Semantic Communication-Empowered Vehicle Count Prediction for Traffic Management

TL;DR

This paper introduces a semantic communication model using CNN-LSTM for vehicle count prediction in traffic management, significantly reducing data overhead and improving prediction accuracy over existing methods.

Contribution

The paper presents a novel CNN-LSTM-based SemCom model that efficiently encodes relevant semantics from images for vehicle counting, outperforming traditional source encoding methods.

Findings

Reduces communication overhead by 54.42%

Outperforms state-of-the-art models in MAE and MSE

Enhances traffic management accuracy

Abstract

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).