# Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction
## 1 Introduction
It can be used to find the quickest route based on the unique DNA of living creatures, which determines everyone's property [11; 12]. The graph theory-based techniques primarily work when various routes are delineated. Further, it works with the 2D transmission. The source to receiver propagation of noise works in 3D, and it does not necessarily follow a 2D road network in a city [13; 14; 15]. There is thus a need to develop a Point-to-Point 3D routing algorithm, customized for accurate noise prediction in the outdoor environment. It should be following the principle of physics for the determination of all the possible propagation routes between every source and receiver pair. Furthermore, the algorithm is needed to input detailed and accurate 3D outdoor terrain data (e.g., LiDAR data) for the best prediction of Point-to-Point 3D routes. Accurate routes should provide an accurate prediction of the noise level in outdoor environment [16; 17; 18]. The Point-to-Point routing algorithm should be compatible with high quality terrain data in 3D for accurate prediction. LiDAR offers detailed 3D terrain data of very high precision. There are also other 3D terrain data sources such as stereo-photo, Digital Elevation Model, contour, 3D Triangulated Irregular Network-TIN, etc. Some of these are in raw terrain data form (e.g., LiDAR, stereo-photo), while others are available as transformed terrain data form [19; 20; 21; 22; 23]. Building and other overground objects are required to be extracted from these unlabeled 3D data before Point-to-Point routes can be estimated. The proposed technique is required to work with all these high-quality 3D terrain data forms. The LiDAR3D point cloud-based technique is targeted to derive an accurate solution for routing over the detailed high quality 3D data [24; 25; 26; 27]. It is expected to improve the accuracy for noise prediction in 3D outdoor environment. The accurately determined routes can improve the accuracies in other urban applications as well, such as, determination of solar irradiance, solar shadow, view shade analysis, setting up wireless tower for a location, etc. [28; 29; 30]. Thus, the researchers have tried to address the following points in this paper. Determination of a routing algorithm that can determine direct and other indirect routes (when the direct transmission is obstructed) between a source and destination (or receiver) points. The establishment of optimal or best possible routes containing the highest energy/flow is tried to be determined. Consider natural propagation of energy or pressure over 3D terrain in an outdoor environment. It did not try to find out any other algorithm which compares the existing city road networks to find the shortest route between two points. The algorithm is required to find out a solution customized to handle propagation problems in 3D. The algorithm should be capable of handling highly detailed and accurate 3D terrain data (e.g., LiDAR data) for the accurate determination of routes. The efficiency of the route determination algorithm is required to be tested in terms of optimality of solutions. The algorithm developed should be useful for accurate noise prediction and applicable for other urban applications, e.g., determination of solar irradiance, urban supply line, view shade analysis, setting up the wireless tower for a location, etc. Research Gap and Need for Point-to-Point 3D Routing Algorithm for Noise Prediction Using LiDAR Data  
Noise propagation modeling in general needs the incorporation of terrain and noise data into the noise prediction model. The collection of detailed terrain information along with the information of time-varying noise data is a challenge. It required the determination of routes through which noise can transmit from a source location to different noise destination (destination) locations. Traditionally noise maps are produced in 2D. These are built for a particular height, which does not show the impact of noise pollution in 3D.