Identifying dangerous trucking firms

Leon N. Moses*, Ian Savage

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

The paper develops a statistical procedure for predicting the safety performance of motor carriers based on characteristics of firms and results of two government safety enforcement programs. One program is an audit of management safety practices, and the other is a program to inspect drivers and vehicles at the roadside for compliance with safety regulations. The technique can be used to provide safety regulators with an empirical approach to identify the most dangerous firms and provide a priority list of firms against which educational and enforcement actions should be initiated. The government needs to use such an approach rather than directly observing accident rates because the most dangerous firms are generally small and, despite relatively high accident rates, accidents remain rare events. The technique uses negative-binomial regression procedures on a dataset of 20,000 firms. The definition of poor performance in roadside inspection is based on both the rate of inspections per fleet mile and the average number of violations found during an inspection. This choice was made because selection for inspection has both a random and nonrandom component. The results of the study suggest that both of the government's safety programs help identify the most dangerous firms. The 2.5% of firms that do poorly in both programs have an average accident rate twice that of the mean for all other firms.

Original languageEnglish (US)
Pages (from-to)359-366
Number of pages8
JournalRisk Analysis
Volume16
Issue number3
DOIs
StatePublished - Jun 1 1996

Keywords

  • Trucks
  • United States
  • accidents
  • inspections

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Physiology (medical)

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