Station grading method and system based on line passenger flow prediction data

Abstract

The invention discloses a station grading method and system based on line passenger flow prediction data, and the method comprises the steps: obtaining the line passenger flow prediction data, calculating a station transportation capability grading coefficient according to the line passenger flow prediction data, and calculating a line transportation capability grading coefficient according to the station transportation capability grading coefficient; according to the line passenger flow prediction data, calculating a station boarding and alighting capability grading coefficient, and according to the station boarding and alighting capability grading coefficient, calculating a line boarding and alighting capability grading coefficient; and determining the step number of the station grading of the current line, and calculating the station grading passenger flow volume of each step according to the line transportation capability grading coefficient and / or the line boarding and alighting capability grading coefficient.

Description

Technical Field

[0001] The present invention belongs to the technical field of rail transit station classification, and more specifically, relates to a station classification method and system based on line passenger flow prediction data. Background Art

[0002] At present, the classification of stations in the domestic rail transit industry is basically based on factors such as the nature of urban planning land use and important urban service functions. It focuses on qualitative rather than quantitative factors and belongs to a macro-qualitative classification method and system. This scheme is clearly stated in the Beijing Landmark "Urban Rail Transit Engineering Design Code". This method emphasizes that under the premise of meeting the basic construction standards and design specifications, it is divided into three categories: Class A travel, Class B leisure gathering, and Class C commuting according to the environmental functional positioning of the service area, the characteristics of the dominant passenger flow behavior of the station, and the degree of influence on the design parameters and configuration standards of station facilities. It is also based on the principle of combining the extensiveness of passenger flow composition types with the risk impact of passenger flow scale, and is classified according to the differences in the dominant passenger flow's requirements for the basic functional use of the station.

[0003] The current classification and grading system is mainly related to the environmental functional positioning of the station's surroundings. As the density of stations increases, the service radius of the station will shrink accordingly, and the functional positioning of the surrounding environment will become clearer, and vice versa; and even though human intervention in urban development may lead to major changes in environmental functional positioning and drastic fluctuations in population size, considering that changes in environmental functional positioning have a long historical cycle, a method has been formed to identify the dominant passenger behavior characteristics according to the environmental functional positioning within the station's service area.

[0004] However, passenger flow forecast data, an important support for the construction of rail transit lines, is only used in the design of rail transit stations for procedural calculations of station platform width, the capacity of escalators and other service facilities, and the evacuation capacity. It remains at the level of capacity calculation, but is not used as the primary basis for guiding the determination of station grade classification, and does not reflect its important position in rail transit design.

[0005] In fact, line passenger flow forecasting is a crucial link in the planning, operation and management of urban rail transit. It is based on historical data, urban development planning, traffic network structure and other factors to predict and analyze the passenger flow distribution and flow characteristics in a certain period of time in the future, providing a scientific basis for the construction, optimization and operation of urban rail transit systems. The forecast results mainly include the peak hour and all-day inter-station OD passenger flow of the line, the peak hour and all-day boarding and alighting passenger flow of each station, the peak hour and all-day transfer volume, etc., which can be used to guide the construction scale, vehicle configuration, operation plan and station facility configuration, operation management, passenger flow organization, etc. of the urban rail transit system. Therefore, the project construction and operation stages attach great importance to the line passenger flow forecast results. By adopting appropriate forecasting models and methods, the accuracy and reliability of the forecast results are ensured, and the line and station design are more accurate and refined. Summary of the invention

[0006] In order to solve the above technical problems, the present invention proposes a station classification method based on line passenger flow prediction data, comprising:

[0007] Acquire route passenger flow forecast data, calculate station transport capacity classification coefficient according to the route passenger flow forecast data, and calculate route transport capacity classification coefficient according to the station transport capacity classification coefficient;

[0008] Calculate the station boarding and alighting capacity classification coefficient according to the line passenger flow forecast data, and calculate the line boarding and alighting capacity classification coefficient according to the station boarding and alighting capacity classification coefficient;

[0009] The number of station-graded steps of the current line is determined, and the station-graded passenger flow of each step is calculated according to the line transport capacity grading coefficient and / or the line boarding and alighting capacity grading coefficient.

