  In the design of traffic monitoring solutions for optimizing the urban
mobility infrastructure, acoustic vehicle counting models have received
attention due to their cost effectiveness and energy efficiency. Although deep
learning has proven effective for visual traffic monitoring, its use has not
been thoroughly investigated in the audio domain, likely due to real-world data
scarcity. In this work, we propose a novel approach to acoustic vehicle
counting by developing: i) a traffic noise simulation framework to synthesize
realistic vehicle pass-by events; ii) a strategy to mix synthetic and real data
to train a deep-learning model for traffic counting. The proposed system is
capable of simultaneously counting cars and commercial vehicles driving on a
two-lane road, and identifying their direction of travel under moderate traffic
density conditions. With only 24 hours of labeled real-world traffic noise, we
are able to improve counting accuracy on real-world data from $63\%$ to $88\%$
for cars and from $86\%$ to $94\%$ for commercial vehicles.
