A road traffic noise prediction method based on a cellular transmission model

Abstract

The application relates to the technical field of road noise prediction, and discloses a road traffic noise prediction method based on a cellular transmission model, which comprises the following steps: dividing a road into cells, and homogenizing vehicles in the same cell; constructing a motion characteristic data prediction model according to the number of vehicle exchanges of adjacent cells and the average speed of vehicles in the cells; calculating motion characteristic data of vehicles in the cells at the next moment according to the characteristic data prediction model, wherein the motion characteristic data comprises the number of vehicles in the cells, the average speed of the vehicles and the acceleration of the vehicles; obtaining noise data of each cell at a standard distance according to the motion characteristic data and a motor vehicle noise emission model; performing distance attenuation on the noise data of the cells at an effective distance; superimposing vehicle noises in the form of cells to obtain noise values of the cells; and realizing road scale traffic noise prediction, which can effectively solve the problem of prediction accuracy while taking into account the scale of road traffic noise research.

Description

Technical Field

[0001] The present invention relates to the technical field of road noise prediction, and in particular to a road traffic noise prediction method based on a cellular transmission model. Background Art

[0002] With the accelerating pace of urbanization, road traffic noise has become a major source of urban environmental noise, significantly impacting residents' quality of life, health, and the urban ecological environment. Traditional methods for predicting road traffic noise rely primarily on on-site monitoring and statistical regression analysis. While these methods can provide estimates of noise levels to a certain extent, they have numerous limitations. For example, on-site monitoring requires significant human and material resources, and data acquisition is time-consuming and costly. Statistical regression analysis relies on extensive historical data and struggles to accurately reflect the immediate impact of dynamic changes in traffic flow on noise levels.

[0003] Furthermore, traditional prediction methods often overlook the complexity and dynamics of road traffic systems, such as vehicle interactions, nonlinear changes in traffic flow, and the diversity of road structures. These factors limit the accuracy and reliability of noise prediction results. Therefore, it is crucial to develop a method that can be applied to road-scale traffic noise prediction and effectively address the issue of prediction accuracy while taking into account the scale of road traffic noise research. Summary of the Invention

[0004] In response to the above-mentioned defects, the purpose of the present invention is to propose a road traffic noise prediction method based on the cellular transmission model to achieve road-scale traffic noise prediction, which can effectively solve the prediction accuracy problem while taking into account the research scale of road traffic noise.

[0005] To achieve this object, the present invention adopts the following technical solutions:

[0006] A road traffic noise prediction method based on a cellular transmission model includes:

[0007] Divide the road into cellular states and homogenize the vehicles in the same cell;

[0008] A motion feature data prediction model is constructed based on the number of vehicle exchanges between adjacent cells and the average speed of vehicles within the cell. The motion feature data of the vehicles within the cell at the next moment is calculated based on the feature data prediction model. The motion feature data includes the number of vehicles within the cell, the average speed of the vehicles, and the acceleration of the vehicles.

[0009] Based on the motion characteristic data and the motor vehicle noise emission model, the noise data of each cell at the standard distance is obtained. The noise data of the cells at the effective distance is attenuated by distance, and the vehicle noise is superimposed with the cell as the basic unit to obtain the noise value of each cell to complete the prediction of road traffic noise.

[0010] Preferably, dividing the road into cellular states and homogenizing vehicles within the same cell includes:

[0011] Obtain the structure of the road to be predicted, divide the road into several equal-length cells that satisfy discrete stability, and set the average speed of vehicles in the same cell to be the same;

[0012] The cell at the ramp entrance is set as a diversion cell, the cell at the ramp exit is set as a confluence cell, and the remaining cells are set as ordinary cells. The diversion cell has ordinary cells and ramp cells connected to it downstream, and the confluence cell has ordinary cells and ramp cells connected to it upstream. The ordinary cell has one cell connected to it upstream and downstream.

[0013] Preferably, along the traffic flow direction of the road to be predicted, the upstream cell of the first cell and the downstream cell of the last cell are set as virtual cells, and the motion characteristic data of the vehicles in the virtual cells are the same as the motion characteristic data of the vehicles in the adjacent cells.

