If you live or work in the city, you’ve probably passed by one of the 700 BIXI stations. And you’ve undoubtedly come across “BIXISTS,” i.e., cyclists riding one of the grey bikes (or perhaps a blue bike). That’s BIXI. It is a hybrid between "BIcycle" and "TaXI" to underline the concept of being able to use a bicycle just like a taxi. While it has been growing in popularity, we thought it will be a good opportunity to analyze various aspects of Bixi Data especially from Montreal. The goal is to find results which can then help improve its quality of the service, find Bixi stations and other services that will benefit the customer. We will be using open datasets for this purpose.
The datasets we will be using in this analysis are.
[OD_2016.csv]
[OD_2017.csv]
[Stations_2016.csv]
[Stations_2017.csv]
[noaa-daily-weather-data_2016.csv]
[noaa-daily-weather-data_2017.csv]
Each dataset of OD.csv has 4 Million records of BIXI ridership details.
Here's the outline of the data process: Find the most popular and least popular bike stations Find the most and least popular days of the week Find the most and least popular times of the day Find the most and least popular ridership by weather conditions Which stations exhaust their bike capacity the fastest and when? Which stations have the most unused bikes? Are there any holidays apart from the week ends when the bike ridership is popular Any difference in any of the above between members and non members less
We will analyze all the entries and calculate the varioyus aspects of bike renting.
BIXI ridership has been in market for over a decade and continues to provide people with rental services. This article can be helpful in improving their services like, stationing more bikes on popular stations, creating new stations on hotspots, creating special offers on popular days and holidays, having backup bikes for stations who exhaust fastest, using weather to an advantage, try to have the station full of bikes at start of an active hour, and the list goes on.
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