High-speed railway trafficability calculation method based on actual operation big data

Abstract

The invention relates to the technical field of high-speed railways, in particular to a high-speed railway trafficability calculation method based on actual operation big data. Comprising the following steps: selecting the shortest mapping time from the spare time of a final arrival station to a starting station; confirming an end-to-end spare time band covering the shortest mapping time; comparing the shortest mapping time with the duration of the end-to-spare time band, and determining the effective mapping time; deleting effective mapping time in the mapping time of other end arriving stations, and determining whether the mapping time exists or not; after the calculation of all mapping time is finished, ending the calculation; through the mode, the accuracy of a calculation result is improved, and support is provided for optimal scheduling and future development of a high-speed railway network.

Description

Technical Field

[0001] The present invention relates to the field of high-speed railway technology, and in particular to a method for calculating the throughput capacity of a high-speed railway based on actual operation big data. Background Art

[0002] Railway capacity refers to the maximum number of train pairs or trains that can pass through in a single day, determined based on existing technical equipment, operating organization methods, and prescribed technical operating procedures. Capacity utilization measures the proportion of capacity occupied by actual train operations. It not only guides daily transportation organization but also serves as an important basis for railway line planning, design, and capacity expansion. my country's high-speed railways are primarily passenger-dedicated lines, and the types of trains and train operation structures they operate differ significantly from those on existing lines. This leads to significant difficulties in calculating capacity using the coefficient reduction method, both in terms of expressing capacity and determining the basic trains. However, whether evaluating the congestion level and optimizing the capacity of existing high-speed railways or conducting feasibility studies for new parallel high-speed railway projects, accurate understanding of high-speed railway capacity utilization is essential. Therefore, research on methods for calculating high-speed railway capacity utilization is particularly important.

[0003] Currently, high-speed railway capacity calculation methods can be divided into the deduction coefficient method, the average minimum interval method, and the UIC406 compression method. The deduction coefficient method can only obtain deduction coefficients for a specific diagram structure, making the results difficult to generalize. The average minimum interval method can only obtain deduction coefficients for a specific diagram structure, making the results difficult to generalize. The UIC406 compression method is suitable for standardized diagrams and cannot consider the network's restrictions on operating lines during compression.

[0004] In recent years, big data technology has developed rapidly, achieving promising results in many fields. Therefore, the emergence of big data technology has provided a new approach to calculating throughput capacity. Therefore, it is crucial to propose a method for calculating throughput capacity for high-speed railways that combines this method with big data technology to improve the accuracy of the results. Summary of the Invention

[0005] The purpose of this invention is to provide a method for calculating the high-speed railway capacity based on actual operation big data, aiming to improve the accuracy of the calculation results and provide support for the optimized scheduling and future development of the high-speed railway network.

[0006] To achieve the above objectives, the present invention adopts a method for calculating the high-speed railway capacity based on actual operation big data, comprising the following steps:

[0007] Select the shortest mapping time from the free time of the terminal station to the origin station;

[0008] Confirm the final free time band covering the shortest mapping time;

[0009] Compare the shortest mapping time with the final free time band length and determine the effective mapping time;

[0010] Deleting the valid mapping time from the mapping time of other terminal stations and determining whether the mapping time exists;

[0011] After all mapping times are calculated, the calculation ends and the settlement results are used to evaluate the capacity of the high-speed railway.

[0012] Among them, in the step of selecting the shortest mapping time from the free time of the terminal station to the originating station:

[0013] Obtain the mapping time data of the intersection of the free time of the terminal station and the effective free time of the departure station after the free time of the terminal station is mapped to the departure station;

[0014] Select the shortest mapping time from the mapping time data.

[0015] Among them, in the step of comparing the shortest mapping time with the length of the final free time band and determining the effective mapping time:

[0016] If the mapping time is greater than the final idle time, the time that is the same as the final idle time is taken from the back to the front as the valid mapping.

[0017] Among them, in the step of comparing the shortest mapping time with the length of the final free time band and determining the effective mapping time:

[0018] If the mapping time is less than or equal to the final free time, the entire mapping time is valid mapping.

