Abstract
Urban demand forecasting plays a critical role in optimizing routing, dispatching, and congestion management within Intelligent Transportation Systems. By leveraging data fusion and analytics techniques, traffic density estimation serves as a key intermediate measure for identifying and predicting emerging demand patterns. In this paper, we propose two gradient boosting model variations, one for classification and one for regression, both capable of generating demand forecasts at various temporal horizons, from 5 minutes up to one hour. Our approach effectively integrates spatial and temporal features, enabling accurate predictions that are essential for improving the efficiency of shared (micro-)mobility services. To evaluate the effectiveness of our approach, we utilize open shared mobility data derived from e-scooters and e-bikes networks in two Dutch metropolitan areas. These real-world datasets enable us to validate our approach and demonstrate its effectiveness in capturing the complexities of modern urban mobility. Ultimately, our methodology offers novel insights on urban micro-mobility management, helping to tackle the challenges arising from rapid urbanization and thus, contributing to more sustainable, efficient, and liveable cities.