Nowadays, Smart Cities applications are becoming steadily popular, thanks to their main objective of improving people daily habits. The services provided by the aforementioned applications may be either addressed to the entire digital population or narrowed towards a specific kind of audience, like drivers and pedestrians. In this sense, the proposed paper describes a Deep Learning solution designed to manage traffic control tasks in Smart Cities. It involves a network of smart lampposts, in charge of directly monitoring the traffic by means of a bullet camera, and equipped with an advanced System-on-Module where the data are efficiently processed. In particular, our solution provides both:i) a risk estimation module, and ii) a license plate recognition module. The first module analyses the scene by means of a Faster R-CNN, trained over an ad-hoc set of synthetically videos, to estimate the risk of potential traffic anomalies. Concurrently, the license plate recognition module, by leveraging on YOLO and Tesseract, is active for retrieving the plate number of the vehicles involved. Preliminary experimental findings, from a prototype of the solution applied in a real-world scenario, are provided.