Information entropy and daily flow fused OD passenger flow classification method between rail transit stations
By using the fusion method of information entropy and daily flow in the rail transit system to classify OD passenger flow between sites, the problem of difficulty in capturing the dynamic changes and complexity of passenger flow is solved, and a more accurate passenger flow analysis and optimization strategy is achieved.
Patent Information
- Application Number
- CN202510086383.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-20
- Publication Date
- 2025-06-13
AI Technical Summary
The traditional OD passenger flow classification method between rail transit stations cannot effectively capture the dynamic changes and complexity of passenger flow, and cannot comprehensively and accurately reflect the relative change relationship of passenger flow and the interrelationship between stations in the network.
The method of integrating information entropy and daily traffic is adopted to obtain OD passenger flow data between sites, sort daily passenger flow and sort ranking levels, and calculate information entropy values for classification.
This method can more accurately capture the dynamic changes and complexity of passenger flow, reveal the relative changes and intrinsic correlation of OD passenger flow between sites, and provide more reliable and accurate classification basis and decision-making support for the optimization of rail transit system.
Smart Images
Figure CN120145100A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of rail transit, and particularly to an OD passenger flow classification method between rail transit stations that integrates information entropy and daily flow. Background Art
[0002] Deeply understanding the OD (origin-destination) passenger flow characteristics between urban rail transit stations is of crucial significance for accurately grasping passenger travel patterns, efficiently allocating transport resources, optimizing the passenger travel experience, and constructing a scientific and reasonable traffic management system and planning blueprint. By deeply analyzing and carefully classifying the OD passenger flow data between stations, the accuracy of the analysis can be greatly improved, potential problems such as low transfer efficiency and unreasonable station layout can be revealed, and targeted optimization strategies can be proposed accordingly. At the same time, the detailed classification of the OD passenger flow data between stations can also help managers accurately identify diverse travel demand patterns and their spatio-temporal distribution characteristics, so as to adjust operation strategies, comprehensively improve the overall efficiency of the rail transit system and passenger satisfaction, and lay a solid data foundation and decision-making support for the sustainable development of rail transit.
[0003] However, the distribution characteristics of the OD passenger flow between rail transit stations are complex and changeable, evolving dynamically with time and space and containing a certain degree of randomness. To comprehensively capture these characteristics and scientifically classify the OD passenger flow between stations, significant challenges will be faced. Traditional methods that use the mean or variance change of the OD passenger flow between stations as the classification criterion are no longer sufficient, because they cannot fully reflect the dynamic changes and complexity of the passenger flow. Even when analyzing the fluctuations of the OD passenger flow between a single pair of stations from the perspective of time series, it is limited to a local perspective and difficult to reveal the global relative change relationship of the passenger flow and the mutual relevance between stations in the network.
[0004] The limitations of traditional methods that use the mean or variance change of the OD passenger flow between stations as the classification criterion cannot comprehensively and accurately capture the dynamics, randomness, global relative change relationship and mutual relevance of the OD passenger flow between rail transit stations, resulting in limited analysis accuracy, difficulty in revealing potential operation problems and optimization strategies, and inability to provide sufficient and accurate data support and decision-making basis for the operation, management and planning of rail transit.
[0005] Therefore, there is a need to provide a method that takes the ranking order of the OD passenger flow between each pair of stations in the daily total OD as the core data, and deeply analyzes the relative change law and internal relevance of the OD passenger flow between stations from the global perspective of the rail transit network. Summary of the Invention
[0006] According to the above-mentioned technical problems, a method for classifying OD passenger flows between rail transit stations by integrating information entropy and daily flow is provided. The present invention mainly introduces information entropy as a key statistical index, quantitatively analyzes the ranking fluctuations of the OD passenger flow volume between each pair of stations over multiple days, effectively copes with the uncertainty and randomness in the passenger flow data, and deeply analyzes the complex characteristics of the OD passenger flow between stations from a global perspective, revealing its relative change law and internal correlation, so as to provide a more reliable and accurate classification basis and decision-making support for the optimization and improvement of the rail transit system.
