Center for Injury Research and Prevention

Learning to Drive: A Reconceptualization

TitleLearning to Drive: A Reconceptualization
Publication TypeJournal Article
Year of Publication2019
AuthorsMirman JH, Curry AE, Mirman D
JournalTransp Res Part F Traffic Psychol Behav
Volume62
Pagination316-326
Date Published04/2019
Type of Articlejournal
ISSN1369-8478
Abstract

Drivers' population-level crash rates incrementally decrease following licensure, which has led to the implicit assumption that an individual driver's crash risk also decreases incrementally after licensure as they accrue experience. However, in the aggregate data an incremental decrease in crash rate can reflect both incremental reductions in crash risk within individuals and an incremental increase in the proportion of drivers who have experienced an abrupt decrease in crash risk. Therefore, while it is true to say that the population of drivers' crash risk reduces in the months following licensure, it is not necessarily true to say that a driver's crash risk reduces in the months following licensure; that is, it cannot be assumed that individual-level changes in crash risk mirror the population-level changes in crash rates. In statistics, this is known as an ecological fallacy and in formal logic it is known as the fallacy of division, a type of category error. Using computational cognitive modeling methods we demonstrate that aggregating individual-level abrupt decreases in crash risk (i.e., non-incremental change trajectories) accurately fits population-level crash rate data from over 1 million adolescents uniquely accounts for effects of two interventions found to reduce police-reported MVCs. Thus, we demonstrate that (1) a power-law artifact is readily observable in newly licensed drivers' aggregate crash data, which is not necessarily indicative of individual-level change processes, (2) interventions can alter crash risk trajectories by inducing immediate phase changes in crash risk into a lower risk stratum, or increasing the probability of such a change, (3) a phase transition model provides a stronger and more parsimonious account of the existing data than an incremental-accrual model.

DOI10.1016/j.trf.2019.01.010
Alternate JournalTransp Res Part F Traffic Psychol Behav
PubMed ID30828257
PubMed Central IDPMC6392458