# Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction
## 4 Results and Discussions
### Accuracy of Noise Prediction
#### 4.2.2 Determination of Error in the Predicted Instantaneous Noise Levels  
Once all the routes are determined, these are used to determine the terrain parameters such as direct distance between source and destination pair, route difference and length for diffracted routes, reflected route length, etc. [17]. This process is repeated for all routes and each pair of sources and destination. The noise data of source(s) and terrain parameters between source and destination pairs are integrated using noise model for prediction of noise levels. The predicted noise levels of different parts of the study site are compared with measured noise levels to estimate the accuracy. Noise mapping is done to show the noise propagation in a particular area. Here, 25 noise sources at the railway line near the RGIPT campus are used to determine all the routes for all the destination locations at the campus shown in (Figure 17). The predicted noise levels are found to be accurate within \(\pm\)6 dB (tested for over 100 destination points at RGIPT campus) and compared with in accuracies of other instantaneous scale noise prediction [40,41] and are found to be significantly better.  
Figure 16: Error propagation due to inaccuracies in terrain data.  
These determined routes are then used to map the noise level for the RGIPT campus due to railway noise sources (Figure 17b) at various locations of noise sources. Predicted noise levels are plotted in a GIS environment using ArcGIS software [26, 42, 43]. The project area of the RGIPT campus noise map in 3D is generated for 3 different instances, as shown in Figure 18a-c. These 3 different maps are for train noise sources when a train is at 3 different positions. Noise prediction of RGIPT campus is performed by gathering information of routes, terrain parameters, noise source, and noise level at the source. Noise source is considered as the train passes through the railway track near the campus for 2 min. These routes, terrain parameters, and noise level at source are further given to the noise model, which will finally calculate the noise level at noise receiver points (destination) of the RGIPT campus. Various routes determined are found to be accurate within \(\pm\) 9 cm, which is a testimony for the effectiveness of the approach. Further, it was found to significantly improve the accuracy of prediction of noise level for a place (for short period). The predicted noise levels are found to be accurate within \(\pm\)6 dB (tested for over 100 points at RGIPT campus), which is about two-fold superior to what was earlier achieved (\(\pm\)10 to 12 dB). The novel route determination algorithm applicable over precise LiDAR point cloud data can easily be extended for significantly improving other urban applications.  
Figure 17: (**a**) Google Earth map of RGIPT campus showing railway line (noise source) with 25 points on line, buildings and the rest area where the destination points are considered. (**b**) Prediction of noise map of RGIPT campus due to noise sources at railway line. Noise levels are projected in DB. Source and destinations’ locations are marked with small circular points.  
Figure 18: RGIPT campus noise map for noise source points at railway line when train is at different location and the noise receiver points (destination) are all over the RGIPT campus including buildings of different height. (**a**) 1 st instance, (**b**) 2 nd instance, and (**c**) 3 rd instance.