[0014] Preferably, the motion feature data prediction model constructed according to the number of vehicle exchanges between adjacent cells includes a cellular vehicle exchange model;

[0015] The cellular vehicle exchange model calculates the number of vehicles in the cell at the current moment by adding the number of vehicles in the cell at the previous moment to the outflow of the upstream cell and the inflow of the downstream cell;

[0016] The cellular vehicle exchange model of the converging cell i satisfies the relationship:

[0017]

[0018]

[0019] Among them, t represents a certain moment, t+1 represents the next moment, i represents the current confluent cell, i o is the upstream ramp cell, i-1 is the upstream ordinary cell, f t,i is the actual outflow of the upstream common cell, is the actual outflow of the upstream ramp cell, f t,i+1 is the actual inflow of the downstream cell, S t,i-1 is the sending capacity of the upstream common cell, is the sending capacity of the upstream ramp cell, p io and pi They represent the confluence ratio of upstream ramp cells and ordinary cells, R t,i is the maximum traffic flow that the current cell can accept, N t,i is the number of vehicles in the cell at time t, N t+1,i is the number of vehicles in the cell at the next moment;

[0020] The cellular vehicle exchange model of the diversion cell i satisfies the relationship:

[0021]

[0022]

[0023] Among them, N t,i is the number of vehicles in the cell at time t, N t+1,i is the number of vehicles in the cell at the next moment, f t,i+1 is the actual inflow of the downstream common cell, is the actual inflow of the downstream ramp cell, f t,i is the actual outflow of the upstream common cell, S t,i is the sending capacity of the shunt cell, p io and p i They represent the diversion ratio of downstream ramp cells and ordinary cells, R t,i+1 is the maximum traffic flow that can be accepted by the downstream ordinary cell, R t,io+1 is the maximum traffic flow that the downstream ramp cell can accept, t represents a certain moment, t+1 represents the next moment, i represents the current diversion cell, i o +1 is the downstream ramp cell, and i+1 is the downstream ordinary cell.

[0024] Preferably, the motion feature data prediction model constructed based on the average speed of vehicles in the cell includes a cellular speed model;

[0025] The cell velocity model of ordinary cell i satisfies the relationship:

[0026]

[0027] The cell velocity model of confluent cell i satisfies the relationship:

[0028]

[0029]

[0030]

[0031] The cell velocity model of the shunt cell i satisfies the relationship:

[0032]

[0033]

[0034] Among them, v t+1,i is the average speed of the vehicles in cell i at the next moment, v t,i is the average speed of vehicles in cell i at time t, f t,i is the actual outflow of the upstream common cell of cell i at time t, ρ t,i is the density of vehicles in cell i at time t, v t,i-1 is the average speed of the vehicles in the upstream common cell at time t, Δt is the time interval, and τ is the expected speed V (ρ t,i ) is the time required, β is the downstream expected adjustment coefficient, ρ s is the comprehensive density evaluation parameter of the downstream of the diversion cell, ρ t,i+1 is the density of vehicles in the downstream common cell of cell i at time t, is the density of vehicles in the downstream ramp cell of cell i at time t, K i+1 and They are respectively expressed as the proportion of vehicles flowing from the diversion cell to the ordinary cell and ramp cell downstream, is the average speed of vehicles in the upstream ramp cell at time t, v c is the calculation speed of the fusion weight of the upstream cell, is the actual outflow of the upstream ramp cell of cell i at time t, ρ t,i-1 is the density of vehicles in the upstream common cell of cell i at time t, is the density of vehicles in the upstream ramp cell of cell i at time t, and ε is the confluence reduction coefficient.

[0035] Preferably, the motion feature data prediction model constructed based on the number of vehicle exchanges between adjacent cells and the average speed of vehicles within the cell includes a cellular spatiotemporal acceleration model;

[0036] The cellular space-time acceleration model satisfies the expression:

[0037]

[0038]

[0039]

[0040]

[0041] Among them, v t+1,i is the average speed of the vehicles in cell i at the next moment, v t,i is the average speed of vehicles in cell i at time t, N t,iis the number of vehicles in the cell at time t, f t,i is the actual outflow of the upstream common cell of cell i at time t, is the time acceleration, is the number of vehicles in the first part of the cell, v t+1,i+1 is the average speed of the vehicles in the downstream cell at the next moment, Δt is the time interval, is the spatial acceleration, is the number of vehicles in the last part of the cell, is the space-time average acceleration.

