Microcosmic traffic simulation parameter calibration value data processing method with minimum group difference value

Abstract

The invention discloses a group difference minimum microscopic traffic simulation parameter calibration value data processing method, and relates to the technical field of traffic safety and traffic simulation. According to the method, optimization of microscopic traffic simulation parameters is realized through accurate measurement and fine operation of simulation model parameters. The method comprises the following steps: firstly, setting parameters such as traffic volume, speed, vehicle type composition, running time and the like, and realizing a simulation calibration program by using a VISSIM simulation model and MATLAB; then, running the program to obtain a parameter calibration result, and calculating an average value and a standard deviation of each parameter; and then, calculating a difference value between the parameter and the average value, comparing the difference value with a standard deviation, and rejecting data which does not meet conditions. And finally, retaining group data of which each parameter error is within a standard deviation range, solving the sum of parameter difference values, then arranging the data, and taking out the data with the minimum data difference value as a final parameter valuing result. According to the method, the accuracy and reliability of the simulation model can be greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of traffic safety and traffic simulation, and belongs to a microscopic traffic simulation parameter calibration value data processing method with the minimum group difference. Background Art

[0002] Traffic simulation plays an important role in theoretical teaching and engineering applications. Among them, VISSIM simulation software, with its high degree of restoration and authenticity, effectively reduces the experimental difficulty of transportation majors. Due to the complexity of the transportation system, there are significant differences in the results under different traffic scenarios. Therefore, a lot of research has been carried out on simulation calibration, and the main content focuses on the selection of simulation calibration parameters and simulation calibration methods. There are few studies on how to screen the simulation calibration results and determine the final value, which leads to the failure of the "last mile" of simulation calibration to be completely unblocked. In addition, in the existing calibration result selection method, the main means is to directly take the average of the parameters that meet the conditions as the final result, but the parameters are discrete in different sizes, and the method of directly using the average value has the disadvantage of fuzzy error.

[0003] 1. The content of simulation calibration focuses on the selection of simulation calibration parameters and simulation calibration methods, including parameter sensitivity analysis, establishing calibration models by respective means, etc., while ignoring the research on the method of obtaining the calibration results. There is a lack of necessary research on how to screen out the most appropriate parameter combination for the numerous parameter combinations completed by the calibration.

[0004] 2. Generally speaking, the calibration result contains multiple parameter combinations, and the method for taking the value of each parameter is a simple averaging or cluster averaging, but they all start from a single parameter and ignore the uniformity of a group of parameters (that is, for a group of parameters, when one of the parameters changes, its parameter combination may not have a good calibration effect. Similarly, the parameter combination formed by averaging the parameters may not have a good calibration effect). Summary of the invention

[0005] In view of the above problems, the present invention intends to study the calibration results of VISSIM traffic simulation software, obtain the best parameter combination through the data processing method with the smallest group error, improve the accuracy of the simulation results, and break through the previous data processing method that only uses the mean value.

[0006] In order to achieve the above object, the present invention is implemented by adopting the following technical scheme: the method comprises:

[0007] Build a VISSIM simulation model and set parameters for traffic volume, speed, vehicle type composition, and operating time;

[0008] Obtain the calibration results of VISSIM simulation model parameters;

[0009] Calculate the mean of each parameter;

[0010] Calculate the standard deviation of each parameter;

[0011] Calculate the difference between the parameter and the mean in each set of data;

[0012] Compare the difference of each parameter to be calibrated with the standard deviation;

[0013] Keep the group data whose error of each parameter is within the standard deviation range;

[0014] The differences of all parameters in the retained parameter combination are summed to obtain the difference sum of each set of data;

[0015] Sort the data and select the group with the smallest difference as the final value result;

[0016] The selected parameter combination is used as the calibration parameters of the simulation software.

[0017] In one embodiment, the method of obtaining the calibration results of the VISSIM simulation model parameters includes: A=[A1, A2, A3……A n ], where A1 = [a1, b1, c1, d1…], a1, b1, c1, d1… are all values ​​of the parameters to be calibrated; A2 = [a2, b2, c2, d2…], a2, b2, c2, d2 are the same as above.