[0007] The technical means adopted by the present invention are as follows:
[0008] A method for classifying OD passenger flows between rail transit stations by integrating information entropy and daily flow, comprising:
[0009] Obtain the OD passenger flow data between stations during the research period;
[0010] Sort the daily passenger flow volumes in the OD passenger flow data between stations every day, set a ranking interval and a ranking level;
[0011] According to the daily passenger flow ranking level of each pair of OD passenger flow data between stations, calculate the entropy value of the daily passenger flow ranking level in the OD passenger flow data between each pair of stations during the research period;
[0012] Analyze the influence of the division method of the ranking interval on the calculation result of the entropy value;
[0013] Classify the OD passenger flow between stations according to the entropy value of the daily flow ranking level.
[0014] Furthermore, the OD passenger flow data between stations includes: date, starting station, terminal station, and passenger flow volume; the date is used to specifically study a certain day within the research period, and the passenger flow volume is the passenger flow quantity for the whole day corresponding to the date.
[0015] Furthermore, the setting of the ranking interval and the ranking level includes:
[0016] Sort the daily passenger flow volumes in the OD passenger flow data between stations, take n ranking positions as a ranking interval, and establish a mapping relationship between the ranking interval and the ranking level, so as to convert the ranking of the daily passenger flow volume between stations into the corresponding ranking level.
[0017] Furthermore, the calculation of the entropy value of the daily passenger flow ranking level in the OD passenger flow data between each pair of stations during the research period includes:
[0018]
[0019] Wherein, H(Y i) represents the information entropy value corresponding to the ranking of the daily flow of OD between the i-th stations in multi-day observations; y i represents the specific ranking of the daily flow of OD between the i-th stations, Y i is the set of y i and contains all the rankings of the daily flow of OD that have occurred between the i-th stations in multi-day observations, p(y i ) represents the probability that the ranking of the daily flow in multi-day observations is y i , H(Z i ) represents the information entropy value corresponding to the ranking level of the daily flow of OD between the i-th stations in multi-day observations, z i represents the specific level of the daily flow ranking of OD between the i-th stations, Z i is the set of z i and contains all the ranking levels of the daily flow that have occurred between the i-th stations in multi-day observations; p(z i ) represents the probability that the ranking level of the daily flow in multi-day observations is z i .
[0020] Furthermore, the influence of the division method of the analysis ranking interval on the entropy value calculation result includes:
[0021] Adjust the division strategy of the ranking interval, calculate the entropy value of the ranking level of the daily passenger flow between each pair of stations after adjustment, conduct a statistical distribution analysis on the calculation results, and select the entropy value H 1 as the stability threshold, select H 2 as the instability threshold, and classify the OD passenger flow between stations according to the key entropy value thresholds H 1 and H 2 .
[0022] Furthermore, classify the OD passenger flow between stations according to the stability threshold H 1 and the instability threshold H 2 , which specifically includes:
[0023] Classify the OD passenger flow between stations into stable OD passenger flow, unstable OD passenger flow, and chaotic OD passenger flow. The stable OD passenger flow is the OD passenger flow between stations where the entropy value H is within the range of 0 < H < H 1 , indicating that the ranking level of the daily passenger flow remains relatively stable in multi-day observations; the unstable OD passenger flow is the OD passenger flow between stations where the entropy value H is within the range of H 1 < H < H 2 , indicating that the ranking level of the daily passenger flow shows a certain degree of volatility in multi-day observations; the chaotic OD passenger flow is the OD passenger flow between stations where the entropy value H > H 2The OD passenger flow between stations within the range indicates that during multi-day observations, the ranking level of daily passenger flow fluctuates significantly and has high instability.
[0024] Compared with the prior art, the present invention has the following advantages:
[0025] 1. The method for classifying OD passenger flow between rail transit stations by integrating information entropy and daily flow provided by the present invention focuses on using the daily passenger flow ranking information between stations OD rather than the daily passenger flow itself for classification.
