CN116595717A - Urban traffic noise grading evaluation method and system - Google Patents
Urban traffic noise grading evaluation method and system Download PDFInfo
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Abstract
The application discloses a method and a system for grading and evaluating urban traffic noise, and belongs to the technical field of noise evaluation. The method comprises the following steps: determining a noise priority key variable based on the road traffic source intensity index and the noise pollution index; scoring the noise priority key variable; determining the weight and the noise grading threshold of each noise priority key variable, and carrying out weighted summation on the scores of each noise priority key variable according to the weight to obtain a noise grading index; and evaluating the noise according to the noise grading index and the threshold value. The noise evaluation method can overcome the defects in the prior art, and comprehensively and accurately reflect the noise pollution level of the region.
Description
Technical Field
The application relates to the technical field of noise evaluation, in particular to a method and a system for classifying and evaluating urban traffic noise.
Background
With the increasing traffic flow on roads, the noise problems that result therefrom also have a serious impact on the residents along the road. In order to effectively prevent and control road noise, noise is usually accurately estimated, so that accurate prevention and control are performed.
The urban acoustic environment quality evaluation system in China is based on the principle of the minimum sampling rate of statistical random samples, and the currently developed urban environmental noise monitoring and evaluation projects mainly comprise: monitoring the ambient noise grid method of the urban area in daytime; monitoring the traffic noise of the urban road in daytime; and (5) monitoring urban environment functional areas at fixed points.
However, the traditional point distribution monitoring cannot fully and accurately reflect the noise pollution level of the area, and on one hand, the monitoring time and coverage range have limitations; on the other hand, the contribution of different noise sources and different influencing factors cannot be distinguished due to the actually measured data of the instrument; in the third aspect, urban traffic noise fluctuates greatly over time, and short-term monitoring of a fixed measurement point often cannot accurately reflect the actual sound level condition of the point. Therefore, the method for acquiring the noise pollution level of the whole area through the point distribution monitoring consumes a great amount of manpower and material resources, cannot meet the current noise management and technical requirements, and cannot provide targeted control measures.
Therefore, how to provide a classification evaluation method of urban traffic noise capable of overcoming the above problems is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the application provides a method and a system for classifying and evaluating urban traffic noise, the noise classifying and evaluating method disclosed by the application can comprehensively and accurately reflect regional noise pollution level by combining parameters such as urban traffic trunk line sound source emission characteristics, pollution space distribution, exposure crowd and the like,
the road section or the area needing to take noise control measures preferentially is conveniently screened out according to a certain rule, so that a reasonable control scheme is planned in a staged action plan, and the problem of road traffic noise pollution is gradually relieved.
In order to achieve the above purpose, the present application adopts the following technical scheme:
on the one hand, the application discloses a classification evaluation method for urban traffic noise, which comprises the following steps:
s1, determining a noise priority key variable based on a road traffic source intensity index and a noise pollution index;
s2, scoring the noise priority key variables to obtain scores of the noise priority key variables;
s3, determining the weight and the noise grading threshold of each noise priority key variable, and carrying out weighted summation on the scores of each noise priority key variable according to the weight to obtain a noise grading index; and evaluating the noise according to the noise grading index and the threshold value.
In order to further optimize the technical scheme, the road traffic source intensity index comprises: hour traffic flow, large vehicle proportion, and/or road traffic noise intensity;
the noise pollution index comprises: noise superscalar exposed population, superscalar sensitive building count, maximum noise superscalar, and/or complaint volume.
In order to further optimize the technical scheme, the maximum noise superscalar is obtained through the following formula:
ΔL max ＝max(L NM -L std )
where Δlmax: road section maximum noise superscalar, unit: dB (dB); l (L) NM : noise value in dB (a) at 1m outside the window of the sensitive building calculated from the noise map; l (L) std : according to the noise limiting requirement of the acoustic environment functional area of the sensitive building, the unit is as follows: dB (A).
In order to further optimize the technical scheme, the noise grading index is the maximum value of the daytime grading index and the night grading index.
