Data Analysis of Bay Area Bike Share Trips between 2013 and 2016 for the Bike Share Project

STA 141 Final Project

Pamela Patterson, Ashkan Saboori, Zamirbek Akimbekov

Bike Sharing

camo.githubusercontent.com

Pricing

bayareabikeshare.com

Expansion Plans (from 700 to 7000 bikes!!!)

1. San Francisco: 4500 bikes

2. East Bay (Oakland, Berkeley and Emeryville): 1400 bikes

3. San Jose: 1000 bikes

bayareabikeshare.com

Project Goals

1. Analyze the Bay Area Bike Share Trip data and obtain insight into biking patterns.

2. Suggest locations for new dock installation and stations that need more docks/bikes.

Data Description

1. Open API (JSON feed)

  • live data

2. Historical data sets for each year between 2013 - 2016 (8-9 GB total)

  • Station (names, station coordinates, installation dates, cities)

  • Status (bike and dock availability per minute per station (36 million rows for each file!!!))

  • Trip (trip start/end day and time, start and end stations, routes, bike number, rider type, subsrciber's home zip code)

  • Weather (weather information per day per service area)

Current Bay Area Bike Share map

Bay Area Bike Share Trips

  • Number of bike rides between 09/01/2015 and 08/31/2016 : 312126
  • Averaging about 13.79 minutes per ride
  • Total riding time is 71729.45 hours
  • or 2988.73 days
  • or 8.19 years

    • Our Strategy

    1. Past and Present

    Analyze how people have been using bike sharing since 2013:

    • most used routes

    • duration of ride

    • days of the week and time of the day

    • the holiday effect (use it to fix bikes)

    • customer types (subscriber vs daily)

    • how the number of bikes/docks has been changing since 2013? what about the number and duration of trips with new docks?

    • highlight the special landmarks, like corporations, caltrain and bark stations


    2. Future

    • Find stations that need more bikes and/or need to be removed

    • Find new locations to expand the bike share business:

      • use information submitted by people through the website, use SF crime and income by neighbourhood data

      • if possible, make some financial analysis too