Long-distance trips include a high value of time compared to short-distance trips; thus, capturing long-distance trips contributes to substantial economic and social benefits. This study utilizes privacy-protected travel data collected from mobile devices in Maryland to identify the link-level proportion of long-distance vehicle trips. We propose applying existing econometric frameworks, i.e., the generalized linear and beta regression models, to predict these link-level long-distance trips. Among the covariates, we utilize highway network-level attributes from the Maryland statewide travel demand model (MSTM), employment, and other open-source datasets available for the state of Maryland. Both the econometric models were compared for their performance in multiple measures like validation, behavioral interpretation, and goodness of fit. The best model results indicate a positive relationship between the proportion of long-distance trips and link attributes, like functional class, speed, and surrounding household density. The proposed framework will provide useful insights and key inputs for practitioners in demand modeling in capturing long-distance travel in a roadway network.

与短途旅行相比，长途旅行的时间价值更高；因此，长途旅行有助于带来巨大的经济和社会效益。本研究利用从马里兰州移动设备收集的受隐私保护的旅行数据来确定长途车辆旅行的链路级比例。我们建议应用现有的计量经济学框架，即广义线性和贝塔回归模型，来预测这些链路级长途旅行。在协变量中，我们利用来自马里兰州全州出行需求模型 (MSTM)、就业和马里兰州可用的其他开源数据集的高速公路网络级属性。比较了这两种计量经济学模型在验证、行为解释和拟合优度等多种指标方面的表现。最佳模型结果表明，长途旅行的比例与链接属性（如功能类别、速度和周围家庭密度）之间存在正相关关系。拟议的框架将为需求建模的从业者提供有用的见解和关键输入，以捕获道路网络中的长途旅行。