[0042] Preferably, the calculating the acceleration of the vehicle in the cell at the next moment according to the feature data prediction model includes:

[0043] Calculating the spatiotemporal average acceleration based on the cellular spatiotemporal acceleration model, using the spatiotemporal average acceleration as the mean of the cellular acceleration distribution in the road, and establishing the acceleration distribution function of the cellular road;

[0044] The acceleration of each cell in the road is calculated according to the acceleration distribution function of the cellular road, and the acceleration of the vehicle in each cell is calculated using Monte Carlo simulation;

[0045] The acceleration distribution function of the cellular road satisfies the relationship:

[0046]

[0047] Among them, the variance and mean are r and s are fitting parameter coefficients, v t,i is the average speed of vehicles in cell i at time t, is the spatiotemporal average acceleration, σ is the standard deviation of the acceleration distribution function, and μ is the mean of the acceleration distribution function.

[0048] Preferably, the noise data of each cell at a standard distance is obtained based on the motion characteristic data and the vehicle noise emission model, the noise data of the cell at an effective distance is subjected to distance attenuation, and the vehicle noise is superimposed using the cell as a basic unit to obtain the noise value of each cell.

[0049] Construct a motor vehicle noise emission model for each cell, which is the same as the number of vehicles in the cell. The motor vehicle noise emission model outputs the noise value of each vehicle in the cell according to the average speed and acceleration of the vehicles in the cell.

[0050] The noise value of each vehicle in each cell is superimposed to obtain the noise value of each cell at the marked distance. The noise value of each cell is distance-attenuated according to the distance between each cell and the detection point. The noise value after distance attenuation is superimposed with the equivalent sound level to obtain the noise value of each cell.

[0051] Preferably, before calculating the motion characteristic data of the vehicle in the cell at the next moment according to the characteristic data prediction model, the method includes: inputting traffic flow demand to the first cell along the traffic flow direction of the road to be predicted.

[0052] One of the above technical solutions has the following advantages or beneficial effects:

[0053] By dividing the road into multiple cells and treating the vehicle states within each cell as homogeneous, this scheme can more precisely simulate the dynamic changes in road traffic flow, taking into account the interactions between vehicles and the nonlinear changes in traffic flow, thereby significantly improving the accuracy of noise prediction. By constructing a motion characteristic data prediction model, the motion characteristic data of cellular road traffic vehicles, such as average speed, number of vehicles, and acceleration distribution, can be calculated and updated in real time. This data provides real-time and accurate input for noise prediction, making the prediction results more accurate to the actual situation. By discretizing the complex road traffic system into a number of cells and assuming that the vehicle states within each cell are the same, the calculation process is simplified and the computational cost is reduced, making the scheme more efficient and feasible in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0055] Figure 1 This is a flow chart of the road traffic noise prediction method based on the cellular transmission model of the present application;

[0056] Figure 2 This is a cell partitioning flowchart of the road traffic noise prediction method based on the cellular transmission model of this application;

[0057] Figure 3 This is a data calculation flow chart of the road traffic noise prediction method based on the cellular transmission model of this application;

[0058] Figure 4 This is a cellular noise prediction flow chart of the road traffic noise prediction method based on the cellular transmission model of the present application;

[0059] Figure 5 This is a cell partitioning structure diagram of the road traffic noise prediction method based on the cellular transmission model of the present application;

[0060] Figure 6This is a ramp cellular structure diagram of the road traffic noise prediction method based on the cellular transmission model of this application;

[0061] Figure 7 This is a structural diagram between detection points and cells in the road traffic noise prediction method based on the cellular transmission model of this application;

[0062] Figure 8 Schematic diagram of the structure of the first and last cells and virtual cells of the road traffic noise prediction method based on the cellular transmission model of the present application;

[0063] Figure 9 This is a noise detection diagram of the road traffic noise prediction method based on the cellular transmission model of this application. DETAILED DESCRIPTION

[0064] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0065] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "axial", "radial", "circumferential" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0066] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0067] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood broadly. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0068] like Figure 1 As shown, a preferred embodiment of the present application is a road traffic noise prediction method based on a cellular transmission model, comprising the steps of:

[0069] S1: Divide the road into cellular states and homogenize the vehicles in the same cell;

[0070] S2: Build a motion feature data prediction model based on the number of vehicle exchanges between adjacent cells and the average speed of vehicles within the cell, and calculate the motion feature data of the vehicles within the cell at the next moment based on the feature data prediction model. The motion feature data includes the number of vehicles within the cell, the average speed of the vehicles, and the acceleration of the vehicles.

[0071] S3: Based on the motion characteristic data and the motor vehicle noise emission model, the noise data of each cell at the standard distance is obtained. The noise data of the cells at the effective distance is attenuated by distance, and the vehicle noise is superimposed with the cell as the basic unit to obtain the noise value of each cell to complete the prediction of road traffic noise.

[0072] Specifically, in step S1, the road is divided into multiple cells, and the vehicles in each cell are considered to be homogeneous, that is, they have similar driving characteristics and speeds. By dividing the road into cells, the complex road system is simplified, so that subsequent motion characteristics and noise prediction can be carried out more effectively.

[0073] In step S2, based on the number of vehicle exchanges between adjacent cells and the average speed of vehicles in the cell, a prediction model is established to calculate the motion characteristic data of vehicles in the cell at the next moment. The motion characteristic data includes the number of vehicles, average speed and acceleration, which reflect the driving status of the vehicles on the road.

[0074] In step S3, the obtained motion feature data is converted into traffic noise data at a standard distance using a single-vehicle noise model. Based on the distance from the detection point to the single vehicle, the effective distance is screened for point source noise attenuation calculation and superposition. Road traffic noise prediction is completed using the cellular road as the basic unit. Through this step, the noise contribution of each cell can be quantitatively estimated, taking into account the distance attenuation effect, making the prediction closer to the actual situation.

[0075] Furthermore, if Figure 2 and Figure 5 As shown, the cellular division of the road and the homogenization of vehicles in the same cell include:

[0076] S101: Obtain the structure of the road to be predicted, divide the road into a number of cells of equal length that satisfy discrete stability, and set the average speed of vehicles in the same cell to be the same;

[0077] S102: Set the cell at the ramp entrance as a diverging cell, the cell at the ramp exit as a merging cell, and the remaining cells as ordinary cells. The downstream of the diverging cell is connected to an ordinary cell and a ramp cell, the upstream of the merging cell is connected to an ordinary cell and a ramp cell, and the ordinary cell has one cell connected to it upstream and downstream.

[0078] Specifically, in step S101, by obtaining the structural information of the road to be predicted, the road is divided into cells of equal length. These cells meet the discrete stability requirements, so that the vehicle driving state in each cell can be stably described and predicted, and the average speed of the vehicles in the cell is the same, which facilitates model establishment and data analysis.

[0079] In step S102, based on the road division, cell types are determined, such as diverging cells (ramp entrances), merging cells (ramp exits), and ordinary cells (main roads). Diverging cells handle ramp vehicle inflows, merging cells handle ramp vehicle outflows, and ordinary cells describe main roads. Cell connectivity is determined. For example, a diverging cell is connected to an ordinary cell and a ramp cell downstream, and an ordinary cell upstream; a merging cell is connected to an ordinary cell and a ramp cell upstream, and an ordinary cell downstream; and an ordinary cell is connected to one cell upstream and one downstream. This connectivity ensures accurate simulation of vehicle flow paths and directions, provides data support for the model, and ensures correct simulation of vehicles in the road network. The determination of connectivity enables the model to accurately simulate vehicle flow and intersections, providing accurate data support for subsequent simulations and predictions.

[0080] Furthermore, along the traffic flow direction of the road to be predicted, the upstream cell of the first cell and the downstream cell of the last cell are set as virtual cells, and the motion feature data of the vehicles in the virtual cells are the same as the motion feature data of the vehicles in the adjacent cells.

