基于专家混合的持续交通流量预测
Continual Traffic Forecasting via Mixture of Experts
AI总结：
针对交通网络持续扩展中的灾难性遗忘问题，提出专家混合模型TFMoE，将交通流分组并由不同专家负责，在PEMSD3-Stream数据集上验证了其持续学习的有效性与效率。
摘要：
现实世界中的交通网络通过安装新传感器不断扩展，这意味着交通模式会随时间持续演变。在新增加的传感器上增量训练模型会使模型遗忘过去的知识，即灾难性遗忘，而在整个网络上重新训练模型以捕捉这些变化则效率极低。为了解决这些挑战，我们提出了一种新颖的交通预测专家混合模型（Traffic Forecasting Mixture of Experts，TFMoE），用于不断演化的网络下的交通预测。其主要思想是将交通流分割成多个同质组，并为每个特定组分配一个专家模型负责。这使得每个专家模型能够专注于学习和适应一组特定的模式，同时在训练过程中最小化专家之间的干扰，从而防止先前知识的稀释或替换，而这是灾难性遗忘的主要原因。通过在真实世界长期流式网络数据集PEMSD3-Stream上进行的大量实验，我们证明了TFMoE的有效性和效率。我们的结果展示了在面对灾难性遗忘时优越的性能和韧性，凸显了我们的方法在处理长期流式网络中交通流量预测的持续学习方面的有效性。
英文摘要：
The real-world traffic networks undergo expansion through the installation of new sensors, implying that the traffic patterns continually evolve over time. Incrementally training a model on the newly added sensors would make the model forget the past knowledge, i.e., catastrophic forgetting, while retraining the model on the entire network to capture these changes is highly inefficient. To address these challenges, we propose a novel Traffic Forecasting Mixture of Experts (TFMoE) for traffic forecasting under evolving networks. The main idea is to segment the traffic flow into multiple homogeneous groups, and assign an expert model responsible for a specific group. This allows each expert model to concentrate on learning and adapting to a specific set of patterns, while minimizing interference between the experts during training, thereby preventing the dilution or replacement of prior knowledge, which is a major cause of catastrophic forgetting. Through extensive experiments on a real-world long-term streaming network dataset, PEMSD3-Stream, we demonstrate the effectiveness and efficiency of TFMoE. Our results showcase superior performance and resilience in the face of catastrophic forgetting, underscoring the effectiveness of our approach in dealing with continual learning for traffic flow forecasting in long-term streaming networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
