Estimation of Average Speed of Road Vehicles by Sound Intensity Analysis

Abstract

Constant monitoring of road traffic is important part of modern smart city systems. The proposed method estimates average speed of road vehicles in the observation period, using a passive acoustic vector sensor. Speed estimation based on sound intensity analysis is a novel approach to the described problem. Sound intensity in two orthogonal axes is measured with a sensor placed alongside the road. Position of the apparent sound source when a vehicle passes by the sensor is estimated by means of sound intensity analysis in three frequency bands: 1 kHz, 2 kHz and 4 kHz. The position signals calculated for each vehicle are averaged in the analysis time frames, and the average speed estimate is calculated using a linear regression. The proposed method was validated in two experiments, one with controlled vehicle speed and another with real, unrestricted traffic. The calculated speed estimates were compared with the reference lidar and radar sensors. Average estimation error from all experiment was 1.4% and the maximum error was 3.2%. The results confirm that the proposed method allow for estimation of time-averaged road traffic speed with accuracy sufficient for gathering traffic statistics, e.g., in a smart city monitoring station.

Acoustic Detector of Road Vehicles Based on Sound Intensity

“…The intensity signals after the integration usually contain a high level of noise which originates from both the sensors and the inaccuracy of the intensity calculation method. Therefore, noise suppression is required before the total intensity is calculated [ 22 ]. We used a simple moving average filter which proved to work very well in this case, providing the necessary amount of smoothing to the intensity signal.…”

“…Figure 3 shows the sound intensity plots (unprocessed and smoothed) calculated from a recording of two vehicles passing by the sensor. It should be noted that the sensor measures the sound intensity of an apparent sound source that is a superposition of all sound sources in a vehicle, and it does not represent a constant point in the vehicle body [ 22 ]. The value of I X is the largest when the apparent sound source is close to the sensor, and it decreases as the distance from the sensor increases, so the plot resembles an inverted parabola.…”

“…It is not possible to measure the physical speed of a road vehicle because the distance y to the source is unknown, and due to the movement of the apparent sound source within the vehicle. These issues are discussed in more detail in our previous publication [22]. Figure 5 presents an example of processing the position signal with a Kalman filter for the intensity signals shown in Figure 3.…”

“…That detector worked well for isolated vehicles, but it was too inaccurate in more intensive traffic. In another publication [ 22 ], we described the average traffic speed estimation by the sound intensity analysis. However, the vehicle detection was not a part of that algorithm.…”

“…This method aims to detect road vehicles (both the isolated ones and vehicles moving in a sequence) and to determine their direction of movement. This detector can also provide data needed by the average speed estimation algorithm [ 22 ].…”