Hybrid-Travellers-Services

This is the project which was developed during my college years with the help of my friends Aman, Devanshu and Priya. We together work as a team and made this project successful and present it as final year project. The aim of this project is to make online booking of a car that can be used temporarily for a fee during a specific period .Getting a rental car helps people that do not have access to their own personal vehicle or do not own a vehicle at all . Recent research on recommender systems gives an idea of uses social network data to travelling system with better prediction and improved accuracy. The individual who needs a car must use this service which gives a comparison cost of all other online car booking services(cost comparison between OLA , UBER etc) by using parsing. This paper expresses views on Google data based travelling systems by considering usage of various recommendation algorithms, functionalities of systems, different types of filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models (Ex- OLA, UBER etc), the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. A hybrid approach for travelling service by using data parsing and enhanced prediction project is an online service that rents automobiles for short period of time for a fee whether in a few hours or a few days or week by using the concept of data parsing and artificial intelligence to take the data from user review and social sites. This project will provide a cost comparison between all other online booking services and also suggest the different places which users can travel by using the concept of prediction and filtering through AI .This basically aims to make traveler’s journey easy and cost effective .This is not for a particular company it is a hybrid approach by which user can choose any of the car booking services and can book car from any online service which is cost effective for him. Traveler system works as a recommender system which uses data from many sources to make predictions and to give advice of different places for a user. Factors such as stability and accuracy are balanced in the generated recommendations. The main aim of using this technique is to generate customized recommendation according to the user preferences and interests. This traveler recommender system has an objective to filter unwanted information and to provide specific results for the particular user. In the travel recommender systems, proposed model learns the user preferences and generates places of attractions according to the user interests. This paper focuses on the recommender systems and their application in tourism by using concept of data parsing and artificial intelligence. World & amplitude; Applications. In this time if a user wants to travel to any place then he becomes confused to choose which traveler service and which place is best on the basis of cost and also on the basis of services .This system makes this problem easy by comparing the price between all online car booking services and it shows the result in increasing order of cost. The travel recommender system uses artificial intelligence techniques to generate personalized suggestion to the user. This project applies the applications of the recommender systems in the field of e- Tourism. Geographical Information Systems are used in this project for the management of geographical data that will be associated with the recommended locations and activities. Hence, the locations, distances, and driving directions are obtained from geographical web service technologies. Continuous calculation of user’s position will be available in this project by using the concept of Google map and it will also show the time required to reach a location so that planning can be made in real time. The planning algorithm will consider only the nearest places through AI.