TITLE:
Accuracy of Smartphone-Based Road Traffic Noise Measurement in Nairobi City, Kenya
AUTHORS:
Akech Elisha Ochungo, Simpson Nyambane Osano, John Francis Gichaga
KEYWORDS:
Noise, Smartphone, Sound Level Meter
JOURNAL NAME:
World Journal of Engineering and Technology,
Vol.13 No.4,
October
17,
2025
ABSTRACT: Conventionally, Sound Level Meters have been used to measure Road Traffic Noise in cities to monitor the acoustic soundscape of neighborhoods. However, today, use of smartphone to record Road Traffic Noise is gaining traction. The key limitation in this shift remains the accuracy gap between the calibrated Sound Level Meter’s data and the smartphone-captured data. In this study, a handheld Android Smartphone, the Samsung Galaxy A12 Model SM-A127F/DS, was used alongside the Lutron SL-4033SD, a Class 1 Sound Level Meter, to establish the accuracy of smartphone integration in the measurement of road traffic noise data in Nairobi, the capital city of Kenya. In previous works, authors have commonly used statistical methods such as; Mean Absolute Error (MAE), Standard Deviation (SD), Root Mean Square Error (RMSE), and Pearson’s correlation coefficient to evaluate the accuracy of smartphone-based noise measurements against reference Sound Level Meters (SLMs) with whose reported error ranges falling typically between ±0.5 dB(A) and ±3.5 dB(A) depending on the device model, environment, and methodology used. The present study used the metrology of the Margin of Error (MoE) formula to give confidence in the integration of smartphones into city’s noisescape’s assessment. The results of the measurement indicate that the Margin of Error is ±0.4247 dB(A). This lends credence to the possible innovative application of smartphones in noise measurement, given their widespread presence. Hence, the potency of applying the citizen science method (crowdsourcing) in real-time noise level monitoring. Its only drawback is the computation of the equivalent continuous sound level (Leq) from raw audio data, where one has to use coding-based applications. However, in this era of Artificial Intelligence (AI) and Machine Learning (ML), such codes can be embedded in the web platforms for automatic transformations. This is presumed to be cheaper than the installation of a noise measurement sensor network. Nairobi city management can therefore, in the future, adopt crowdsourced noise data from smartphones to update its real-time noise map.