In order to further optimize the above technical solution, the process of determining the weights of the key variables of the noise priority and the noise classification threshold is as follows:
(1) Obtaining scores of a plurality of key variables, and dividing the scores into an experience group and a control group;
(2) Initializing the respective corresponding weights; respectively calculating noise grading indexes of the experience group and the control group according to the initialization weight;
(3) Obtaining a positive judgment rate according to the noise classification index of the experience group, and taking the noise classification index corresponding to the positive judgment rate of m% as a threshold value;
(4) And calculating the positive judgment rate of the comparison group according to the threshold value, if the positive judgment rate is less than n%, re-determining weights corresponding to a plurality of key variables, and if the positive judgment rate is more than or equal to n%, outputting a final weight and noise classification threshold value.
In another aspect, the application discloses a hierarchical urban traffic noise assessment system, the system comprising: the key variable scoring module is connected with the noise grading index calculation module and the noise grading evaluation module in sequence;
the key variable scoring module is internally used for storing scoring rules corresponding to the key variables and scoring according to the collected key variable data to obtain scoring results;
the noise grading index calculation module is used for internally storing weights corresponding to the key variables, acquiring the grading result and obtaining a noise grading index according to the weights;
and the noise classification evaluation module is internally preset with a noise classification threshold value and is used for acquiring and evaluating the noise according to the noise classification index and the noise classification threshold value.
In order to further optimize the technical scheme, the system further comprises a parameter setting module, wherein the parameter setting module is used for setting the key variable weight and the noise grading threshold according to the following method.
(1) Obtaining scores of a plurality of key variables, and dividing the scores into an experience group and a control group;
(2) Initializing the respective corresponding weights; respectively calculating noise grading indexes of the experience group and the control group according to the initialization weight;
(3) Obtaining a positive judgment rate according to the noise classification index of the experience group, and taking the noise classification index corresponding to the positive judgment rate of m% as a threshold value;
(4) And calculating the positive judgment rate of the comparison group according to the threshold value, if the positive judgment rate is less than n%, re-determining weights corresponding to a plurality of key variables, and if the positive judgment rate is more than or equal to n%, outputting a final weight and noise classification threshold value.
Compared with the prior art, the urban traffic noise grading evaluation method fully considers parameters such as urban traffic trunk sound source emission characteristics, pollution space distribution, exposure population and the like based on the road traffic source intensity index and the noise pollution index, and accordingly provides a grading evaluation method which comprehensively and accurately reflects regional noise pollution levels so as to carry out grading evaluation on road sections needing measures according to noise problems according to the priority order, thereby planning reasonable prevention and treatment schemes in stages so as to relieve the road traffic noise pollution problems.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for classifying and evaluating urban traffic noise provided by the application;
FIG. 2 is a flow chart of the determination of weights and thresholds of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The embodiment of the application discloses a grading evaluation method for urban traffic noise, and aims to establish a set of noise pollution control priority grading method suitable for urban road traffic trunk lines. By combining the parameters of urban traffic trunk line sound source emission characteristics, pollution space distribution, exposure crowd and the like, road sections or areas needing to take noise control measures preferentially are screened out according to certain rules, so that a reasonable control scheme is planned in a staged action plan, and the problem of road traffic noise pollution is gradually relieved.
Specifically, the urban traffic noise grading evaluation method of the application comprises the following steps:
s1, determining a noise priority key variable based on a road traffic source intensity index and a noise pollution index;
s2, scoring the noise priority key variables to obtain scores of the noise priority key variables;
s3, determining the weight and the noise grading threshold of each noise priority key variable, and carrying out weighted summation on the scores of each noise priority key variable according to the weight to obtain a noise grading index; and evaluating the noise according to the noise grading index and the threshold value.
The urban road traffic trunk noise pollution control priority grading method is based on the road traffic noise grading index (I RN ) Calculated from a number of key variables (V RN ) Obtained by weighting them according to their importance.
