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 demand forecasting serves as a key intermediate measure for identifying emerging spatial and temporal demand patterns. In this paper, we tackle this challenge by proposing two gradient boosting model variations, one for classiffication and one for regression, both capable of generating demand forecasts at various temporal horizons, from 5 minutes up to one hour. Our overall approach effectively integrates temporal and contextual features, enabling accurate predictions that are essential for improving the efficiency of shared (micro-) mobility services. To evaluate its effectiveness, we utilize open shared mobility data derived from e-scooter and e-bike networks in five metropolitan areas. These real-world datasets allow us to compare our approach with state-of-the-art methods as well as a Generative AI-based model, demonstrating 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 livable cities.

翻译：城市需求预测在智能交通系统中对优化路径规划、车辆调度与拥堵管理起着至关重要的作用。通过融合数据分析技术，交通需求预测作为一项关键中间手段，能够识别新兴的时空需求模式。本文针对这一挑战，提出了两种梯度提升模型的变体——一种用于分类，一种用于回归，两者均能生成从5分钟到1小时不同时间跨度的需求预测。我们的整体方法有效整合了时序特征与上下文特征，实现了对共享（微）出行服务效率提升至关重要的精准预测。为评估其有效性，我们利用来自五个大都市区电动滑板车与电动自行车网络的开放共享出行数据。这些真实世界数据集使我们能够将所提方法与现有先进方法以及一种基于生成式人工智能的模型进行比较，结果证明了其在捕捉现代城市出行复杂性方面的有效性。最终，我们的方法为城市微出行管理提供了新的见解，有助于应对快速城市化带来的挑战，从而为建设更可持续、高效与宜居的城市作出贡献。