[0026] First of all, from the perspective of relativity, as an absolute numerical index, the daily passenger flow is difficult to intuitively reveal the passenger flow differences and dynamic changes between different OD pairs of stations. By using the daily passenger flow ranking for analysis, this absolute value can be transformed into a relative value, making the passenger flow situations between different OD pairs of stations comparable. This relative analysis perspective helps to more clearly identify which stations or OD pairs have stable passenger flow performance and which ones have large fluctuations, thus providing a more accurate reference basis for the formulation of operation strategies.
[0027] Secondly, the daily passenger flow ranking analysis shows significant advantages in capturing the dynamic changes of passenger flow. The passenger flow fluctuates due to various factors such as time, weather, and holidays, and it is difficult to accurately capture these subtle changes by directly analyzing the passenger flow. However, the daily passenger flow ranking analysis can reflect the relative position changes of the passenger flow at multiple time points or time periods, thus more intuitively showing the volatility and trend of the passenger flow. This capture of dynamics is crucial for deeply understanding the change rules of passenger flow and predicting future passenger flow situations.
[0028] In addition, the daily passenger flow ranking analysis also has excellent anti-noise ability. During the actual operation process, the passenger flow data may be interfered by various accidental events and noises, which may mislead the analysis of the passenger flow itself. However, the daily passenger flow ranking analysis is based on relative relationships and has strong resistance to small-amplitude noise fluctuations, so it can more accurately reflect the true situation of the passenger flow.
[0029] 2. The method for classifying OD passenger flow between rail transit stations by integrating information entropy and daily flow provided by the present invention innovatively uses information entropy as the core statistical index to achieve a fine quantitative analysis of the ranking fluctuations of OD passenger flow between stations over multiple days. The application of this method not only significantly improves the accuracy of capturing the dynamic characteristics of OD passenger flow between stations, but also deeply reveals the internal laws of passenger flow changes, providing a solid data basis and strong support for the scientific formulation of operation strategies.
[0030] In the complex field of data analysis, although mean analysis can provide an overall view of a dataset, it often fails to comprehensively and meticulously describe the essence of a phenomenon due to neglecting the influence of random events. Especially in the analysis of OD passenger flow between stations in urban rail transit, if only relying on the daily passenger flow mean within the research period as the classification criterion, its limitations cannot be ignored. Traditionally, variance analysis has been used as a means to reveal the volatility of data, which can show the degree of deviation between the data and the mean. However, variance mainly focuses on the dispersion of data points and cannot directly and comprehensively reflect the inherent uncertainty and randomness of passenger flow.
[0031] To more accurately capture uncertainty and deeply explore the characteristics of passenger flow dynamics, the present invention introduces the key statistical indicator of information entropy. In information theory, the entropy value is used to quantify the uncertainty or degree of chaos of information and is suitable for dealing with data with probability distribution characteristics. In the analysis scenario of OD passenger flow between stations, the entropy value can accurately reflect the uncertainty and complexity presented by passenger flow due to various influencing factors. Compared with variance, the entropy value not only considers the discreteness of data but also directly measures the uncertainty of information, thus providing a more in-depth perspective for understanding the characteristics of passenger flow dynamics.
[0032] In addition, the entropy value also has excellent anti-noise performance. Since its calculation is based on probability distribution, it has high robustness to small changes in data and can effectively resist the interference of noise, thereby more accurately revealing the true situation of passenger flow. In contrast, variance may produce misjudgments due to the influence of noise, thus affecting the accurate understanding of passenger flow changes.
[0033] Based on the above reasons, the present invention can be widely promoted in fields such as rail transit. BRIEF DESCRIPTION OF THE DRAWINGS
[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.
[0035] Figure 1 It is a flowchart of the OD passenger flow classification method between rail transit stations that integrates information entropy and daily flow in the present invention.
[0036] Figure 2 It is the sensitivity analysis result of different ranking interval division strategies in the embodiments of the present invention. DETAILED DESCRIPTION OF THE INVENTION
[0037] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.
[0038] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only a part rather than all of the embodiments of the present invention. The description of at least one exemplary embodiment is actually only illustrative and in no way restrictive of the present invention and its application or use. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.
[0039] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments of the present invention. As used herein, unless otherwise clearly specified in the context, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of the described features, steps, operations, devices, components and / or their combinations.
