Learning to drive: A reconceptualization

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 the authors 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 novice drivers and uniquely accounts for effects of two interventions found to reduce police-reported MVCs. Thus, the authors 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, and (3) a phase transition model provides a stronger and more parsimonious account of the existing data than an incremental-accrual model.

• Record URL:
  • https://doi.org/10.1016/j.trf.2019.01.010
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1369847818305771
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13698478
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Mirman, Jessica Hafetz
  • Curry, Allison E
  • Mirman, Daniel
  • 0000-0001-5472-0220
• Publication Date: 2019-4

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 316-326
• Serial:
  • Transportation Research Part F: Traffic Psychology and Behaviour
  • Volume: 62
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1369-8478
  • Serial URL: http://www.sciencedirect.com/science/journal/13698478

Subject/Index Terms

• TRT Terms: Crash rates; Driving; Learning; Mathematical models; Recently qualified drivers; Strategic planning; Teenage drivers; Traffic safety
• Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01693078
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 19 2019 11:53AM