Glances That Matter: Applying Quantile Regression to Assess Driver Distraction from Off-Road Glances. (September 2018)

• Record Type: Journal Article
• Authors: Liu, Shu-Yuan; Lee, John D.; Lee, Ja Young; Venkatraman, Vindhya
• Is Part Of: Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Volume 62:Part 1(2018)
• Page Start: 1954
• Page End: 1958
• Publication Date: 2018-09
• Subjects: Human engineering -- Congresses; 620.8205
• DOI: 10.1177/1541931218621443
• Languages: English
• ISSNs: 1541-9312

Abstract

This study assessed whether quantile regression can identify design specifications that lead to particularly long glances, which might go unnoticed with traditional analyses focusing on conditional means of off-road glances. Although substantial research indicates that long glances contribute disproportionately to crash risk, few studies have directly assessed the tails of the distribution. Failing to examine the distribution tails might underestimate the disproportionate risk on long glances imposed by secondary tasks. We applied quantile regression to assess the effects of secondary task type (reading or entry), system delay (delay or no delay), and text length (long or short) on off-road glance duration at 15th, 50th, and 85th quantiles. The results show that entry task, long text, and some combinations of variables led to longer glances than that would be expected given the central tendency of glance distributions. Quantile regression identifies secondary task features that produce long glances, which might be neglected by traditional analyses with conditional means.