[0019] Among them, in the step of deleting the valid mapping time from the mapping time of other terminal stations and determining whether the mapping time exists:

[0020] If a mapping time exists after deleting the valid mapping time from the mapping time of other terminal stations, the shortest mapping time is reselected and the judgment is repeated.

[0021] Among them, in the step of deleting the valid mapping time from the mapping time of other terminal stations and determining whether the mapping time exists:

[0022] If the mapping time does not exist after deleting the valid mapping time from the mapping time of other terminal stations, the calculation of all mapping times is completed.

[0023] The present invention provides a method for calculating the throughput capacity of a high-speed railway based on actual operation big data, which selects the shortest mapping time from the free time of the terminal station to the departure station; confirms the terminal free time band covering the shortest mapping time; compares the shortest mapping time with the length of the terminal free time band, and determines the effective mapping time; deletes the effective mapping time from the mapping time of other terminal stations, and determines whether the mapping time exists; and ends the calculation after all mapping times are calculated. Through the above method, the accuracy of the calculation results is improved, providing support for the optimized scheduling and future development of the high-speed railway network. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] 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 only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 It is a flow chart of the method for calculating the high-speed railway capacity based on actual operation big data of the present invention.

[0026] Figure 2 This is a flowchart of the steps of the method for calculating the high-speed railway capacity based on actual operation big data of the present invention. DETAILED DESCRIPTION

[0027] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different drawings represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this application.

[0028] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0029] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0030] See also Figure 1 and Figure 2 The present invention provides a method for calculating the high-speed railway capacity based on actual operation big data, comprising the following steps:

[0031] S100: Select the shortest mapping time from the free time of the terminal station to the originating station;

[0032] In this embodiment, priority is given to the shortest duration (i.e., mapping time) of the free time of the terminal station after it is mapped to the departure station and the intersection with the effective free time of the departure station. Because the longer the mapping time, the larger the range of available additional trains. Selecting the shortest mapping time first can ensure that trains can be operated as much as possible during the most difficult time period for adding trains to the terminal station. Among them, in the step of selecting the shortest mapping time of the free time of the terminal station to the departure station: first obtain the mapping time data of the intersection duration of the free time of the terminal station after it is mapped to the departure station and the effective free time of the departure station; then select the shortest mapping time among the mapping time data.

[0033] S200: confirming the final free time band covering the shortest mapping time;

[0034] S300: Compare the shortest mapping time with the final free time band length and determine the effective mapping time;

[0035] In this embodiment, after selecting the shortest mapping time from the free time at the final destination to the originating destination, the final free time band that covers the shortest mapping time is identified. The shortest mapping time is then compared with the length of the free time band to determine the valid mapping time. If the mapping time is greater than the free time at the final destination, the time corresponding to the free time at the final destination is selected as the valid mapping time. If the mapping time is less than or equal to the free time at the final destination, all mapping times are valid mappings.

[0036] When determining a valid mapping for a destination time zone at the originating station, the duration of the intersection with the originating station's available time zone is compared with the duration of the destination station's available time zone. If the intersection is shorter, the entire intersection is considered a valid mapping. If the destination's available time zone is shorter, a valid mapping is selected from the intersection, starting from the end and ending with the same duration as the destination's available time zone. This ensures the determined originating time is as late as possible, maximizing travel speed. If the mapping is covered by multiple destination time zones, the destination time zone with the least capacity is prioritized.

[0037] S400: Delete the valid mapping time from the mapping times of other destination stations, and determine whether the mapping time exists.

[0038] S500: After all mapping times are calculated, the calculation ends.

[0039] In this embodiment, after determining the effective mapping time, the effective mapping time is deleted from the mapping time of other terminal stations, and it is determined whether the mapping time exists; if the mapping time exists after deleting the effective mapping time from the mapping time of other terminal stations, the shortest mapping time is reselected and the judgment is repeated; if the mapping time does not exist after deleting the effective mapping time from the mapping time of other terminal stations, all mapping times are calculated and the calculation ends.

