Data Analysis on NYC Yellow Taxi

Posted on May 2, 2016
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Contributed by Frank Wang. He  is currently in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between April 11th to July 1st, 2016. This post is based on his first class project - R visualization (due on the 2nd week of the program).


This  note briefly reports the data analysis of the NYC 2014 yellow taxi data. Records include pick-up and drop-off dates/times, pick-up and drop-off locations, trip distances, itemized fares, rate types, payment types, and driver-reported passenger counts. The motivation of this study is to learn the pattern behind the data, for instance, where the people to go? and when?

Where the most people go? The following two pictures show that large number of passengers head to the central area of the city (east of Time Square) in the morning time (top) while they leave the central area in the evening (Bottom). In the pictures, the red and blue dots represent pick-up and drop-off,respectively. The size of the circle is proportional to the number of taxi. In other words, people move from the red area to the blue area by taxi.


Data Analysis on NYC Yellow Taxi

Density of passenger pick-up (red) and drop-off (blue) at Friday morning

Data Analysis on NYC Yellow Taxi

FIG.1 Density of pick-up (red) and drop-off (blue) at Friday evening (6pm-11pm)

Location and Time

It is more interesting to explore the net flow of the taxi at a particular location and time. For this purpose, we divide the whole regime into small area and calculate the difference between the number of drop-off and pick-up as the net flow of the taxi at that area and that particular time. The net flow results are shown in FIG.2 for Friday morning time (top) and evening time (bottom). The red and blue dots represent more pick-up and drop-off, respectively, while the size of the dots represents the amount of difference.

In other words, people leave the red area and arrive the blue area. In the morning time (top plot), people from the surrounding area flow to the central regime, while people leave central area in the evening (bottom).  It is worthy to note that more people take taxi to Brooklyn, Queen and Harlem area in the evening time while they less likely take taxi at the morning time when they go to work. The two big circles on the right part of the pictures are located at LaGuardia Airport. There are similar number of pick-up and drop-off at morning time, but there are much more pick-up at evening time because more passengers arrive at evening time.

Net taxi flow at Friday Morning (red means net pick-up, blue means net drop-off)

Net taxi flow at Friday Morning (red means net pick-up, blue means net drop-off)

Data Analysis on NYC Yellow Taxi

FIG.2: Net taxi flow at Friday evening (red means net pick-up, blue means net drop-off)


The hourly taxi activities for Friday, Saturday and Sunday are shown in the picture below. The hour starts from middle night of the day to the middle night of the next day.  The vertical axis shows the total taxi income, which approximately represents the number of taxi on the road at that time. There is a minimum taxi activity around 5am for all the three days.  There is a rush hour around 8-9am of the work day, while there is no such peak at the weekend.

It is interesting to note that there is always a low taxi activity near 4pm. It is turn out that is correlated with taxi driver shift time. They change shift at that time. Therefore less number of taxi is available. This explains the puzzle why it is difficult to find a taxi near 4pm during the work day.

Hourly taxi activity for Friday, Saturday and Sunday

FIG.3: Hourly taxi activity for Friday, Saturday and Sunday


We also looked at the tip for different trips. The tip rate is similar for Friday, Saturday and Sunday as shown below. There are three peaks  located at 17%, 20% and 23%, respectively. The tip rate for different hour at Friday is shown in FIG.5. It is interesting that passengers like to pay more tip around 4am and 4pm.   However, there is no such clear pattern on the weekend.

Fig.6 shows the trip distance for the three days. Most of the trips are really short trip with median distance about 2 miles. This agrees with the data shown in FIG.1. The median trip time and  cost is 10 minutes and $12, respectively.

Tip rate distribution for three different days

FIG.4 Tip rate distribution for three different days

Tip at different hour of the day (Friday)

FIG. 5: Tip at different hour of the day (Friday)

Trip distance for Friday, Saturday and Sunday

FIG.6: Trip distance for Friday, Saturday and Sunday


  • Large number of passengers go to the central area of the middle town in the morning time of the work day.
  • More people, especially from Brooklyn, Queen and Harlem area, take taxi to home at evening time comparing the number of people taking taxi to work at morning time
  • There is minimum daily taxi activity at 5am.
  • It can be difficult to find a taxi around 4pm, especially during the weekday. This is correlated with the taxi driver schedule.
  • People like to pay more tip at 4am and 4pm during the work day


About Author

Frank Wang

Frank (Lanfa) Wang have worked in several research laboratories as a physicist. He has over a decade of experience in modeling and scientific computing and had access to the large supercomputer NERSC. He participated several national/international projects: Japanese...
View all posts by Frank Wang >

Related Articles

Leave a Comment

Google November 19, 2019
Google Here are several of the websites we suggest for our visitors.
Google October 15, 2019
Google Below you will obtain the link to some websites that we consider you should visit.
free fifa coins no offers no download July 22, 2017
Thank you a lot for sharing this with all of us you actually understand what you are speaking approximately! Bookmarked. Please additionally seek advice from my web site =). We can have a hyperlink change arrangement between us
free fifa 17 coins July 20, 2017
What's up friends, how is the whole thing, and what you desire to say on the topic of this paragraph, in my view its in fact awesome for me.
Centro Multisalud barrio pilar July 18, 2017
Camino de las Exquisiteces – cuarenta y mil trece Sevilla (España).
comment-7963 July 15, 2017
Estos son platos de inusual calidad y gustosísimos.
hut coins nhl 18 June 11, 2017
I really like reading through a post that can make people think. Also, thank you for permitting me to comment!
pdextrading March 28, 2017
Great , Do you have the same info about UBER Taxis?
xiangfacai March 15, 2017
here we go!
xiangfacai March 14, 2017
here we go!
xiangfacai March 14, 2017
here we go!
xiangfacai March 13, 2017
here we go!
xiangfacai March 13, 2017
here we go!
xiangfacai March 12, 2017
here we go!
xiangfacai March 11, 2017
here we go!
xiangfacai March 11, 2017
here we go!
MICHELLE August 30, 2016
You've got very good stuff in this article MICHELLE
nhl coins August 27, 2016
Wow, stunning website. Thnx .. nhl coins
fifa coins August 24, 2016
Terrific Website, Keep up the good work. Thank you!.
nhl 17 coins August 23, 2016
You have superb information in this case nhl 17 coins
nfl 17 news August 21, 2016
Keep up the exceptional work !! Lovin' it! nfl 17 news
nhl 17 August 20, 2016
Sustain the good job and delivering in the group! nhl 17
Kyle June 29, 2016
Thank you for giving the info. It will help me lot.
Werner June 20, 2016
Oh my goodness! an amazing article dude. Thank you Nevertheless I am experiencing problem with ur rss . Do’t understand why Unable to subscribe to it. Is there anyone getting identical rss problem? Anyone who knows kindly react. Thnkx

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans 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 boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis 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 seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI