CN108254069A - Urban road noise Forecasting Methodology - Google Patents
Urban road noise Forecasting Methodology Download PDFInfo
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- CN108254069A CN108254069A CN201810132713.1A CN201810132713A CN108254069A CN 108254069 A CN108254069 A CN 108254069A CN 201810132713 A CN201810132713 A CN 201810132713A CN 108254069 A CN108254069 A CN 108254069A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Abstract
The present invention relates to road traffics and environmental protection technical field, and in particular to urban road noise Forecasting Methodology, its key points of the technical solution are that including the following steps：Establish the noise relationship statistical model based on vehicle vehicle, car speed and total wagon flow equivalent sound level；Select state parameter, by vehicle than, bicycle road mean hours vehicle flowrate, by state parameters input noise relationship statistical models such as each vehicle mean hours flow of future position, future positions to the subtended angle and correction amount for there are limit for length section both ends, calculate each vehicle hour equivalent sound level corresponding with the state parameter and calculate total wagon flow equivalent sound level；Urban road will be generated in the noise relationship statistical model that the larger factor of noise effect is established as state parameter input, and the vehicle in urban road is classified according to vehicle, the accuracy predicted urban road noise is improved, formulation is facilitated to be more suitable for the Noise Prevention and Treatment means of current predictive road.
Description
Technical field
The present invention relates to road traffic and environmental protection technical field, more specifically, it relates to which urban road noise is pre-
Survey method.
Background technology
With China's expanding economy, also getting worse, noise pollution are one kind of environmental pollution to problem of environmental pollution,
A big harm of people's daily life is influenced through becoming.Noise pollution is regarded as with water pollution, atmosphere pollution in world wide
Three main Environmental Problems.
According in People's Republic of China's noise pollution prevention method to the sorting technique of noise, noise pollution it is main
Source includes traffic noise, man-made noise, building noise and noise of social activities, while traffic noise is urban environment noise
Most important source, the prevention to traffic noise are to improve the key task of urban environment noise.It is prevented to traffic noise
When, it needs to carry out noise prediction to urban road, to formulate the means of prevention adapted to.
Invention content
The object of the present invention is to provide urban road noise Forecasting Methodologies, make an uproar to the urban road prevented
Sound is predicted, facilitates the means of prevention formulated and adapted to.
The present invention above-mentioned technical purpose technical scheme is that：Urban road noise prediction side
Method, which is characterized in that include the following steps：
Establish the noise relationship statistical model based on vehicle vehicle, car speed and total wagon flow equivalent sound level；
State parameter is selected, including vehicle than, bicycle road mean hours vehicle flowrate, each vehicle mean hours by future position
Flow, future position to the subtended angle and correction amount for having limit for length section both ends；
By the state parameter input noise relationship statistical model of selection, it is corresponding with the state parameter small to calculate each vehicle
When equivalent sound level；
Total wagon flow equivalent sound level is calculated according to the hour equivalent sound level of each vehicle.
By using above-mentioned technical proposal, using being generated to urban road, the larger factor of noise effect is defeated as state parameter
In the noise relationship statistical model for entering foundation, and the vehicle in urban road is classified according to vehicle, respectively to different vehicles
Noise caused by type is calculated, and improves the accuracy predicted urban road noise, formulation is facilitated to be more suitable for current pre-
Survey the Noise Prevention and Treatment means of road.
Preferably, the computation model of the wherein hour equivalent sound level of i types vehicle is：
Wherein total wagon flow equivalent sound level computation model is：
Wherein Leq（T）For total wagon flow equivalent sound level, Leq（h）The greatly hour equivalent sound level of large car, Leq（h）In be medium-sized
The hour equivalent sound level of vehicle, Leq（h）The small hour equivalent sound level for compact car.
By using above-mentioned technical proposal, equivalent sound level of the different automobile types on different roads is different, according to it is corresponding into
The different automobile types situation of the road of row Noise Prevention and Treatment carries out the calculating of equivalent sound level, then total wagon flow equivalent sound level is calculated,
The accuracy predicted urban road noise is improved, formulation is facilitated to be more suitable for the Noise Prevention and Treatment means of current predictive road.
Preferably, the computation model of the average speed of i types vehicle is：
I types vehicle is in the computation model that horizontal distance is the average sound level at 7.5m：
By using above-mentioned technical proposal, the shadow of speed is predicted in the prediction of the average sound level of different automobile types by corresponding vehicle
It rings, and the prediction speed of vehicle is related with the equivalent vehicle number of corresponding vehicle, can be calculated according to vehicle flowrate, the vehicle ratio of corresponding vehicle
Go out corresponding equivalent vehicle number, and then the average sound level of different automobile types can be calculated, improve prediction accuracy.
Preferably, the computation model of wherein correction amount is：
By using above-mentioned technical proposal, the route, Acoustic Wave Propagation approach and reflection of urban road are introduced to urban road
The influence of noise is modified urban road noise prediction, improves prediction accuracy, formulation is facilitated to be more suitable for current predictive
The Noise Prevention and Treatment means of road.
Preferably, the computation model of wherein road longitudinal grade correction amount is：
Wherein β is the road longitudinal grade gradient；
When road is asphalt concrete pavement, correction amount caused by Expressway Pavement Material is 0dB, and road is cement concrete pavement
And car speed be less than 30km/h when, correction amount caused by Expressway Pavement Material be 1dB, road be cement concrete pavement and vehicle
When speed is 30-50km/h, correction amount caused by Expressway Pavement Material is 1.5dB, and road is cement concrete pavement and vehicle
When speed is more than 50km/h, correction amount caused by Expressway Pavement Material is 2dB.
By using above-mentioned technical proposal, for different vehicles, the different road longitudinal grade gradients and Expressway Pavement Material
Correction amount caused by generated lane factor is also different, for different automobile types, in conjunction with the road longitudinal grade gradient of corresponding road
The calculating of correction amount caused by carrying out lane factor with Expressway Pavement Material, improves prediction accuracy.
Preferably, the computation model of wherein barrier attenuation is：
Wherein f is frequency of sound wave, and c is the velocity of sound, and δ is path difference；
The computation model of attenuation is caused by air absorbs：
By using above-mentioned technical proposal, for different vehicles, different frequency of sound wave, path difference and air absorb system
Caused attenuation is also different in Acoustic Wave Propagation approach caused by number, for different automobile types, in conjunction with the sound wave of corresponding road
Frequency, path difference and air absorption coefficient carry out the calculating of caused attenuation in Acoustic Wave Propagation approach, improve prediction accuracy.
Preferably, wherein the correction amount as caused by reflection includes urban road junction noise correction amount and both sides are built
The reflected sound correction amount of object；
When the distance of point affected by noise to nearest fast traffic lane central axes crosspoint is less than 40m, urban road junction is made an uproar
Sound correction amount is 3dB, when the distance of point affected by noise to nearest fast traffic lane central axes crosspoint is 40-70m, urban road
Intersection noise correction amount is 2dB, when the distance of point affected by noise to nearest fast traffic lane central axes crosspoint is 70-100m
When, urban road junction noise correction amount be 1dB, when point affected by noise to nearest fast traffic lane central axes crosspoint away from
During from more than 100m, urban road junction noise correction amount is 0dB；
When both sides of the road distance between buildings is less than total computed altitude 30%, reflected sound correction amount is：
By using above-mentioned technical proposal, for different roads, urban road junction noise correction amount and both sides
The reflected sound correction amount of building is also different, the reflection to different cities road junction noise correction amount and both sides building
Sound correction amount is calculated, and calculates the correction amount as caused by reflection, improves prediction accuracy.
