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.
PERSONA & USER JOURNEY MAP
ITP Spring Show 2018 Highlights:
Link to code: https://github.com/effyfan/Nature_of_Code_2018/tree/master/final/airtnt
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:
- Draw all the points of interest in NYC using MapboxGL.
2. Use Genetic Algorithm (powered by Daniel Shiffman) to find the shortest route that goes through all the locations the user picked.
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
- Include some fun animations of physics simulations on the page.
- Transfer this to other cities.
- Include different transportation methods.