TRAVEL ITINERARY CREATION BASED ON DATA TECHNIQUES

An optimized itinerary plan for visiting n number of points in a tourist trip can be obtained by developing the Hamilton clustering system on top of N points by considering K numbers of NN with calculating the reachability distance between points. Please find Rab!=Rba. Which decides which point should come first. This model is developed based on the Hamilton graph/ Salesman travel problem algorithm, and HDBSCAN clustering along with considering tourist constraints like limited total spent time per day, estimated time to visit the place, etc. While this algorithm is great for a multi-day trip, it is especially quite useful and makes the process easy, if someone wants to cover a great number of points in a trip in less number of days or when someone is trying to go for a world tour / big tour with a considerably high number of visiting points