Optimization of Tourist Routes in the Old City of Fez Using a Hopfield Neural Network

The tourism sector is an indispensable and flourishing industry that has contributed significantly to the global economy. It has become one of the most significant and dominant sectors worldwide, driving economic growth, creating jobs, and promoting cultural exchange. The advent of technology in the modern era has played a pivotal role in the evolution of traditional tourism practices worldwide. However, despite the technological advancements, tourists still face a critical challenge in planning and optimizing their visits to their preferred points of interest, akin to the street vendor problem. To address this problem, this paper proposes using the Hopfield neural network that can effectively improve tourist visits. Specifically, the Hopfield neural network can recommend visitors with personalized travel itineraries, taking into account their preferences, route distance, time constraints etc. To validate our proposed method, we conducted an experiment in the city of Fez, Morocco. Our results indicated that the Hopfield neural network is a promising approach to help tourists plan a better visit to Fez. By improving the tourist service, we can enhance the overall tourism experience and expand the circle of points of interest that tourists choose. Ultimately, our proposed solution can help boost the local economy and promote sustainable tourism practices.