Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they can also return it to the same location if they'd like to just go for a ride. Regardless, each bike can serve several users per day.
Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used.
In this project, we use the data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. You will compare the system usage between three large cities: Chicago, New York City, and Washington, DC.
Randomly selected data for the first six months of 2017 are provided for all three cities. All three of the data files contain the same core six (6) columns:
Start Time (e.g., 2017-01-01 00:07:57) End Time (e.g., 2017-01-01 00:20:53) Trip Duration (in seconds - e.g., 776) Start Station (e.g., Broadway & Barry Ave) End Station (e.g., Sedgwick St & North Ave) User Type (Subscriber or Customer)
The Chicago and New York City files also have the following two columns: Gender Birth Year
To answer these questions using Python, we have a Python script called bikeshare.py as well as the datasets in a zip file called bikeshare_data containing the following files: chicago.csv new_york_city.csv washington.csv
We compute the following statistics:
#1 Popular times of travel (i.e., occurs most often in the start time):
most common month
most common day of week
most common hour of day
#2 Popular stations and trip:
most common start station
most common end station
most common trip from start to end (i.e., most frequent combination of start station and end station)
#3 Trip duration:
total travel time
average travel time
#4 User info: counts of each user type counts of each gender (only available for NYC and Chicago) earliest, most recent, most common year of birth (only available for NYC and Chicago)