In the embodiment, S1, determining a noise priority key variable based on a road traffic source intensity index and a noise pollution index; wherein the noise priority ranking key variable comprises (V RN ) 7, 4 of which are noise pollution indicators, including: the noise exceeding exposure population, the number of exceeding sensitive buildings, the maximum noise exceeding amount and/or complaint amount mainly reflect the influence degree of noise sources on the surrounding sensitive buildings or the exposure population;
the other 3 are road traffic source intensity indexes, including: hour traffic flow, large vehicle proportion, and/or road traffic noise intensity; mainly reflecting variables affecting the intensity and characteristics of the source of traffic noise itself.
Further, S2, scoring the noise priority key variables to obtain scores of the noise priority key variables; in the present embodiment of the present application,
(1) Noise-overstandard exposed population (P) exp ) And the population total number of the sensitive buildings exposed in the noise exceeding range in the influence range of 200m around the represented road section is obtained by comprehensively calculating and analyzing the noise map calculation result and the population distribution data.
The scoring method is as in table 1,
TABLE 1 noise oversubstance exposure population (Pexp) partitioning criteria and scoring values
(2) Number of superscalar sensitive buildings (Nb)
And the number (Nb) of the out-of-standard sensitive buildings is obtained by comprehensively calculating and analyzing the noise map calculation result and the sensitive building distribution data, and the number of the sensitive buildings exposed in the out-of-standard noise range is represented within the influence range of 200m around the represented road section.
The scoring method is as shown in table 2,
TABLE 2 number of oversubstance sensitive buildings (Nb) dividing criteria and scoring values
(3) Maximum noise superscalar (ΔLmax)
The maximum noise superscalar (Δlmax) data is derived from noise map calculations and characterizes the maximum noise value of road traffic noise affecting surrounding sensitive buildings. The variables are variables which are jointly influenced by indexes such as road type, road sound source intensity, distance between buildings and roads, noise reduction measures and the like. It reflects the maximum degree of pollution and is one of the important indexes for noise reduction scheme selection.
Wherein the maximum noise superscalar is obtained by the following formula:
ΔL max ＝max(L NM -L std )
where Δlmax: road section maximum noise superscalar, unit: dB (dB); l (L) NM : noise value in dB (a) at 1m outside the window of the sensitive building calculated from the noise map; l (L) std : according to the noise limiting requirement of the acoustic environment functional area of the sensitive building, the unit is as follows: dB (A).
And the scoring method is shown in table 3,
TABLE 3 maximum noise superscalar (ΔLmax) partitioning criteria and scoring values
(5) Complaint index (N) C )
The complaint index mainly reflects the number of road traffic noise complaints;
the scoring method is shown in table 4,
TABLE 4 complaint index N C Division criteria and scoring values
(5) Traffic flow (AHT)
The main traffic flow data of the traffic flow (AHT) per hour or on-site monitoring is obtained and represents the traffic flow data of the road section passing through in unit time (1 h).
The scoring method is as shown in table 5,
TABLE 5 traffic flow (AHT) partitioning criteria and scoring values
(6) Large vehicle proportion (% HV)
The main traffic flow data of the large vehicle proportion (% HV) or on-site monitoring is obtained, and the road section represents the percentage of the total traffic flow of the large vehicle passing through the road section within a certain time. The large-sized vehicle has high passing noise intensity, and is an important factor for exceeding the standard at night or disturbing the noise.
The scoring method is as shown in table 6,
TABLE 6 Large vehicle Scale (% HV) division criteria and scoring values
(7) Road traffic noise intensity (RNL)
Road traffic noise intensity (RNL) characterizes road traffic noise intensity indicators reflecting the radiation level of road origin, the scores of which are shown in table 7. The index was evaluated with reference to the traffic intensity classification level in the environmental noise monitoring technical Specification urban Acoustic Environment conventional monitoring (HJ 640-2012), as shown in Table 8,
TABLE 7 road traffic noise intensity (RNL) partitioning criteria and scoring values
TABLE 8 road traffic noise intensity classification
Further, S3, determining the weight and the noise grading threshold of each noise priority key variable, and carrying out weighted summation on the scores of each noise priority key variable according to the weight to obtain a noise grading index; and evaluating the noise according to the noise grading index and the threshold value.