[0081] Specifically, if Figure 8 As shown in the figure, within the traffic flow direction of the road being predicted, virtual cells are identified as cells upstream of the first cell and downstream of the last cell. The vehicle motion characteristic data within the virtual cells is identical to that within the adjacent actual cells. The motion characteristics of the virtual cells are not directly calculated. Instead, they are used to connect actual cells in the model, maintaining data continuity and compensating for noise differences in gaps. The virtual cell configuration ensures data consistency and stability across the entire road area, reducing model prediction errors.

[0082] Furthermore, the motion feature data prediction model constructed based on the number of vehicle exchanges between adjacent cells includes a cellular vehicle exchange model;

[0083] The cellular vehicle exchange model calculates the number of vehicles in the cell at the current moment by adding the number of vehicles in the cell at the previous moment to the outflow of the upstream cell and the inflow of the downstream cell;

[0084] The cellular vehicle exchange model of the converging cell i satisfies the relationship:

[0085]

[0086]

[0087] The cellular vehicle exchange model of the diversion cell i satisfies the relationship:

[0088]

[0089]

[0090] like Figure 6 As shown, i is the current cell, i-1 is the upstream ordinary cell, i+1 is the downstream cell. If i-1 is the current shunt cell, then i is the ordinary cell downstream of the shunt cell, i o is the ramp cell downstream of the diverging cell; if i is the current converging cell, then i-1 is the ordinary cell upstream of the converging cell, i o -1 is the ramp cell upstream of the merging cell. For vehicle flow between ordinary cells, three basic factors are considered: outflow from the previous cell, inflow into the next cell, and changes in vehicle density. In the case of merging cells, the model needs to consider the outflow of vehicles from upstream ordinary cells and ramp cells, as well as the inflow of vehicles into downstream cells, and use these quantities to calculate the change in the number of vehicles within the merging cell.

[0091] In the case of diverging cells, the model considers the number of vehicles flowing out of upstream cells and the number of vehicles flowing into downstream ordinary cells and ramp cells, and also calculates the change in the number of vehicles within the diverging cells. By accurately calculating the inflow and outflow volume and changes in vehicle density, it can more accurately predict vehicle flow and congestion on the road. The model uses a time-stepping method to ensure the continuity of the traffic flow model at different times, avoiding sudden changes and discontinuities in the prediction.

[0092] Furthermore, the motion characteristic data prediction model constructed based on the average speed of the vehicles in the cell includes a cell speed model;

[0093] The cell velocity model of ordinary cell i satisfies the relationship:

[0094]

[0095] The cell velocity model of confluent cell i satisfies the relationship:

[0096]

[0097]

[0098]

[0099] The cell velocity model of the shunt cell i satisfies the relationship:

[0100]

[0101]

[0102] Specifically, the cell velocity model of an ordinary cell describes the velocity change of an ordinary cell, taking into account the vehicle density of the current cell, the outflow, the relationship between the vehicle speed and density of the upstream and downstream cells, and the adjustment of the difference between the expected speed and the actual speed (through the parameters τ and V(ρ t,i )). β is an adjustment factor used to adjust the speed of the current cell when the downstream traffic density changes.

[0103] The cellular velocity model of the confluence cell describes the velocity change of the confluence cell, taking into account not only the influence of ordinary cells but also the influence of ramp cells. The velocity v is calculated by the confluence reduction coefficient ε and the fusion weight of the upstream cell. c To adjust the speed of the current cell, this allows the merging cell to more accurately reflect the traffic flow during merging.

[0104] The cell velocity model of the diversion cell describes the velocity change of the diversion cell, which takes into account the downstream comprehensive density evaluation parameter ρ s By comprehensively considering the vehicle density ratio of ordinary cells and ramp cells, the speed of the current cell is adjusted to reflect the traffic flow under the diversion situation, K i+1 and They are respectively expressed as the ratio of ordinary cells and ramp cells in the downstream cell flow, and their values ​​are affected by the actual road conditions.

