Metro and Bike-sharing Systems in Washington DC, Boston and San Francisco: a data-driven approach
Published in Research project with professor David Shmoys, 2020
With the rapid popularity of bike-sharing systems, stationed and now dock- less bikes have played an increasingly important role in people’s daily travel routine. More and more people use shared bikes to commute from home to nearby metro stations; others may even choose to ride bikes instead of walking, taking a metro or taxi. The high level goal of this project is to understand how each method in the transportation system interacts with each other. In particular, we focus on the metro system and bike-sharing system. Since the bike-sharing systems complete the trip chain by connecting metro stations with points of interest, it is necessary to understand the various trans-shipment characteristics of bike-sharing systems for metro stations. Through case studies in Washington DC (DC), Boston and San Francisco (SF), we get a better understanding of operating characteristics of dockless and stationed bikes near metro stations and compare the ridership patterns between metros and shared bikes. In this project, we use a data-driven approach. We collected data from dockless bikes system in DC through a live API and also worked on various published datasets for metro ridership and stationed bikes trip history. We prepossessed and analyzed data through various methods, including K-means clustering.