A Two-Stage Algorithm for Origin-Destination Matrices Estimation Considering Dynamic Dispersion Parameter for Route Choice.
A Two-Stage Algorithm for Origin-Destination Matrices Estimation Considering Dynamic Dispersion Parameter for Route Choice.
复制标题
考虑动态色散参数的路径选择起点-终点矩阵估计两阶段算法
DOI：
10.1371/journal.pone.0146850
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Wang Y
中科院分区：
文献类型：
--
作者：
Wang Y;Ma X;Liu Y;Gong K;Henrickson KC;Xu M;Wang Y
本文提出了一种两阶段算法，利用拥塞网络中的部分流量计数同时估计起点-目的地（OD）矩阵、链路选择比例和分散参数。开发了一种包含动态分散参数的非线性优化模型，然后是迭代应用广义最小二乘法 (GLS) 估计和随机用户均衡 (SUE) 分配模型的两阶段算法，直到达到收敛。为了评估算法的性能，所提出的方法使用具有高误差的输入数据在假设网络中实现，并在一系列变异系数下进行测试。估计的 OD 需求和链路流量的均方根误差 (RMSE) 用于评估模型估计结果。结果表明，估计的色散参数θ对变异系数的选择不敏感。所提出的方法被证明优于两种已建立的 OD 估计方法，并产生接近真实值的参数估计。此外，所提出的方法应用于华盛顿州西雅图的实证网络，以验证该方法的稳健性和实用性。总之，本研究提出并评估了一种创新的计算方法，可使用链路级交通流数据准确估计 OD 矩阵，并为旅客路线选择行为建模中的最佳参数选择提供有用的见解。
This paper proposes a two-stage algorithm to simultaneously estimate origin-destination (OD) matrix, link choice proportion, and dispersion parameter using partial traffic counts in a congested network. A non-linear optimization model is developed which incorporates a dynamic dispersion parameter, followed by a two-stage algorithm in which Generalized Least Squares (GLS) estimation and a Stochastic User Equilibrium (SUE) assignment model are iteratively applied until the convergence is reached. To evaluate the performance of the algorithm, the proposed approach is implemented in a hypothetical network using input data with high error, and tested under a range of variation coefficients. The root mean squared error (RMSE) of the estimated OD demand and link flows are used to evaluate the model estimation results. The results indicate that the estimated dispersion parameter theta is insensitive to the choice of variation coefficients. The proposed approach is shown to outperform two established OD estimation methods and produce parameter estimates that are close to the ground truth. In addition, the proposed approach is applied to an empirical network in Seattle, WA to validate the robustness and practicality of this methodology. In summary, this study proposes and evaluates an innovative computational approach to accurately estimate OD matrices using link-level traffic flow data, and provides useful insight for optimal parameter selection in modeling travelers’ route choice behavior.