Preferably, set sampled point, sampled point between the vehicle ratio of i type vehicles, bicycle road mean hours vehicle flowrate, daytime,
Night is acquired, and conduct by i type vehicles mean hours flow, the sampled point of sampled point to the subtended angle for having limit for length section both ends
State parameter.
By using above-mentioned technical proposal, in sampled point to pair of the total required state parameter of wagon flow equivalent sound level of calculating
As parameter is acquired, predetermined speed is improved.
In conclusion the invention has the advantages that：Sampled point is set, and equivalent to calculating total wagon flow in sampled point
The state parameter that sound level generates large effect is acquired, and is passed according to the route of different vehicle and urban road, sound wave
It broadcasts the road conditions such as approach and reflection and is calculated and corrected to calculating total wagon flow equivalent sound level, improve pre- to urban road noise
The accuracy of survey, the Noise Prevention and Treatment means to formulate current predictive road provide more accurately reference data.
Description of the drawings
Fig. 1 is the flow diagram of the present embodiment；
Fig. 2 is the present embodiment future position to the subtended angle schematic diagram for having limit for length section both ends.
Specific embodiment
The present invention is described in further detail below in conjunction with attached drawing.
Specific examples below is only explanation of the invention, is not limitation of the present invention, art technology
Personnel can as needed make the present embodiment the modification of no creative contribution after this specification is read, but as long as
It is all protected in the right invented below by Patent Law.
Urban road noise Forecasting Methodology, as shown in Figure 1, including the following steps：
Sampled point is set in the place influenced by road noise, the acquisition of state parameter is carried out in sampled point；
Establish the noise relationship statistical model based on vehicle vehicle, car speed and total wagon flow equivalent sound level；
State parameter is selected, including vehicle than, bicycle road mean hours vehicle flowrate, each vehicle mean hours by future position
Flow, future position to the subtended angle and correction amount for having limit for length section both ends, wherein future position are overlapped with sampled point；
By the state parameter input noise relationship statistical model of selection, each vehicle hour corresponding with state parameter etc. is calculated
Imitate sound level；
Total wagon flow equivalent sound level is calculated according to the hour equivalent sound level of each vehicle.
Large car, in-between car and compact car will be divided into, and be referred to as i type vehicles by the vehicle of road according to vehicle size.
Wherein state parameter further include i types vehicle horizontal distance at 7.5m average sound level, from lane center to prediction
The distance of point, frequency of sound wave, path difference, air absorption coefficient, the distance of reference position, sampled point to nearest fast traffic lane central axes
Distance, the spacing of both sides of the road building reflecting surface and the average height of structures in crosspoint.
Noise relationship statistical model based on vehicle vehicle, car speed and total wagon flow equivalent sound level is：
Wherein Leq（T）For total wagon flow equivalent sound level, Leq（h）The greatly hour equivalent sound level of large car, Leq（h）In be medium-sized
The hour equivalent sound level of vehicle, Leq（h）The small hour equivalent sound level for compact car.
For i type vehicles, the computation model of hour equivalent sound level is：
In the computation model of the hour equivalent sound level of above-mentioned i types vehicle, i types vehicle is the average sound level at 7.5m in horizontal distance
Computation model be：
For different vehicles, the average speed on different roads is different, the meter of the average speed of i type vehicles
Calculating model is：
1 speed of table calculates equation coefficients
| Vehicle | k 1 | k 2 | k 3 | k 4 | m i |
| Compact car | -0.061748 | 149.65 | -0.000023696 | -0.02099 | 1.2102 |
| In-between car | -0.057537 | 149.38 | -0.000016390 | -0.01245 | 0.8044 |
| Large car | -0.051900 | 149.39 | -0.000014202 | -0.01254 | 0.70957 |
It is the average sound level at 7.5m that the average speed of i type vehicles according to calculating, which calculates i types vehicle in horizontal distance,.
