Case Study: citiBike

Citibike System Data:

https://www.citibikenyc.com/system-data

Use sample of all the trips for the period: June 1, 2017 to May 31, 2018

Problem: How does citiBike deal with this mismatch between demand and supply ?

Question 1: How many bikes to stock in each station at the beginning of the day?

Objective: To find the number of bikes to stock in each station at the beginning of the day to maximize the number of daily bike trips we need to answer the below questions

Questions:

How many trips between stations every day?

How many stations and bikes?

Analysis:

Level I: Descriptive Analytics: Identifying trip patterns,

Question: Where is the demand, Where do bikes go? Show Trip Visualisation

Level II: Predictive Analytics: Forecasting daily trips between stations

Question: Demand Forecast ( Forecast Demand vs Regression)

Level III: Prescriptive analytics: Determining the number of bikes to stock in each station at the beginning of the day :

Question: Decision ( Using Optimization Model), Show the optimal number of trips

Please provide the answers to the question using R code