用多视角地理表征学习提升滴滴出行预测精度
Enhancing Ride-Hailing Forecasting at DiDi with Multi-View Geospatial Representation Learning from the Web
- 融合兴趣点与移动模式，从语义和时序双视角建模区域特征
- 在滴滴真实数据上实现当前最优预测性能，显著提升准确性
- 适合城市交通规划与出行平台优化人员参考
网约车服务的普及深刻改变了城市出行模式，精准预测网约车需求对提升乘客体验和交通效率至关重要。但受地理异质性和外部事件干扰，预测面临挑战。本文提出MVGR-Net框架，采用两阶段方法：预训练阶段通过整合兴趣点（Points-of-Interest）和时间移动模式，从语义属性与时空行为双视角学习全面的地理表征；预测阶段采用提示增强的框架，微调大语言模型并融入外部事件信息。在滴滴真实数据集上的大量实验表明，该方法达到当前最佳性能。
原文摘要 · Abstract (English)
The proliferation of ride-hailing services has fundamentally transformed urban mobility patterns, making accurate ride-hailing forecasting crucial for optimizing passenger experience and urban transportation efficiency. However, ride-hailing forecasting faces significant challenges due to geospatial heterogeneity and high susceptibility to external events. This paper proposes MVGR-Net(Multi-View Geospatial Representation Learning), a novel framework that addresses these challenges through a two-stage approach. In the pretraining stage, we learn comprehensive geospatial representations by integrating Points-of-Interest and temporal mobility patterns to capture regional characteristics from both semantic attribute and temporal mobility pattern views. The forecasting stage leverages these representations through a prompt-empowered framework that fine-tunes Large Language Models while incorporating external events. Extensive experiments on DiDi's real-world datasets demonstrate the state-of-the-art performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。