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).

翻译：车辆计数预测是智慧城市交通管理的重要方面。大多数主干道路由具备计算与传输能力的摄像头监控，这些摄像头向负责交通控制管理的中央交通控制器（CTC）提供数据。本文提出一种基于CNN-LSTM的联合语义通信（SemCom）模型，其中摄像头的语义编码器从原始图像中提取相关语义信息，经编码后的语义由发射器以符号形式发送至CTC。CTC的语义解码器基于接收到的符号序列预测各道路的车辆数量，并据此制定交通管理策略。通过数值结果验证，该SemCom模型相比传统源编码/解码方法可降低54.42%的开销。此外，仿真结果表明该模型在平均绝对误差（MAE）和均方误差（MSE）指标上均优于现有最优模型。