[0040] Unless otherwise specifically stated, the relative arrangements, numerical expressions and values of the components and steps set forth in these embodiments do not limit the scope of the present invention. At the same time, it should be clear that, for the convenience of description, the dimensions of the various parts shown in the drawings are not drawn in actual proportional relationship. Technologies, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the said technologies, methods and devices should be regarded as part of the authorized specification. In all the examples shown and discussed here, any specific value should be construed as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.
[0041] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by orientation words such as "front, back, top, bottom, left, right", "horizontal, vertical, perpendicular, horizontal" and "top, bottom", etc. is usually based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description. Without contrary explanations, these orientation words do not indicate and imply that the devices or elements referred to must have a specific orientation or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation on the protection scope of the present invention: the orientation words "inside, outside" refer to the inside and outside relative to the contour of each component itself.
[0042] For the sake of convenience of description, spatial relative terms such as "above...", "over...", "on the upper surface of...", "above-mentioned", etc. can be used here to describe the spatial positional relationship between a device or feature shown in the drawings and other devices or features. It should be understood that the spatial relative terms are intended to include different orientations in use or operation in addition to the orientation described in the drawings for the device. For example, if the device in the drawings is inverted, the device described as "above other devices or structures" or "over other devices or structures" will then be positioned as "below other devices or structures" or "under other devices or structures". Thus, the exemplary term "above..." can include both the orientations of "above..." and "below...". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and corresponding explanations are made for the spatial relative descriptions used here.
[0043] In addition, it should be noted that using words such as "first", "second", etc. to limit components is only for the convenience of distinguishing the corresponding components. Without additional declarations, the above words have no special meanings. Therefore, it should not be construed as a limitation on the protection scope of the present invention.
[0044] As Figure 1 shown, the present invention provides an OD passenger flow classification method between rail transit stations that combines information entropy and daily flow, including:
[0045] Obtain the OD passenger flow data between stations during the research period;
[0046] Specifically in implementation, as a preferred implementation manner of the present invention, the OD passenger flow data between stations includes: date, origin station, destination station, and passenger flow; the date is used to specifically study a certain day within the research period, and the passenger flow is the passenger flow quantity for the whole day corresponding to the date. As shown in Table 1.
[0047] Table 1 OD Passenger Flow Data between Stations
[0048] Field Description Date A certain day during the research period Origin station Origin station name Destination station Destination station name Passenger flow Number of trips throughout the day
[0049] In implementation, obtain the OD passenger flow data between stations during the research period from the AFC system (Automatic Fare Collection) of the analysis object.
[0050] Sort the daily passenger flow volume in the OD passenger flow data between stations daily, and set the ranking interval and ranking level.
[0051] In specific implementation, as a preferred implementation manner of the present invention, setting the ranking interval and ranking level includes:
[0052] Sort the daily passenger flow volume in the OD passenger flow data between stations, take n ranking positions as a ranking interval, establish the mapping relationship between the ranking interval and the ranking level, and convert the ranking of the daily passenger flow volume between stations OD into the corresponding ranking level.
[0053] According to the ranking level of the daily passenger flow volume of each pair of OD passenger flow data between stations, calculate the entropy value of the ranking level of the daily passenger flow volume in each pair of OD passenger flow data between stations during the research period.
[0054] In specific implementation, as a preferred implementation manner of the present invention, calculating the entropy value of the ranking level of the daily passenger flow volume in each pair of OD passenger flow data between stations during the research period includes:
[0055]
[0056] Among them, H(Y i ) represents the information entropy value corresponding to the ranking position of the daily flow volume of the i-th OD between stations in multi-day observations; y i represents the specific ranking position of the daily flow volume ranking of the i-th OD between stations, Y i is the set of y i , including all the ranking positions of the daily flow volume that have occurred for the i-th OD between stations in multi-day observations, p(y i ) represents the probability that the ranking position of the daily flow volume in multi-day observations is y i , H(Z i ) represents the information entropy value corresponding to the ranking level of the daily flow volume of the i-th OD between stations in multi-day observations, z i represents the specific level of the daily flow volume ranking of the i-th OD between stations, Z i is the set of z i , including all the ranking levels of the daily flow volume that have occurred for the i-th OD between stations in multi-day observations; p(z i ) represents the probability that the ranking level of the daily flow volume in multi-day observations is z i .
