Crashes under **normal conditions** dominate in volume, but adverse conditions show subtle but important differences:
* **Adverse weather** accounts for 345,487 crashes but has *lower fatality rates per crash* (0.030) than clear conditions (0.037).
* Fatal crashes spike in **rain on wet roads** and **fog on dry surfaces**—a dangerous and often underestimated combo.
Compiled using 40 years for FMCSA / DOT Data. Visualization done using matplotlib.
inversemodel on
Shouldn’t these be normalized in some way by the number of journeys undertaken in such conditions? Otherwise, these are just a measure of how many journeys are taken in good weather (lots).
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Source:
[https://research.runalphaloop.com/uncovering-the-patterns-a-deep-dive-into-motor-carrier-crash-data/](https://research.runalphaloop.com/uncovering-the-patterns-a-deep-dive-into-motor-carrier-crash-data/)
Crashes under **normal conditions** dominate in volume, but adverse conditions show subtle but important differences:
* **Adverse weather** accounts for 345,487 crashes but has *lower fatality rates per crash* (0.030) than clear conditions (0.037).
* Fatal crashes spike in **rain on wet roads** and **fog on dry surfaces**—a dangerous and often underestimated combo.
Top 3 weather × road combinations by fatalities:
1. **No Adverse + Dry:** 59,854 fatalities
2. **Rain + Wet:** 5,767 fatalities
3. **Fog + Dry:** 1,137 fatalities
Compiled using 40 years for FMCSA / DOT Data. Visualization done using matplotlib.
Shouldn’t these be normalized in some way by the number of journeys undertaken in such conditions? Otherwise, these are just a measure of how many journeys are taken in good weather (lots).