Motor vehicle crashes are a global public health concern. Most analysis have used zero-inflated count models for examining crash counts. However, few methods are available to account for safety metrics that have semi-continuous observations. This article considers the problem of variable selection for the semi-continuous zero-inflated (SCZI) models. These models include two parts: a zero-inflated part and a nonzero continuous part. A special group regularization is designed to accommodate the unique structure of two-part SCZI models, and a type of Bayesian information criterion is proposed to select tuning parameters. We illustrate the variable selection process of the proposed model using lane position data from a driving simulator study. In the study, drivers stay in the intended lane for the majority of their drive (zero-inflated part). On occasion, some drivers do drift out of their intended driving lane (nonzero continuous part). Our findings show that individual differences can be captured with the proposed model, which has implications for driving safety and the design of in-vehicle alerting systems.

中文翻译：

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半连续交通数据的稀疏群正则化

机动车事故是一个全球性的公共卫生问题。大多数分析都使用零膨胀计数模型来检查碰撞计数。然而，很少有方法可用于解释具有半连续观察的安全指标。本文考虑了半连续零膨胀 (SCZI) 模型的变量选择问题。这些模型包括两部分：零膨胀部分和非零连续部分。设计了一种特殊的群正则化以适应两部分 SCZI 模型的独特结构，并提出了一种贝叶斯信息准则来选择调整参数。我们使用来自驾驶模拟器研究的车道位置数据来说明所提出模型的变量选择过程。在研究中，司机大部分时间都在预定车道上行驶（零充气部分）。有时，一些司机确实偏离了他们预定的车道（非零连续部分）。我们的研究结果表明，所提出的模型可以捕获个体差异，这对驾驶安全和车载警报系统的设计具有影响。