Correction amount in the computation model of the hour equivalent sound level of above-mentioned i types vehicle is made of 3 parts, is drawn including lane factor
The correction amount that rises, in Acoustic Wave Propagation approach caused attenuation and the correction amount as caused by reflection, i.e. correction amount computation model
For：
For correction amount caused by lane factor, including being corrected caused by road longitudinal grade correction amount and Expressway Pavement Material
Amount, for different vehicles, the correction amount on the road of the different road longitudinal grade gradients has differences, and the road of different automobile types is indulged
The computation model of slope correction amount is specially：
Wherein β is the road longitudinal grade gradient.
For correction amount caused by Expressway Pavement Material, when road is asphalt concrete pavement, Expressway Pavement Material causes
Correction amount for 0dB, when road is less than 30km/h for cement concrete pavement and car speed, repaiied caused by Expressway Pavement Material
Positive quantity is 1dB, when road is cement concrete pavement and car speed is 30-50km/h, is corrected caused by Expressway Pavement Material
It measures as 1.5dB, when road is more than 50km/h for cement concrete pavement and car speed, correction amount caused by Expressway Pavement Material
For 2dB.
Caused attenuation includes attenuation and barrier attenuation caused by air absorbs in Acoustic Wave Propagation approach, hollow
The computation model of attenuation is caused by aspiration：
The computation model of barrier attenuation is：
Wherein f is frequency of sound wave, and c is the velocity of sound, and δ is path difference.
Attenuation caused by air is absorbed is added with barrier attenuation can obtain caused in Acoustic Wave Propagation approach decline
Decrement.
Correction amount includes the anti-of urban road junction noise correction amount and both sides building wherein as caused by reflection
Penetrate sound correction amount：
For urban road junction noise correction amount, when point affected by noise to nearest fast traffic lane central axes crosspoint away from
During from less than 40m, urban road junction noise correction amount is 3dB, when point affected by noise to nearest fast traffic lane central axes
When the distance in crosspoint is 40-70m, urban road junction noise correction amount is 2dB, when point affected by noise is to fast recently
When the distance in track central axes crosspoint is 70-100m, urban road junction noise correction amount is 1dB, when by noise shadow
When the distance for ringing point to nearest fast traffic lane central axes crosspoint is more than 100m, urban road junction noise correction amount is 0dB.
For the reflected sound correction amount of both sides building, when both sides of the road distance between buildings is less than total computed altitude 30%,
Its reflected sound correction amount is：
The noise relationship statistics established the larger factor of noise effect is generated to urban road as state parameter input
In model, and the vehicle in urban road is classified according to vehicle, making an uproar according to caused by different automobile types in corresponding road
Correction amount caused by sound and other environmental factors predicts the equivalent sound level of total wagon flow, improves pre- to urban road noise
The accuracy of survey facilitates formulation to be more suitable for the Noise Prevention and Treatment means of current predictive road.
Claims (8)
1. urban road noise Forecasting Methodology, which is characterized in that include the following steps：
Establish the noise relationship statistical model based on vehicle vehicle, car speed and total wagon flow equivalent sound level；
State parameter is selected, including vehicle than, bicycle road mean hours vehicle flowrate, each vehicle mean hours by future position
Flow, future position to the subtended angle and correction amount for having limit for length section both ends；
By the state parameter input noise relationship statistical model of selection, it is corresponding with the state parameter small to calculate each vehicle
When equivalent sound level；
Total wagon flow equivalent sound level is calculated according to the hour equivalent sound level of each vehicle.
2. urban road noise Forecasting Methodology according to claim 1, which is characterized in that
The computation model of the wherein hour equivalent sound level of i types vehicle is：
,
WhereinFor the hour equivalent sound level of i type vehicles,In horizontal distance it is the average sound level at 7.5m for i types vehicle,
For the i type vehicle mean hours flows by future position, r is the distance from lane center to future position,Average vehicle for i type vehicles
Speed, T are the time for calculating equivalent sound level,、For future position to the subtended angle for having limit for length section both ends,For correction amount；
Wherein total wagon flow equivalent sound level computation model is：
,
Wherein Leq（T）For total wagon flow equivalent sound level, Leq（h）The greatly hour equivalent sound level of large car, Leq（h）In be medium-sized
The hour equivalent sound level of vehicle, Leq（h）The small hour equivalent sound level for compact car.
