Rail transit passenger flow scheduling processing method, system and electronic device

Abstract

The application provides a rail transit passenger flow scheduling processing method, system and electronic equipment. The method comprises the following steps: receiving a first input, the first input being used for indicating to obtain passenger flow early warning data; in response to the first input, displaying the passenger flow early warning data, the passenger flow early warning data being used for representing the size relationship between passenger flow prediction data and a warning threshold; receiving a second input, the second input being used for indicating to obtain a first passenger flow scheduling scheme; and in response to the second input, displaying the first passenger flow scheduling scheme. In the case that the passenger flow prediction data is not within the range defined by the warning threshold, it is indicated that large passenger flow will occur, the second input can be received, in response to the second input, an optimization model is solved based on the first transport capacity data and the passenger flow prediction data to obtain the first passenger flow scheduling scheme, and the first passenger flow scheduling scheme can be displayed so that the scheduling personnel can perform passenger flow scheduling in advance based on the first passenger flow scheduling scheme, and the efficient determination of the passenger flow scheduling scheme for large passenger flow can be realized.

Description

Technical Field

[0001] This invention relates to the field of rail transit technology, and in particular to a method, system and electronic equipment for handling rail transit passenger flow scheduling. Background Technology

[0002] With the continuous expansion of urban rail transit networks, passenger flow intensity is also increasing, and mismatches between train capacity and passenger demand frequently occur. In related technologies, passenger flow control measures are typically used to alleviate large passenger flows, but these measures have low response efficiency. How to efficiently determine passenger flow scheduling schemes for large passenger flows is a problem that the industry urgently needs to solve. Summary of the Invention

[0003] To address the problems existing in the prior art, embodiments of the present invention provide a method, system, and electronic device for handling passenger flow scheduling in rail transit.

[0004] In a first aspect, the present invention provides a method for processing passenger flow scheduling in rail transit, comprising:

[0005] Receive a first input, which is used to instruct the acquisition of passenger flow early warning data;

[0006] In response to the first input, the passenger flow warning data is displayed, which is used to represent the magnitude relationship between the passenger flow prediction data and the warning threshold.

[0007] Receive a second input, which is used to indicate the acquisition of a first passenger flow scheduling plan;

[0008] In response to the second input, the first passenger flow scheduling plan is displayed;

[0009] The first passenger flow scheduling scheme is obtained by solving an optimization model based on the first transport capacity data and the passenger flow prediction data.

[0010] Optionally, according to the processing method for rail transit passenger flow scheduling provided by the present invention, the first passenger flow scheduling scheme includes capacity allocation strategy data, flow restriction strategy data, and combined strategy data, wherein the combined strategy data includes the capacity allocation strategy data and the coordinated flow restriction strategy data, and the receiving of the second input includes:

[0011] Receive the first sub-input, which is used to determine the passenger flow scheduling period;

[0012] Receive a second sub-input, which is used to determine the target strategy display method among multiple strategy display methods;

[0013] The response to the second input, displaying the first passenger flow scheduling plan, includes:

[0014] In response to the first sub-input and the second sub-input, the first passenger flow scheduling scheme is displayed based on the target strategy data display method;

[0015] The multiple strategy data display methods include: a first display method for displaying the capacity allocation strategy data, a second display method for displaying the flow restriction strategy data, and a third display method for displaying the combined strategy data;

[0016] The optimization model includes a capacity optimization sub-model and a flow restriction optimization sub-model. The capacity allocation strategy data is obtained by solving the capacity optimization sub-model based on the passenger flow scheduling period, the first capacity data, and the passenger flow forecast data. The flow restriction strategy data is obtained by solving the flow restriction optimization sub-model based on the passenger flow scheduling period, the first capacity data, and the passenger flow forecast data. The coordinated flow restriction strategy data is obtained by solving the flow restriction optimization sub-model based on the passenger flow scheduling period, the second capacity data, and the passenger flow forecast data. The second capacity data is obtained by adjusting the first capacity data based on the capacity allocation strategy data.

[0017] Optionally, according to the rail transit passenger flow scheduling processing method provided by the present invention, the step of displaying the first passenger flow scheduling scheme based on the target strategy data display method in response to the first sub-input and the second sub-input includes:

[0018] When the target strategy data display method is the first display method, the capacity allocation strategy data and the first evaluation result are displayed. The first evaluation result is determined based on the capacity allocation strategy data and the preset capacity allocation strategy evaluation index.

[0019] Alternatively, if the target strategy data display method is the second display method, the rate limiting strategy data and the second evaluation result are displayed, wherein the second evaluation result is determined based on the rate limiting strategy data and the preset rate limiting strategy evaluation index;

[0020] Alternatively, if the target strategy data display method is the third display method, the combined strategy data and the third evaluation result are displayed, wherein the third evaluation result is determined based on the combined strategy data and the preset combined strategy evaluation index.

[0021] Optionally, according to the rail transit passenger flow scheduling processing method provided by the present invention, the multiple strategy data display methods further include: a fourth display method for displaying the capacity allocation strategy data, the flow restriction strategy data and the combined strategy data;

[0022] The step of displaying the first passenger flow scheduling scheme in response to the first sub-input and the second sub-input, based on the target strategy data display method, includes:

[0023] When the target strategy data display method is the fourth display method, the capacity allocation strategy data, the flow restriction strategy data, the combined strategy data, and the strategy comparison data are displayed.

[0024] The strategy comparison data is determined based on the first evaluation result, the second evaluation result, and the third evaluation result.

[0025] Optionally, according to the rail transit passenger flow scheduling processing method provided by the present invention, after displaying the first passenger flow scheduling scheme in response to the second input, the method further includes:

[0026] Receive a third input, which is used to determine a second passenger flow scheduling scheme;

[0027] In response to the third input, the second passenger flow scheduling plan and the fourth evaluation result are displayed;

[0028] The fourth evaluation result is determined based on the second passenger flow scheduling scheme and the scheduling scheme evaluation index. The scheduling scheme evaluation index is any one of the preset capacity allocation strategy evaluation index, the preset flow restriction strategy evaluation index, and the preset combined strategy evaluation index.

[0029] Optionally, according to the processing method for rail transit passenger flow scheduling provided by the present invention, the passenger flow prediction data includes: OD passenger flow prediction data, station entry passenger flow prediction data, and cross-sectional passenger flow prediction data, and further includes, before receiving the third input:

[0030] Receive a fourth input, which is used to instruct the acquisition of passenger flow forecast data;

[0031] In response to the fourth input, the OD passenger flow prediction data, the station entry passenger flow prediction data, and the cross-sectional passenger flow prediction data are displayed.

[0032] The OD passenger flow prediction data is obtained based on historical OD passenger flow data, historical inbound passenger flow data, weather forecast data, and a first deep learning model. The inbound passenger flow prediction data is obtained based on the historical inbound passenger flow data, the weather forecast data, and a second deep learning model. The cross-sectional passenger flow prediction data is obtained based on the OD passenger flow prediction data by allocating OD passenger flow to trains.

[0033] Secondly, the present invention also provides a processing system for passenger flow scheduling in rail transit, comprising:

[0034] The first input module is used to receive a first input, which is used to indicate the acquisition of passenger flow early warning data;

[0035] The first response module is used to respond to the first input and display the passenger flow warning data, wherein the passenger flow warning data is used to represent the magnitude relationship between the passenger flow prediction data and the warning threshold.

[0036] The second input module is used to receive a second input, which is used to indicate the acquisition of the first passenger flow scheduling plan.

[0037] The second response module is used to respond to the second input and display the first passenger flow scheduling scheme;

[0038] The first passenger flow scheduling scheme is obtained by solving an optimization model based on the first transport capacity data and the passenger flow prediction data.

