A low-cost noise measurement device for noise mapping based on mobile sampling

Traffic noise assessment based on mobile measurements

“…Low-cost hardware provide accurate noise measurements [9] that, combined to the low speed (< 5 m/s) and low self-noise of the bicycle, ensures the reliability of the samples. However, it should be possible to use another vehicle but other factors should be taken into account such as the air flow, outliers detection, max speed, among others [12,14,15].…”

“…To perform the mobile noise measurements, a low-cost noise sensor with an integrated GPS module [12], which stores the georeferenced L Aeq and its date/time information at each time-step (t = 1 s), was mounted on a bicycle as shown in Figure 3. In order to avoid the screening effect of the bicycle itself (driver), of the surrounding vehicles and the parked cars [23], a study was performed in the same street, where microphones were placed every 0.5 m height in a tube, ranging from 0.5 m to 4 m. It was observed that the noise levels measured above 1.5 m height were almost the same.…”

“…To reduce the resources needed to acquire the noise levels, low-cost noise sensors, accurate enough to provide representative measurements [9], have been developed mainly to be used as nodes in sensor networks [10], which are characterized by their ubiquity. On the other hand, with the growth of the use of mobile communication devices, citizens are now enabled to perform noise measurements using their own smartphones or even low-cost hardware specially designed for that purpose [11,12]. In any case, there are drawbacks that should be addressed for these new sampling approaches.…”

“…In this way, the temporal and spatial coverage is enhanced but in a more controlled environment. It has been shown that noise mapping using samples taken by pedestrians [14,16] or using bicycles [12,18,19] may be a good option for mobile sampling, that, besides reducing the time needed to perform the measurements, it could also help to evaluate the spatial variations of noise levels along the streets [20]. For example, in [17], the requirements to reduce the estimation error of mobile measurements of traffic noise (number of passes-by, distance of aggregation of mobile samples and distance to intersection) are obtained through a modeling framework based on micro-traffic simulations, where the mobile sensor are bicycles.…”

A Survey of Technologies and Recent Developments for Sustainable Smart Cycling

“…Vagnoli et al [76] Environment monitoring Noise, humidity, temperature, air and road quality Liu et al [75] Air quality Air pollution Taniguchi et al [78] Path quality Pavement quality indicators Wijerathne et al [79] Path quality Pavement quality indicators Zhao et al [80] Path quality Pavement quality indicators, bike speed Corno et al [73] Environment monitoring Temperature, humidity, pressure Grama et al [81] Environment monitoring Multiple scalar data Shen et al [74] Environment monitoring Multiple scalar data Katto et al [69] Path quality Images Quintero et al [82] Noise level Audio (noise) sensed data Oliveira et al [71] Monitoring of any variable Scalar and multimedia data…”