[0010] Further, calculating the station transportation capacity classification coefficient includes:

[0011] K y =Q 上下 *α / Y 运

[0012] Calculating the line transport capacity classification coefficient includes:

[0013] MK y =(K y1 +K y2 +K y3 +……+K yn ) / n

[0014] Among them, K y is the station transport capacity classification coefficient, Q 上下 is the number of passengers getting on and off the train during peak hours at the station, Y 运is the peak hour system transport capacity, MK y is the line transport capacity classification coefficient, K yn It is the station transport capacity classification coefficient of the nth non-transfer station in the current line.

[0015] Further, calculating the station boarding and alighting capacity classification coefficient includes:

[0016] K J =Q 上下 *α / Q MAX

[0017] Calculating the line boarding and alighting capacity classification coefficient includes:

[0018] MK J =(K J1 +K J2 +K J3 +……+K Jn ) / n

[0019] Among them, K J is the station's passenger and alighting capacity classification coefficient, Q MAX MK is the passenger volume value of the station with the largest number of passengers getting on and off during peak hours among all non-transfer stations on the line. J is the line boarding and alighting capacity classification coefficient, K Jn It is the station boarding and alighting capacity classification coefficient of the nth non-transfer station in the current line.

[0020] Furthermore, determining the number of steps of the station classification of the current line includes:

[0021] If K Yn Smaller than MK Y or K Jn Smaller than MK J If the number of is greater than 70%, then K B =2;

[0022] If K Yn Smaller than MK Y or K Jn Smaller than MK J If the number of is less than 70%, then K B =3;

[0023] Among them, K B is the number of steps in the current line station classification, if K B =2, it is divided into 2 levels. If K B =3, it is divided into 3 levels.

[0024] Furthermore, the calculation of the station-level passenger flow of each level includes:

[0025] The station-level passenger flow of the first level of the current line station level is FQ1:

[0026] FQ1={Y 运 *MK y , Q MAX *MK J}

[0027] Among them, FQ1 is to take Y 运 *MK y and Q MAX *MK J The relatively larger value of the two calculation results is used as the station-level passenger flow of the first tier;

[0028] The station-level passenger flow of the second level of the current line station level is FQ2:

[0029] FQ2=FQ1*K t

[0030] Among them, K t The station classification adjustment coefficient for the second level of the current line station classification, based on MK y The calculation result is used to take a balance coefficient of 1.4 to 1.6 to reasonably determine the graded passenger flow of the second echelon and achieve a match in the number of stations of the second and third echelons.

[0031] The present invention also proposes a station classification system based on route passenger flow prediction data, comprising:

[0032] A module for determining a line transport capacity classification coefficient is used to obtain line passenger flow forecast data, calculate a station transport capacity classification coefficient based on the line passenger flow forecast data, and calculate a line transport capacity classification coefficient based on the station transport capacity classification coefficient;

[0033] A module for determining a line boarding and alighting capacity classification coefficient is used to calculate a station boarding and alighting capacity classification coefficient according to the line passenger flow forecast data, and to calculate a line boarding and alighting capacity classification coefficient according to the station boarding and alighting capacity classification coefficient;

[0034] The station classification module is used to determine the number of station classification levels of the current line, and calculate the station classification passenger flow of each level according to the line transportation capacity classification coefficient and / or the line boarding and alighting capacity classification coefficient.

[0035] Further, calculating the station transportation capacity classification coefficient includes:

[0036] K y =Q 上下 *α / Y 运

[0037] Calculating the line transport capacity classification coefficient includes:

[0038] MK y =(K y1 +K y2 +K y3 +……+K yn ) / n

[0039] Among them, K y is the station transport capacity classification coefficient, Q 上下 is the number of passengers getting on and off the train during peak hours at the station, Y 运 is the peak hour system transport capacity, MK y is the line transport capacity classification coefficient, K yn It is the station transport capacity classification coefficient of the nth non-transfer station in the current line.

[0040] Further, calculating the station boarding and alighting capacity classification coefficient includes:

[0041] K J =Q 上下 *α / Q MAX

[0042] Calculating the line boarding and alighting capacity classification coefficient includes:

[0043] MK J =(K J1 +K J2 +K J3 +……+K Jn ) / n

[0044] Among them, K J is the station's passenger and alighting capacity classification coefficient, Q MAX MK is the passenger volume value of the station with the largest number of passengers getting on and off during peak hours among all non-transfer stations on the line. J is the line boarding and alighting capacity classification coefficient, K Jn It is the station boarding and alighting capacity classification coefficient of the nth non-transfer station in the current line.