[0018] In one embodiment, calculating the mean of each parameter includes:

[0019] Denoted as a, b, c, d..., where a = (a1 + a2 + ...) / n, and b = (b1 + b2 + ...) / n. In one embodiment, the calculation of the standard deviation of each parameter includes:

[0020] S a ,S b ,S c ,S d ......in

[0021] In one embodiment, the calculation of the difference between the parameter and the mean in each set of data includes: forming a parameter group denoted as A 11 ,A 22 ,A 33 ……A nn , where A 11 =[a1-a, b1-b, c1-c, d1-d...], A 22 =[a2-a, b2-b, c2-c, d2-d...].

[0022] In one embodiment, the group data in which each parameter error is retained within the standard deviation range includes: B = [B1, B2, B3 ... B m ](m≤n), where B1, B2, B3…are the data in A that meet the conditions. For example, if each parameter of A1 meets the difference requirement, then B1=A1=[a1,b1,c1,d1……], if c2 among the parameters of A2 does not meet the conditions, then A2 is eliminated. If each parameter of A3 meets the difference requirement, then B2=A3, and so on.

[0023] In one embodiment, obtaining the difference of each set of data includes:

[0024] B 11 =|a1-a|+|b1-b|+|c1-c|+|d1-d|…, and so on B 22 ,B 33 ......B mm , for B 11 ,B 22 ,B 33 ......B mm Sort the data.

[0025] Beneficial effects of the present invention:

[0026] Breaking through the previous method of directly taking the average value for calibration results, the present invention takes parameter combination as the starting point, comprehensively considers the difference of different parameters, and retains the relevant data that meets the difference requirements by analyzing the group difference. A data processing method for taking the calibration parameter of microscopic traffic simulation based on the minimum group difference is proposed, which improves the accuracy of calibration result selection. The data is processed in a way that minimizes the group error to determine the appropriate simulation calibration parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flow chart of the method of the present invention;

[0028] Figure 2 The present invention eliminates the group data graphs whose differences are outside the standard deviation. DETAILED DESCRIPTION

[0029] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Typical embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described in the present invention. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0030] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used in the present invention in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Typical embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described in the present invention. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0031] like Figure 1 As shown in the figure, a data processing method for calibrating the values ​​of microscopic traffic simulation parameters with the smallest group difference is proposed.

[0032] S1. By setting parameters such as traffic volume, speed, vehicle type composition, and running time, a VISSIM simulation model is built, and a simulation calibration program is implemented in conjunction with MATLAB. This step mainly includes setting various parameters, such as traffic volume, speed, vehicle type composition, and running time. Then a VISSIM simulation model is built, which can accurately simulate the actual traffic flow. At the same time, MATLAB programming is used, and combined with the set parameters, a simulation calibration program is implemented to achieve the study of traffic flow.

[0033] S2. According to the parameters and calibration procedures set by the above model, the parameter calibration results generated by the above calibration procedures are obtained by running VISSIM software, which is recorded as A = [A1, A2, A3 ... A n ], where A1 = [a1, b1, c1, d1 ...], a1, b1, c1, d1 ... are all possible values ​​of the parameters to be calibrated; A2 = [a2, b2, c2, d2 ...], a2, b2, c2, d2 are the same as above. Run VISSIM software to obtain the parameter calibration results obtained under the simulation calibration program, where the possible values ​​of these parameters are all set by the previous step. This step is of great significance for understanding the simulation results under various parameter values.

[0034] S3. Calculate the mean of each parameter, that is, add up the values ​​of each parameter and divide by the total number of parameter values. For example, for parameters a = a1, a2, ... an, the average value is a = (a1 + a2 + ... an) / n. This can greatly reduce the error caused by the value of a single parameter and make it closer to the real environment.

[0035] S4. Calculate the standard deviation of each parameter, denoted as S a ,S b ,S c ,S d......in Standard deviation is an important way to measure data variation, which represents the degree of dispersion between data.