[0105] Furthermore, the motion feature data prediction model constructed based on the number of vehicle exchanges between adjacent cells and the average speed of vehicles within the cell includes a cellular spatiotemporal acceleration model;

[0106] The cellular space-time acceleration model satisfies the expression:

[0107]

[0108]

[0109]

[0110]

[0111] Specifically, because some vehicles within a cell will remain within the cell during a temporal change, while others will move to downstream cells, it is necessary to calculate the temporal acceleration of the leading vehicles and the spatial acceleration of the trailing vehicles, thereby deriving the spatiotemporal average acceleration. To construct a cellular spatiotemporal acceleration model, it is necessary to combine the average vehicle speed within the cell and the number of vehicle exchanges between cells. The resulting spatiotemporal acceleration will serve as the basis for noise calculations.

[0112] Furthermore, if Figure 3 As shown, the calculation of the acceleration of the vehicle in the cell at the next moment based on the feature data prediction model includes the following steps:

[0113] S201: Calculating a spatiotemporal average acceleration based on the cellular spatiotemporal acceleration model, using the spatiotemporal average acceleration as the mean of the cellular acceleration distribution in the road, and establishing an acceleration distribution function for the cellular road;

[0114] S202: Calculating the acceleration of each cell in the road according to the acceleration distribution function of the cellular road, and calculating the acceleration of the vehicle in each cell using Monte Carlo simulation;

[0115] The acceleration distribution function of the cellular road satisfies the relationship:

[0116]

[0117] Specifically, in step S201, the spatiotemporal average acceleration calculated by the above method is used as the mean value of the cellular acceleration distribution in the road, that is, N~(μ,σ 2 ), where the variance and mean are r and s are the fitting parameter coefficients. This function describes the distribution of vehicle acceleration within each cell on the road and can accurately predict the acceleration of the vehicle within each cell on the road.

[0118] In step S202, a Monte Carlo simulation is performed based on the established cellular road acceleration distribution function to calculate the specific acceleration values ​​of each vehicle within the road cell. This Monte Carlo simulation not only provides acceleration predictions for individual cells but also accounts for randomness and uncertainty, making the predictions more comprehensive and accurate.

[0119] like Figure 4As shown, the noise data of each cell at a standard distance is obtained based on the motion characteristic data and the motor vehicle noise emission model, the noise data of the cell at an effective distance is subjected to distance attenuation, and the vehicle noise is superimposed with the cell as the basic unit to obtain the noise value of each cell, including the following steps:

[0120] S301: Constructing a motor vehicle noise emission model for each cell, the same number as the number of vehicles in the cell, wherein the motor vehicle noise emission model outputs a noise value for each vehicle in the cell based on the average speed and acceleration of the vehicles in the cell;

[0121] S302: The noise value of each vehicle in each cell is superimposed to obtain the noise value of each cell at the marked distance, and the noise value of each cell is distance-attenuated according to the distance between each cell and the detection point. The noise values ​​after distance attenuation are superimposed by equivalent sound levels to obtain the noise value of each cell.

[0122] In step S301, a motor vehicle noise emission model is constructed for each cell, and the noise value is calculated based on the average speed and acceleration of the vehicles in the cell to provide basic data. In step S302, the noise value of each vehicle is superimposed to obtain the total noise value of each cell at the standard distance. Subsequently, the noise value is attenuated according to the distance between the cell and the detection point, and the equivalent sound level is superimposed to finally obtain the actual noise value of each cell. Figure 7 It is shown that due to the different distances between the cells and the detection points, the noise value needs to be attenuated to obtain more accurate noise prediction results, such as Figure 9 As shown in the figure, this figure describes the results of the noise calculation of each cell by this scheme.

[0123] Furthermore, before calculating the motion characteristic data of the vehicle in the cell at the next moment according to the characteristic data prediction model, the method includes: inputting a traffic flow demand to the first cell along the traffic flow direction of the road to be predicted.

[0124] Specifically, in a road traffic flow model, the first cell represents the starting point for a specific road direction. Before predicting the movement characteristics of vehicles within the cell at the next moment, it is necessary to determine the traffic demand and vehicle inflow in that direction. This can be based on historical data or real-time traffic observations. This step determines the initial vehicle flow in the road direction to be predicted, provides input conditions for the prediction model, and influences the accuracy of future traffic forecasts.