[0039] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the processing method for rail transit passenger flow scheduling as described above.

[0040] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the rail transit passenger flow scheduling processing method as described above.

[0041] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the rail transit passenger flow scheduling processing method as described above.

[0042] The present invention provides a method, system, and electronic device for processing passenger flow scheduling in rail transit. By receiving a first input, it can determine if a user instructs it to obtain passenger flow early warning data. In response to this first input, it can display the passenger flow early warning data, which represents the relationship between passenger flow forecast data and an early warning threshold. This allows dispatchers to determine whether the passenger flow forecast data is within the range defined by the early warning threshold. If the passenger flow forecast data is outside the range defined by the early warning threshold, it indicates that a large passenger flow will occur in a future period. Then, by receiving a second input, it can determine if a user instructs it to obtain a first passenger flow scheduling plan. In response to this second input, based on the first capacity data and the passenger flow forecast data, it can obtain the first passenger flow scheduling plan by solving an optimization model. The first passenger flow scheduling plan can then be displayed, allowing dispatchers to perform passenger flow scheduling in advance based on it. This enables efficient determination of passenger flow scheduling plans for large passenger flows. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0044] Figure 1 This is a flowchart illustrating the rail transit passenger flow scheduling method provided by the present invention.

[0045] Figure 2 This is a schematic diagram of the passenger flow alarm and early warning interface provided by the present invention;

[0046] Figure 3 This is a flowchart illustrating the passenger flow scheduling scheme provided by the present invention;

[0047] Figure 4 This is a schematic diagram of the passenger flow scheduling scheme interface provided by the present invention;

[0048] Figure 5 This is a schematic diagram of the first optimization model provided by the present invention;

[0049] Figure 6 This is a schematic diagram of the train frequency increase scheme provided by the present invention;

[0050] Figure 7 This is a schematic diagram of the second optimization model provided by the present invention;

[0051] Figure 8 This is a schematic diagram of the passenger flow prediction interface provided by the present invention;

[0052] Figure 9 This is a schematic diagram of the passenger flow prediction process provided by the present invention;

[0053] Figure 10 This is a schematic diagram of the passenger flow monitoring interface provided by the present invention;

[0054] Figure 11 This is one of the structural schematic diagrams of the rail transit passenger flow scheduling processing system provided by the present invention;

[0055] Figure 12 This is the second schematic diagram of the processing system for rail transit passenger flow scheduling provided by the present invention;

[0056] Figure 13 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0058] Figure 1 This is a flowchart illustrating the rail transit passenger flow scheduling method provided by the present invention, as shown below. Figure 1 As shown, the subject executing the method can be an electronic device, such as a server. The method includes:

[0059] Step 101: Receive the first input, which is used to indicate the acquisition of passenger flow early warning data.

[0060] Specifically, in order to efficiently determine passenger flow scheduling plans for large passenger flows, a first input can be received. By receiving the first input, user instructions can be determined and passenger flow early warning data can be obtained.

[0061] Step 102: In response to the first input, display the passenger flow warning data, which is used to represent the magnitude relationship between the passenger flow prediction data and the warning threshold.

[0062] Specifically, after receiving the first input, the system can respond to the first input to obtain passenger flow warning data, and then display the passenger flow warning data. The passenger flow warning data can represent the relationship between the passenger flow forecast data and the warning threshold, so that dispatchers can determine whether the passenger flow forecast data is within the range limited by the warning threshold based on the passenger flow warning data.

[0063] Step 103: Receive the second input, which is used to indicate the acquisition of the first passenger flow scheduling plan.

[0064] Specifically, after displaying passenger flow warning data, if the passenger flow forecast data is not within the range limited by the warning threshold, it indicates that a large passenger flow will occur in a certain period of time in the future, and the capacity supply and passenger flow demand will not match during that period. Then, by receiving the second input, the user's instruction can be determined to obtain the first passenger flow scheduling plan, and then the system can respond to the second input.

[0065] Step 104: In response to the second input, display the first passenger flow scheduling scheme;

[0066] The first passenger flow scheduling scheme is obtained by solving an optimization model based on the first transport capacity data and the passenger flow prediction data.

[0067] Specifically, after receiving the second input, in response to the second input, a first passenger flow scheduling plan can be obtained by solving an optimization model based on the first capacity data and passenger flow forecast data. The first passenger flow scheduling plan can then be displayed so that dispatchers can schedule passenger flow in advance based on the first passenger flow scheduling plan.

[0068] Optionally, Figure 2 This is a schematic diagram of the passenger flow alarm and early warning interface provided by the present invention, as shown below. Figure 2 As shown, passenger flow early warning data can represent the relationship between predicted inbound passenger flow and the first early warning threshold, the relationship between predicted transfer passenger flow and the second early warning threshold, and the relationship between predicted cross-sectional passenger flow and the second early warning threshold. The interface can also include real-time alarm information, which can indicate whether real-time inbound passenger flow, outbound passenger flow, transfer passenger flow, and station capacity utilization exceed the thresholds. Transfer passenger flow is obtained based on passenger flow allocation, and station capacity utilization is determined by the number of people gathered per square meter on the platform, obtained from in-station video surveillance. Figure 2 As shown, the leftmost button is the indicator selection button. If an indicator triggers an alarm or warning, an alarm or warning message will appear in the upper right corner of that indicator button. The right side displays the detailed information of the alarm or warning indicator. Dispatchers can switch the left-hand indicator selection button (i.e., receive the first input) to view the specific details of the alarm or warning. Simultaneously, the upper right corner of the alarm or warning indicator information display interface has a "Formulate Passenger Flow Dispatch Strategy" button. Clicking this button will automatically generate a passenger flow dispatch strategy and redirect to the passenger flow dispatch plan interface.

[0069] The rail transit passenger flow scheduling method provided by this invention, by receiving a first input, can determine that the user indicates the need to obtain passenger flow early warning data. In response to the first input, the method can display the passenger flow early warning data, which represents the relationship between passenger flow forecast data and an early warning threshold. This allows dispatchers to determine whether the passenger flow forecast data is within the range defined by the early warning threshold. If the passenger flow forecast data is outside the range defined by the early warning threshold, it indicates that a large passenger flow will occur in a future period. Then, by receiving a second input, the method can determine that the user indicates the need to obtain a first passenger flow scheduling plan. In response to the second input, based on the first capacity data and the passenger flow forecast data, the method obtains the first passenger flow scheduling plan by solving an optimization model. The method can then display the first passenger flow scheduling plan, allowing dispatchers to perform passenger flow scheduling in advance based on this plan. This enables efficient determination of passenger flow scheduling plans for large passenger flows.

[0070] Optionally, according to the processing method for rail transit passenger flow scheduling provided by the present invention, the first passenger flow scheduling scheme includes capacity allocation strategy data, flow restriction strategy data, and combined strategy data, wherein the combined strategy data includes the capacity allocation strategy data and the coordinated flow restriction strategy data, and the receiving of the second input includes:

[0071] Receive the first sub-input, which is used to determine the passenger flow scheduling period;

[0072] Receive a second sub-input, which is used to determine the target strategy display method among multiple strategy display methods;

[0073] The response to the second input, displaying the first passenger flow scheduling plan, includes:

[0074] In response to the first sub-input and the second sub-input, the first passenger flow scheduling scheme is displayed based on the target strategy data display method;

[0075] The multiple strategy data display methods include: a first display method for displaying the capacity allocation strategy data, a second display method for displaying the flow restriction strategy data, and a third display method for displaying the combined strategy data;

[0076] The optimization model includes a capacity optimization sub-model and a flow restriction optimization sub-model. The capacity allocation strategy data is obtained by solving the capacity optimization sub-model based on the passenger flow scheduling period, the first capacity data, and the passenger flow forecast data. The flow restriction strategy data is obtained by solving the flow restriction optimization sub-model based on the passenger flow scheduling period, the first capacity data, and the passenger flow forecast data. The coordinated flow restriction strategy data is obtained by solving the flow restriction optimization sub-model based on the passenger flow scheduling period, the second capacity data, and the passenger flow forecast data. The second capacity data is obtained by adjusting the first capacity data based on the capacity allocation strategy data.

