  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.
