用梯度提升模型预测共享出行需求，支持5分钟到1小时的精准调度。
MoDE-Boost: Boosting Shared Mobility Demand with Edge-Ready Prediction Models
- 设计分类与回归两类梯度提升模型，融合时间与上下文特征。
- 在五个城市的真实电单车/电滑板车数据上验证，优于主流方法。
- 适合城市交通管理、共享出行平台优化调度场景。
城市需求预测在智能交通系统中对路径规划、调度和拥堵管理至关重要。通过数据融合与分析技术，交通需求预测成为识别时空需求模式的关键中间手段。本文提出两种梯度提升模型变体，分别用于分类与回归，可生成从5分钟至1小时不同时间尺度的需求预测。该方法有效整合时间与上下文特征，实现高精度预测，对提升共享（微）出行服务效率具有重要意义。我们利用来自五个大都市区的电动滑板车与电动自行车网络的真实开放数据进行评估，对比了当前最优方法及生成式AI模型，验证了其在捕捉现代城市出行复杂性方面的有效性。本研究为城市微出行管理提供了新视角，有助于应对快速城市化带来的挑战，推动更可持续、高效、宜居的城市发展。
原文摘要 · Abstract (English)
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.
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