Abstract:

The accuracy of demand forecasting for ride-hailing services will affect the efficiency of urban transportation resource allocation and traffic operation directly. The existing ride-hailing demand forecasting methods suffer significant accuracy degradation during non-recurrent traffic events due to insufficient global multimodal pattern capturing capability and inadequate refined abnormal flow modeling mechanism. To address this issue， a data augmentation method based on Llama3 was proposed， integrating domain-adaptive fine-tuning with semantic event information fusion， and a quantitative representation system was constructed， incorporating event importance and congestion impact coefficients， thereby converting unstructured event descriptions into structured numerical features. At the same time， a dual-path collaboration architecture named InformerGRU （Informer with Gated Recurrent Unit） was designed， combining the Informer encoder’s long-sequence global modeling capability with the dynamic local-sequence capturing advantages of bidirectional Gated Recurrent Units （GRU）， so as to realize intelligent aggregation of multi-scale features through an adaptive feature fusion layer. Real-world experimental results demonstrate that the proposed data augmentation method reduces the model Mean Squared Error （MSE） by 71.44% in abnormal traffic flow scenarios， with enhanced peak forecasting capability. Compared to Informer， InformerGRU model achieves 4.33% lower Mean Absolute Error （MAE） and improves the direction accuracy by 2.68%.

Key words: Large Language Model (LLM), ride-hailing demand, event representation, semantic quantification, spatio-temporal forecasting, data augmentation

摘要：

网约车需求预测的精度会直接影响城市运力资源配置的效率与交通运行的效能。现有的预测方法在应对非常规交通事件时，因缺乏对时间序列多模态模式的全局捕获能力及对异常流量的精细化建模机制而预测精度显著下降。针对该问题，提出基于Llama3模型的领域适配微调与事件信息语义融合的数据增强方法，并构建包含事件重要系数和拥堵影响系数等多维特征的量化表征体系，从而实现非结构化事件描述到结构化数值特征的转换。同时，融合Informer编码器的长时序全局建模能力与双向门控循环单元（GRU）的局部序列动态捕捉优势，设计InformerGRU （Informer with Gated Recurrent Unit）双路径协同架构，通过自适应特征融合层实现多尺度特征智能聚合。基于真实数据的实验结果表明，所提出的数据增强方法使异常流量场景下的模型均方误差（MSE）降低了71.44%，高流量峰值预测能力显著提升；相较于Informer，InformerGRU模型的平均绝对误差（MAE）降低了4.33%，方向准确度相对提升了2.68%。

关键词: 大语言模型, 网约车需求, 事件表征, 语义量化, 时空预测, 数据增强