[0045] Furthermore, determining the number of steps of the station classification of the current line includes:

[0046] If K Yn Smaller than MK Y or K Jn Smaller than MK J If the number of is greater than 70%, then K B =2;

[0047] If K Yn Smaller than MK Y or K Jn Smaller than MK JIf the number of is less than 70%, then K B =3;

[0048] Among them, K B is the number of steps in the current line station classification, if K B =2, it is divided into 2 levels. If K B =3, it is divided into 3 levels.

[0049] Furthermore, the calculation of the station-level passenger flow of each level includes:

[0050] The station-level passenger flow of the first level of the current line station level is FQ1:

[0051] FQ1={Y 运 *MK y , Q MAX *MK J}

[0052] Among them, FQ1 is to take Y 运 *MK y and Q MAX *MK J The relatively larger value of the two calculation results is used as the station-level passenger flow of the first tier;

[0053] The station-level passenger flow of the second level of the current line station level is FQ2:

[0054] FQ2=FQ1*K t

[0055] Among them, K t The station classification adjustment coefficient for the second level of the current line station classification. y The calculation result is used to take a balance coefficient of 1.4 to 1.6 to reasonably determine the graded passenger flow of the second echelon and achieve a match in the number of stations of the second and third echelons.

[0056] like Figure 3-6 As shown, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art:

[0057] The present invention compares and analyzes the station classification system with the peak hour passenger and alighting volumes and line transportation capacity data that best reflect the passenger flow conditions of the station, thereby realizing that a rail transit line can determine the different levels of large, medium and small stations according to the actual needs of the station users. This lays a good foundation for the subsequent determination of the standards for various service facilities inside and outside the station, and is also the first step in achieving the differentiation and precision of station service facilities, and is an urgent need for the high-quality development of rail transit.

[0058] The station capacity classification coefficient and passenger and alighting classification coefficient proposed in the present invention fully take into account the differences in passenger flow levels of lines with different marshaling, and innovatively use the comparison logic of peak hour passenger flow with line transportation capacity and maximum peak hour passenger flow in line stations as the key indicator for station classification, and then determine the critical peak hour passenger and alighting volume value for station classification, thereby realizing the refined design of non-transfer stations and avoiding the homogenization of standards and the waste of construction scale and service facilities.

[0059] Taking into account that the operation organization plans and vehicle formations of different rail transit lines will vary, the selection of peak hour passenger and alighting volume also requires the reasonable selection of various station classification coefficients to meet the passenger and alighting volume characteristics of each line and station, and by refining the graded passenger flow indicators of large, medium and small stations in different periods of different marshalling lines, to achieve detailed guidance on station design. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a flow chart of the method of embodiment 1 of the present invention;

[0061] Figure 2 is a system structure diagram of Embodiment 2 of the present invention;

[0062] Figure 3 This is an example diagram of the ratio of the peak hour passenger and alighting volume of a station and the peak hour line system capacity in the fifth phase line of a certain city in the south of the present invention;

[0063] Figure 4 This is a recommended map of station classification for the 4A line of the fifth phase of a certain city in the south of the present invention;

[0064] Figure 5 This is a recommended map of station classification for the 6A line of the fifth phase of a certain city in the south of the present invention;

[0065] Figure 6 This is a recommended map of station classification for the 8A line of the fifth phase of a certain city in the south of the present invention. DETAILED DESCRIPTION

[0066] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0067] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0068] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0069] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0070] The display is used to show the interactive cross-section of each application.

[0071] All subscripts in the formulas of the present invention are only used to distinguish parameters and have no actual meaning.

[0072] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0073] Example 1

[0074] like Figure 1 As shown, the embodiment of the present invention provides a station classification method based on line passenger flow prediction data, which divides the stations into different levels according to the core elements such as the peak hour passenger and alighting volume of the station and the line transportation capacity that best reflect the line and station passenger flow index status, proposes quantitative indicators such as station classification coefficient, corresponds to the predicted amount of each station in a line, and determines the station classification system for the entire line of this line, which is specifically shown as follows:

[0075] (1) Calculation of station classification coefficient based on line transport capacity

[0076] 1. Station transport capacity classification coefficient K y

[0077] The station should determine the station transport capacity classification coefficient K according to the ratio of peak hour passenger and alighting volume to peak hour system transport capacity (K). y

[0078] K y =Q 上下 *α / Y 运

[0079] Where: Q 上下 ——The number of passengers getting on and off the train during peak hours at the station,

[0080] α——super peak coefficient of the station;

[0081] Y 运 ——Peak hour system transport capacity is determined based on the vehicle marshaling capacity and the number of train pairs in the operation organization;

[0082] 2. Line transport capacity classification coefficient MK y

[0083] The station transport capacity classification coefficient K of all non-transfer stations y Perform weighted calculation, weighted average K yn It is the line transport capacity classification coefficient, which can be used as the value parameter of the first level passenger flow FQ1 of station classification.