[0077] Specifically, in order to meet the diverse scheduling needs during a specific scheduling period, the passenger flow scheduling period can be determined by receiving the first sub-input, and the target strategy data display method can be determined from multiple strategy data display methods by receiving the second sub-input.

[0078] Specifically, after receiving the first sub-input, in response to the first sub-input, the system can obtain capacity allocation strategy data by solving the capacity optimization sub-model based on the passenger flow scheduling period, the first capacity data, and the passenger flow forecast data. It can also obtain flow restriction strategy data by solving the flow restriction optimization sub-model based on the passenger flow scheduling period, the first capacity data, and the passenger flow forecast data. Based on the capacity allocation strategy data, the system can adjust the first capacity data to obtain the second capacity data. Finally, based on the passenger flow scheduling period, the second capacity data, and the passenger flow forecast data, the system can obtain coordinated flow restriction strategy data by solving the flow restriction optimization sub-model.

[0079] Specifically, upon receiving the second sub-input, the system can respond to the second sub-input by displaying the first passenger flow scheduling scheme based on the target strategy data display method.

[0080] Optionally, Figure 3 This is a flowchart illustrating the passenger flow scheduling scheme provided by the present invention, as shown below. Figure 3 As shown, the process for obtaining the passenger flow scheduling plan includes:

[0081] Step 301: Obtain passenger flow scheduling time periods.

[0082] Specifically, in response to the first sub-input, the passenger flow scheduling period can be obtained, and then the corresponding passenger flow scheduling plan can be determined for the passenger flow scheduling period.

[0083] Understandably, if dispatchers predict a large passenger flow during a certain period of the day based on passenger flow warning data, they can determine that period as the passenger flow dispatch period by receiving the first sub-input.

[0084] Understandably, if dispatchers predict a large passenger flow at a certain time on a subsequent day based on passenger flow warning data, they can determine that time period as the passenger flow dispatch period by receiving the first sub-input.

[0085] Step 302: When the passenger flow scheduling period is the current midday period, in each first scheduling cycle of the passenger flow scheduling period, based on the first transport capacity data and the first passenger flow data of the first scheduling cycle, solve the first optimization model (in this case, the optimization model is the first optimization model, the transport capacity optimization sub-model is the first transport capacity optimization sub-model, and the flow restriction optimization sub-model is the first flow restriction optimization sub-model) to determine the train frequency increase scheme and the first flow restriction scheme for the first scheduling cycle (in this case, the transport capacity allocation strategy data is the train frequency increase scheme, and the flow restriction strategy data is the first flow restriction scheme).

[0086] Alternatively, if the passenger flow scheduling task is for the subsequent midday period, in each second scheduling cycle during the passenger flow scheduling period, based on the first capacity data and the second passenger flow data of the second scheduling cycle, solve the second optimization model (in this case, the optimization model is the second optimization model, the capacity optimization sub-model is the second capacity optimization sub-model, and the flow restriction optimization sub-model is the second flow restriction optimization sub-model) to determine the second flow restriction scheme and at least one operation scheme for the second scheduling cycle (in this case, the capacity allocation strategy data includes at least one operation scheme, and the flow restriction strategy data is the second flow restriction scheme);

[0087] The duration of the first scheduling cycle is shorter than the duration of the second scheduling cycle; the first optimization model includes a first capacity optimization sub-model for determining the train frequency increase scheme and a first flow restriction optimization sub-model for determining the first flow restriction scheme; the second optimization model includes at least one second capacity optimization sub-model for determining at least one operation scheme and a second flow restriction optimization sub-model for determining the second flow restriction scheme.

[0088] Specifically, by distinguishing between the current midday period and the subsequent midday period, the passenger flow scheduling plan can be determined according to the first scheduling cycle when the passenger flow scheduling period is the current midday period, and according to the second scheduling cycle when the passenger flow scheduling period is the subsequent midday period. The first scheduling cycle being shorter than the second scheduling cycle can meet the scheduling needs of the current day at a higher frequency.

[0089] Optionally, the first scheduling cycle can be 15 minutes, and the second scheduling cycle can be 1 hour.

[0090] It is understandable that passenger flow forecast data includes first-passenger flow data and second-passenger flow data.

[0091] Specifically, when the passenger flow scheduling period is the current midday period, by solving the first capacity optimization sub-model and the first flow restriction optimization sub-model, the train frequency increase scheme and the first flow restriction scheme for the first scheduling cycle can be determined. The train frequency increase scheme and the first flow restriction scheme can be used to allocate capacity and restrict passenger flow to ensure that the capacity supply and passenger flow demand in the first scheduling cycle are matched. Moreover, the train frequency increase scheme is a scheme that increases the train frequency based on the current train operation scheme, which can avoid changing the train operation routes.

[0092] It is understandable that both the increased train frequency plan and the first flow restriction plan are passenger flow scheduling plans, and dispatchers can choose between the increased train frequency plan and / or the first flow restriction plan to carry out passenger flow scheduling.

[0093] Specifically, when the passenger flow scheduling period is the midday period of the following day, by solving the second flow restriction optimization sub-model and at least one second capacity optimization sub-model, the second flow restriction scheme and at least one operation scheme can be determined. The at least one operation scheme and the second flow restriction scheme can be used to allocate capacity and restrict passenger flow to ensure that the capacity supply of the second scheduling cycle matches the passenger flow demand. Furthermore, the train operation routes of the following day can be changed, and the at least one operation scheme can be used to change the train operation routes of the following day to meet the capacity supply.

[0094] It is understandable that the second flow restriction plan and at least one operation plan are both passenger flow scheduling plans. The dispatchers can select one or more plans from the second flow restriction plan and at least one operation plan to carry out passenger flow scheduling.

[0095] Optionally, the first capacity optimization sub-model may include: a first capacity allocation objective function and a first capacity allocation constraint condition. The first capacity allocation objective function takes the train operation frequency of each route as the decision variable and the minimum train travel distance as the optimization objective.

[0096] The first flow restriction optimization sub-model may include: a first flow restriction objective function and a first flow restriction constraint. The first flow restriction objective function takes the set of flow restriction stations and the flow restriction intensity of each flow restriction station as decision variables, and takes the maximum passenger flow in the first scheduling cycle and the minimum variance of the flow restriction intensity of the set of flow restriction stations as optimization objectives.

[0097] The train frequency increase plan may include: increasing the train frequency configuration for each route; the first flow restriction plan includes: the first flow restriction station set and the flow restriction intensity of each first flow restriction station.

[0098] Optionally, the first transport capacity data may include: the first preset maximum cross-sectional load factor, the preset maximum train frequency on the line, the preset maximum number of trains used on the line, the total number of stations on the line, the capacity index of the entrance gates, the platform capacity index, and the maximum transport capacity index of each section; the first passenger flow data includes: the maximum cross-sectional passenger flow of each section;

[0099] The first set of constraints on transport capacity allocation includes: maximum cross-sectional load factor constraint, section train frequency constraint, route train frequency constraint, maximum train frequency constraint, and number of vehicles in use constraint.