3. urban road noise Forecasting Methodology according to claim 2, which is characterized in that the meter of the average speed of i type vehicles
Calculating model is：
,
WhereinFor the prediction speed of i type vehicles,For the equivalent vehicle number of i type vehicles,For the vehicle ratio of i type vehicles, vol is bicycle road
Mean hours vehicle flowrate,For the weighting coefficient of i type vehicles, k1, k2, k3, k4 are design factor；
I types vehicle is in the computation model that horizontal distance is the average sound level at 7.5m：
。
4. urban road noise Forecasting Methodology according to claim 2, which is characterized in that the wherein computation model of correction amount
For：
,
WhereinFor correction amount caused by lane factor,For caused attenuation in Acoustic Wave Propagation approach,For by anti-
Caused correction amount is penetrated,For road longitudinal grade correction amount,For correction amount caused by Expressway Pavement Material,For sky
Decay caused by aspiration,For barrier attenuation.
5. urban road noise Forecasting Methodology according to claim 4, which is characterized in that wherein road longitudinal grade correction amount
Computation model is：
,
Wherein β is the road longitudinal grade gradient；
When road is asphalt concrete pavement, correction amount caused by Expressway Pavement Material is 0dB, and road is cement concrete pavement
And car speed be less than 30km/h when, correction amount caused by Expressway Pavement Material be 1dB, road be cement concrete pavement and vehicle
When speed is 30-50km/h, correction amount caused by Expressway Pavement Material is 1.5dB, and road is cement concrete pavement and vehicle
When speed is more than 50km/h, correction amount caused by Expressway Pavement Material is 2dB.
6. urban road noise Forecasting Methodology according to claim 4, which is characterized in that the wherein meter of barrier attenuation
Calculating model is：
,
Wherein f is frequency of sound wave, and c is the velocity of sound, and δ is path difference；
The computation model of attenuation is caused by air absorbs：
,
Wherein a is air absorption coefficient,Distance for reference position.
7. urban road noise Forecasting Methodology according to claim 4, which is characterized in that corrected wherein as caused by reflection
Amount includes urban road junction noise correction amount and the reflected sound correction amount of both sides building；
When the distance of point affected by noise to nearest fast traffic lane central axes crosspoint is less than 40m, urban road junction is made an uproar
Sound correction amount is 3dB, when the distance of point affected by noise to nearest fast traffic lane central axes crosspoint is 40-70m, urban road
Intersection noise correction amount is 2dB, when the distance of point affected by noise to nearest fast traffic lane central axes crosspoint is 70-100m
When, urban road junction noise correction amount be 1dB, when point affected by noise to nearest fast traffic lane central axes crosspoint away from
During from more than 100m, urban road junction noise correction amount is 0dB；
When both sides of the road distance between buildings is less than total computed altitude 30%, reflected sound correction amount is：
When both sides building is reflecting surface：≤ 3.2dB,
When both sides building is general absorbent surfaces：≤ 3.2dB,
When both sides building is hypersorption surface：,
Wherein w is the spacing of both sides of the road building reflecting surface,Average height for structures.
8. urban road noise Forecasting Methodology according to claim 3, which is characterized in that setting sampled point, in sampled point
Between the vehicles of i type vehicles than, bicycle road mean hours vehicle flowrate, daytime, night by the i type vehicles mean hours flow of sampled point,
Sampled point is acquired to the subtended angle for having limit for length section both ends, and as state parameter.
