Urban traffic prediction becomes difficult when historical data is insufficient, as most deep learning models depend on large, location-specific datasets. To overcome this challenge, the Multi-Scale Traffic Pattern Bank (MTPB) framework focuses on learning transferable spatio-temporal traffic patterns from data-rich cities and applying them to data-scarce cities through few-shot learning. Due to a lack of detailed operational data at the intersection level, MTPB’s application in real-time traffic signal control is still restricted, despite its encouraging results in cross-city traffic forecasts. This paper examines the benefits and drawbacks of the main components of the MTPB framework, including multi-scale temporal patch extraction, masked spatio-temporal representation learning, traffic pattern clustering, graph reconstruction, and meta-adaptation. An extended framework including signal timing data, parking demand indicators, queue length prediction, and closed-loop feedback systems is proposed to enable adaptive traffic signal management. All things considered, the study shows how MTPB may be enhanced to become a more useful and scalable intelligent urban traffic management system.