论文标题
在不同的数据可用性条件下，转移学习和在线学习预测流量预测：替代方案和陷阱
Transfer Learning and Online Learning for Traffic Forecasting under Different Data Availability Conditions: Alternatives and Pitfalls
论文作者
论文摘要
这项工作旨在揭示转移学习的潜力（TL），以在缺乏数据的情况下开发交通流预测模型。在TL范式下，从高质量预测模型中的知识转移变得可行，从而使新的适当模型的生成很少。为了探索这种能力，我们确定了三个不同级别的数据，没有方案，其中在深度学习（DL）方法中应用TL技术进行了预测。然后，使用真实的交通流数据将传统的批量学习与基于TL的模型进行比较，该模型由马德里市议会（西班牙）管理的部署循环收集。此外，我们还采用在线学习（OL）技术，在每个预测之后，模型都会收到更新，以适应流量流趋势变化并从新的传入流量数据中逐步学习。获得的实验结果阐明了转移和在线学习对交通流的预测的优势，并在相互作用上与他们在感兴趣的位置与可用培训数据的数量进行了实践见解。
This work aims at unveiling the potential of Transfer Learning (TL) for developing a traffic flow forecasting model in scenarios of absent data. Knowledge transfer from high-quality predictive models becomes feasible under the TL paradigm, enabling the generation of new proper models with few data. In order to explore this capability, we identify three different levels of data absent scenarios, where TL techniques are applied among Deep Learning (DL) methods for traffic forecasting. Then, traditional batch learning is compared against TL based models using real traffic flow data, collected by deployed loops managed by the City Council of Madrid (Spain). In addition, we apply Online Learning (OL) techniques, where model receives an update after each prediction, in order to adapt to traffic flow trend changes and incrementally learn from new incoming traffic data. The obtained experimental results shed light on the advantages of transfer and online learning for traffic flow forecasting, and draw practical insights on their interplay with the amount of available training data at the location of interest.
