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Mapbox Cities tackles Vision Zero in collaboration with DDOT

Mapbox performed a Vision Zero data analysis to identify high risk areas to prioritize in DDOTs street design and safety measures in Washington, DC.

A custom collision frequency model took into account a wide range of datasets, not just crash counts.
Model results identify clearly which areas of DC are high risk areas
Outcome: Lively urban streets, with higher density of users, in cars and pedestrians, due businesses and restaurants, are prone to accidents. Intersection density is another factor to increase this risk of accidents.
This can directly be used to inform DDOT’s Vision Zero resource distribution and is ultimately making urban areas of DC safer for pedestrians & cyclists.

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Mapbox Cities tackles Vision Zero in collaboration with DDOT

  1. 1. Changing the way people move around cities
  2. 2. Get the data to the people
  3. 3. Citizen sentiment
  4. 4. mySidewalk.com
  5. 5. Real-time traffic data
  6. 6. 225,000,000 miles / day Mapbox Traffic
  7. 7. Sign up for free ➡ mapbox.com/cities cityzenith.com cityzenith.com
  8. 8. Open Source for Cities Sign up for free ➡ mapbox.com/cities Get better, faster
  9. 9. Open Cities are Smarter Cities Sign up for free ➡ mapbox.com/cities > 200% Return on Investment (ROI) of Open Source
  10. 10. Copy what works How does it work?
  11. 11. github.com/DCgov/
  12. 12. github.com/DCgov/
  13. 13. Vision Zero DC
  14. 14. Assumption 1 More vehicles or pedestrians = more opportunity for incidents
  15. 15. Assumption 2 Higher speeds = more accidents
  16. 16. Assumption 3 More shops, restaurants & schools = higher frequency of crashes CC BY-ND 2.0 by Daniel M. Hendricks | Flickr
  17. 17. Data available opendata.dc.gov Crash data (before 2017) Census data Intersection data DDOT + Howard University Traffic Data Center Traffic counts Mapbox Mobile sensor data (speeding)
  18. 18. Modeling Collision Frequency Various conditions Traffic counts Employment data census block Intersections School locations Mapbox speed data* Density of crashes
  19. 19. Lively urban streets. More accidents.
  20. 20. Intersection density matters. CC BY-ND 2.0 by Sonara Arnav | Flickr
  21. 21. Higher speeds More incidents = unrelated
  22. 22. School locations Crash frequency = unrelated
  23. 23. Next actions?
  24. 24. 1. Test model in other cities Next steps
  25. 25. Next steps 1. Test model in other cities 2. Open source the model
  26. 26. Mapbox ❤ Cities Sign up for free ➡ mapbox.com/cities

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