[0084] MK y =(K y1 +K y2 +K y3 +……+K yn ) / n

[0085] Where: K yn ——Station transport capacity classification coefficient of the nth non-transfer station on this line;

[0086] n——the number of non-transfer stations on this line;

[0087] MK y ——The weighted average of the transport capacity classification coefficients of the n non-transfer stations on this line;

[0088] FQ1 – passenger flow at the first level in the station classification system;

[0089] (2) Calculation of station classification coefficient based on predicted passenger flow at the station

[0090] 1. Station passenger and alighting capacity classification coefficient K J

[0091] The station should determine the station's passenger and alighting capacity classification coefficient K according to the ratio of the station's peak hour passenger and alighting volume to the maximum peak hour passenger and alighting volume of non-transfer stations on the entire line (K). J

[0092] K J =Q 上下 *α / Q MAX

[0093] Where: Q 上下 ——The number of passengers getting on and off the train during peak hours at the station,

[0094] α——super peak coefficient of the station;

[0095] QMAX ——The passenger volume value of the station with the largest number of passengers getting on and off during peak hours among all non-transfer stations on the entire line;

[0096] 2. Line passenger and alighting capacity classification coefficient MK J

[0097] The station transport capacity classification coefficient K of all non-transfer stations J Perform weighted calculation, weighted average K Jn It is the line transport capacity classification coefficient, which can be used as the value parameter of the first level passenger flow FQ1 of station classification.

[0098] MK J =(K J1 +K J2 +K J3 +……+K Jn ) / n

[0099] Where: K Jn ——The station boarding and alighting capacity classification coefficient of the nth non-transfer station on this line;

[0100] n——the number of non-transfer stations on this line;

[0101] MK J ——The weighted average of the boarding and alighting capacity classification coefficients of the n non-transfer stations on this line;

[0102] (3) Calculation of station classification system based on multi-level management

[0103] 1. Station step coefficient K B

[0104] If K Yn Smaller than MK Y or K Jn Smaller than MK J The number is greater than 70%, K B =2;

[0105] If K Yn Smaller than MK Y or K Jn Smaller than MK J The number is less than 70%, K B =3;

[0106] Where: K B ——The number of steps in the station classification of this line, if K B =2, it should be divided into 2 levels. If K B =3, it should be divided into 3 levels.

[0107] 2. The passenger flow of the first level of the line station FQ1

[0108] FQ1={Y 运 *MK y， Q MAX *MK J}

[0109] Where: FQ1 is Y 运 *MK y and Q MAX *MK J The larger value of the two calculated results is used as the graded passenger flow of the first level.

[0110] 3. The passenger flow of the second level of the line station FQ2

[0111] FQ2=FQ1*K t

[0112] Where: K t ——Station classification adjustment coefficient for the second level of the line. y The calculation result is used to take a balance coefficient of 1.4 to 1.6 to reasonably determine the graded passenger flow of the second echelon and achieve a match in the number of stations of the second and third echelons.

[0113] It also includes a size classification of stations, as follows:

[0114] SRI=(Q 上下 ) α *(Y 运 ) β *(K y ) a *(K J ) b / [(MaxPCD) γ *(MK y ) δ *(MK J ) d ]

[0115] Thresh1=(MK y ) η *(MK J ) μ *MaxPCD*e -p*SRI

[0116] Thresh2=(MK J ) θ *MaxPCD*e ρ*SRI

[0117] Among them, SRI is the station classification index, α is the first weight, β is the second weight, a is the third weight, b is the fourth weight, γ is the fifth weight, MaxPCD is the maximum peak passenger and alighting volume of non-transfer stations on the entire line, δ is the sixth weight, d is the seventh weight, Thresh1 is the first classification critical value, η is the eighth weight, μ is the ninth weight, ρ′ is the tenth weight, Thresh2 is the second classification critical value, θ is the eleventh weight, and ρ is the twelfth weight.

[0118] When,SRI<Thresh1, it is a small station;

[0119] When,Thresh2>SRI≥Thresh1, it is a medium-sized station;

[0120] When SRI ≥ Thresh2, it is a large station.

[0121] Example 2

[0122] like Figure 2 As shown, the embodiment of the present invention also proposes a station classification system based on line passenger flow prediction data, including:

[0123] A module for determining a line transport capacity classification coefficient is used to obtain line passenger flow forecast data, calculate a station transport capacity classification coefficient based on the line passenger flow forecast data, and calculate a line transport capacity classification coefficient based on the station transport capacity classification coefficient;

[0124] Specifically, calculating the station transportation capacity classification coefficient includes:

[0125] K y =Q 上下 *α / Y 运

[0126] Calculating the line transport capacity classification coefficient includes:

[0127] MK y =(K y1 +K y2 +K y3 +……+K yn ) / n

[0128] Among them, K y is the station transport capacity classification coefficient, Q 上下 is the number of passengers getting on and off the train during peak hours at the station, Y 运 is the peak hour system transport capacity, MK y is the line transport capacity classification coefficient, K yn It is the station transport capacity classification coefficient of the nth non-transfer station in the current line.

[0129] A module for determining a line boarding and alighting capacity classification coefficient is used to calculate a station boarding and alighting capacity classification coefficient according to the line passenger flow forecast data, and to calculate a line boarding and alighting capacity classification coefficient according to the station boarding and alighting capacity classification coefficient;

[0130] Specifically, calculating the station's boarding and alighting capacity classification coefficient includes:

[0131] K J =Q 上下 *α / Q MAX

[0132] Calculating the line boarding and alighting capacity classification coefficient includes:

[0133] MK J =(K J1 +K J2 +K J3 +……+K Jn ) / n

[0134] Among them, K J is the station's passenger and alighting capacity classification coefficient, Q MAX MK is the passenger volume value of the station with the largest number of passengers getting on and off during peak hours among all non-transfer stations on the line. J is the line boarding and alighting capacity classification coefficient, K Jn It is the station boarding and alighting capacity classification coefficient of the nth non-transfer station in the current line.

[0135] The station classification module is used to determine that all stations on a current line are divided into multiple levels, and calculate the station classification passenger flow of each level based on the line transportation capacity classification coefficient and / or the line boarding and alighting capacity classification coefficient.

[0136] Specifically, the number of steps for determining the station classification of the current line includes:

[0137] If K Yn Smaller than MK Y or K Jn Smaller than MK J If the number of is greater than 70%, then K B =2;

[0138] If K Yn Smaller than MK Y or K Jn Smaller than MK J If the number of is less than 70%, then K B =3;

[0139] Among them, K B is the number of steps in the current line station classification, if K B =2, it is divided into 2 levels. If K B=3, it is divided into 3 levels.

[0140] Specifically, the calculation of the station-level passenger flow of each level includes:

[0141] The station-level passenger flow of the first level of the current line station level is FQ1:

[0142] FQ1={Y 运 *MK y , Q MAX *MK J}

[0143] Among them, FQ1 is to take Y 运 *MK y and Q MAX *MK J The relatively larger value of the two calculation results is used as the station-level passenger flow of the first tier;

[0144] The station-level passenger flow of the second level of the current line station level is FQ2:

[0145] FQ2=FQ1*K t

[0146] Among them, K t The station classification adjustment coefficient for the second level of the current line station classification. y The calculation result is used to take a balance coefficient of 1.4 to 1.6 to reasonably determine the graded passenger flow of the second echelon and achieve a match in the number of stations of the second and third echelons.

[0147] Example 3

[0148] The embodiment of the present invention further proposes a storage medium storing a plurality of instructions, wherein the instructions are used to implement the station classification method based on line passenger flow prediction data.

[0149] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0150] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: obtaining route passenger flow prediction data, calculating a station transport capacity classification coefficient according to the route passenger flow prediction data, and calculating a route transport capacity classification coefficient according to the station transport capacity classification coefficient;

[0151] Specifically, calculating the station transportation capacity classification coefficient includes:

[0152] K y =Q 上下*α / Y 运

[0153] Calculating the line transport capacity classification coefficient includes:

[0154] MK y =(K y1 +K y2 +K y3 +……+K yn ) / n

[0155] Among them, K y is the station transport capacity classification coefficient, Q 上下 is the number of passengers getting on and off the train during peak hours at the station, Y 运 is the peak hour system transport capacity, MK y is the line transport capacity classification coefficient, K yn It is the station transport capacity classification coefficient of the nth non-transfer station in the current line.

[0156] Calculate the station boarding and alighting capacity classification coefficient according to the line passenger flow forecast data, and calculate the line boarding and alighting capacity classification coefficient according to the station boarding and alighting capacity classification coefficient;

[0157] Specifically, calculating the station's boarding and alighting capacity classification coefficient includes:

[0158] K J =Q 上下 *α / Q MAX

[0159] Calculating the line boarding and alighting capacity classification coefficient includes:

[0160] MK J =(K J1 +K J2 +K J3 +……+K Jn ) / n

[0161] Among them, K J is the station's passenger and alighting capacity classification coefficient, Q MAX MK is the passenger volume value of the station with the largest number of passengers getting on and off during peak hours among all non-transfer stations on the line. J is the line boarding and alighting capacity classification coefficient, K Jn It is the station boarding and alighting capacity classification coefficient of the nth non-transfer station in the current line.

[0162] The number of station-graded steps of the current line is determined, and the station-graded passenger flow of each step is calculated according to the line transport capacity grading coefficient and / or the line boarding and alighting capacity grading coefficient.

[0163] Specifically, the number of steps for determining the station classification of the current line includes:

[0164] If K Yn Smaller than MK Y or K Jn Smaller than MK J If the number of is greater than 70%, then K B =2;

[0165] If K Yn Smaller than MK Y or K Jn Smaller than MK J If the number of is less than 70%, then K B =3;

[0166] Among them, K B is the number of steps in the current line station classification, if K B =2, it is divided into 2 levels. If K B =3, it is divided into 3 levels.

[0167] Specifically, the calculation of the station-level passenger flow of each level includes:

[0168] The station-level passenger flow of the first level of the current line station level is FQ1:

[0169] FQ1={Y 运 *MK y , Q MAX *MK J}

[0170] Among them, FQ1 is to take Y 运 *MK y and Q MAX *MK J The relatively larger value of the two calculation results is used as the station-level passenger flow of the first tier;

[0171] The station-level passenger flow of the second level of the current line station level is FQ2:

[0172] FQ2=FQ1*K t

[0173] Among them, K t The station classification adjustment coefficient for the second level of the current line station classification. y The calculation result is used to take a balance coefficient of 1.4 to 1.6 to reasonably determine the graded passenger flow of the second echelon and achieve a match in the number of stations of the second and third echelons.

[0174] Example 4

[0175] An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor to enable the processor to execute a station classification method based on line passenger flow prediction data.

[0176] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0177] Among them, the storage medium can be used to store software programs and modules, such as a station classification method based on line passenger flow prediction data in an embodiment of the present invention, and corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned station classification method based on line passenger flow prediction data. The storage medium may include high-speed random storage media, and may also include non-volatile storage media, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include storage media remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, corporate intranet, local area network, mobile communication network and combinations thereof.

[0178] The processor can call the information and application program stored in the storage medium through the transmission system to execute the steps of: obtaining route passenger flow prediction data, calculating the station transportation capacity classification coefficient according to the route passenger flow prediction data, and calculating the route transportation capacity classification coefficient according to the station transportation capacity classification coefficient;

[0179] Specifically, calculating the station transportation capacity classification coefficient includes:

[0180] K y =Q 上下 *α / Y 运

[0181] Calculating the line transport capacity classification coefficient includes:

[0182] MK y =(K y1 +K y2 +K y3 +……+K yn ) / n

[0183] Among them, K y is the station transport capacity classification coefficient, Q 上下 is the number of passengers getting on and off the train during peak hours at the station, Y 运 is the peak hour system transport capacity, MK yis the line transport capacity classification coefficient, K yn It is the station transport capacity classification coefficient of the nth non-transfer station in the current line.

[0184] Calculate the station boarding and alighting capacity classification coefficient according to the line passenger flow forecast data, and calculate the line boarding and alighting capacity classification coefficient according to the station boarding and alighting capacity classification coefficient;

[0185] Specifically, calculating the station's boarding and alighting capacity classification coefficient includes:

[0186] K J =Q 上下 *α / Q MAX

[0187] Calculating the line boarding and alighting capacity classification coefficient includes:

[0188] MK J =(K J1 +K J2 +K J3 +……+K Jn ) / n

[0189] Among them, K J is the station's passenger and alighting capacity classification coefficient, Q MAX MK is the passenger volume value of the station with the largest number of passengers getting on and off during peak hours among all non-transfer stations on the line. J is the line boarding and alighting capacity classification coefficient, K Jn It is the station boarding and alighting capacity classification coefficient of the nth non-transfer station in the current line.

[0190] The number of station-graded steps of the current line is determined, and the station-graded passenger flow of each step is calculated according to the line transport capacity grading coefficient and / or the line boarding and alighting capacity grading coefficient.

[0191] Specifically, the number of steps for determining the station classification of the current line includes:

[0192] If K Yn Smaller than MK Y or K Jn Smaller than MK J If the number of is greater than 70%, then K B =2;

[0193] If K Yn Smaller than MK Y or K Jn Smaller than MK J If the number of is less than 70%, then K B =3;

[0194] Among them, K Bis the number of steps in the current line station classification, if K B =2, it is divided into 2 levels. If K B =3, it is divided into 3 levels.

[0195] Specifically, the calculation of the station-level passenger flow of each level includes:

[0196] The station-level passenger flow of the first level of the current line station level is FQ1:

[0197] FQ1={Y 运 *MK y , Q MAX *MK J}

[0198] Among them, FQ1 is to take Y 运 *MK y and Q MAX *MK J The relatively larger value of the two calculation results is used as the station-level passenger flow of the first tier;

[0199] The station-level passenger flow of the second level of the current line station level is FQ2:

[0200] FQ2=FQ1*K t

[0201] Among them, K t The station classification adjustment coefficient for the second level of the current line station classification. y The calculation result is used to take a balance coefficient of 1.4 to 1.6 to reasonably determine the graded passenger flow of the second echelon and achieve a match in the number of stations of the second and third echelons.

[0202] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0203] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0204] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0205] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0206] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0207] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0208] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A station classification method based on route passenger flow prediction data, characterized in that: include: Acquire route passenger flow forecast data, calculate station transport capacity classification coefficient according to the route passenger flow forecast data, and calculate route transport capacity classification coefficient according to the station transport capacity classification coefficient; Calculate the station boarding and alighting capacity classification coefficient according to the line passenger flow forecast data, and calculate the line boarding and alighting capacity classification coefficient according to the station boarding and alighting capacity classification coefficient; The number of station-graded steps of the current line is determined, and the station-graded passenger flow of each step is calculated according to the line transport capacity grading coefficient and / or the line boarding and alighting capacity grading coefficient.

2. A station classification method based on route passenger flow prediction data as claimed in claim 1, characterized in that: Calculation of the station transport capacity classification coefficient includes: K y =Q 上下 *α / Y 运 Calculating the line transport capacity classification coefficient includes: MK y =(K y1 +K y2 +K y3 +……+K yn ) / n Among them, K y is the station transport capacity classification coefficient, Q 上下 is the number of passengers getting on and off the train during peak hours at the station, Y 运 is the peak hour system transport capacity, MK y is the line transport capacity classification coefficient, K yn It is the station transport capacity classification coefficient of the nth non-transfer station in the current line.

3. A station classification method based on route passenger flow prediction data as claimed in claim 2, characterized in that: Calculation of the station's boarding and alighting capacity classification coefficient includes: K J =Q 上下 *α / Q MAX Calculating the line boarding and alighting capacity classification coefficient includes: MK J =(K J1 +K J2 +K J3 +……+K Jn ) / n Among them, K J is the station's passenger and alighting capacity classification coefficient, Q MAX MK is the passenger volume value of the station with the largest number of passengers getting on and off during peak hours among all non-transfer stations on the line. J is the line boarding and alighting capacity classification coefficient, K Jn It is the station boarding and alighting capacity classification coefficient of the nth non-transfer station in the current line.

4. A station classification method based on route passenger flow prediction data as claimed in claim 3, characterized in that: The number of steps that determine the station classification of the current line includes: If K Yn Smaller than MK Y or K Jn Smaller than MK J If the number of is greater than 70%, then K B =2; If K Yn Smaller than MK Y or K Jn Smaller than MK J If the number of is less than 70%, then K B =3; Among them, K B is the number of steps in the current line station classification, if K B =2, it is divided into 2 levels. If K B =3, it is divided into 3 levels.

