I have some trips, and for each trip contains different steps, the data frame looks like following:
tripId duration (s) distance (m) speed Km/h
1819714 NaN NaN NaN
1819714 6.0 8.511452 5.106871
1819714 10.0 6.908963 2.487227
1819714 5.0 15.960625 11.491650
1819714 6.0 26.481649 15.888989
... ... ... ... ...
1865507 6.0 16.280313 9.768188
1865507 5.0 17.347482 12.490187
1865507 5.0 14.266625 10.271970
1865507 6.0 22.884008 13.730405
1865507 5.0 21.565655 15.527271
I want to know if, on a trip X, the cyclist has braked (speed has decreased by at least 30%). The problem is that the duration between every two steps is each time different. For example, in 6 seconds, the speed of a person X has decreased from 28 km/h to 15 km/h.. here we can say, he has braked, but if the duration was high, we will not be able to say that My question is if there is a way to apply something to know if there is a braking process, for all data frame in a way that makes sense
CodePudding user response:
The measure of braking is the "change in speed" relative to "change in time". From your data, I created a column 'acceleration', which is change in speed (Km/h) divided by duration (seconds). Then the final column to detect braking if the value is less than -1 (Km/h/s).
Note that you need to determine if a reduction of 1km/h per second is good enough to be considered as braking.
df['speedChange'] = df['speedKm/h'].diff()
df['acceleration'] = df['speedChange'] / df['duration(s)']
df['braking'] = df['acceleration'].apply(lambda x: 'yes' if x<-1 else 'no')
print(df)
Output:
tripId duration(s) distance(m) speedKm/h speedChange acceleration braking
0 1819714.0 6.0 8.511452 5.106871 NaN NaN no
1 1819714.0 10.0 6.908963 2.487227 -2.619644 -0.261964 no
2 1819714.0 5.0 15.960625 11.491650 9.004423 1.800885 no
3 1819714.0 6.0 26.481649 15.888989 4.397339 0.732890 no
4 1865507.0 6.0 16.280313 9.768188 -6.120801 -1.020134 yes
5 1865507.0 5.0 17.347482 12.490187 2.721999 0.544400 no
6 1865507.0 5.0 14.266625 10.271970 -2.218217 -0.443643 no
7 1865507.0 6.0 22.884008 13.730405 3.458435 0.576406 no