Optimizing Travel Itineraries: A Multi-Objective Machine Learning Model Approach

A travel itinerary is a complex problem that involves multiple objectives and constraints, such as cost, time, transportation modes, and comfort levels. This research study focuses on creating a real-time eco-friendly itinerary generator by integrating machine learning techniques. This research work explores two machine-learning algorithms: Artificial Neural Networks (ANNs) and Multi-Layer Perceptron (MLP). These algorithms are evaluated for their suitability in generating user-friendly and environment-friendly itineraries. Through experimentation, the accuracy and precision of the proposed models are assessed, leading to the identification of an optimal travel planner model. The ultimate goal of this model is to provide a detailed itinerary that considers prominent modes of transportation while addressing the pressing environmental crisis. By prioritizing eco-friendly options, this approach sets itself apart from conventional travel planning tools.