Wherein the road traffic noise grading index (I RN ) Is a number between 0 and 100. The higher the value, the more serious the influence of the road section on the surrounding noise, the higher the priority in the noise control action plan. Because ofHere, the definition can be expressed by the following equation:
in the application, 7 indexes respectively have two monitoring values of daytime and nighttime, so two grading indexes are defined to respectively correspond to the daytime and nighttime data: i RN-D 、I RN-N The application takes the maximum value of the two to consider the quantitative index of road traffic noise pollution, namely:
I RN ＝MAX[I RN-D ，I RN-N ]
in one embodiment, the weights corresponding to the key variables are, as shown in table 9,
table 9 weights corresponding to key variables
The application further discloses a method for setting the weight of the key variable and the noise classification threshold, which comprises the following steps:
(1) Obtaining scores of a plurality of key variables, and dividing the scores into an experience group and a control group;
(2) Initializing the respective corresponding weights; respectively calculating noise grading indexes of the experience group and the control group according to the initialization weight;
(3) Obtaining a positive judgment rate according to the noise classification index of the experience group, and taking the noise classification index corresponding to the positive judgment rate of m% as a threshold value;
(4) And calculating the positive judgment rate of the comparison group according to the threshold value, if the positive judgment rate is less than n%, re-determining weights corresponding to a plurality of key variables, and if the positive judgment rate is more than or equal to n%, outputting a final weight and noise classification threshold value.
In one embodiment, the specific calculation steps are shown in fig. 2, and include:
(1) And (3) data arrangement: the 7 key variable values of each road section are obtained from on-site investigation data and/or numerical simulation results;
(2) Initializing weights: initializing 7 weights according to the current situation of the literature and the urban traffic trunk line;
(3) And (3) calculating: respectively calculating noise grading indexes of an experience group and a control group according to the dividing standard and the grading value of each key variable;
(4) Calculating a threshold value: setting a grading threshold according to 80% of the positive judgment rate of the experience group;
(5) Positive judgment rate judgment of a control group: calculating the positive judgment rate of the control group according to the threshold value, and determining the weight and the threshold value if the positive judgment rate is more than or equal to 90%; if the positive judgment rate is less than 90%, the weight is adjusted, and the steps (3) - (5) are repeated until the positive judgment rate of the comparison group is more than or equal to 90%, and the weight and the threshold are determined.
In addition, the embodiment of the application also discloses a grading evaluation system for urban traffic noise, which comprises the following steps: the key variable scoring module is connected with the noise grading index calculation module and the noise grading evaluation module in sequence;
the key variable scoring module is internally used for storing scoring rules corresponding to the key variables and scoring according to the collected key variable data to obtain scoring results;
the noise grading index calculation module is used for internally storing weights corresponding to the key variables, acquiring the grading result and obtaining a noise grading index according to the weights;
and the noise classification evaluation module is internally preset with a noise classification threshold value and is used for acquiring and evaluating the noise according to the noise classification index and the noise classification threshold value.
In addition, the system also comprises a parameter setting module for setting key variable weights and noise classification thresholds according to the following method.
(1) Obtaining scores of a plurality of key variables, and dividing the scores into an experience group and a control group;
(2) Initializing the respective corresponding weights; respectively calculating noise grading indexes of the experience group and the control group according to the initialization weight;
(3) Obtaining a positive judgment rate according to the noise classification index of the experience group, and taking the noise classification index corresponding to the positive judgment rate of m% as a threshold value;
(4) And calculating the positive judgment rate of the comparison group according to the threshold value, if the positive judgment rate is less than n%, re-determining weights corresponding to a plurality of key variables, and if the positive judgment rate is more than or equal to n%, outputting a final weight and noise classification threshold value.
To further illustrate the above-described determination of weights and thresholds, the present application is described in connection with examples,
selecting 17 key traffic trunks of a certain city to be divided into 286 road sections, wherein the length of each road section is 500 meters, 69 preselected road sections are arranged, 217 complementary road sections are arranged, the preselected road section data are set as experience groups, and the complementary road section data are set as comparison groups;
the 286 road sections selected are classified and calculated according to different evaluation parameters, and the results are shown in table 10.
