Travel Planning Management System

This project, "TRAVEL PLANNING MANAGEMENT SYSTEM", is used to automate all processes of travel and tourism, which deals with the creation, booking, confirmation, and user details. The project is designed with React JS as the front end and Spring boot as the backend, which works in any browser. A travel planning management system is used to book a tour from anywhere in the world by a single dynamic website which will help the user to know all about the places and travel details in a single web application. The admin can add travel packages to the system, transport services, place management, and hotels to create travel packages. Then the users can sign in and book each travel package, and also, they can book custom travel packages. The user can confirm their bookings by paying for the package. It is the most accessible platform for travellers who can easily book and know all details

Zipping Segment Trees

Stabbing queries in sets of intervals are usually answered using segment trees. A dynamic variant of segment trees has been presented by van Kreveld and Overmars, which uses red-black trees to do rebalancing operations. This paper presents zipping segment trees - dynamic segment trees based on zip trees, which were recently introduced by Tarjan et al. To facilitate zipping segment trees, we show how to uphold certain segment tree properties during the operations of a zip tree. We present an in-depth experimental evaluation and comparison of dynamic segment trees based on red-black trees, weight-balanced trees and several variants of the novel zipping segment trees. Our results indicate that zipping segment trees perform better than rotation-based alternatives

Dynamic Fuzzy Logic-Ant Colony System-Based Route Selection System

Route selection in metropolises based on specific desires is a major problem for city travelers as well as a challenging demand of car navigation systems. This paper introduces a multiparameter route selection system which employs fuzzy logic (FL) for local pheromone updating of an ant colony system (ACS) in detection of optimum multiparameter direction between two desired points, origin and destination (O/D). The importance rates of parameters such as path length and traffic are adjustable by the user. In this system, online traffic data are supplied directly by a traffic control center (TCC) and further minutes traffic data are predicted by employing artificial neural networks (ANNs). The proposed system is simulated on a region of London, United Kingdom, and the results are evaluated