[0100] The maximum cross-sectional load factor constraint condition is used to constrain the maximum cross-sectional load factor of a section to be less than or equal to the first preset maximum cross-sectional load factor.

[0101] The section train frequency constraint is used to constrain the relationship between the train frequency of each section and the maximum cross-sectional passenger flow of each section.

[0102] The train frequency constraint is used to constrain the relationship between the train frequency of a section and the train frequency of the section.

[0103] The maximum train frequency constraint is used to constrain the relationship between the train frequency of a section of the route and the preset maximum train frequency of the line.

[0104] The vehicle number constraint is used to constrain the relationship between the number of vehicles used on a route and the maximum number of vehicles used on a preset route.

[0105] The first flow restriction constraints include: flow restriction intensity constraints, flow restriction station quantity constraints, station capacity constraints, and interval transport capacity constraints.

[0106] The current limiting strength constraint condition is used to constrain the range of values ​​for the current limiting strength;

[0107] The constraint on the number of stations with flow restrictions is used to constrain the proportional relationship between the number of stations with flow restrictions and the total number of stations on the line;

[0108] The station capacity constraint is used to constrain the relationship between the passenger flow entering the station and the throughput capacity of the entrance gates, as well as the relationship between the passenger flow entering the station and the platform capacity.

[0109] The interval transport capacity constraint is used to constrain the relationship between the passenger flow through each interval and the maximum transport capacity index of each interval.

[0110] It is understandable that the area between adjacent stations is a section, and the line can be divided into different sections according to the current route pattern. A section can include multiple sections.

[0111] Optionally, the objective function for the first capacity allocation takes minimizing the train travel distance Z1 as the optimization objective, and the objective function for the first capacity allocation can be expressed by the following formula:

[0112] MinZ1=2·∑f j ·L j j = 1, 2, 3…j max ;

[0113] The first constraint on capacity allocation may include: the maximum cross-sectional load factor constraint, the section train frequency constraint, the route train frequency constraint, the maximum train frequency constraint, and the number of vehicles in use constraint.

[0114] The first preset maximum cross-sectional load factor can be 100%, and the maximum cross-sectional load factor constraint can be expressed by the following formula:

[0115]

[0116]

[0117] The frequency of trains operating in each section should be able to guarantee the passenger flow demand of that section. The constraint on the frequency of trains operating in a section can be expressed by the following formula:

[0118]

[0119] The sum of the operating frequencies of all sections should meet the operating frequency requirements of that section. The operating frequency constraint of the train routes can be expressed by the following formula:

[0120] ∑x ij ·f j ≥D i j = 1, 2, 3…j max ;

[0121] The train frequency for each section cannot exceed the section's capacity. The maximum train frequency constraint can be expressed by the following formula:

[0122] ∑x ij ·f j ≤f m j = 1, 2, 3…j max ;

[0123] The total number of vehicles used on all routes cannot exceed the number of available vehicles. The constraint on the number of vehicles used can be expressed by the following formula:

[0124] Z 运用 ≤Z 可用 ;

[0125] Z 运用 =∑f j ·n,j=1,2,3…j max ;

[0126] The decision variable for the primary capacity allocation objective function can be the train frequency f on each route. j ;

[0127] Among them, f j Let j be the train frequency for route j. max Indicates the total number of routes; L j P is the length of intersection j; i,max Let i be the maximum cross-sectional passenger flow of segment i. max Indicates the total number of segments; C 定员 n represents the passenger capacity per train formation; n represents the number of cars in the train formation; x represents the passenger capacity per train formation. ij The relationship between intersection j and segment i is defined as follows: if intersection j includes segment i, the value is 1; otherwise, it is 0. i f is the train frequency for segment i; mThe maximum train frequency for the preset line is subject to capacity limitations; Z 运用 Z represents the total number of vehicles used across all routes. 可用 This is the preset maximum number of vehicles for a route (the number of vehicles available for the route).

[0128] Understandably, heuristic algorithms such as genetic algorithms can be used to solve the above-mentioned first transportation capacity allocation objective function.

[0129] Optionally, the first flow restriction objective function takes the maximum inbound passenger flow Z1 and the minimum flow restriction intensity variance Z2 of the set of flow-restricted stations within the first scheduling period as its optimization objectives. The first flow restriction objective function can be expressed by the following formula:

[0130]

[0131]

[0132] The first flow restriction constraint may include: flow restriction intensity constraint, flow restriction number of stations constraint, station capacity constraint, and interval transport capacity constraint.

[0133] To ensure the service level of the station, the flow restriction intensity cannot be too high or too low. The constraint condition of the flow restriction intensity can be expressed by the following formula:

[0134]

[0135] The constraint on the number of stations implementing flow control measures is that the number of stations implementing flow control measures should not be too large, and the number of stations with flow control measures should not exceed 20% of the total number of stations on the line. This constraint can be expressed by the following formula:

[0136]

[0137]

[0138] The station capacity constraint, which states that the passenger flow entering the station cannot exceed the throughput capacity of the entrance gates and the platform capacity, can be expressed by the following formula:

[0139]

[0140]

[0141] The constraint on the transport capacity of a section, namely that the passenger flow through any section should not exceed the maximum transport capacity of that section, firstly requires determining whether each traffic origin-destination (OD) in the inbound traffic passes through section dm. The OD volumes passing through section dm are then accumulated to determine the station's passenger flow contribution to section dm. The ratio of this passenger flow contribution to the station's inbound traffic is the station's passenger flow contribution rate to section dm. The maximum transport capacity of the section is then considered. The constraints on the transport capacity of a section, which are related to the frequency of train operations, the number of cars in a train formation, and the train's passenger capacity, can be expressed by the following formula:

[0142]

[0143]

[0144]

[0145]

[0146]

[0147] The decision variable of the first flow restriction objective function can be the set of flow-restricted stations, s of each flow-restricted station. i Current limiting intensity

[0148] Where T is the scheduling period; t i For each flow restriction period (e.g., 15 minutes), if the passenger flow scheduling period is during the current midday period, T can include only one t. i S represents the set of stations; DM represents the set of cross-sections. S after rate limiting i Standing at t i Passenger flow entering the station during a specific time period; For S i Standing at t i Demand for station entry volume during a specific time period; CR aver N is the average of the rate limiting intensity across all sites. S n is the total number of stations on the line. Si For S i A 0-1 variable indicating whether the station will implement traffic control measures; For S i Standing at t i The throughput capacity of the entrance gates during a specific time period; For S i Standing at t i The platform's capacity during different time periods; For S iThe contribution of station entry passenger flow to the up-direction section dm is determined by whether each OD in the entry flow passes through section dm and the ODs that pass through the up-direction section dm are accumulated. For S i The contribution of station entry passenger flow to the downstream cross-section dm; For S i The contribution rate of station entry passenger flow to the upstream cross-section dm, that is, the ratio of the contribution to the station entry passenger flow. For S i The contribution rate of station entry passenger flow to the downstream cross-section dm; For t i Within a given time interval, the transport capacity of dm; The train frequency for the line to which section dm belongs; n is the number of cars in the train formation; C 定员 The number of passengers is set for each train set.

[0149] Understandably, the first current-limiting objective function mentioned above can be solved using machine learning algorithms such as reinforcement learning.

[0150] Optionally, if the passenger flow scheduling period is the current midday period, the configuration can be increased based on the train operation frequency of each route, and the maximum transport capacity index of each section in the first transport capacity data can be updated to determine the second transport capacity data.

