Travelling New York Man

Travelling New York Man is a travel optimizing tool that showcases a real-world adaptation of the classic “Traveling Salesman Problem”. It will improve the experience for travelers to plan, record, and share their trip.

EXPERIENCE MAP

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PERSONA & USER JOURNEY MAP

USER FLOWCHART

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WEBSITE PROTOTYPE

 

ITP Spring Show 2018 Highlights:

 

Link to code: https://github.com/effyfan/Nature_of_Code_2018/tree/master/final/airtnt

 

Concept development:

Personally, I love traveling and making my own traveler plan. I am wondering what roll machine learning can play in optimizing one’s travel route. For my final project, I want to create a interactive web base project that uses machine learning methods to give the users information about the points of interests in New York City.

Elements of the project:

  1. Draw all the points of interest in NYC using MapboxGL.

Screen Shot 2018-03-28 at 15.57.47

2. Use Genetic Algorithm (powered by Daniel Shiffman) to find the shortest route that goes through all the locations the user picked.

Screen Shot 2018-04-17 at 12.20.26

3. Use Kmeans unsupervised learning to classify the points of interest. Reference examples by Andrew Lee: https://beta.observablehq.com/@vndrewlee. I will try to use Python to scrape images from Trip Advisor “Things To Do” pageas my data set. https://www.tripadvisor.com/Attractions-g60763-Activities-New_York_City_New_York.html

 

Additional thoughts:

  1. Include some fun animations of physics simulations on the page.
  2. Transfer this to other cities.
  3. Include different transportation methods.
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