[0036] S5. Calculate the difference between the parameter and the mean in each group of data to form a parameter group denoted as A 11 ,A 22 ,A 33 ……A nn , where A 11 =[a1-a, b1-b, c1-c, d1-d...], A 22 =[a2-a, b2-b, c2-c, d2-d…] The difference in the parameter group reflects the degree of dispersion among the data. The larger the difference, the greater the degree of dispersion, and the smaller the difference, the smaller the degree of dispersion.

[0037] S6. Compare the difference of each parameter to be calibrated with the standard deviation, and eliminate the group data whose difference is outside the standard deviation (see Appendix Figure 2 ), and remove the data that exceeds the upper and lower sides of the horizontal line. If the difference exceeds the standard deviation, it means that this parameter value will cause the model prediction to deviate from the actual value, so it should be removed. This step is to ensure the accuracy of the filtered data and remove parameter values ​​that may have a greater impact on the final result.

[0038] S7, repeat step 5 to complete the data elimination of all parameters to be calibrated, and retain the group data whose error of each parameter is within the standard deviation range, recorded as B = [B1, B2, B3 ... B m ](m≤n), where B1, B2, B3... are the data in A that meet the conditions. For example: if each parameter of A1 meets the difference requirement, then B1=A1=[a1,b1,c1,d1...], if c2 of A2 does not meet the condition, then A2 is eliminated. If each parameter of A3 meets the difference requirement, then B2=A3, and so on. Eliminate all parameter groups that do not meet the conditions. The remaining parameter groups are the ones with the best performance, and their errors are within the allowable standard deviation range.

[0039] S8. Sum the differences of all parameters in the retained parameter combination B to obtain the sum of the differences of each set of data, B 11 =|a1-a|+|b1-b|+|c1-c|+|d1-d|…, and so on B 22 ,B 33 ......B mm This value represents the total error of this set of parameters. The smaller the total error, the closer this set of parameters is to the ideal value.

[0040] S9, against B11 ,B 22 ,B 33 ......B mm The purpose of this step is to find the parameter combination with the smallest difference among all parameter combinations, which will be used for the optimal parameter selection in the next step.

[0041] S10. Filter out the group of data with the smallest difference as the final value result. Selecting the group with the smallest difference as the final parameter value means that the model prediction result corresponding to this group of parameters is closest to the actual result.

[0042] S11. Use the selected parameter combination as the calibration parameter of the simulation software. Use the optimal parameter combination in the simulation model of VISSIM software to conduct research on actual traffic flow.

[0043] Overall, this method uses computer simulation technology to calculate various possible parameter combinations and screen out the optimal parameter combination according to certain principles, thereby improving the accuracy of model predictions.

[0044] Example:

[0045] In order to improve the traffic flow of urban trunk roads and reduce traffic accidents, we selected a two-way street with 6 lanes each and several branches perpendicular to it as the research object, assuming that the traffic flow is 800 vehicles per hour, the vehicle type combination is 70% sedans, 20% trucks, and 10% buses, and the operating time will be set to the morning rush hour from 7:00 to 9:00. We used VISSIM to establish the corresponding traffic flow simulation model and used Matlab to implement the simulation calibration program.

[0046] Running VISSIM, we get the following parameters to be calibrated: average speed on the main street is 60km / h, average speed on the branch road is 40km / h, average traffic flow on the main street is 400 vehicles / hour, average traffic flow on the branch road is 200 vehicles / hour, average accident rate is 1 per hour, etc. Calculate the average value of each parameter, for example, average speed = (60+40) / 2 = 50km / h, average traffic flow = (400+200) / 2 = 300 vehicles / hour, accident rate = 1 / hour.

[0047] Then, we calculate the standard deviation of each parameter. For example, the standard deviation of the accident rate is σ = sqrt((1-1) 2 / 1)=0, standard deviation of average speed σ=sqrt((60-50) 2 +(40-50) 2 / 2) = 10, the standard deviation of the average flow σ = sqrt((400-300) 2 +(200-300)2 / 2)=100.

[0048] Calculate the difference between each parameter group and the average value. For example, the difference between the average speed of the street and the average speed is 60-50=10, which is less than the standard deviation of 10, so it is retained. Similarly, the difference between the average flow of the branch and the average flow is 200-300=-100. The absolute value of the difference is greater than the standard deviation of 100, so it is eliminated. The final parameter group retained is: the average speed of the street is 60km / h and the average traffic flow is 400 vehicles / hour.