5. A station classification method based on route passenger flow prediction data as claimed in claim 4, characterized in that: Calculating the station-level passenger flow for each step includes: The station-level passenger flow of the first level of the current line station level is FQ1: FQ1={Y 运 *MK y ,Q MAX *MK J } Among them, FQ1 is to take Y 运 *MK y and Q MAX *MK J The relatively larger value of the two calculation results is used as the station-level passenger flow of the first tier; The passenger flow of the second level of the current line station classification is FQ2 FQ2=FQ1*K t Among them, K t The station classification adjustment coefficient for the second level of the current line station classification, based on MK y The calculation result is used to take a balance coefficient of 1.4 to 1.6 to reasonably determine the graded passenger flow of the second echelon and achieve a match in the number of stations of the second and third echelons.

6. A station classification system based on route passenger flow forecast data, characterized in that: include: A module for determining a line transport capacity classification coefficient is used to obtain line passenger flow forecast data, calculate a station transport capacity classification coefficient based on the line passenger flow forecast data, and calculate a line transport capacity classification coefficient based on the station transport capacity classification coefficient; A module for determining a line boarding and alighting capacity classification coefficient is used to calculate a station boarding and alighting capacity classification coefficient according to the line passenger flow forecast data, and to calculate a line boarding and alighting capacity classification coefficient according to the station boarding and alighting capacity classification coefficient; The station classification module is used to determine the number of station classification levels of the current line, and calculate the station classification passenger flow of each level according to the line transportation capacity classification coefficient and / or the line boarding and alighting capacity classification coefficient.

7. A station classification system based on route passenger flow prediction data as claimed in claim 6, characterized in that: Calculation of the station transport capacity classification coefficient includes: K y =Q 上下 *α / Y 运 Calculating the line transport capacity classification coefficient includes: MK y =(K y1 +K y2 +K y3 +……+K yn ) / n Among them, K y is the station transport capacity classification coefficient, Q 上下 is the number of passengers getting on and off the train during peak hours at the station, Y 运 is the peak hour system transport capacity, MK y is the line transport capacity classification coefficient, K yn It is the station transport capacity classification coefficient of the nth non-transfer station in the current line.

8. A station classification system based on route passenger flow prediction data as claimed in claim 7, characterized in that: Calculation of the station's boarding and alighting capacity classification coefficient includes: K J =Q 上下 *α / Q MAX Calculating the line boarding and alighting capacity classification coefficient includes: MK J =(K J1 +K J2 +K J3 +……+K Jn ) / n Among them, K J is the station's passenger and alighting capacity classification coefficient, Q MAX MK is the passenger volume value of the station with the largest number of passengers getting on and off during peak hours among all non-transfer stations on the line. J is the line boarding and alighting capacity classification coefficient, K Jn It is the station boarding and alighting capacity classification coefficient of the nth non-transfer station in the current line.

9. A station classification system based on route passenger flow prediction data as claimed in claim 8, characterized in that: The number of steps that determine the station classification of the current line includes: If K Yn Smaller than MK Y or K Jn Smaller than MK J If the number of is greater than 70%, then K B =2; If K Yn Smaller than MK Y or K Jn Smaller than MK J If the number of is less than 70%, then K B =3; Among them, K B is the number of steps in the current line station classification, if K B =2, it is divided into 2 levels. If K B =3, it is divided into 3 levels.

10. A station classification system based on route passenger flow prediction data as claimed in claim 9, characterized in that: Calculating the station-level passenger flow for each step includes: The station-level passenger flow of the first level of the current line station level is FQ1: FQ1={Y 运 *MK y ,Q MAX *MK J } Among them, FQ1 is to take Y 运 *MK y and Q MAX *MK J The relatively larger value of the two calculation results is used as the station-level passenger flow of the first tier; The station-level passenger flow of the second level of the current line station level is FQ2: FQ2=FQ1*K t Among them, K t The station classification adjustment coefficient for the second level of the current line station classification, based on MK y The calculation result is used to take a balance coefficient of 1.4 to 1.6 to reasonably determine the graded passenger flow of the second echelon and achieve a match in the number of stations of the second and third echelons.

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