融合天气等上下文信息，提升恶劣天气下共享单车需求预测精度
Contextual Data Integration for Bike-sharing Demand Prediction with Graph Neural Networks in Degraded Weather Conditions
- 用图神经网络融合天气、时间嵌入和交通流等上下文数据
- 在恶劣天气下预测误差降低超20%，优于当前最佳模型
- 适合关注城市交通预测与多模态数据融合的研究者
共享单车需求受天气、时段、其他交通方式等多种因素影响，这些因素之间存在复杂依赖关系或地理位置相关的用户行为差异。现有研究尚未明确哪些因素是历史需求中未包含的额外信息，且不同交通方式间的相互依赖性在恶劣条件下仍缺乏探索。本文分析了引入天气、时间嵌入和道路交通流等上下文数据对预测共享单车起止点（OD）流量的影响。研究表明，道路交通流与需求预测质量间存在较弱关联，而引入时间嵌入显著提升了性能，尤其在异常天气条件下表现突出。将天气数据作为额外输入后，相较于基础的ST-ED-RMGC模型，在恶劣天气条件下的预测误差降低超过20%。
原文摘要 · Abstract (English)
Demand for bike sharing is impacted by various factors, such as weather conditions, events, and the availability of other transportation modes. This impact remains elusive due to the complex interdependence of these factors or locationrelated user behavior variations. It is also not clear which factor is additional information which are not already contained in the historical demand. Intermodal dependencies between bike-sharing and other modes are also underexplored, and the value of this information has not been studied in degraded situations. The proposed study analyzes the impact of adding contextual data, such as weather, time embedding, and road traffic flow, to predict bike-sharing Origin-Destination (OD) flows in atypical weather situations Our study highlights a mild relationship between prediction quality of bike-sharing demand and road traffic flow, while the introduced time embedding allows outperforming state-of-the-art results, particularly in the case of degraded weather conditions. Including weather data as an additional input further improves our model with respect to the basic ST-ED-RMGC prediction model by reducing of more than 20% the prediction error in degraded weather condition.
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