In modern traffic management, one of the most essential yet challenging tasks is accurately and timely predicting traffic. It has been well investigated and examined that deep learning-based Spatio-temporal models have an edge when exploiting Spatio-temporal relationships in traffic data. Typically, data-driven models require vast volumes of data, but gathering data in small cities can be difficult owing to constraints such as equipment deployment and maintenance costs. To resolve this problem, we propose TrafficTL, a cross-city traffic prediction approach that uses big data from other cities to aid data-scarce cities in traffic prediction. Utilizing a periodicity-based transfer paradigm, it identifies data similarity and reduces negative transfer caused by the disparity between two data distributions from distant cities. In addition, the suggested method employs graph reconstruction techniques to rectify defects in data from small data cities. TrafficTL is evaluated by comprehensive case studies on three real-world datasets and outperforms the state-of-the-art baseline by around 8 to 25 percent.

翻译：在现代交通管理中，准确及时地预测交通状况是最基本但也最具挑战性的任务之一。大量研究和实践表明，基于深度学习的时空模型在挖掘交通数据的时空关系方面具有优势。数据驱动模型通常需要海量数据支撑，但小城市由于设备部署和维护成本等限制，数据采集往往面临困难。为解决这一问题，我们提出TrafficTL——一种利用其他城市大数据辅助数据稀缺城市进行交通预测的跨城市交通预测方法。该方法采用基于周期性的迁移范式，识别数据相似性并减少因不同城市数据分布差异导致的负迁移现象。此外，所提方法还采用图重构技术来修复小数据城市的数据缺陷。通过在三个真实数据集上的综合案例评估，TrafficTL相比现有最优基线方法性能提升约8%至25%。