Hubway Station Examiner

Charles Cohen
Posted on Jul 1, 2019

Introduction

In 2014, the city of Boston held a data visualization challenge asking data scientists to pour over three years of ridership data along their subscriber bike share system, Hubway. Some of the results of that challenge are incredible, and as an avid cyclist myself, I wanted to take a shot at building my own.

The aim of this project was to build a tool to allow station managers to analyze who is riding a Hubway bike, where they are riding to/from and when. This project was coded in R and launched as a live Shiny web app to shinyapps.io.

Analyzing the Data

Hubway published three years of ridership, however, due to the memory constraints of shinyapps.io, I constrained my dataset to only consider rides taken in 2012, leaving 528,202 rides, from 141 stations in the Greater Boston Area. Hubway did not publish user specific information, however they do specify if a ride is taken by a Registered or Casual user.

Throughout 2012, there were twice as many rides taken by registered users as there were to casual, and three times as many rides taken by males to females. This fits Hubway's business model in that they aim to help commuters get to and from work. For a newly released transportation system, it would appear that working age males are the quickest to adopt it.

App Features

The target audience of this interactive app is the managers who maintain and balnace the Hubway System. At a glance, the app provides detailed information on three aspects of ridership usage.

  1. The net flow of trips to and from the station.
  2. The breakdown of rider types (subscription)
  3. The gender of riders using that station.

All of this information can be analyzed on a per hour, weekday or month basis to demonstrate different trends. (Note: those stations that appear to be missing the first months in the year, or stations that became active at that time.)

The design of the app was intended to mimic a console on a desktop display. Managers can easily and quickly change stations by dragging the map and clicking the desired icon. The app changes its display to reflect the change.

Hubway Station Metrics Display

Lastly, the lines emanating from the selected station identify the 5 most paired stations to and from, with red being from, blue being to and purple being the overlap.

Conclusion

This app demonstrates very quickly the relevant information a station manager might need to make more informed decisions.

  • Understanding who is using the station provides more data on how to advertise to its users. For example: casual-centric stations should be identified by companies promoting recreation and tourism.
  • Understanding when users use the station informs them when bikes need to be present and how rebalancing may need to occur.

There is a lot of information and potential to be tapped by this dataset. I hope to revisit it and expand the app to show information on how the bikes move throughout the city by tracking individual bikes during their lifetime.

I encourage you to play with the app yourself. It's live at: Hubway Station Examiner. You can also see the code behind this project (and others) at my Github.

About Author

Charles Cohen

Charles Cohen

Charles Cohen is currently teaching at the NYC Data Science Academy. Charles studied Physical Sciences at the City College of New York and subsequently worked in research and non-profit environments. Charles is a self-motivated learner who eagerly adapts...
View all posts by Charles Cohen >

Related Articles

Leave a Comment

Avatar
Distilled News | AnalytiXon July 4, 2019
[…] Hubway Station Metrics […]

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

2019 airbnb Alex Baransky alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus API Application artist aws beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep Bundles California Cancer Research capstone Career Career Day citibike clustering Coding Course Demo Course Report D3.js data Data Analyst data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization Deep Learning Demo Day Discount dplyr employer networking feature engineering Finance Financial Data Science Flask gbm Get Hired ggplot2 googleVis Hadoop higgs boson Hiring hiring partner events Hiring Partners Industry Experts Instructor Blog Instructor Interview Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter lasso regression Lead Data Scienctist Lead Data Scientist leaflet linear regression Logistic Regression machine learning Maps matplotlib Medical Research Meet the team meetup Networking neural network Neural networks New Courses nlp NYC NYC Data Science nyc data science academy NYC Open Data NYCDSA NYCDSA Alumni Online Open Data painter pandas Part-time Portfolio Development prediction Prework Programming PwC python python machine learning python scrapy python web scraping python webscraping Python Workshop R R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn Selenium sentiment analysis Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau team TensorFlow Testimonial tf-idf Top Data Science Bootcamp twitter visualization web scraping Weekend Course What to expect word cloud word2vec XGBoost yelp