In the Internet-of-Things (IoT) domain, modeling the intricate relationships within spatiotemporal data from sensors is a significant challenge. Conventional methods, which often rely on tabular or time-series formats, frequently fail to capture the underlying complex dynamics. In contrast, graph-based representations excel at this task, but they have seldom been explored in tourism and remain unexplored in small villages, where IoT networks involve only a handful of sensors or nodes. We propose an explainable and optimized Graph Neural Network (GNN) methodology to predict return visits in smart villages. We collected data from License Plate Recognition cameras across three villages, tracking 467,773 distinct vehicles over 2 years and 11 months. For each vehicle, we construct a unique graph that maps its specific route across all cameras. We conduct a comprehensive study of various GNN architectures, including spectral, spatial, recurrent, and attention-based models, with a special focus on the impact of incorporating edge features. We also apply explainability methods to identify the most influential graph components driving model predictions. To optimize performance, we integrate Neural Architecture Search and Hyperparameter Optimization (NAS-HPO) using a multivariate objective that maximizes the F1-score while minimizing computational time. Analysis of the results reveals that models using edge attributes perform better; specifically, the E-GAT model. We apply the NAS-HPO approach to this E-GAT base model, and the improved version achieves the highest performance across all metrics, demonstrating a 5.71% improvement in F1-score and a 20.87% decrease in execution time.