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Driver crash risk factors and prevalence evaluation using naturalistic driving data.
| Content Provider | Semantic Scholar |
|---|---|
| Author | Dingus, Thomas A. Guo, Feng Lee, Suzie Antin, Jonathan F. Pérez, Miguel A. Buchanan-King, Mindy Hankey, Jonathan M. |
| Copyright Year | 2016 |
| Abstract | The accurate evaluation of crash causal factors can provide fundamental information for effective transportation policy, vehicle design, and driver education. Naturalistic driving (ND) data collected with multiple onboard video cameras and sensors provide a unique opportunity to evaluate risk factors during the seconds leading up to a crash. This paper uses a National Academy of Sciences-sponsored ND dataset comprising 905 injurious and property damage crash events, the magnitude of which allows the first direct analysis (to our knowledge) of causal factors using crashes only. The results show that crash causation has shifted dramatically in recent years, with driver-related factors (i.e., error, impairment, fatigue, and distraction) present in almost 90% of crashes. The results also definitively show that distraction is detrimental to driver safety, with handheld electronic devices having high use rates and risk. |
| File Format | PDF HTM / HTML |
| PubMed reference number | 26903657 |
| Journal | Medline |
| Volume Number | 113 |
| Issue Number | 10 |
| Alternate Webpage(s) | http://www.pnas.org/content/113/10/2636.full.pdf |
| Alternate Webpage(s) | https://doi.org/10.1073/pnas.1513271113 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
