发表机构
Dongbei University of Finance & Economics; Hong Kong University of Science and Technology (Guangzhou)（东北财经大学; 香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对多变量交通流预测，推出保留三类原始测量的PEMSB-3V基准，提出模型无关的RiskTraf方法，通过风险外推残差学习缓解状态依赖捷径，提升预测性能且优于相关适配方法。
AI 中文摘要
交通传感器通常会记录流量、速度和占用率，但标准交通流预测基准与模型很少能可靠地利用这三类原始测量数据。尽管速度和占用率能提供远超流量的传感器原生交通状态信息，但现有基准发布版本常忽略这些变量、用代理变量替代，或包含逻辑不一致的记录。此外，对三变量输入直接进行经验风险最小化可能会利用依赖状态的捷径，因为流量、速度和占用率之间的关系在自由流和拥堵状态间存在显著差异。我们推出了PEMSB-3V，这是一个公共基准套件，保留了PeMS检测器用于流量预测的原始流量、速度和占用率测量数据。我们还提出了RiskTraf，这是一种模型无关的风险外推残差插件。对于每个经过训练的时空骨干网络，RiskTraf会冻结选定的检查点，并从历史速度和占用率中学习轻量级的零启动残差头。该残差头构建有序的交通风险环境，并通过风险外推目标优化水平相关的流量修正，从而在不修改骨干网络的情况下缓解特定状态的捷径相关性。大量实验表明，RiskTraf能持续改进多种预测骨干网络，且优于去偏和分布偏移适配方法。我们的代码和基准可在此https URL获取。
英文摘要
Traffic sensors commonly record flow, speed, and occupancy, but standard traffic flow forecasting benchmarks and models rarely exploit all three raw measurements reliably. Although speed and occupancy provide sensor-native traffic-state information beyond flow alone, existing releases often omit these variables, replace them with proxies, or contain logically inconsistent records. Moreover, direct empirical risk minimization over three-variable inputs may exploit regime-dependent shortcuts, as the relationships among flow, speed, and occupancy vary substantially between free-flow and congested states. We introduce \textbf{PEMSB-3V}, a public benchmark suite that preserves raw flow, speed, and occupancy measurements from PeMS detectors for flow prediction. We also propose \textbf{RiskTraf}, a model-agnostic risk-extrapolated residual plug-in. For each trained spatio-temporal backbone, RiskTraf freezes the selected checkpoint and learns a lightweight zero-start residual head from historical speed and occupancy. The residual head constructs ordered traffic-risk environments and optimizes horizon-wise flow corrections with a risk extrapolation objective, thereby mitigating regime-specific shortcut correlations without modifying the backbone. Extensive experiments demonstrate that RiskTraf consistently improves diverse forecasting backbones and outperforms debiasing and distribution-shift adaptation methods. Our code and benchmark are available at https://github.com/Guangyu4/RiskTraf.
CommentsAccepted by CIKM 2026 Oral
Journal refCIKM' 2026
