Predicting overnights in smart villages: the importance of context information

Abstract

The tourism industry increasingly employs sensors and machine learning for tasks such as demand prediction and mobility forecasting. However, some challenges in data collection remain, especially with information privacy and resource management. We propose a vehicle classification model based on License Plate Recognition (LPR) sensor data, incorporating contextual datasets not explored in the existing literature to predict the number of nights a vehicle will stay in a mountain tourist area. We also study the importance of each dataset in the results. Our analysis utilizes data from four LPR cameras spanning 17 months. We compare different classification models optimized through ensemble techniques. Additionally, an ablation study assesses the impact of each dataset, with variables categorized by expert knowledge into seasonal, socio-economic or visit-related. Optimal dataset selection demonstrates a 22.2% reduction in processing time and an 80% decrease in the number of variables, with only a slight decrease of 0.01 in the Area Under the Curve (AUC) compared to using all available variables. This research provides information to develop tourism prediction models, guiding which datasets and calculated variables are the most important while balancing the processing time and AUC.