The state-of-the-art in the application of artificial intelligence-based models for traffic noise prediction: a bibliographic overview

Umar, Ibrahim Khalil; Adamu, Musa; Mostafa, Nour; Riaz, Malik Sarmad; Haruna, Sadi I.; Hamza, Mukhtar Fatihu; Ahmed, Omar Shabbir; Azab, Marc

doi:10.1080/23311916.2023.2297508

Cogent Engineering

2024

DOI: 10.1080/23311916.2023.2297508

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The state-of-the-art in the application of artificial intelligence-based models for traffic noise prediction: a bibliographic overview

Ibrahim Khalil Umar,

Musa Adamu,

Nour Mostafa

et al.

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2024

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Cited by 3 publications

References 79 publications

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From Early to Recent Models: A Review of the Evolution of Road Traffic and Single Vehicles Noise Emission Modelling

Guarnaccia,

Mascolo,

Aumond

et al. 2024

Curr Pollution Rep

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Purpose of the Review This paper is devoted to the review of the most popular literature Road Traffic Noise Models (RTNMs) frameworks, from the oldest ones to the recent machine learning techniques. A dedicated section is reserved to the review of Noise Emission Models (NEMs), with specific focus on approaches that allow the assessment of single vehicles’ emissions. Finally, some propagation models are also briefly presented, along with the assessment of the impact on the population of road traffic noise, in terms of time-averaged indicators and exposure descriptors. Recent Findings In recent years, many efforts have been devoted to developing methods and models to assess the impact of environmental noise. Considering the primary role of road traffic as a noise source, estimating its impact is fundamental when evaluating the acoustic environment of a specific urban area. The scope of RTNMs is to provide an assessment of the noise emitted by the source in terms of traffic flows, propagate it at any desired point, including possible corrective factors, assess the impact at the receiver, and use this information to provide maps and other useful outputs. Summary This review summarizes the so-far developed approaches for road traffic noise evaluation and furthermore underscores the ongoing necessity for research to develop more precise tools useful for managing road traffic noise’s adverse effects on urban environments and public well-being. Challenges and limitations of such models are discussed in the conclusions, highlighting the need for providing high quality input data and avoiding site-dependent approaches.

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From Early to Recent Models: A Review of the Evolution of Road Traffic and Single Vehicles Noise Emission Modelling

Guarnaccia,

Mascolo,

Aumond

et al. 2024

Curr Pollution Rep

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Traffic Flow Prediction Based on Downsampled Interactive Spatio-Temporal Attention Network

季

2024

MOS

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Development, Calibration and Validation of Time Series Analysis and Artificial Neural Network Joint Model for Urban Noise Prediction

Rossi,

Singh,

Guarnaccia

2024

Applied Sciences

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Noise in large urban areas, which is mainly generated by road traffic and by the human activities carried out nearby and inside the area under study, is a relevant problem. The continuous exposure to high noise levels, in fact, can lead to several problems, largely documented in the scientific literature. The analysis and forecasting of the noise level in a given area are, then, fundamental for control and prevention, especially when field measurements present peculiar trends and slopes, which can be modeled with a Time Series Analysis approach. In this paper, a hybrid model is presented for the analysis and the forecasting of noise time series in urban areas: this technique is based on the application of a deterministic decomposition model followed in cascade by a predictor of the forecasting errors based on an artificial neural network. Two variants of the hybrid model have been implemented and presented. The time series used to calibrate and validate the model is composed of sound pressure level measurements detected on a busy road near the commercial port of an Italian city. The proposed hybrid model has been calibrated on a part of the entire time series and validated on the remaining part. Residuals and error analysis, together with a detailed statistical description of the simulated noise levels and error metrics describe in detail the method’s performances and its limitations.

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The state-of-the-art in the application of artificial intelligence-based models for traffic noise prediction: a bibliographic overview

Cited by 3 publications

References 79 publications

From Early to Recent Models: A Review of the Evolution of Road Traffic and Single Vehicles Noise Emission Modelling

From Early to Recent Models: A Review of the Evolution of Road Traffic and Single Vehicles Noise Emission Modelling

Traffic Flow Prediction Based on Downsampled Interactive Spatio-Temporal Attention Network

Development, Calibration and Validation of Time Series Analysis and Artificial Neural Network Joint Model for Urban Noise Prediction

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