A micro-traffic simulation parameter calibration value data processing method with minimum group difference

Abstract

The application discloses a kind of microcosmic traffic simulation parameter calibration value data processing method of group difference minimum, it is related to traffic safety and traffic simulation technical field.The method is realized by accurate measurement and fine operation to simulation model parameter, and the optimization of microcosmic traffic simulation parameter.Firstly, by setting traffic volume, speed, vehicle type composition, running time and other parameters, using VISSIM simulation model and MATLAB realizes simulation calibration program.Then, the program is run, and the parameter calibration result is obtained, and the average value and standard deviation of each parameter are calculated.Subsequently, the difference between the parameter and the average value is calculated, and the size of the difference and the standard deviation is compared, and the data that does not meet the condition is eliminated.Finally, the group data that the error of each parameter is within the standard deviation range is retained, the difference value of each parameter is obtained, then the data is arranged, and the data difference minimum is taken out as the final parameter value result.The method can greatly improve the accuracy and reliability of simulation model.

Description

Technical Field

[0001] This invention relates to the fields of traffic safety and traffic simulation technology, and belongs to a method for processing data of microscopic traffic simulation parameter calibration values ​​with the minimum group difference. Background Technology

[0002] Traffic simulation plays a vital role in theoretical teaching and engineering applications. VISSIM simulation software, with its high degree of realism and fidelity, effectively reduces the experimental difficulty for transportation engineering majors. However, due to the complexity of transportation systems, results vary significantly across different traffic scenarios. Consequently, extensive research has been conducted on simulation calibration, primarily focusing on parameter selection and calibration methods. Less research has been done on how to screen and determine the final values ​​of the calibration results, resulting in a failure to fully realize the "last mile" of simulation calibration. Furthermore, existing methods for determining calibration results mainly involve directly averaging the parameters that meet the conditions as the final result. However, since parameters can have varying degrees of dispersion, directly using the average value introduces fuzzy errors.

[0003] 1. The content of simulation calibration focuses on the selection of simulation calibration parameters and simulation calibration methods, including parameter sensitivity analysis and the establishment of calibration models using various methods. However, it neglects the research on the methods for obtaining calibration results and lacks necessary research on how to select the most suitable parameter combination from the many parameter combinations that have been calibrated.

[0004] 2. Generally speaking, the calibration results contain multiple parameter combinations. The method for determining the value of each parameter is simply to calculate the mean or to take the mean of clusters. However, these methods start from a single parameter and ignore the uniformity of a set of parameters (i.e., when one parameter changes, the 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] To address the aforementioned issues, this invention aims to study the calibration results of the VISSIM traffic simulation software. By employing a data processing method that minimizes group errors, the optimal parameter combination can be obtained, thereby improving the accuracy of the simulation results and breaking through the limitations of previous data processing methods that only used the mean value.

[0006] To achieve the above objectives, the present invention employs the following technical solution: the method comprises:

[0007] Build a VISSIM simulation model and set parameters such as traffic volume, speed, vehicle type composition, and running time.

[0008] Obtain the VISSIM simulation model parameter calibration results;

[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 for each set of data;

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

[0013] Retain the data sets where the error of each parameter is within the standard deviation range;

[0014] Sum the differences of all parameters in the retained parameter combinations to obtain the sum of differences for each set of data;

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

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

[0017] In one approach, obtaining the VISSIM simulation model parameter calibration results includes: denoted as... ,in ,

[0018] ...all of these are values ​​for the parameters to be calibrated; , Same as above.

[0019] In one approach, calculating the mean of each parameter includes:

[0020] Let them be a, b, c, d... where a = (a1 + a2 + ...) / n; b = (b1 + b2 + ...) / n.

[0021] In one approach, calculating the standard deviation of each parameter includes:

[0022] Recorded as in ; .

[0023] In one approach, calculating the difference between the parameter and the mean in each data set includes: forming a parameter set denoted as... ,in , .

[0024] In one scheme, the group of data in which the error of each parameter is within the standard deviation includes: denoted as ,in for Data that partially meets the conditions, for example: If each parameter satisfies the difference requirement, then , In the parameters If the conditions are not met, then remove. This group, If each parameter satisfies the difference requirement, then And so on.

[0025] In one approach, obtaining the sum of differences for each set of data includes:

[0026] And so on. ,right The data is sorted.

[0027] The beneficial effects of this invention are:

[0028] Breaking away from the traditional method of directly averaging calibration results, this invention takes parameter combinations as its starting point, comprehensively considering the differences between different parameters. Through analysis of group differences, it retains relevant data that meet the difference requirements and proposes a data processing method for microscopic traffic simulation calibration parameters based on minimizing group differences, thus improving the accuracy of calibration result selection. It uses a method that minimizes group errors to process data and determine suitable simulation calibration parameters. Attached Figure Description

[0029] Figure 1 This is a flowchart of the method of the present invention;

[0030] Figure 2 This is a data chart for removing groups of data whose differences fall outside the standard deviation, as per the present invention. Detailed Implementation

[0031] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0032] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. To facilitate understanding, the invention will now be described more fully with reference to the accompanying drawings, which illustrate exemplary embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the invention more thorough and complete.

