NYC Vision Zero incorporates the collaborations with different Transportation associate agencies in the city: City Hall, Police Department, Department of Transportation, Taxi and Limousine Commission, Department of Citywide Administrative Services and Department of Health and Mental Hygiene. These departments will be working together setting up rules and regulations to improve the safety of the action plan. The plan will include the following: more enforcement against speeding with more precinct-level personnel, adding personnel at the Highway Division of the NYPD. Also,  redesigning intersections and expanding upon the slow zone in residential neighborhood and school zone.
Most importantly and effectively is the increase in the number of speed humps, speed boards, red lights, and speed cameras to ensure the vehicles are following the rules and regulations. Please note: the data set used in this report is from NYPD Collision Reports. Nonetheless, other agencies have their input in organizing and finding mitigation measures.
Some of the mitigation measures include related to some of the main contributing factors to collision within the five boroughs. 
  1. Implement safety engineering improvements at 50 intersections and corridors 
  2. Create 25 new arterial slow zones
  3. Install speed cameras at 20 new authorized locations 
  4. Install 250-speed bumps, including in neighborhood slow zones
  5. Enhance maintenance of street markings
  6. Additional street reconstruction safety projects
  7. Survey national and international best practices to expand potential strategies
  8. Undertake a high-quality ad campaign aimed at reducing speeding, failure-to-yield and other forms of reckless driving
  9. Increase extent of “Choices” anti-DUI campaign
  10. Double number of programmable speed boards for intensive education/enforcement initiative
  11. Make effective, age-appropriate safety curriculum available to schools throughout the city
  12. Partner with senior centers to increase communication and get specific feedback from aging New Yorkers about street safety improvements
  13. Increase the number and visibility of hands-on safety demonstrations
  14. Add safety flyers and messaging in DOT mailings such as Alternate Side Parking regulations and construction permits

Declaring the null hypothesis 

For Traffic Analysis and Pattern in relation to the adoption of Vision Zero in terms of the cumulative number of accidents prior to the adoption of Vision Zero,  which the data frame begins from 2012 and the Vision Zero starts on January 15, 2015.  Null Hypothesis: is testing the probability of daily accidents in term of the adopting improvements.  Using the K-S testing and Chi-Square was used to test if the null hypothesis is true. Also, the hypothesis will test the time series to reflect the changes in the number of accidents throughout the five Boroughs. 

Vision Zero Data: 

The data used for this analysis driven from:
NYC Open Data NYPD Motor Vehicle Collison. (https://data.cityofnewyork.us/api/views/h9gi-nx95/rows.csv?accessType=DOWNLOAD
  1. Filtered the data set to before and after the adoption of Vision Zero
  2. Used the cumulative number of accident 
  3. Using a time frame using Year, Month and Day (To create the time seriers plot) 
Used the following the link for understandings: 
https://www1.nyc.gov/site/visionzero/index.page
http://www.nyc.gov/html/dot/html/about/datafeeds.shtml#vision

Calculation Mythology: 

 Summary: KS-test tests whether two samples are drawn from the same distribution. It returns two floats: the first is KS statistic, the second is two-tailed p-value. In terms of the Null hypothesis, if the K-S statistic is small or the p-value is high, then we cannot reject the hypothesis that the distributions of the two samples are the same. Applying to the Vision Zero case, since the p-value is just pvalue=5.712013011686036e-30 which is far smaller than critical value 0.05, we reject the Null hypothesis that there is no statistical difference in the for the before and after the adoption of Vision Zero. 

Conclusions:

The scipy.stats KS test already tells us the significance and the p-value. 
eject the null hypothesis that the two samples were from the same distribution if the p-value is less than your significance level. You can find tables online for the conversion of the D statistic into a p-value if you are interested in the procedure. 
Remember: the Null hypothesis is rejected if
The p-value returned by the k-s test has the same interpretation as other p-values. You r