[0151] For example, if the original frequency was 4 pairs of trains per 15 minutes, with 6 train sets and a capacity of 240 passengers per car, then the maximum transport capacity of the section was 4 × 6 × 240 = 5760 passengers per 15 minutes. After updating the maximum transport capacity of each section based on the increased train frequency of each route, the capacity can be increased to 5 pairs of trains per 15 minutes. Therefore, when solving the first flow restriction objective function, the maximum transport capacity of the section is 7200 passengers per 15 minutes.

[0152] Optionally, at least one second capacity optimization sub-model may include the following sub-models: single route operation scheme optimization sub-model, large and small route operation scheme optimization sub-model, and connecting route operation scheme optimization sub-model;

[0153] The second capacity optimization sub-model may include a second capacity allocation objective function and second capacity allocation constraints.

[0154] The second capacity allocation objective function of the single-route operation scheme optimization sub-model uses the single-route train operation frequency as the decision variable, and the optimization objectives are to minimize passenger waiting time and train travel distance.

[0155] The second capacity allocation objective function of the sub-model for optimizing the operation of the long and short routes uses the frequency of trains on the short route, the first ratio, the first turnaround station of the short route and the second turnaround station of the short route as decision variables, and the optimization objectives are to minimize passenger waiting time and minimize train travel distance. The first ratio is the ratio between the frequency of trains on the long route and the frequency of trains on the short route.

[0156] The second capacity allocation objective function of the connecting route operation scheme optimization sub-model uses the train frequency of the first route, the train frequency of the second route, and the connecting stations as decision variables, and takes the minimum passenger waiting time and the minimum train travel distance as optimization objectives.

[0157] The second flow restriction optimization sub-model includes: a second flow restriction objective function and a second flow restriction constraint. The second flow restriction objective function uses the set of flow-restricted stations and the flow restriction intensity of each flow-restricted station as decision variables, and takes the maximum passenger flow entering the station and the minimum variance of the flow restriction intensity of the set of flow-restricted stations within the second scheduling cycle as optimization objectives.

[0158] Optionally, at least one operation scheme includes: a single route operation scheme, a combined route operation scheme and a connecting route operation scheme; the second flow restriction scheme includes: a third flow restriction scheme for each flow restriction period, the third flow restriction scheme includes the set of second flow restriction stations and the flow restriction intensity of each second flow restriction station, and the flow restriction period is a period in the second scheduling cycle.

[0159] Optionally, the first capacity data may include: the minimum train frequency of the preset line, the maximum train frequency of the preset line, the maximum number of trains used on the preset line, the second preset maximum cross-section full load rate, the total number of stations on the line, the capacity index of the entrance gate, the platform capacity index, and the maximum transport capacity index of each section.

[0160] The second constraint on transport capacity allocation includes: minimum train frequency constraint, maximum train frequency constraint, number of vehicles in use constraint, and maximum cross-sectional load factor constraint.

[0161] The minimum train frequency constraint is used to constrain the relationship between the train frequency of the route and the preset minimum train frequency of the line.

[0162] The maximum train frequency constraint is used to constrain the relationship between the train frequency of the route and the preset maximum train frequency of the line.

[0163] The vehicle number constraint is used to constrain the relationship between the number of vehicles used on a route and the maximum number of vehicles used on a preset route.

[0164] The maximum cross-section full load rate constraint condition is used to constrain the maximum cross-section full load rate of the line to be less than or equal to the second preset maximum cross-section full load rate.

[0165] The second flow restriction constraint conditions include: flow restriction intensity constraint conditions, flow restriction station number constraint conditions, station capacity constraint conditions, and interval transport capacity constraint conditions;

[0166] The current limiting strength constraint condition is used to constrain the range of values ​​for the current limiting strength;

[0167] The constraint on the number of stations with flow restrictions is used to constrain the proportional relationship between the number of stations with flow restrictions and the total number of stations on the line;

[0168] The station capacity constraint is used to constrain the relationship between the passenger flow entering the station and the throughput capacity of the entrance gates, as well as the relationship between the passenger flow entering the station and the platform capacity.

[0169] The interval transport capacity constraint is used to constrain the relationship between the passenger flow through each interval and the maximum transport capacity index of each interval.

[0170] Optionally, the second capacity allocation objective function of the single-route operation scheme optimization sub-model can take minimizing passenger waiting time Z1 and train travel distance Z2 as optimization objectives, which can be expressed by the following formula:

[0171]

[0172] MinZ2=2·L 1N ·f1·n;

[0173] MinM1=w1·Z1+w2·Z2;

[0174] The second capacity allocation constraints of the single-route operation scheme optimization sub-model may include: minimum train frequency constraints, maximum train frequency constraints, number of vehicles in use constraints, and maximum cross-sectional load factor constraints.

[0175] Minimum train frequency constraints: To ensure a high level of service, the maximum headway for urban rail trains should not be too large; generally, the preset minimum train frequency f for the line is taken. min The minimum train frequency constraint, which is 10 pairs / hour, can be expressed by the following formula:

[0176] f1≥f min ;

[0177] The maximum train frequency constraint, which is subject to the minimum train headway and the turnaround capacity of the turnaround station, stipulates that the maximum frequency should not exceed the preset maximum train frequency f for the line. max (For example, 30 pairs / hour), the maximum train frequency constraint can be expressed by the following formula:

[0178] f1≤f max ;

[0179] The constraint on the number of vehicles used, namely that the number of vehicles used in the train operation plan cannot exceed the preset maximum number of vehicles used on the line (the number of available vehicles), can be expressed by the following formula:

[0180] Z 运用 ≤Z 可用 ;

[0181]

[0182]

[0183] The maximum cross-sectional load factor constraint condition, i.e., the cross-sectional load factor cannot be too large, the second preset maximum cross-sectional load factor can be 120%, and the maximum cross-sectional load factor constraint condition can be expressed by the following formula:

[0184] γ max ≤120%;

[0185]

[0186]

[0187]

[0188] The objective function for the second capacity allocation of the single-route operation scheme optimization sub-model can use the train frequency f1 as a decision variable.

[0189] Among them, T k The second scheduling cycle (e.g., 1 hour); f1 is the train frequency for a single route; Q is the total passenger flow entering the station on this line; Q in To bring in passenger traffic to other lines; L 1N The total length of the entire line is n; the number of cars in the train formation is n; w1 and w2 are the weights of the objective function; f min The minimum train frequency for the preset line; f max Z is the preset maximum train frequency for the line; 运用 Z represents the number of vehicles used in the capacity allocation plan. 可用 T1 represents the maximum number of trains that can be used on the preset line; T1 represents the turnaround time for a single route; N represents the total number of stations on the line; R i S represents the train travel time in interval i; j t represents the train's dwell time at station j; 折 γ represents the turnaround time of the train at the turnaround station. max This represents the maximum cross-sectional load factor. Let i be the load factor of section i in the upward direction; Let i be the load factor of section i in the upward direction; The passenger flow at section i in the upward direction; C represents the passenger flow at section i in the downhill direction; 定员 The number of passengers is set for each train set.

[0190] Optionally, the second capacity allocation objective function of the sub-model for optimizing the operation of both long and short routes can be optimized by minimizing passenger waiting time Z1 and train travel distance Z2, which can be expressed by the following formula:

[0191]

[0192] MinZ2=2·L 1N ·f1·n+2·L ab ·f2·n;

[0193] MinM1=w1·Z1+w2·Z2;

[0194] The second capacity allocation constraints of the sub-model for optimizing the operation scheme of large and small routes may include: minimum train frequency constraints, maximum train frequency constraints, number of vehicles in use constraints, and maximum cross-sectional load factor constraints.