[0049] Repeating the above steps, we get multiple sets of parameter combinations that meet the conditions. For example, another set of parameters is that the average speed on the street is 55km / h and the average traffic flow is 350 vehicles / h. Sum the differences of these parameters to get the difference sum. For example, the difference sum of the first set is 10+0=10, and the difference sum of the second set is 5+50=55. Sorting the difference sums, we can see that the difference sum of the first set of parameters is the smallest, so we choose this set of parameters as the calibration parameters, input them into VISSIM, and use this parameter combination to conduct simulation research on real traffic flow.

[0050] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0051] It should be understood that the detailed description of the technical solutions of the present invention by means of the preferred embodiments is illustrative rather than restrictive. A person skilled in the art may modify the technical solutions described in the embodiments, or replace some of the technical features by equivalents, based on reading the specification of the present invention; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for processing data for calibrating and obtaining values ​​of microscopic traffic simulation parameters with minimum group difference, characterized in that: The method includes: Build a VISSIM simulation model and set parameters for traffic volume, speed, vehicle type composition, and operating time; Obtain the calibration results of VISSIM simulation model parameters; Calculate the mean of each parameter; Calculate the standard deviation of each parameter; Calculate the difference between the parameter and the mean in each set of data; Compare the difference of each parameter to be calibrated with the standard deviation; Keep the group data whose error of each parameter is within the standard deviation range; The differences of all parameters in the retained parameter combination are summed to obtain the difference sum of each set of data; Sort the data and select the group with the smallest difference as the final value result; The selected parameter combination is used as the calibration parameters of the simulation software.

2. The method for processing data for calibrating and obtaining values ​​of microscopic traffic simulation parameters with the smallest group difference according to claim 1 is characterized in that: The method for obtaining the calibration results of the VISSIM simulation model parameters includes: A=[A1, A2, A3……A n ], where A1 = [a1, b1, c1, d1…], a1, b1, c1, d1… are all values ​​of the parameters to be calibrated; A2 = [a2, b2, c2, d2…], a2, b2, c2, d2 are the same as above.

3. The method for processing data for calibrating and obtaining values ​​of microscopic traffic simulation parameters with the smallest group difference according to claim 1 is characterized in that: The calculation of the mean of each parameter includes: denoted as a, b, c, d..., where a = (a1 + a2 + ...) / n,; b = (b1 + b2 + ...) / n.

4. The method for processing data for calibrating and obtaining values ​​of microscopic traffic simulation parameters with the smallest group difference according to claim 1 is characterized in that: The calculation of the standard deviation of each parameter includes: denoted as S a ,S b ,S c ,S d ......in 5. The method for processing data for calibrating microscopic traffic simulation parameters with the smallest group difference according to claim 1 is characterized in that: The calculation of the difference between the parameter and the mean in each set of data includes: forming a parameter group denoted as A 11 ,A 22 ,A 33 ……A nn , where A 11 =[a1-a, b1-b, c1-c, d1-d...], A 22 =[a2-a, b2-b, c2-c, d2-d...].

6. The method for processing data for calibrating and obtaining values ​​of microscopic traffic simulation parameters with the smallest group difference according to claim 1 is characterized in that: The group data in which each parameter error is retained within the standard deviation range includes: B = [B1, B2, B3 ... B m ](m≤n), where B1, B2, B3…are the data in A that meet the conditions. For example, if each parameter of A1 meets the difference requirement, then B1=A1=[a1,b1,c1,d1……], if c2 among the parameters of A2 does not meet the conditions, then A2 is eliminated. If each parameter of A3 meets the difference requirement, then B2=A3, and so on.

7. The method for processing data for calibrating and obtaining values ​​of microscopic traffic simulation parameters with the smallest group difference according to claim 1 is characterized in that: The difference of each set of data obtained includes: B 11 =|a1-a| + |b1-b| + |c1-c| + |d1-d|..., and so on B 22 ,B 33 ......B mm , for B 11 ,B 22 ,B 33 ......B mm Sort the data.

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