[0033] like Figure 1 As shown, a data processing method for calibrating microscopic traffic simulation parameters with the minimum group difference is presented.

[0034] S1. By setting parameters such as traffic volume, speed, vehicle type composition, and travel time, a VISSIM simulation model is built, and a simulation calibration program is implemented using MATLAB. This step mainly involves setting various parameters, such as traffic volume, speed, vehicle type composition, and travel time. Then, a VISSIM simulation model is built, which can accurately simulate real traffic flow. Simultaneously, using MATLAB programming and combining the set parameters, a simulation calibration program is implemented to facilitate the study of traffic flow.

[0035] S2. Following the parameters and calibration procedure set in the above model, obtain the parameter calibration results generated by the above calibration procedure by running the VISSIM software, and record them as follows: ,in , ...all of these are possible values ​​for the parameter to be calibrated; , Same as above. Run the VISSIM software and obtain the parameter calibration results obtained under the simulation calibration program. The possible values ​​of these parameters are all set in the previous step. This step is of great significance for understanding the simulation results under various parameter values.

[0036] S3. Calculate the mean of each parameter, which is to sum 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, making it closer to the real environment.

[0037] S4. Calculate the standard deviation of each parameter, denoted as... in ; Standard deviation is an important way to measure data variation; it represents the degree of dispersion among data points.

[0038] S5. Calculate the difference between the parameter and the mean for each data set, forming a parameter set denoted as . ,in , The differences in the parameter group reflect the degree of dispersion among the data. The larger the difference, the greater the dispersion, and the smaller the difference, the less dispersion.

[0039] S6. Compare the difference of each parameter to be calibrated with the standard deviation, and discard the data sets whose differences are outside the standard deviation (see appendix). Figure 2), and delete data that exceeds the horizontal line above and below it. If the difference exceeds the standard deviation, it means that this parameter value will cause the model's prediction to deviate from the actual value, so it should be removed. This step is to ensure the accuracy of the selected data and remove parameter values ​​that may have a significant impact on the final result.

[0040] S7. Repeat step 5 to complete the data removal process for all parameters to be calibrated, retaining the data sets where the error of each parameter is within the standard deviation range, denoted as . ,in for Data that partially meets the conditions, for example: If each parameter satisfies the difference requirement, then , In the parameters If the conditions are not met, then remove. This group, If each parameter satisfies the difference requirement, then And so on. All parameter sets that do not meet the conditions are eliminated. The remaining parameter sets are the ones with optimal performance, and their errors are all within the allowable standard deviation range.

[0041] S8, Reserved parameter combinations Sum the differences of all parameters to obtain the sum of differences for each data set. And so on. This value represents the total error of this set of parameters. The smaller the total error, the closer the set of parameters is to the ideal value.

[0042] S9, to The data is sorted. The purpose of this step is to find the parameter combination with the smallest sum of differences among all parameter combinations, which will be used for the next step of optimal parameter selection.

[0043] S10. Select the group with the smallest difference as the final value. Choosing the group with the smallest sum of differences as the final parameter value means that the model prediction result corresponding to this set of parameters is closest to the actual result.

[0044] S11. The selected parameter combination is used as the calibration parameters for the simulation software. The optimal parameter combination is then used in the VISSIM software simulation model to study actual traffic flow.

[0045] Overall, this method uses computer simulation technology to calculate various possible combinations of parameters and selects the optimal combination of parameters according to certain principles, thereby improving the accuracy of model predictions.

[0046] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0047] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or make equivalent substitutions for some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for processing data of microscopic traffic simulation parameter calibration values ​​with minimum group differences, characterized in that: The method includes: Build a VISSIM simulation model and set parameters such as traffic volume, speed, vehicle type composition, and running time. Obtain the VISSIM simulation model parameter calibration results; Calculate the mean of each parameter; Calculate the standard deviation of each parameter; Calculate the difference between the parameter and the mean for each set of data; Compare the difference of each parameter to be calibrated with the standard deviation; Retain the data sets where the error of each parameter is within the standard deviation range; Sum the differences of all parameters in the retained parameter combinations to obtain the sum of differences for each set of data; Sort the data and select the group with the smallest difference as the final value; The selected parameter combination is used as the calibration parameters for the simulation software. The acquisition of VISSIM simulation model parameter calibration results includes: denoted as ,in , ...all of these are values ​​for the parameters to be calibrated; , ...all of these are values ​​for the parameters to be calibrated; The calculation of the difference between the parameter and the mean in each set of data includes: forming a parameter set denoted as... ,in , ; The group of data in which the error of each parameter is within the standard deviation range includes: denoted as , ,in for The data that meets the conditions, wherein the conditions for meeting the conditions are that each parameter meets the difference requirement; The process of obtaining the sum of differences for each set of data includes: And so on. ,right The data is sorted.

2. The method for processing data of microscopic traffic simulation parameter calibration values ​​with the minimum group difference as described in claim 1, characterized in that: The calculation of the mean of each parameter includes: Let them be a, b, c, d, ..., where a = (a1 + a2 + ...) / n; b = (b1 + b2 + ...) / n.

3. The method for processing data of microscopic traffic simulation parameter calibration values ​​with the minimum group difference as described in claim 1, characterized in that: The calculation of the standard deviation of each parameter includes: Recorded as ,in ; .

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