ABSTRACT A significant aspect of the U.S. Department of Transportation’s Highway Safety Improvement Program (HSIP) rulemaking is the prerequisite that states must gather and utilize Model Inventory of Roadway Elements (MIRE) for all public paved roads, including low-volume roadways (LVR). States are particularly not equipped with the ability to collect traffic volumes of LVRs due to issues such as budgetary constraints. One alternative is to estimate traffic volumes of LVRs using regression or machine learning (ML) models. The present study accomplishes this by developing a ML framework to estimate traffic volumes on LVRs. By using available traffic counts on low-volume roads in Minnesota, this study applies and validates three different ML models (random forest, support vector regression, and Cubist) to estimate traffic volumes. The models include various traffic and non-traffic (e.g. demographic and socio-economic) variables. Overall, the Cubist model shows better performance compared to support vector regression and random forests. Additionally, the Cubist approach provides rule-based equations for different subsets of data. The findings of this study can be beneficial for transportation communities associated with LVRs.

中文翻译：

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低流量道路的交通量预测：立体派方法

摘要 美国交通部公路安全改进计划 (HSIP) 规则制定的一个重要方面是各州必须为所有公共铺砌道路收集和利用道路要素模型清单 (MIRE)，包括低流量道路 (LVR)。由于预算限制等问题，各州尤其不具备收集 LVR 流量的能力。一种替代方法是使用回归或机器学习 (ML) 模型来估计 LVR 的流量。本研究通过开发 ML 框架来估计 LVR 上的流量来实现这一点。通过使用明尼苏达州低流量道路上的可用交通量，本研究应用并验证了三种不同的 ML 模型（随机森林、支持向量回归和 Cubist）来估计交通量。这些模型包括各种交通和非交通（例如人口和社会经济）变量。总体而言，与支持向量回归和随机森林相比，Cubist 模型表现出更好的性能。此外，Cubist 方法为不同的数据子集提供了基于规则的方程。这项研究的结果可能有益于与 LVR 相关的交通社区。