Journal of Advanced Transportation

Volume 2018, Article ID 7031418, 10 pages

https://doi.org/10.1155/2018/7031418

## Urban Traffic Noise Maps under 3D Complex Building Environments on a Supercomputer

^{1}School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China^{2}Guangdong Provincial Key Laboratory of Intelligent Transportation System, Guangzhou, China^{3}Guangdong Provincial Engineering Research Center for Traffic Environmental Monitoring and Control, Guangzhou, China^{4}School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, China

Correspondence should be addressed to Haibo Wang; nc.ude.usys.liam@9bhgnaw

Received 13 April 2018; Revised 12 June 2018; Accepted 25 June 2018; Published 9 July 2018

Academic Editor: Konstantinos Vogiatzis

Copyright © 2018 Ming Cai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The complexity of the 3D buildings and road networks gives the simulation of urban noise difficulty and significance. To solve the problem of computing complexity, a systematic methodology for computing urban traffic noise maps under 3D complex building environments is presented on a supercomputer. A parallel algorithm focused on controlling the compute nodes of the supercomputer is designed. Moreover, a rendering method is provided to visualize the noise map. In addition, a strategy for obtaining a real-time dynamic noise map is elaborated. Two efficiency experiments are implemented. One experiment involves comparing the expansibility of the parallel algorithm with various numbers of compute nodes and various computing scales to determine the expansibility. With an increase in the number of compute nodes, the computing time increases linearly, and an increased computing scale leads to computing efficiency increases. The other experiment is a comparison of the computing speed between a supercomputer and a normal computer; the computing node of Tianhe-2 is found to be six times faster than that of a normal computer. Finally, the traffic noise suppression effect of buildings is analyzed. It is found that the building groups have obvious shielding effect on traffic noise.

#### 1. Introduction

With rapid growth of population and the continued expansion of transportation systems, traffic noise pollution becomes quite a nuisance to urban residents. In Europe, the recent Environmental Noise Directive (END) revision has updated the current situation on the application of the END [1] and noise pollution continues to be a major health problem. The growing amount of noise pollution can lead to a number of serious diseases, including sleep disturbance [2, 3], stroke [4], male infertility [5], and learning impairment [6]. Moreover, long-term exposure to traffic noise can easily lead to high blood pressure [7] and affect the quality of life in the neighborhood [8].

Since END ’s prescription for noise maps and action plans, there have been many efforts by the scientific community to conduct high-level study and propose new mitigation systems for the main sources of noise in urban areas: road traffic, railway traffic, airport, and industrial, ranging from regulations to operational approaches [9–11]. A noise map is an important tool for observing and controlling noise pollution and the basis for epidemiology study relating annoyance to noise and noise exposure in urban areas. In urban planning, a noise map is used to analyze the sound qualities of the soundscapes in a specific urban area to generate recommendations for the urban design of the soundscapes [12]. For example, Palma de Mallorca (Spain) conducted a study analyzing various noise mitigation measures, which consider not only the reduction of noise and the number of people who can benefit from these measures but also the net monetary benefits generated, by using a traffic noise map [13]. Another study proposed a method for sorting road stretches by priority by using a noise map. The method is based on the so-called “road stretch priority index”, in which the index involves a number of variables that are weighted according to their influence on the road traffic noise problem, and road stretches of different priorities thus require different plan actions [14]. Similarly, Daniel Naish proposed a regional road traffic noise management strategy (RRTNMS); in this strategy, the road was ranked according to the predicted sound pressure level, and different control measures were implemented according to the results of the ranking [15]. In both studies, noise maps play a key role in the prediction of traffic noise and the display of final result. Furthermore, noise impact indicators and the mean of the expected individual annoyance scores in the population are calculated on the basis of a noise map. Those indicators include the percentages of people being highly annoyed, annoyed, and slightly annoyed and are used to compare the effect of noise reduction [16].

The research on noise maps started with the publication of END; since then, many scholars have researched noise maps, covering the calculating model and the update method for a noise map [17–23]. The calculation of a noise map is a complex process that involves a traffic noise prediction model and sound propagation attenuation. Nicolas Fortin et al. implemented a noise prediction method within the OrbisGIS2 software [24]; the method can produce large noise maps on a personal computer in a few hours, but the noise map is two-dimensional rather than three-dimensional. A small-scale 3D noise map can be implemented well by combining the prediction model and measured data to obtain a high-precision noise map [25]. However, since the complexity of the noise map calculation is the square of the computing scale which includes receiver point’s density and map size, a large-scale 3D noise map requires an excessive amount of time when using a normal computer with a CPU containing no more than 16 computing cores (the normal computer information used in the experiments in this article is described in Section 9). A study in 2009 considered computing a noise map on a supercomputer; in this effort, software called Noise Propagation Model is used to obtain the noise map in a supercomputer [26]; the parallel algorithm assigns tasks at one time rather than using dynamic assignment, and the noise map is two-dimensional. In addition, a traffic noise map was computed in the cloud computing environment [27]; however, in that study, the noise map produced was an existing service in the cloud computing system, and the computational framework was not explained.

