Sparse group regularization for semi-continuous transportation data

Tianshu Feng, Linda Ng Boyle

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)3267-3285
Number of pages19
JournalStatistics in Medicine
Issue number14
StatePublished - Jun 30 2021


  • semi-continuous zero-inflated model
  • transportation data
  • variable selection

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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