[0195] The ratio m of the operating frequencies of long-distance trains to short-distance trains is a positive integer, and m can be a positive integer from 1 to 3. The relationship between the operating frequencies of long-distance trains and short-distance trains can be expressed by the following formula:

[0196] m = 1 or 2 or 3;

[0197] f1 = m·f2;

[0198] Minimum train frequency constraints: To ensure a high level of service, the maximum headway for urban rail trains should not be too large; generally, the preset minimum train frequency f for the line is taken. min The minimum train frequency constraint, which is 10 pairs / hour, can be expressed by the following formula:

[0199] f1≥f min ;

[0200] The maximum train frequency constraint, which is subject to the minimum train headway and the turnaround capacity of the turnaround station, stipulates that the maximum frequency should not exceed the preset maximum train frequency f for the line. max (For example, 30 pairs / hour), the maximum train frequency constraint can be expressed by the following formula:

[0201] (f1+f2)≤f max ;

[0202] The constraint on the number of vehicles used, namely that the number of vehicles used in the train operation plan cannot exceed the preset maximum number of vehicles used on the line (the number of available vehicles), can be expressed by the following formula:

[0203] Z 运用 ≤Z 可用 ;

[0204]

[0205]

[0206]

[0207] The maximum cross-sectional load factor constraint condition, i.e., the cross-sectional load factor cannot be too large, the second preset maximum cross-sectional load factor can be 120%, and the maximum cross-sectional load factor constraint condition can be expressed by the following formula:

[0208] γ max ≤120%;

[0209]

[0210]

[0211]

[0212] The objective function of the second capacity allocation in the sub-model for optimizing the operation of short-route and long-route trains can take the train frequency f2, the first ratio m, the first short-route turnaround station a, and the second short-route turnaround station b as decision variables;

[0213] Among them, T k For the second scheduling cycle (e.g., 1 hour), f1 is the train frequency of the long route; f2 is the train frequency of the short route; the line can be divided into different sections according to the route pattern. Q1 is the passenger flow whose origin or destination is in the non-overlapping section of the long and short routes, that is, the passenger flow taking the long route train; Q2 is the passenger flow whose origin and destination are both in the short route section, that is, passengers who can take either the long or short route train. For passengers whose origin or destination is in a non-overlapping section of the major and minor routes, the passenger flow is transferred to other lines; This refers to passenger traffic originating and terminating in short-distance routes that are being transferred to other lines; L 1N L represents the total length of the entire line. ab T1 is the total length of the short-route train; T2 is the turnaround time of the long-route train; n is the number of cars in the train; w1 and w2 are the weights of the objective function; f min The minimum train frequency for the preset line; f max Z is the preset maximum train frequency for the line;运用 Z represents the number of vehicles used in the capacity allocation plan. 可用 R represents the maximum number of vehicles that can be used on the preset line; N represents the total number of stations on the line; R represents the maximum number of vehicles that can be used on the preset line. i S represents the train travel time in interval i; j t represents the train's dwell time at station j; 折 γ represents the turnaround time of the train at the turnaround station. max This represents the maximum cross-sectional load factor. Let i be the load factor of section i in the upward direction; Let i be the load factor of section i in the upward direction; The passenger flow at section i in the upward direction; C represents the passenger flow at section i in the downhill direction; 定员 The number of passengers is set for each train set.

[0214] Optionally, the second capacity allocation objective function of the connecting route operation scheme optimization sub-model can take minimizing passenger waiting time Z1 and train travel distance Z2 as optimization objectives, which can be expressed by the following formula:

[0215]

[0216] MinZ2=2·L 1c ·f1·n+2·L cN ·f2·n;

[0217] MinM1=w1·Z1+w2·Z2;

[0218] The second capacity allocation constraints of the sub-model for optimizing the operation plan of connecting routes may include: minimum train frequency constraints, maximum train frequency constraints, number of vehicles in use constraints, and maximum cross-sectional load factor constraints.

[0219] Minimum train frequency constraints: To ensure a high level of service, the maximum headway for urban rail trains should not be too large; generally, the preset minimum train frequency f for the line is taken. min The minimum train frequency constraint, which is 10 pairs / hour, can be expressed by the following formula:

[0220] f1≥f min ;

[0221] f2≥f min ;

[0222] The maximum train frequency constraint, which is subject to the minimum train headway and the turnaround capacity of the turnaround station, stipulates that the maximum frequency should not exceed the preset maximum train frequency f for the line. max (For example, 30 pairs / hour), the maximum train frequency constraint can be expressed by the following formula:

[0223] f1≤f max ;

[0224] f2≤f max ;

[0225] The constraint on the number of vehicles used, namely that the number of vehicles used in the train operation plan cannot exceed the preset maximum number of vehicles used on the line (the number of available vehicles), can be expressed by the following formula:

[0226] Z 运用 ≤Z 可用 ;

[0227]

[0228]

[0229]

[0230] The maximum cross-sectional load factor constraint condition, i.e., the cross-sectional load factor cannot be too large, the second preset maximum cross-sectional load factor can be 120%, and the maximum cross-sectional load factor constraint condition can be expressed by the following formula:

[0231] γ max ≤120%;

[0232]

[0233]

[0234]

[0235] The objective function of the second capacity allocation in the sub-model for optimizing the operation plan of connecting routes can take the train frequency f1 of the first route, the train frequency f2 of the second route, and the connecting station c as decision variables;

[0236] Among them, T k For the second scheduling cycle (e.g., 1 hour), f1 is the train frequency of the first route; f2 is the train frequency of the second route; Q1 is the passenger flow with both origin and destination on the first route, i.e., the passenger flow taking the trains on the first route; Q2 is the passenger flow with both origin and destination on the second route, i.e., the passenger flow taking the trains on the second route; Q3 is the passenger flow with origin and destination not on the same route. This is to transfer passenger traffic from other lines where the origin and destination are both on the first route; This is to transfer passenger traffic from other lines where both the origin and destination are on the second route; For passengers whose origin and destination are not on the same route, transfer them to another line; L 1c L is the total length of the first intersection route; cNT1 is the total length of the second route; T2 is the turnaround time of the first route; T3 is the turnaround time of the second route; n is the number of cars in the train; w1 and w2 are the weights of the objective function; f min The minimum train frequency for the preset line; f max Z is the preset maximum train frequency for the line; 运用 Z represents the number of vehicles used in the capacity allocation plan. 可用 R represents the maximum number of vehicles that can be used on the preset line; N represents the total number of stations on the line; R represents the maximum number of vehicles that can be used on the preset line. i S represents the train travel time in interval i; j t represents the train's dwell time at station j; 折 γ represents the turnaround time of the train at the turnaround station. max This represents the maximum cross-sectional load factor. Let i be the load factor of section i in the upward direction; Let i be the load factor of section i in the upward direction; The passenger flow at section i in the upward direction; C represents the passenger flow at section i in the downhill direction; 定员 The number of passengers is set for each train set.

[0237] It is understandable that heuristic algorithms such as genetic algorithms can be used to solve the above-mentioned second transportation capacity allocation objective function.

