Bike Sharing Demand Analysis for Capital Bikeshare by Deborah AfolarinBike Sharing Demand Analysis for Capital Bikeshare by Deborah Afolarin

Bike Sharing Demand Analysis for Capital Bikeshare

Deborah Afolarin

Completed work

Data Analyst

Data Scientist

GitHub

Python

Transportation

Bike Sharing Analysis

Description

This project focuses on analyzing bike sharing data from Capital Bikeshare in Washington, D.C., USA, for the period between January 1, 2011, and December 31, 2012. The data is aggregated on an hourly basis. This means that no initial and final locations of the individual rides are available, but only the total number of rides per hour. Nevertheless, additional meteorological information is available in the data, which could serve as a driving factor for identifying the total number of requests for a specific time frame (bad weather conditions could have a substantial impact on bike sharing demand)

Problem Statement

This project seeks to investigate how temporal features and weather conditions affect hourly bike-sharing demand, aiming to uncover meaningful insights that can enhance operational strategies and service responsiveness

Dataset

Bike-sharing rental process is highly correlated to the environmental and seasonal settings. For instance, weather conditions, precipitation, day of week, season, hour of the day, etc. can affect the rental behaviors. The core data set is related to the two-year historical log corresponding to years 2011 and 2012 from Capital Bikeshare system, Washington D.C., USA which is publicly available in http://capitalbikeshare.com/system-data. We aggregated the data on two hourly and daily basis and then extracted and added the corresponding weather and seasonal information.

Dataset characteristics

- instant: record index
- dteday : date
- season : season (1:winter, 2:spring, 3:summer, 4:fall)
- yr : year (0: 2011, 1:2012)
- mnth : month ( 1 to 12)
- hr : hour (0 to 23)
- holiday : weather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)
- weekday : day of the week
- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.
+ weathersit : 
	- 1: Clear, Few clouds, Partly cloudy, Partly cloudy
	- 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
	- 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
	- 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
- temp : Normalized temperature in Celsius. The values are divided to 41 (max)
- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)
- hum: Normalized humidity. The values are divided to 100 (max)
- windspeed: Normalized wind speed. The values are divided to 67 (max)
- casual: count of casual users
- registered: count of registered users
- cnt: count of total rental bikes including both casual and registered

Like this project

Completed work

Posted Aug 28, 2025

Analyzed Capital Bikeshare data to understand bike-sharing demand influenced by weather and temporal features.

Likes

Views

Timeline

Dec 31, 2010 - Dec 30, 2012

Clients

Capital Bikeshare