Generation of road traffic noise maps using traffic data derived from aerial photographs - Zhang, Xinyi; Sakamoto, Shinichi (Acoustical Science and Technology, 2025)
Road traffic noise is one of the most widespread environmental noise sources, significantly impacting public health. To control traffic noise pollution, the European Union requires countries to prepare strategic road traffic noise maps using prediction models every five years since 2007. Similarly, Japan has developed its own road traffic noise maps based on regular on-site observations. However, traditional methods of collecting traffic data through on-site measurements are labor-intensive and costly. Therefore, in this work, we present a method for creating road traffic noise maps using an object detection deep learning algorithm to extract road traffic conditions, such as vehicle types and speeds, from aerial photographs. On the basis of ASJ RTN-Model 2018, the traffic condition data serves as the foundation for calculating the sound power levels of road traffic noise for different roads. The road traffic noise map is then created directly from the sound pressure level distribution within the considered regions. We validated the accuracy of the detection model and the calculated sound pressure level along the road from the aerial photographs against the published measurements. Using the proposed method, we created the road traffic noise map for Meguro City, Tokyo.