[0238] Optionally, the second flow restriction objective function takes the maximum inbound passenger flow Z1 and the minimum flow restriction intensity variance Z2 of the set of restricted stations within the second scheduling cycle as its optimization objectives. The second flow restriction objective function can be expressed by the following formula:

[0239]

[0240]

[0241] The second flow restriction constraint may include: flow restriction intensity constraint, flow restriction station number constraint, station capacity constraint, and interval transport capacity constraint;

[0242] To ensure the service level of the station, the flow restriction intensity cannot be too high or too low. The constraint condition of the flow restriction intensity can be expressed by the following formula:

[0243]

[0244] The constraint on the number of stations implementing flow control measures is that the number of stations implementing flow control measures should not be too large, and the number of stations with flow control measures should not exceed 20% of the total number of stations on the line. This constraint can be expressed by the following formula:

[0245]

[0246]

[0247] The station capacity constraint, which states that the passenger flow entering the station cannot exceed the throughput capacity of the entrance gates and the platform capacity, can be expressed by the following formula:

[0248]

[0249]

[0250] The constraint on the transport capacity of a section is that the passenger flow through any section should not exceed the maximum transport capacity of that section. First, it is necessary to determine whether each originating point (OD) in the inbound traffic passes through section dm. The ODs that pass through section dm are accumulated to determine the station's passenger flow contribution to section dm. The ratio of this contribution to the station's inbound traffic is the station's passenger flow contribution rate to section dm. The maximum transport capacity of the section is then considered. The constraints on the transport capacity of a section, which are related to the frequency of train operations, the number of cars in a train formation, and the train's passenger capacity, can be expressed by the following formula:

[0251]

[0252]

[0253]

[0254]

[0255]

[0256] The decision variable for the second flow restriction objective function can be the set of flow-restricted stations, s of each flow-restricted station. i Current limiting intensity

[0257] Where T is the scheduling period (e.g., 1 hour) t i For each flow restriction period (e.g., 15 minutes), if the passenger flow scheduling period is a subsequent midday period, T can include multiple t. i (For example, if T is 1 hour and the flow restriction period is 15 minutes, then T can include 4 t's.) i S represents the set of stations; DM represents the set of cross-sections. S after rate limiting i Standing at t i Passenger flow entering the station during a specific time period; For S i Standing at t i Demand for station entry volume during a specific time period; CR aver N is the average of the rate limiting intensity across all sites.S n is the total number of stations on the line. Si For S i A 0-1 variable indicating whether the station will implement traffic control measures; For S i Standing at t i The throughput capacity of the entrance gates during a specific time period; For S i Standing at t i The platform's capacity during different time periods; For S i The contribution of station entry passenger flow to the up-direction section dm is determined by whether each OD in the entry flow passes through section dm and the ODs that pass through the up-direction section dm are accumulated. For S i The contribution of station entry passenger flow to the downstream cross-section dm; For S i The contribution rate of station entry passenger flow to the upstream cross-section dm, that is, the ratio of the contribution to the station entry passenger flow. For S i The contribution rate of station entry passenger flow to the downstream cross-section dm; For t i Within a given time interval, the transport capacity of dm; The train frequency for the line to which section dm belongs; n is the number of cars in the train formation; C 定员 The number of passengers is set for each train set.

[0258] Understandably, the second current-limiting objective function mentioned above can be solved using machine learning algorithms such as reinforcement learning.

[0259] Optionally, when the passenger flow scheduling period is during the subsequent midday period, the single-route operation plan, the large-small-small-routes operation plan, and the connecting route operation plan can be evaluated based on preset evaluation indicators to obtain the evaluation results of each operation plan; then, based on the evaluation results of each operation plan, one of the single-route operation plan, the large-small-small-routes operation plan, and the connecting route operation plan can be determined as the target operation plan; then, based on the target operation plan, the maximum transport capacity indicators of each section in the first transport capacity data can be updated to determine the second transport capacity data.

[0260] Therefore, by receiving the first sub-input to determine the passenger flow scheduling period and by receiving the second sub-input to determine the target strategy data display method, it is possible to meet the diverse scheduling needs during specific scheduling periods.

[0261] Optionally, according to the rail transit passenger flow scheduling processing method provided by the present invention, the step of displaying the first passenger flow scheduling scheme based on the target strategy data display method in response to the first sub-input and the second sub-input includes:

[0262] When the target strategy data display method is the first display method, the capacity allocation strategy data and the first evaluation result are displayed. The first evaluation result is determined based on the capacity allocation strategy data and the preset capacity allocation strategy evaluation index.

[0263] Alternatively, if the target strategy data display method is the second display method, the rate limiting strategy data and the second evaluation result are displayed, wherein the second evaluation result is determined based on the rate limiting strategy data and the preset rate limiting strategy evaluation index;

[0264] Alternatively, if the target strategy data display method is the third display method, the combined strategy data and the third evaluation result are displayed, wherein the third evaluation result is determined based on the combined strategy data and the preset combined strategy evaluation index.

[0265] To facilitate dispatchers' evaluation of strategy data, the strategy data and evaluation results can be displayed together. Specifically, when the target strategy data is displayed in the first display mode, the capacity allocation strategy data can be evaluated based on preset capacity allocation strategy evaluation indicators to determine the first evaluation result, and then the capacity allocation strategy data and the first evaluation result can be displayed together. When the target strategy data is displayed in the second display mode, the flow restriction strategy data can be evaluated based on preset flow restriction strategy evaluation indicators to determine the second evaluation result, and then the flow restriction strategy data and the second evaluation result can be displayed together. When the target strategy data is displayed in the third display mode, the combined strategy data can be evaluated based on preset combined strategy evaluation indicators to determine the third evaluation result, and then the combined strategy data and the third evaluation result can be displayed together.

[0266] Therefore, by displaying the strategy data and evaluation results together, it is easier for dispatchers to evaluate the strategy data and improve the efficiency of determining dispatch strategies.

[0267] Optionally, according to the rail transit passenger flow scheduling processing method provided by the present invention, the multiple strategy data display methods further include: a fourth display method for displaying the capacity allocation strategy data, the flow restriction strategy data and the combined strategy data;

[0268] The step of displaying the first passenger flow scheduling scheme in response to the first sub-input and the second sub-input, based on the target strategy data display method, includes:

[0269] When the target strategy data display method is the fourth display method, the capacity allocation strategy data, the flow restriction strategy data, the combined strategy data, and the strategy comparison data are displayed.

[0270] The strategy comparison data is determined based on the first evaluation result, the second evaluation result, and the third evaluation result.

[0271] To facilitate dispatchers in summarizing and analyzing various strategy data, multiple strategy data and their evaluation results can be displayed together. Specifically, when the target strategy data display method is the fourth display method, the evaluation results can be summarized and compared based on the first, second, and third evaluation results to determine the strategy comparison data. Thus, capacity allocation strategy data, flow restriction strategy data, combined strategy data, and strategy comparison data can be displayed together.

[0272] Optionally, Figure 4 This is a schematic diagram of the passenger flow scheduling scheme interface provided by the present invention, as shown below. Figure 4 As shown, the interface includes a passenger flow scheduling time slot input control, which can receive the first sub-input. The interface includes four controls: a comprehensive strategy information control (corresponding to the fourth display mode), a capacity allocation strategy control (corresponding to the first display mode), a flow restriction strategy control (corresponding to the second display mode), and a combined strategy control (corresponding to the third display mode). Figure 4 As shown, the strategy comprehensive information control summarizes the capacity allocation strategy, flow restriction strategy control and combined strategy. In order to make it easier for dispatchers to directly see all strategy information and evaluation indicator comparisons, detailed information of each strategy can be viewed by clicking the buttons of other controls (that is, receiving the second sub-input).

[0273] Optionally, Figure 5 This is a schematic diagram of the first optimization model provided by the present invention, as shown below. Figure 5 As shown, the first optimization model may include a first capacity optimization sub-model and a first flow restriction optimization sub-model; the first capacity optimization sub-model may include a first capacity allocation objective function and a first capacity allocation constraint, the first capacity allocation constraint may include a maximum cross-sectional full load rate constraint, a section train frequency constraint, a route train frequency constraint, a maximum train frequency constraint, and a number of operating vehicles constraint; the first flow restriction optimization sub-model may include a first flow restriction objective function and a first flow restriction constraint, the first flow restriction constraint may include a flow restriction intensity constraint, a flow restriction station number constraint, a station capacity constraint, and a section transport capacity constraint.

