  Vehicle count prediction is an important aspect of smart city traffic
management. Most major roads are monitored by cameras with computing and
transmitting capabilities. These cameras provide data to the central traffic
controller (CTC), which is in charge of traffic control management. In this
paper, we propose a joint CNN-LSTM-based semantic communication (SemCom) model
in which the semantic encoder of a camera extracts the relevant semantics from
raw images. The encoded semantics are then sent to the CTC by the transmitter
in the form of symbols. The semantic decoder of the CTC predicts the vehicle
count on each road based on the sequence of received symbols and develops a
traffic management strategy accordingly. Using numerical results, we show that
the proposed SemCom model reduces overhead by $54.42\%$ when compared to source
encoder/decoder methods. Also, we demonstrate through simulations that the
proposed model outperforms state-of-the-art models in terms of mean absolute
error (MAE) and mean-squared error (MSE).
