Exploring the potential of deep regression model for next-location prediction

Location-based services are gaining popularity; prediction of future destinations and crowd movement patterns are crucial components of these services. This article presents an attention-based neural network regression model designed to forecast future user locations, a critical aspect of location-based services. Leveraging two real-world mobility datasets, New York City check-in data from the Foursquare API and Porto taxi trajectories from Portugal. The model employs an attention-based encoder–decoder neural network to predict user destination coordinates in latitude and longitude. Beyond its predictive capabilities, this research delves into the intricacies of human mobility patterns, contributing to a deeper understanding of movement behavior and shedding light on challenges in current models for mobility prediction. The study explores the impact of various optimization algorithms on model performance, analyzing their effects on accuracy, with the mean haversine distance error serving as the evaluation metric. Notably, the model achieves remarkable results, giving a mean haversine distance error of 1.3336 for the Porto dataset and 1.6379 for the Foursquare NYC dataset when employing the Adam optimizer. We have extended our study by implementing our model on Universal Transverse Mercator coordinate systems. These findings underscore the model’s superiority over previous approaches, offering valuable insights for developing more precise location-based services and advancing mobility and human behavior analysis.