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| CN109405962A (en) * | 2018-11-21 | 2019-03-01 | 中山大学 | A kind of road traffic noise frequency spectrum calculation method |
| CN109785621A (en) * | 2019-02-02 | 2019-05-21 | 重庆港力环保股份有限公司 | A kind of road traffic noise intelligent optimized control method relying on big data |
| CN111428285A (en) * | 2020-03-12 | 2020-07-17 | 深圳小库科技有限公司 | Noise evaluation method and device and storage medium |
| CN111504451A (en) * | 2020-04-29 | 2020-08-07 | 湖南建工集团有限公司 | Method and system for judging standard exceeding of environmental noise emission of construction site |
| CN111696369A (en) * | 2020-04-10 | 2020-09-22 | 北京数城未来科技有限公司 | Whole-city road time-division vehicle type traffic flow prediction method based on multi-source geographic space big data |
| CN111753432A (en) * | 2020-07-01 | 2020-10-09 | 合肥学院 | Noise reduction method for urban tramcar sound |
| CN112071073A (en) * | 2020-09-18 | 2020-12-11 | 上海市环境科学研究院 | Road noise automatic correction method of noise map system |
| CN112233428A (en) * | 2020-10-10 | 2021-01-15 | 腾讯科技（深圳）有限公司 | Traffic flow prediction method, traffic flow prediction device, storage medium and equipment |
| CN113622332A (en) * | 2021-09-17 | 2021-11-09 | 无锡希格声声学科技有限公司 | Vibration and noise reduction method based on overhead traffic flow squeal outside building |
| CN114973657A (en) * | 2022-05-12 | 2022-08-30 | 中南大学 | Urban traffic noise pollution analysis and evaluation method based on trajectory data |
| CN116186865A (en) * | 2023-04-25 | 2023-05-30 | 成都欢聚堂科技有限公司 | Noise evaluation method, device and storage medium |
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| CN111696369A (en) * | 2020-04-10 | 2020-09-22 | 北京数城未来科技有限公司 | Whole-city road time-division vehicle type traffic flow prediction method based on multi-source geographic space big data |
| CN111504451A (en) * | 2020-04-29 | 2020-08-07 | 湖南建工集团有限公司 | Method and system for judging standard exceeding of environmental noise emission of construction site |
| CN111753432A (en) * | 2020-07-01 | 2020-10-09 | 合肥学院 | Noise reduction method for urban tramcar sound |
| CN112071073A (en) * | 2020-09-18 | 2020-12-11 | 上海市环境科学研究院 | Road noise automatic correction method of noise map system |
| CN112071073B (en) * | 2020-09-18 | 2021-08-17 | 上海市环境科学研究院 | Road noise automatic correction method of noise map system |
| CN112233428A (en) * | 2020-10-10 | 2021-01-15 | 腾讯科技（深圳）有限公司 | Traffic flow prediction method, traffic flow prediction device, storage medium and equipment |
| CN112233428B (en) * | 2020-10-10 | 2023-09-22 | 腾讯科技（深圳）有限公司 | Traffic flow prediction method, device, storage medium and equipment |
| CN113622332A (en) * | 2021-09-17 | 2021-11-09 | 无锡希格声声学科技有限公司 | Vibration and noise reduction method based on overhead traffic flow squeal outside building |
| CN113622332B (en) * | 2021-09-17 | 2023-12-29 | 阿贝龙(北京)智能科技有限公司 | Vibration and noise reduction method based on overhead traffic flow howling outside building |
| CN114973657A (en) * | 2022-05-12 | 2022-08-30 | 中南大学 | Urban traffic noise pollution analysis and evaluation method based on trajectory data |
| CN116186865A (en) * | 2023-04-25 | 2023-05-30 | 成都欢聚堂科技有限公司 | Noise evaluation method, device and storage medium |
| CN116186865B (en) * | 2023-04-25 | 2023-07-04 | 成都欢聚堂科技有限公司 | Noise evaluation method, device and storage medium |
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