Integrating Vehicle Acoustic Data for Enhanced Urban Traffic Management: A Study on Speed Classification in Suzhou

TL;DR

This paper introduces a new acoustic dataset and a deep learning model for classifying vehicle speeds in urban traffic, demonstrating high accuracy and potential for real-time traffic management and noise pollution reduction.

Contribution

The study provides a publicly available acoustic dataset and proposes a bimodal deep CNN with attention mechanisms for vehicle speed classification, improving accuracy and robustness.

Findings

Achieved 87.56% accuracy on SZUR-Acoustic Dataset

Achieved 96.28% accuracy on IDMT-Traffic dataset

Validated effectiveness of the bimodal fusion and attention modules

Abstract

This study presents and publicly releases the Suzhou Urban Road Acoustic Dataset (SZUR-Acoustic Dataset), which is accompanied by comprehensive data-acquisition protocols and annotation guidelines to ensure transparency and reproducibility of the experimental workflow. To model the coupling between vehicular noise and driving speed, we propose a bimodal-feature-fusion deep convolutional neural network (BMCNN). During preprocessing, an adaptive denoising and normalization strategy is applied to suppress environmental background interference; in the network architecture, parallel branches extract Mel-frequency cepstral coefficients (MFCCs) and wavelet-packet energy features, which are subsequently fused via a cross-modal attention mechanism in the intermediate feature space to fully exploit time-frequency information. Experimental results demonstrate that BMCNN achieves a classification accuracy of 87.56% on the SZUR-Acoustic Dataset and 96.28% on the public IDMT-Traffic dataset. Ablation studies and robustness tests on the Suzhou dataset further validate the contributions of each module to performance improvement and overfitting mitigation. The proposed acoustics-based speed classification method can be integrated into smart-city traffic management systems for real-time noise monitoring and speed estimation, thereby optimizing traffic flow control, reducing roadside noise pollution, and supporting sustainable urban planning.