Table 10 results of the evaluation of the important road section classification
Through analysis and calculation, grading threshold I RN The classification threshold of (2) should be 45, will be I RN The road sections more than or equal to 45 are set as important road sections, the positive judgment rate of the experience group is 84.1%, the positive judgment rate of the comparison group is 97.2%, and the model design requirement is met. The classification evaluation verification results are shown in table 11.
Table 11 Classification evaluation check analysis Table
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. A method for hierarchical assessment of urban traffic noise, comprising:
s1, determining a noise priority key variable based on a road traffic source intensity index and a noise pollution index;
s2, scoring the noise priority key variable to obtain the score of the noise priority key variable;
s3, determining the weight of the noise priority key variable and a noise grading threshold, and obtaining a noise grading index according to the weight and the score of the noise priority key variable; and carrying out grading evaluation on the noise according to the noise grading index and the noise grading threshold.
2. The urban traffic noise classification evaluation method according to claim 1, wherein,
the road traffic source intensity index comprises: hour traffic flow, large vehicle proportion, and/or road traffic noise intensity;
the noise pollution index comprises: noise superscalar exposed population, superscalar sensitive building count, maximum noise superscalar, and/or complaint volume.
3. The urban traffic noise classification evaluation method according to claim 2, wherein said maximum noise superscalar is obtained by the following formula:
ΔL max ＝max(L NM -L std )
where Δlmax: road section maximum noise superscalar, unit: dB (dB); l (L) NM : noise value in dB (a) at 1m outside the window of the sensitive building calculated from the noise map; l (L) std : according to the noise limiting requirement of the acoustic environment functional area of the sensitive building, the unit is as follows: dB (A).
4. The urban traffic noise classification evaluation method according to claim 1, wherein the noise classification index is a maximum value of a daytime classification index and a nighttime classification index.
5. The urban traffic noise classification evaluation method according to claim 1, wherein the process of determining the weights of the noise priority key variables and the noise classification threshold is:
(1) Obtaining scores of a plurality of key variables, and dividing the scores into an experience group and a control group;
(2) Initializing the respective corresponding weights; respectively calculating noise grading indexes of the experience group and the control group according to the initialization weight;
(3) Obtaining a positive judgment rate according to the noise classification index of the experience group, and taking the noise classification index corresponding to the positive judgment rate of m% as a threshold value;
(4) And calculating the positive judgment rate of the comparison group according to the threshold value, if the positive judgment rate is less than n%, re-determining weights corresponding to a plurality of key variables, and if the positive judgment rate is more than or equal to n%, outputting a final weight and noise classification threshold value.
6. An urban traffic noise classification evaluation system, comprising: the key variable scoring module is connected with the noise grading index calculation module and the noise grading evaluation module in sequence;
the key variable scoring module is internally used for storing scoring rules corresponding to the key variables and scoring according to the collected key variable data to obtain scoring results;
the noise grading index calculation module is used for internally storing weights corresponding to the key variables, acquiring the grading result and obtaining a noise grading index according to the weights;
and the noise grading evaluation module is internally preset with a noise grading threshold and is used for acquiring and grading and evaluating the noise according to the noise grading index and the noise grading threshold.
7. The urban traffic noise classification evaluation system according to claim 6, further comprising a parameter setting module for setting key variable weights and noise classification thresholds according to the following method:
(1) Obtaining scores of a plurality of key variables, and dividing the scores into an experience group and a control group;
(2) Initializing the respective corresponding weights; respectively calculating noise grading indexes of the experience group and the control group according to the initialization weight;
(3) Obtaining a positive judgment rate according to the noise classification index of the experience group, and taking the noise classification index corresponding to the positive judgment rate of m% as a threshold value;
(4) And calculating the positive judgment rate of the comparison group according to the threshold value, if the positive judgment rate is less than n%, re-determining weights corresponding to a plurality of key variables, and if the positive judgment rate is more than or equal to n%, outputting a final weight and noise classification threshold value.
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