In September 2017, dockless bikeshare joined the transportation options in the District of Columbia. In March 2018, scooter share followed. During the pilot of these technologies, Python has helped District Department of Transportation answer some critical questions. This talk will discuss how Python was used to answer research questions and how it supported the evaluation of this demonstration.
In this talk we will show how different libraries in python are used to analyze trip data from multiple dockless bikeshare and scootershare companies in the District of Columbia. The key points of this talk are: • Development of performance measurements and visualizations for the new mobility technologies, • Hotspot analysis of dockless trips using hexagonal binning and density-based spatial clustering, and • Comparative analysis of new mobility technologies with District of Columbia station-based bikeshare system, including the use of Random Forest and Logistic Regression to label dockless trips into casual and member trips. This presentation is suitable for participants interested in a beginner-level data science project who wish to see the practical use of libraries in python and R. This presentation also highlights the use of data science in local government to evaluate the new technologies and improve services for residents.