A Deep Wavelet-Fourier Method for Monaural Vehicle Speed Estimation in Real-Life Settings

Vehicle speed estimation using acoustic data offers a cost-effective and nonintrusive approach to enhance traffic safety and efficiency. Previous studies are limited by controlled settings, constraints on vehicle types, and reliance on manual tuning. This article proposes an end-to-end framework for monaural settings in a multilane roadway, subject to real-life ambient noise without restriction on vehicle types. First, we develop a multimodal feature vector by processing raw audio data using a hybrid Fourier–Wavelet method. Second, a careful examination of the ambient noise and vehicular audio guarantees the validity of the suggested feature vector. Third, we design two deep neural networks to handle both regression and classification tasks. Our method is evaluated against state-of-the-art on a benchmark dataset comprising 304 samples. The results demonstrate a substantial improvement in accuracy, increasing by 29.4% (achieving 83.26% accuracy for the target class) and enhancement in the root-mean-square error (RMSE) for the regression task by 5.6%. In addition, we provide a proprietary dataset collected and curated as part of this study in four urban locations in Melbourne, VIC, Australia. This dataset represents the first real-world compilation of complex acoustic speed data, comprising 364 samples. Subsequent tests yield a classification accuracy of 84.02%, as well as mean absolute error (MAE) and RMSE values of 6.38 and 8.09 for regression, respectively.

I. Introduction

Intelligent transportation systems (ITSs) utilize sensors and equipment installed on roads and highways to collect data on the number, type, flow, and speed of vehicles. This data helps improve transportation safety and efficiency by dynamically updating traffic management policy [31]. Speed data are critical in maintaining the safety of the traffic network. In 2021, the United States lost 12330 lives as a result of speeding violations [26]. In 2022, overspeeding accounted for 41% of fatal events and 24% of serious injuries in NSW, Australia, resulting in 135 fatalities and 1141 injuries [28]. Traffic monitoring (TM) systems in smart cities provide reliable data on vehicle count, speed, and type, improving transportation performance, safety, and accident avoidance [3], [4].