insights.
Keyword:
OD demand estimation
Distributionally robust optimization
Quasi-sparsity
期刊
IF:
7.9
论文数:
4.7K
被引数:
3.2W
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