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 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 per day 
  3. Using a time frame using Year, Month and Day (To create the time series 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 p-value = 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. See Figure 1.