  As the demand for vehicles continues to outpace construction of new roads, it
becomes imperative we implement strategies that improve utilization of existing
transport infrastructure. Traffic sensors form a crucial part of many such
strategies, giving us valuable insights into road utilization. However, due to
cost and lead time associated with installation and maintenance of traffic
sensors, municipalities and traffic authorities look toward cheaper and more
scalable alternatives. Due to their ubiquitous nature and wide global
deployment, cellular networks offer one such alternative. In this paper we
present a novel method for traffic flow estimation using standardized LTE/4G
radio frequency performance measurement counters. The problem is cast as a
supervised regression task using both classical and deep learning methods. We
further apply transfer learning to compensate that many locations lack traffic
sensor data that could be used for training. We show that our approach benefits
from applying transfer learning to generalize the solution not only in time but
also in space (i.e., various parts of the city). The results are very promising
and, unlike competing solutions, our approach utilizes aggregate LTE radio
frequency counter data that is inherently privacy-preserving, readily
available, and scales globally without any additional network impact.
