tam-ng / Divvy_Chicago_BikeSharing

Developed a relational database that will enable quick response and analysis on the current state of Divvy’s operations in regard to ridership, station locations, other factors affecting them. Then built a scoring model to optimize the number of stations and bikes allocated by zip codes

Repository from Github https://github.comtam-ng/Divvy_Chicago_BikeSharingRepository from Github https://github.comtam-ng/Divvy_Chicago_BikeSharing

Divvy_Chicago_BikeSharing

Background

  • June 28, 2019 will mark the sixth Anniversary of Divvy. As the only major bike sharing system in Chicago, the blue bikes have become a regular mode of transportation for Chicagoans since its inception. (reaching more than 20k rides per day in peak seasons).
  • One year ago, Divvy provided its 15 millionth ride and its expected to pass 20 millionth mark by the end of 2019 / early 2020.

Recent Development

  • March 2019, Lyft, which took over Divvy-operator Motivate, was given the exclusive rights for 9 years to operate the city-owned bike-sharing system and is proposing to invest US$50 million to:
  • Add 175 stations and 10,500 bikes
  • Expand to all 50 city wards by 2021

Project Objectives

To assist with the expansion plan, our team developed a relational database that will enable quick response and analysis on the current state Divvy operations in regard to ridership, station locations and various other factors affecting them. And:

  • Provide methodologies and various tools used in the process
  • Provide data analysis and visualization
  • Put forward a future state blueprint for the new stations and bikes allocation process

Data

  1. Divvy trip
  2. Divvy station
  3. Weather
  4. Bike racks
  5. Population
  6. Bike route
  7. Zipcode

Tools

  • OpenRefine
  • SQL
  • Tableau
  • R
  • Python

About

Developed a relational database that will enable quick response and analysis on the current state of Divvy’s operations in regard to ridership, station locations, other factors affecting them. Then built a scoring model to optimize the number of stations and bikes allocated by zip codes