[0125] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0126] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A road traffic noise prediction method based on a cellular transmission model, characterized in that: include: Divide the road into cellular states and homogenize the vehicles in the same cell; A motion feature data prediction model is constructed based on the number of vehicle exchanges between adjacent cells and the average speed of vehicles within the cell. The motion feature data of the vehicles within the cell at the next moment is calculated based on the feature data prediction model. The motion feature data includes the number of vehicles within the cell, the average speed of the vehicles, and the acceleration of the vehicles. Based on the motion characteristic data and the vehicle noise emission model, the noise data of each cell at the standard distance is obtained. The noise data of the cells at the effective distance is attenuated by distance. The vehicle noise is superimposed on the cell as the basic unit to obtain the noise value of each cell to complete the prediction of road traffic noise. The cellular division of the road and the homogenization of vehicles within the same cell include: Obtain the structure of the road to be predicted, divide the road into several equal-length cells that satisfy discrete stability, and set the average speed of vehicles in the same cell to be the same; The cell at the ramp entrance is set as a diverging cell, the cell at the ramp exit is set as a merging cell, and the remaining cells are set as ordinary cells. The downstream of the diverging cell is connected to an ordinary cell and a ramp cell, and the upstream of the merging cell is connected to an ordinary cell and a ramp cell. The ordinary cell has one cell connected to it upstream and downstream respectively. The motion feature data prediction model constructed based on the number of vehicle exchanges between adjacent cells includes a cellular vehicle exchange model; The cellular vehicle exchange model calculates the number of vehicles in the cell at the current moment by adding the number of vehicles in the cell at the previous moment to the outflow of the upstream cell and the inflow of the downstream cell; confluent cell The cellular vehicle exchange model satisfies the relationship: ； ； in, Indicates a moment, For the next moment, Represents the current confluent cell, is the upstream ramp cell of the confluence cell, is the actual outflow of the upstream ordinary cell of the confluent cell, is the actual outflow of the upstream ramp cell, is the actual inflow of the downstream cell of the confluent cell, is the sending capacity of the upstream common cell of the confluent cell, is the sending capacity of the upstream ramp cell of the confluence cell, and They represent the confluence ratio of the upstream ramp cell and the ordinary cell of the confluence cell, is the maximum traffic flow that the current merging cell can accept, for The number of vehicles in the merging cell at a given moment, is the number of vehicles in the merging cell at the next moment; Shunt Cell The cellular vehicle exchange model satisfies the relationship: ； ； in, for The number of vehicles in the diversion cell at any moment, is the number of vehicles in the diversion cell at the next moment, is the actual inflow of the downstream ordinary cell of the diversion cell, is the actual inflow of the downstream ramp cell of the diversion cell, is the actual outflow of the upstream ordinary cell of the diversion cell, is the sending capacity of the shunt cell, and They represent the diversion ratios of the downstream ramp cells and ordinary cells of the diversion cells, is the maximum traffic flow that the downstream ordinary cell of the diversion cell can accept, is the maximum traffic flow that the downstream ramp cell of the diversion cell can accept, Indicates a moment, For the next moment, represents the current shunt cell, is the downstream ramp cell, It is a common downstream cell.

2. The road traffic noise prediction method according to claim 1, characterized in that: Along the traffic flow direction of the road to be predicted, the upstream cell of the first cell and the downstream cell of the last cell are set as virtual cells, and the motion feature data of the vehicles in the virtual cells are the same as the motion feature data of the vehicles in the adjacent cells.

