Abstract: Highway traffic flow during holidays exhibits significant spatiotemporal heterogeneity, making accurate short-term Origin-Destination (OD) flow prediction a key technology for enhancing the intelligent level of road network management. To address issues such as the high-dimensional sparsity of OD data, complex spatiotemporal dependencies, and holiday pattern shifts, this paper proposes a short-term highway OD flow prediction method based on spatiotemporal fusion and holiday adjustment, and constructs a Dual-stage Spatio-Temporal Fusion Network (DSTF) model. First, a spatiotemporal feature extraction architecture for multi-source data fusion is designed: a dual-branch Graph Attention Network (GAT) is used to extract and fuse spatial features representing macroscopic travel correlations from the OD perspective and microscopic node state dependencies from the entrance and exit flow perspectives. Then, a gated fusion module combining a Temporal Convolutional Network (TCN) and a Convolutional Long Short-Term Memory network (CNN-LSTM) collaboratively captures both the short-term fluctuations and long-term periodic trends of traffic flow. Simultaneously, a Cross-Attention mechanism is introduced to achieve multi-task collaborative prediction of entrance flow, exit flow, and baseline OD flow. To adapt to the special travel patterns during holidays, the model adopts a two-stage training strategy: the first stage trains the baseline prediction model using sufficient and stable non-holiday data; the second stage introduces a lightweight Sequence-to-Sequence (Seq2Seq) holiday adjustment module, focusing on learning the deviation of holiday patterns from the baseline, and performs adaptive fine-tuning on the baseline OD flow predictions. Experimental results based on real highway toll data show that the proposed DSTF model significantly outperforms various baseline models across multiple evaluation metrics in holiday short-term OD prediction tasks, achieving reductions of 11.7% in MAE and 12.2% in RMSE compared to the best baseline model STGCN in 1-step prediction, demonstrating higher prediction accuracy, stronger robustness, and superior scenario adaptability.

摘要： 高速公路节假日期间的交通流具有显著的时空异质性，对起讫点（OD）流量进行精准短时预测是提升路网管理智能化水平的关键技术。针对OD数据的高维稀疏性、复杂时空依赖性及节假日模式偏移等问题，本文提出一种基于时空融合与节假日修正的高速公路OD流量短时预测方法，并构建双阶段时空融合网络（DSTF）模型。首先设计一种多源数据融合的时空特征提取架构：利用双分支图注意力网络（GAT）分别从OD层面与入口、出口流量层面提取并融合宏观出行关联与微观节点状态依赖的空间特征；进而通过门控融合的时间卷积网络（TCN）与卷积-长短期记忆网络（CNN-LSTM）组合模块，协同捕捉交通流的短时波动与长周期趋势；同时引入交叉注意力（Cross-Attention）机制实现入口流量、出口流量与基础OD流量的多任务协同预测。为适配节假日特殊出行模式，模型采用两阶段训练策略：第一阶段利用数据充足、模式稳定的非节假日数据训练基础预测模型；第二阶段引入轻量级的序列到序列（Seq2Seq）节假日修正模块，专注于学习节假日相对于基础模式的偏移量，对基础OD流量预测值进行自适应微调。基于真实高速公路收费数据的实验结果表明，所提DSTF模型在节假日OD短时预测任务中，在多项评价指标上均显著优于多种基线模型，在1步预测中MAE和RMSE较最优基线模型STGCN分别降低了11.7%和12.2%，展现了更高的预测精度、更强的鲁棒性以及更优秀的场景适应性。