[0057] Analyze the influence of the division method of the ranking interval on the entropy value calculation result.
[0058] In specific implementation, as a preferred implementation manner of the present invention, analyze the influence of the division method of the ranking interval on the entropy value calculation result, including:
[0059] Adjust the division strategy of the ranking interval, calculate the entropy value of the daily passenger flow ranking level between each pair of stations after adjustment, conduct a statistical distribution analysis on the calculation results, and select the entropy value H 1 As the stability threshold, select H 2 As the instability threshold, and according to the key entropy value threshold H 1 And H 2 Classify the OD passenger flow between stations.
[0060] Classify the OD passenger flow between stations according to the entropy value of the daily flow ranking level.
[0061] In specific implementation, as a preferred implementation manner of the present invention, classify the OD passenger flow between stations according to the stability threshold H 1 And the instability threshold H 2 Specifically include:
[0062] Divide the OD passenger flow between stations into stable OD passenger flow, unstable OD passenger flow and chaotic OD passenger flow. The stable OD passenger flow is the OD passenger flow between stations where the entropy value H is in the range of 0 < H < H 1 Range, indicating that the daily passenger flow ranking level remains relatively stable during multi-day observations; the unstable OD passenger flow is the OD passenger flow between stations where the entropy value H is in the range of H 1 < H < H 2 Range, indicating that during multi-day observations, the daily passenger flow ranking level shows a certain degree of volatility; the chaotic OD passenger flow is the OD passenger flow between stations where the entropy value H > H 2 Range, indicating that during multi-day observations, the daily passenger flow ranking level fluctuates significantly and has a high degree of instability.
[0063] Embodiment
[0064] As Figure 1 Shown, the present invention provides a method for classifying the OD passenger flow between rail transit stations by integrating information entropy and daily flow. In this embodiment, eight different ranking interval division schemes are studied and designed, and each scheme sets the ranking level to cover 5, 10, 15, 20, 25, 30, 40, 50 ranking positions respectively. Subsequently, the entropy value of the daily passenger flow ranking level between stations under each division scheme is calculated, and a statistical distribution analysis is conducted on the calculation results.
[0065] Figure 2 In, the horizontal axis represents the range of the entropy value of the daily passenger flow ranking between stations calculated, and the vertical axis shows the number of OD between stations corresponding to each specific entropy value. According to Figure 2From the results, it can be observed that under the eight partitioning strategies, when the entropy values are 0, 0.72, 0.97, 1.37, and 1.52 respectively, the number of OD between stations shows relatively high stability and the change in quantity is relatively gentle. However, with the adjustment of the ranking interval partitioning, especially when the entropy value reaches 1.92, the number of OD between stations begins to increase significantly; and when the entropy value exceeds 1.92, this quantity begins to decrease significantly. This finding indicates that for the OD between stations with a small entropy value, the change in the width of the ranking interval covered by a single ranking level has no significant impact on it. A small entropy value means that the ranking levels of these OD between stations are relatively stable over multiple days. Therefore, it can be inferred that the change in the width of the ranking interval covered by a single ranking level will not have a significant impact on using the classification method proposed in this study to identify the stable OD passenger flow between stations.
[0066] According to the calculation results of the entropy value of the daily passenger flow ranking level, the OD passenger flow between stations is classified in detail. Taking the selected case as the research object, 1.52 and 1.92 are selected as the key entropy value thresholds, and accordingly, the OD passenger flow between stations is divided into three types. Specifically, the OD passenger flow between stations with a low entropy value (less than or equal to 1.52) is classified into the first type. This type of passenger flow has maintained a relatively stable daily passenger flow ranking level during multi-day observations and is defined as "stable" OD passenger flow; the OD passenger flow between stations with an entropy value of 1.92 constitutes the second type, and its daily passenger flow ranking level shows a certain degree of volatility and is classified as "unstable" OD passenger flow; while the OD passenger flow between stations with an entropy value exceeding 1.92 is classified as "chaotic" OD passenger flow due to the significant fluctuation of the daily passenger flow ranking level over multiple days, which reflects the high instability of this type of OD passenger flow.
