Massive and passive Automatic Vehicle Identification (AVI) data provides samples of whereabouts and movements of vehicles, which is a potential source of information for route choice behavior modeling. However, the AVI observations are too sparse to infer the specific chosen route and OD pair, which discourages its application on route choice model estimation. To tackle this issue, this paper develops a semi-supervised learning method that can train the route choice model with sparse AVI observations. First of all, the likelihood function in Maximum Likelihood Estimation procedure was derived by decomposing the AVI trace into observation pairs. Combined with high-resolution GPS observations, the measurement equation and OD inference model were then defined to deal with the sparsity problem of AVI observations. At the same time, the Mixed Logit model was introduced to capture the correlation and heterogeneity across the choice behavior between different observation pairs. Finally, the relationship between route choice model and the likelihood function was established and the unknown parameters in route choice model can be estimated by seeking a maximum to the log-likelihood function. Empirical studies were conducted with field-testing data in this paper. The estimated results show that the proposed semi-supervised method improved the identification accuracy of route choice model significantly without sacrificing interpretability. The evaluation of the computational efficiency presented the potential of the semi-supervised method to learn route choice behavior for a large-size sample set. The sensitivity analysis was also performed to illustrate how robust the proposed method is. This is the first research that attempts to apply AVI data on route choice model and it endows the high-penetration AVI data with great practical value for modeling the route choice behavior of city-wide samples over a long period.

大量和被动的自动车辆识别（AVI）数据提供了车辆的下落和移动的样本，这是进行路线选择行为建模的潜在信息源。但是，AVI观测值太稀疏，无法推断出特定选择的路线和OD对，这不利于其在路线选择模型估计中的应用。为了解决这个问题，本文开发了一种半监督学习方法，该方法可以训练具有稀疏AVI观测值的路线选择模型。首先，通过将AVI迹线分解为观测对，得出最大似然估计程序中的似然函数。结合高分辨率GPS观测值，定义了测量方程和OD推断模型，以处理AVI观测值的稀疏性问题。同时，引入混合Logit模型以捕获不同观察对之间选择行为的相关性和异质性。最后，建立了路径选择模型与似然函数之间的关系，可以通过寻求对数似然函数的最大值来估计路径选择模型中的未知参数。本文采用现场测试数据进行了实证研究。估计结果表明，本文提出的半监督方法在不影响可解释性的前提下，极大地提高了路径选择模型的识别精度。计算效率的评估显示了半监督方法学习大型样本集路径选择行为的潜力。还进行了敏感性分析，以说明所提出方法的鲁棒性。这是首次尝试将AVI数据应用到路线选择模型中的研究，它赋予高渗透率AVI数据对于长期建模全市范围样本的路线选择行为具有很大的实用价值。