Collective responsibility for freeway rear-ending accidents?. An application of probabilistic causal models

Gary A. Davis, Tait Swenson

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


Determining whether or not an event was a cause of a road accident often involves determining the truth of a counterfactual conditional, where what happened is compared to what would have happened had the supposed cause been absent. Using structural causal models, Pearl and his associates have recently developed a rigorous method for posing and answering causal questions, and this approach is especially well suited to the reconstruction and analysis of road accidents. Here, we applied these methods to three freeway rear-end collisions. Starting with video recordings of the accidents, trajectory information for a platoon of vehicles involved in and preceding the collision was extracted from the video record, and this information was used to estimate each driver's initial speed, following distance, reaction time, and braking rate. Using Brill's model of rear-end accidents, it was then possible to simulate what would have happened, other things being equal, had certain driver actions been other than they were. In each of the three accidents we found evidence that: (1) short following headways by the colliding drivers were probable causal factors for the collisions, (2) for each collision, at least one driver ahead of the colliding vehicles probably had a reaction time that was longer than his or her following headway, and (3) had that driver's reaction time been equal to his or her following headway, the rear-end collision probably would not have happened.

Original languageEnglish (US)
Pages (from-to)728-736
Number of pages9
JournalAccident Analysis and Prevention
Issue number4
StatePublished - Jul 2006

Bibliographical note

Funding Information:
The authors would like to thank John Hourdos and Vishnu Garg for providing the video recordings of the rear-end collisions, and Paula Mesa for assisting in the extraction of the vehicle trajectories. This research was supported by the Intelligent Transportation Systems Institute at the University of Minnesota.


  • Car-following
  • Causal inference
  • Markov Chain Monte Carlo
  • Rear-end crash


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