[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.
Claims
1. A rail transit station OD passenger flow classification method integrating information entropy and daily flow, characterized in that: Including: Obtain the OD passenger flow data between stations during the research period; Sort the daily passenger flow in the OD passenger flow data between stations daily, set a ranking interval and ranking levels; According to the ranking levels of the daily passenger flow of each pair of OD passenger flow data between stations, calculate the entropy value of the ranking levels of the daily passenger flow in each pair of OD passenger flow data between stations during the research period; Analyze the influence of the division method of the ranking interval on the entropy value calculation result; Classify the OD passenger flow between stations according to the entropy value of the daily flow ranking level.
2. The OD passenger flow classification method between rail transit stations integrating information entropy and daily flow according to claim 1 is characterized in that: The OD passenger flow data between stations includes: date, starting station, terminal station, and passenger flow; the date is for a specific day within the research period, and the passenger flow is the passenger flow quantity for the whole day corresponding to the date.
3. The method for classifying OD passenger flow between rail transit stations by integrating information entropy and daily flow according to claim 1 is characterized in that: The setting of the ranking interval and ranking levels includes: Sort the daily passenger flow in the OD passenger flow data between stations, take n ranking positions as a ranking interval, establish a mapping relationship between the ranking interval and ranking levels, and convert the ranking of the daily passenger flow between stations into the corresponding ranking levels.
4. The OD passenger flow classification method between rail transit stations integrating information entropy and daily flow according to claim 1 is characterized in that: The calculation of the entropy value of the ranking levels of the daily passenger flow in each pair of OD passenger flow data between stations during the research period includes: Among them, H(Y i ) represents the information entropy value corresponding to the daily flow ranking of OD between the i-th stations in multi-day observations; y i represents the specific ranking of the OD daily traffic ranking between the i-th sites, Y i Yes i The set contains the ranking of all daily flow rates of OD between the i-th stations in multi-day observations, p(y i ) indicates that the daily flow ranking in multi-day observations is y i The probability of H(Z i ) represents the information entropy value corresponding to the daily flow ranking of OD between the i-th stations in multi-day observations, z i represents the specific level of the OD daily traffic ranking between the i-th sites, Z i Yes i The set of all daily flow rankings of OD between the i-th stations in multi-day observations; p(z i ) indicates that the daily flow ranking level in multi-day observation is z i probability.
5. The method for classifying OD passenger flow between rail transit stations by integrating information entropy and daily flow according to claim 1 is characterized in that: The analysis of the influence of the division method of the ranking interval on the entropy value calculation result includes: Adjust the division strategy of the ranking interval, calculate the entropy value of the ranking levels of the daily passenger flow in each pair of OD passenger flow data between stations after adjustment, conduct a statistical distribution analysis on the calculation results, select the entropy value H1 as the stability threshold, select H2 as the instability threshold, and classify the OD passenger flow between stations according to the key entropy value thresholds H1 and H2.
6. The method for classifying OD passenger flow between rail transit stations by integrating information entropy and daily flow according to claim 1 is characterized in that: Classify the OD passenger flow between stations according to the stability threshold H1 and instability threshold H2, specifically including: Classify the OD passenger flow between stations into stable OD passenger flow, unstable OD passenger flow, and chaotic OD passenger flow. The stable OD passenger flow is the OD passenger flow between stations where the entropy value H is within the range of 0 < H < H1, indicating that the daily passenger flow ranking level remains relatively stable during multi-day observations; the unstable OD passenger flow is the OD passenger flow between stations where the entropy value H is within the range of H1 < H < H2, indicating that during multi-day observations, the daily passenger flow ranking level shows a certain degree of volatility; the chaotic OD passenger flow is the OD passenger flow between stations where the entropy value H > H2, indicating that during multi-day observations, the daily passenger flow ranking level fluctuates significantly and has a high degree of instability.