Smart Mobility Support for Vehicle-based Tourism: Theoretical and Technological Foundations

Abstract

Vehicle-based tourism becomes more and more important in the era of the pandemic. Tourism management is an important challenge for the tourist region development. The construction of a personalized attraction visiting route for tourists with personal vehicles has a great impact on the tourist flows. The authors propose theoretical and technological foundations for smart mobility support of vehicle-based tourists. We propose to predict tourist preferences by using deep neural networks for the prediction model’s implementation and demonstrated 70-80% accuracy in training on completed tourist trips to St. Petersburg, Russia. The tourist route attractiveness prediction was used to assess the constructed route quality. The attraction attractiveness and attendance prediction together with potential tourist trajectory prediction were used for attraction selection process personification. The obtained results can be used in smart mobility support systems to improve the travel experience.