An Effective Decision Support System for Travel in COVID'19 Pandemic using Fuzzy Rules and Intelligent Algorithms

Abstract

Travel is important for every human being and it impacts in all aspects of life ranging from personal to societal development. COVID'19 pandemic has changed the way we think to travel. Exploring the impact of the Covid-19 pandemic in the place the user needs to travel thereby facilitating user perception on travel is becoming a mandate nowadays. Travel perception is also important for variety of day-to-day activities like transportation of goods and services, health related travel etc., This work aims to create comprehensive and efficient prediction models, facilitated by a convenient user interface to predict how risky or convenient it is for a user to travel in a time where COVID-19 is prevalent. The predictions made are based on the location they wish to travel using various Machine Learning models. The results are combined with the individual's health history to arrive at an optimized decision. The model is trained using a comorbidities dataset as well as a location- wise weather dataset, which allows us to make the prediction of whether travelling is dangerous for the user or not. The user interface is designed to show predictions for various districts. The trained model is tested using the data provided in social media and government websites provided for prediction