In the paper, a method for computing a large-scale noise map in three dimensions on a supercomputer is presented to solve the problem of time-consuming calculation of dynamic three-dimensional noise maps. The supercomputer used in this study is Tianhe-2, and a computation strategy using Tianhe-2 is introduced and its performance is analyzed. The entire noise map calculation process includes a noise prediction model, a parallel calculation algorithm, and a visualization scheme of noise map. Then, combined with taxi trajectory data, a real-time noise map implementation method was proposed to dynamically update the noise map of the entire city. In addition, two experiments were carried out to analyze the efficiency of the parallel algorithm, one is about the expansibility analysis of the noise map parallel computing algorithm proposed in this paper, and the other is the analysis of the computing efficiency of Tianhe-2. Finally, we analyze the suppression effect on traffic noise of buildings from the changes in noise distribution on the ground and building surface caused by building groups.

#### 2. Traffic Noise Prediction among Building Groups

Noise map is composed of noise receiver points and it reflects the noise distribution in an urban area. Vehicles running on the road generate noise that propagates across buildings in a city before reaching the receiver point. Therefore, a model for predicting traffic noise has three steps: the first step is to predict the acoustical power of road, the second step is to predict sound attenuation caused by the building group in an urban area, and the last step is to combine all of the road’s acoustic power with the sound attenuation caused by the building group to calculate the noise value of the noise receiver point [28].

The factors affecting road noise emissions include traffic volume, road speed, and road surface materials. Among them, road surface materials are inherent attributes of roads, and good road surface materials can effectively reduce road noise emissions [29, 30].This paper is based on China’s road environment. China’s roads are basically asphalt pavements, so the impact of pavement materials as a fixed item is not reflected in the model parameters. As a result, the acoustical power of road is calculated using (1) and (2):where is equivalent sound level of the type traffic flow to the receiver point without shelter, is Equivalent sound level of the road to the receiver point without shelter, is average sound level of the type traffic flow to the reference point, is traffic flow volume of the type traffic flow, is average speed of the type traffic flow, is the prediction time, is distance between the reference point and the lane (), is distance between the receiver point and the lane, and is the field angle formed by the receiver point, the origin point of the road, and the end point of the road, with the peak of the angle being the receiver point.

In the model, the sound attenuation caused by the building group has two parts:

denotes the immediate shelter effect on the propagation path of sound caused by building group, and the relevant prediction equation iswhere is the density of the building group on the propagation path of sound and is the length of the sound propagation path.

is the shelter effect of sound diffusion caused by the building group, and the relevant prediction equation iswhere is the ratio of the projection length on the road of buildings near the road divided by the road length.

In total, the noise value of the receiver point should combine all roads and the attenuation of those roads:where is the equivalent sound level of receiver point , is the equivalent sound level of road to a receiver point without shelter, and is the sound attenuation caused by the building group between road and the receiver point.

#### 3. Parallel Algorithm in a Supercomputer

Urban areas contain a large number of buildings with complicated layouts and irregular outlines, which increases the computation complexity of a noise map, especially a three-dimensional one. However, simplifying the prediction model would cause the noise map to have low prediction accuracy. In this condition, a supercomputer is applied to compute a large-scale 3D noise map. In this study, Tianhe-2 is used to compute a noise map in three dimensions. Tianhe-2 was announced in June 2013, with Intel Xeon Phi accelerators contributing more than 85% of the 55pflops peak performance [31]. The strong computing ability of Tianhe-2 supports the computation of a large-scale 3D noise map on the basis of its hardware; thus, it is critical to have an appropriate and efficient parallel algorithm. The parallel algorithm presented here can be divided into two sections: one is the map tiling, which is the structural basis of parallel computation, and the other section is the coordination of the compute nodes, which implements the computation of noise map.

##### 3.1. Map Tiling

When the distance between the sound source and the receiver point is sufficiently large, the impact of the sound source to the receiver point is so small that it can be ignored. Therefore, breaking up the noise map into a tiling map composed of many smaller rectangle blocks is a reasonable approach for calculating the noise.

The map is tiled into a set of rectangle blocks that are 200 meters in length and 200 meters in width. In each block, the distance between noise receiver points is always set as 4m, so there are always 2500 receiver points in a block. While computing the noise of a block, the roads and buildings in a wider area are brought into such a state that each receiver point in the block can receive all impacts of the roads near it. Range of the area is determined by the number of roads inside it. As shown in Figure 1, when computing block 0, the roads and buildings in the grey area are also brought into the computation. The initial size of the grey area is 600*m* × 600*m*, and it gradually expands until the number of roads inside it is larger than a threshold .