Worked on a team with Anqing Chen, Jianchen Gu, Nicholas Chu, and Nikhil Kolluri to analyze hundreds of thousands of pieces of data from Austin's BCycle system.
What it does
-Plots and analyzes various trajectories of movement of BCycle ride data to make inferences based on ride times, paths of travel, and locations
How we built it
Used Python (pandas) code to generate graphical representations of hundreds thousands of pieces of BCycle ride data, including trajectory data between UT students and Austin locals, average frequency of visit per location, frequency of visits per location at varying times of day.
Challenges we ran into
Difficulties processing all of the data at once: due to the large size of the data set, computer took several minutes to process it
Accomplishments that we're proud of
-Determining Walk Up and Kiosk usage are the largest use categories (shows that BCycle is most frequently used on one-off occasions to get around)
-Determining paths of travel at night for UT students to show how BCycle is most frequently used (directly taken to West Campus, or taken to a bus station to get to North Campus)
-Finding that common usage times for UT students BCycle extend further than expected: until late in to the night (3AM-4AM), but not into the early morning (5AM-6AM)
-Finding out that BCycle is most commonly used by UT students for short trips from the PCL and returned at the same location, rather than as a form of transportation over a larger distance. Going across Speedway was a close second, but this is still a short journey
What's next for BCycle Interesting Data
-In the future, compare UT data versus data from the Austin population
-See how traffic varies on various days of the week
-Compare BCycle traffic data with holiday data, landmark data, etc