3. The road traffic noise prediction method according to claim 1, characterized in that: The motion feature data prediction model constructed based on the average speed of vehicles in the cell includes a cell speed model; Ordinary cells The cellular velocity model satisfies the relationship: ； confluent cell The cellular velocity model satisfies the relationship: ； ； ； Shunt Cell The cellular velocity model satisfies the relationship: ； ； in, 、 and are the average speeds of vehicles in ordinary cells, merging cells and diverging cells at the next moment, 、 and are the average speeds of vehicles in ordinary cells, merging cells and diverging cells at time t, 、 and are the actual outflows of the upstream ordinary cells of ordinary cells, confluent cells and divergent cells at time t, 、 and is the density of vehicles in ordinary cells, converging cells and diverging cells at time t, 、 and are the average speeds of vehicles in the upstream ordinary cells of ordinary cells, merging cells and diverging cells at time t, is the time interval, 、 and are the expected velocities corresponding to the densities of ordinary cells, confluent cells, and divergent cells, respectively. The time required, is the downstream expected adjustment coefficient, is the comprehensive density evaluation parameter of the downstream of the diversion cell, 、 and are the density of vehicles in ordinary cells, converging cells and downstream ordinary cells of diverging cells at time t, is the density of vehicles in the downstream ramp cell of the diversion cell at time t, and They are respectively expressed as the proportion of vehicles flowing from the diversion cell to the ordinary cell and ramp cell downstream, is the average speed of vehicles in the upstream ramp cell of the merging cell at time t, is the calculation speed of the fusion weight of the upstream cell of the confluent cell, is the actual outflow of the upstream ramp cell of the confluence cell at time t, is the density of vehicles in the upstream common cell of the confluent cell at time t, is the density of vehicles in the upstream ramp cell of the merging cell at time t, is the confluence reduction factor.

4. The road traffic noise prediction method according to claim 3, characterized in that: The motion feature data prediction model constructed based on the number of vehicle exchanges between adjacent cells and the average speed of vehicles within the cell includes a cellular space-time acceleration model; The space-time acceleration models of ordinary cells, converging cells, and diverging cells satisfy the expression: ； ； ； ； in, is the average speed of vehicles in ordinary cells, merging cells or diverging cells at the next moment, is the average speed of vehicles in ordinary cells, merging cells or diverging cells at time t, is the number of vehicles in ordinary cells, converging cells or diverging cells at time t, is the actual outflow of the upstream ordinary cell of the ordinary cell, confluent cell or divergent cell at time t, is the time acceleration, is the number of vehicles in the front part of a normal cell, a converging cell, or a diverging cell, is the average speed of all vehicles in the downstream cells at the next moment, time interval, is the spatial acceleration, is the number of vehicles in the ordinary cell, converging cell or the part after the diverging cell, is the space-time average acceleration.

5. The road traffic noise prediction method according to claim 4, characterized in that: The calculation of the acceleration of the vehicle in the cell at the next moment according to the feature data prediction model includes: Calculating the spatiotemporal average acceleration based on the cellular spatiotemporal acceleration model, using the spatiotemporal average acceleration as the mean of the cellular acceleration distribution in the road, and establishing the acceleration distribution function of the cellular road; The acceleration of each cell in the road is calculated according to the acceleration distribution function of the cellular road, and the acceleration of the vehicle in each cell is calculated using Monte Carlo simulation; The acceleration distribution function of the cellular road satisfies the relationship: ， Among them, the variance and mean are , r and s are fitting parameter coefficients, is the average speed of vehicles in cell i at time t, is the space-time average acceleration, is the standard deviation of the acceleration distribution function, is the mean of the acceleration distribution function.

6. The road traffic noise prediction method according to claim 5, characterized in that: The noise data of each cell at the standard distance is obtained based on the motion characteristic data and the motor vehicle noise emission model, the noise data of the cell at the effective distance is subjected to distance attenuation, and the vehicle noise is superimposed with the cell as the basic unit to obtain the noise value of each cell. Construct a motor vehicle noise emission model for each cell, which is the same as the number of vehicles in the cell. The motor vehicle noise emission model outputs the noise value of each vehicle in the cell according to the average speed and acceleration of the vehicles in the cell. The noise value of each vehicle in each cell is superimposed to obtain the noise value of each cell at the marked distance. The noise value of each cell is distance-attenuated according to the distance between each cell and the detection point. The noise value after distance attenuation is superimposed with the equivalent sound level to obtain the noise value of each cell.

7. The road traffic noise prediction method according to any one of claims 1 to 6, characterized in that: Before calculating the motion characteristic data of the vehicle in the cell at the next moment according to the characteristic data prediction model, the method includes: inputting a traffic flow demand to the first cell along the traffic flow direction of the road to be predicted.

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