Authors: Yogesh Kumar
Certificate: View Certificate
Even after the presence of multiple services which helps us in travelling like Uber, Ola, Make My Trip, Goibibo etc, the travelling enthusiasts or peoples who are going on vacation don’t have proper platform where they can plan their entire trip at one place. There are so many places where only local rental services is available means no online cab services, no platform to contact a guide, platform to book self-driving vehicles, to check the current and predicted weather conditions of the destination, proper expense splitter if travelling in group and a platform which can predict the proper ways through which you can travel to the destination in a set budget. These problems demands an application which helps to manage all the travelling requirements like rental vehicles, hotels, activities, budget manager, expense splitter if travelling in group etc. Also to provide direct contacts of local drivers, help centres, emergencies etc. An application which can use machine learnings prediction system like Support Vector Regression Model and Adaptive Neuro Fuzzy Inference Systems, Expectation Maximization and Self-Organizing Map for clustering techniques and for dimensionality reduction, Principal Component Analysis.It will be really helpful to all the travel enthusiasts as well as this platform can also provide broader scope to the local vendors and peoples of the entered destination as it will directly connect the travellers to them, and it can recommend best services to ease the travel. It will also provide air pollution report and alternative tour plan.
It seems very easy, but an organized travel is still a very challenging job one has to perform for hassle-free travels. A traveler has to go through n number of websites and applications to organize his full travel like bookings, rentals, location condition and budget management. It is also very challenging to plan the travel cost-effective and resource efficient. A traveler requires an application which can behave like a travel companion in travelling as it can help to plan the entire travel from start to end.The basic services likerecommending best hotels, buses, flights and trains in a set budget for travelling as well as status of air pollution, weather, covid cases and any natural disaster based on which it can provide alternate tour plan. Also, direct connection to local vendors and emergency services. Other services also include rentals, expense splitter and budget management. This paper presents the comprehensive investigation of the available solutions to the multiple problems for a proper travel management and it also suggests the further integrations which can be done to improve the traveler requirements fulfillment and easy navigation to experience a full fledged adaptive pre trip, on trip and post trip plan.
II. RELATED WORK
There are several related works available which has been already published. In this section we will analyze the related surveys approaches for the problems and there solutions and extend it to make more useful for a traveller. Below are some literature reviews:
This study has a descriptive research characteristic because learning technology is a new area in social studies, especially in tourism. Descriptive study has an inductive nature and such as a typology, researcher attempts to find out the relationships and pattern which have helped to understand the concept before producing the data. Researchers offer different descriptive research strategies based on new data generated or existing data. According to literature, there is a rare exact developed scale or measurement tool for learning technology. However, it has not been encountered with a specific methodological approach because of the conceptual nature of the learning technology. This study will first try to capture and evaluate the conceptual and theoretical basics. Despite receiving a few sources, some secondhand data (related literature, some statistical indicators) and researchers’ experiences or observations can provide sufficient infrastructure for this study. Therefore, this study methodology is constructed as descriptive based on theoretical approaches without using quantitative and qualitative analysis.
IV. ANALYSIS AND INTERPRETATION
This paper analyses that there are multiple applications and websites available to book a tour based on location and searching multiple offers and analysis just to select one of the travel packages, deciding on selecting travel mode, routes, checking the location environment and searching for attractions and locations to go to etc. All these troubles requires an Artificial Intelligence and Machine Learning models to help in an individuals and groups tour management along with providing functionalities of knowing the location a traveler is going to and assistant in all the requirements and need fulfillment during travelling.
A. System Design
The application is designed in such a way that all the requirements of an individual or groups to travel is available at the same place. The TFT provides all the travel needs through its recommendation engine and it provides a Chabot to suggest the best travel modes and routes to a destination along with providing all the travel related information.
The TFT has included multiple features and functionalities from all the basic to advance requirements for travelling based on Pre trip, on trip and post trip model suggesting the best bookings of multiple travelling modes as well as the accommodation. It provides users direct interactions to the local service providers along with providing weather, air and disease reports at the destination location. The TFT have basic services like checklists, bill splitter, experience sharing and recommender system.
C. Experimental Results
The application has provided multiple outputs based on the set budget to select the travel modes and accommodation along with the weather, air and disease reports. The TFT application has also recommended the local service providers for the rentals and shopping’s so that the traveler can experience the actual environment and culture of the destination and be always availed with the required services during their trips and the attractions to visit.
Despite the wide range of travel software products available in the market, chances are that none of them will exactly fit everyone’s current needs, as all businesses and situations are unique. Data driven technology helps both the consumers and the brands to make the best possible use of their resources be it money, time or manpower without cutting out on benefits from both the sides. Machine learning is clearly one of the most promising technology fields that are changing the way we travel. With the continuous development of the tourism industry, integrating the Machine learning into tourism projects is a very promising technology. Smart tourism uses the ML to maximize information analysis and information integration, as well as to achieve a fast and convenient information exchange between users. In other words, the Machine Learning will become an important element to meet the needs of the new generation of tourists. Based on this, we propose a travel management system using machine learning’s algorithms like KNN classification, Support Vector Regression Model and Adaptive Neuro Fuzzy Inference Systems, Expectation Maximization and Self-Organizing Map for clustering techniques and for dimensionality reduction, Principal Component Analysis. The machine learning can effectively help tourists make a decision on whether to choose a certain travel destination based on historical order data and historical browsing information. The results obtained from the experiments prove the effective and efficient travel management using machine learning. Tourism needs to focus on ML technology to develop a better service system for tourists and to support the travelers through technological advancement in the organization. This research contributes to the tourism and technology literature by shedding light on the use of ML in tourism advancement to predict future business conditions, revenue, challenges and also to identify the current trend of tourist demand. Moreover, it helps to improve traveling ML approaches and indicate the impacts in addition to the above mentioned what best applies to management in the travel industry.
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