[0040] Example

[0041] Part of the Zhengzhou-Chongqing High-Speed Railway sections are used as the objects for capacity calculation in this embodiment. The faster running time, slower running time and tracking interval time of each section are used as key parameters for train capacity calculation. Taking into account the errors in train arrival, departure and passing times caused by the sensitivity of the track circuit and the actual operation of the train, 5% of the abnormal values in the total number of train running time statistics are removed, and the remaining method is used to determine the faster running time and slower running time of the train in each section according to the mode within the fastest / slowest 20%, and set a range limit of 3-5min for the tracking interval time. Combined with big data technology, the tracking interval time of the Dengzhou East-Sihou Line Station section of the Zhengzhou-Chongqing High-Speed Railway and their faster running time and slower running time data of the section can be obtained, as shown in Table 1, Table 2 and Table 3:

[0042]

[0043] Table 1. Statistics of train tracking intervals in the Dengzhou East-Sihou section of the Zhengzhou-Chongqing High-Speed Railway

[0044]

[0045]

[0046] Table 2. Statistics of fast-running train times in each section of the Dengzhou East-Sihou section of the Zhengzhou-Chongqing High-Speed Railway

[0047]

[0048] Table 3. Statistics of faster and slower train running time in the Dengzhou East-Sihou section of the Zhengzhou-Chongqing High-Speed Railway

[0049] This example selects the Dengzhou East-Sihou section of the Zhengzhou-Chongqing High-Speed Railway on April 25, 2024 as the calculation object, and obtains the capacity utilization data of the Dengzhou East-Sihou section of the Zhengzhou-Chongqing High-Speed Railway, as shown in Table 4:

[0050]

[0051]

[0052] Table 4. Calculation results of capacity utilization rate of the Dengzhou East-Sihou section of the Zhengzhou-Chongqing High-Speed Railway

[0053] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the contents disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed in this application.

[0054] It will be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A method for calculating the throughput capacity of high-speed railways based on actual operation big data, characterized in that: The steps include: Select the shortest mapping time from the free time of the terminal station to the origin station; Confirm the final free time band covering the shortest mapping time; Compare the shortest mapping time with the final free time band length and determine the effective mapping time; Deleting the valid mapping time from the mapping time of other terminal stations and determining whether the mapping time exists; After all mapping times are calculated, the calculation ends.

2. The method for calculating high-speed railway capacity based on actual operation big data according to claim 1, characterized in that: In the step of selecting the shortest mapping time from the free time of the terminal station to the originating station: Obtain the mapping time data of the intersection of the free time of the terminal station and the effective free time of the departure station after the free time of the terminal station is mapped to the departure station; Select the shortest mapping time from the mapping time data.

3. The method for calculating high-speed railway capacity based on actual operation big data according to claim 1, characterized in that: In the step of comparing the shortest mapping time with the length of the final free time band and determining the effective mapping time: If the mapping time is greater than the final idle time, the time that is the same as the final idle time is taken from the back to the front as the valid mapping.

4. The method for calculating high-speed railway capacity based on actual operation big data according to claim 3, characterized in that: In the step of comparing the shortest mapping time with the length of the final free time band and determining the effective mapping time: If the mapping time is less than or equal to the final free time, the entire mapping time is valid mapping.

5. The method for calculating high-speed railway capacity based on actual operation big data according to claim 1, characterized in that: In the step of deleting the valid mapping time from the mapping time of other terminal stations and determining whether the mapping time exists: If a mapping time exists after deleting the valid mapping time from the mapping time of other terminal stations, the shortest mapping time is reselected and the judgment is repeated.

6. The method for calculating high-speed railway capacity based on actual operation big data according to claim 5, characterized in that: In the step of deleting the valid mapping time from the mapping time of other terminal stations and determining whether the mapping time exists: If the mapping time does not exist after deleting the valid mapping time from the mapping time of other terminal stations, the calculation of all mapping times is completed.

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