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. An optimization problem to improve the quality of experience (QoE) is introduced and numerically solved, taking into account constraints such as vehicle user safety, transmit power of camera devices, vehicle count prediction accuracy, and semantic entropy. 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 QoE.

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。本文提出了一种基于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。编码后的语义随后以符号形式由发射器发送至CTC。CTC的语义解码器根据接收符号序列预测每条道路上的车辆数量，并据此制定交通管理策略。本文引入并数值求解了一个提升体验质量（QoE）的优化问题，同时考虑了车辆用户安全性、摄像头设备发射功率、车辆计数预测精度及语义熵等约束条件。数值结果表明，与源编码器/解码器方法相比，所提出的SemCom模型降低了54.42%的开销。此外，仿真实验证明，该模型在平均绝对误差（MAE）和QoE指标上均优于现有最先进模型。