[0274] Optionally, such as Figure 5As shown, by solving the objective function of the first capacity allocation, a train frequency increase plan can be obtained. This plan includes the frequency increase configuration for each route and the time slots for adding trains. The first scheduling cycle can be divided into several time slots, with the time slot with the highest passenger flow designated as the time slot for adding trains.

[0275] For example, if the line operates both long and short routes, with routes 1 and 2 both operating at a frequency of 3 pairs / 15 minutes, the line can be divided into two sections based on the coverage area of ​​each route. Figure 6 This is a schematic diagram of the train frequency increase scheme provided by the present invention, as shown below. Figure 6 As shown, section 1 is only covered by route 1, while section 2 is covered by both routes 1 and 2. Based on the maximum cross-sectional passenger flow of each section, the required operating frequency for section 1 is 3 pairs / 15min, and the required operating frequency for section 2 is 7 pairs / 15min. To ensure the capacity demand of each section and minimize train travel distance, the required operating frequency for route 1 is 3 pairs / 15min, and for route 2 it is 4 pairs / 15min. Therefore, one additional pair of trains should be added to route 2. The train frequency increase plan is shown in Table 1, including the original line operation plan, the train frequency increase configuration, and evaluation indicators. Figure 4 As shown, the data in Table 1 can be displayed through the capacity allocation strategy control.

[0276] Table 1. Configuration Table for Increased Train Frequency

[0277]

[0278]

[0279] Optionally, such as Figure 5 As shown, by solving the first flow restriction objective function, the first flow restriction scheme can be obtained. The first flow restriction scheme includes the first flow restriction station set and the flow restriction intensity of each first flow restriction station.

[0280] For example, if we solve the objective function for the first flow restriction on the line from 8:00 to 8:15, the resulting first flow restriction scheme is shown in Table 2, including the set of stations subject to the first flow restriction and the flow restriction intensity and evaluation index for each station. Figure 4 As shown, the data in Table 2 can be displayed using the rate limiting strategy control.

[0281] Table 2. Flow restriction intensity of each first-level flow restriction station.

[0282]

[0283] Optionally, such as Figure 5As shown, by solving the first capacity allocation objective function, the train frequency increase configuration for each route can be obtained. Based on the train frequency increase configuration for each route, the maximum transport capacity index of each section is updated. Then, under the constraints of flow restriction intensity constraints, flow restriction station number constraints, station capacity constraints, and updated section transport capacity constraints, by solving the first flow restriction objective function, the first flow restriction station set and the flow restriction intensity of each first flow restriction station can be determined, and the first combination scheme can be determined. The first combination scheme includes the train frequency increase configuration for each route, the time period for adding trains, the first flow restriction station set, and the flow restriction intensity of each first flow restriction station.

[0284] For example, the first combined scheme obtained can be as shown in Table 3, including the original line operation scheme, the configuration of increased train frequency, the set of first flow-limiting stations, the flow-limiting intensity of each first flow-limiting station, and the evaluation indicators. Figure 4 As shown, the data in Table 3 can be displayed using the combined strategy control.

[0285] Table 3: Increased Train Frequency and Intensity of Current Restriction

[0286]

[0287] Optionally, Figure 7 This is a schematic diagram of the second optimization model provided by the present invention, as shown below. Figure 7 As shown, the second optimization model may include at least one second capacity optimization sub-model and a second flow restriction optimization sub-model; the second capacity optimization sub-model may include a second capacity allocation objective function and a second capacity allocation constraint, the second capacity allocation constraint may include a minimum train frequency constraint, a maximum train frequency constraint, a number of operating vehicles constraint, and a maximum cross-sectional load factor constraint; the second flow restriction optimization sub-model may include a second flow restriction objective function and a second flow restriction constraint, the second flow restriction constraint may include a flow restriction intensity constraint, a flow restriction station number constraint, a station capacity constraint, and an interval transport capacity constraint.

[0288] Optionally, such as Figure 7 As shown, at least one second capacity optimization sub-model includes the following sub-models: single route operation scheme optimization sub-model, large and small route operation scheme optimization sub-model, and connecting route operation scheme optimization sub-model.

[0289] Optionally, such as Figure 7 As shown, the single-route operation scheme can be obtained by solving the single-route operation scheme optimization sub-model.

[0290] Optionally, such as Figure 7 As shown, the large and small route operation schemes can be obtained by solving the optimization sub-model of the large and small route operation schemes.

[0291] Optionally, such as Figure 7 As shown, the operation scheme of connecting routes can be obtained by solving the optimization sub-model of the connecting route operation scheme.

[0292] For example, the second scheduling cycle can be 1 hour, and the determined single-route operation plan, large-small-route operation plan, and connecting route operation plan can be shown in Table 4. Figure 4 As shown, the data in Table 4 can be displayed through the capacity allocation strategy control.

[0293] Table 4 Configuration Table of Multiple Train Operation Schemes

[0294]

[0295] Optionally, such as Figure 7 As shown, by solving the second flow restriction optimization sub-model, the second flow restriction scheme can be obtained. The second flow restriction scheme includes the third flow restriction scheme for each flow restriction period. The third flow restriction scheme includes the set of second flow restriction stations and the flow restriction intensity of each second flow restriction station. The flow restriction period is a period in the second scheduling cycle.

[0296] For example, the second current-limiting optimization sub-model can be solved for the line from 7:30 to 8:30 to obtain the second current-limiting scheme, as shown in Table 5, which includes the third current-limiting scheme and evaluation indicators for each current-limiting period. Figure 4 As shown, the data in Table 5 can be displayed using the rate limiting strategy control.

[0297] Table 5 Configuration Table of Second Current Limiting Scheme

[0298]

[0299] Optionally, such as Figure 7 As shown, after determining the single-route operation plan, the large-small-small-routes operation plan, and the connecting-route operation plan, the single-route operation plan, the large-small-small-routes operation plan, and the connecting-route operation plan are evaluated based on preset evaluation indicators to obtain the first evaluation result of each operation plan. Then, based on the first evaluation result of each operation plan, the target operation plan can be determined. Then, based on the target operation plan, the maximum transport capacity index of each section can be updated. After updating the maximum transport capacity index of each section, under the constraints of the flow restriction intensity constraint, the flow restriction station number constraint, the station capacity constraint, and the updated section transport capacity constraint, the second flow restriction plan can be determined by solving the second flow restriction objective function. Then, the second combined plan can be determined. The second combined plan includes the target operation plan and the second flow restriction plan.

[0300] For example, the obtained second combination scheme can be as shown in Table 6, including the target train operation scheme, the second flow restriction scheme, and evaluation indicators. Figure 4 As shown, the data in Table 6 can be displayed using the combined strategy control.

[0301] Table 6 Configuration Table for the Second Combination Scheme

[0302]

[0303] Therefore, by displaying capacity allocation strategy data, flow restriction strategy data, combined strategy data, and strategy comparison data together, dispatchers can easily summarize and analyze various strategy data, thereby improving the efficiency of determining dispatch strategies.

[0304] Optionally, according to the rail transit passenger flow scheduling processing method provided by the present invention, after displaying the first passenger flow scheduling scheme in response to the second input, the method further includes:

[0305] Receive a third input, which is used to determine a second passenger flow scheduling scheme;

[0306] In response to the third input, the second passenger flow scheduling plan and the fourth evaluation result are displayed;

[0307] The fourth evaluation result is determined based on the second passenger flow scheduling scheme and the scheduling scheme evaluation index. The scheduling scheme evaluation index is any one of the preset capacity allocation strategy evaluation index, the preset flow restriction strategy evaluation index, and the preset combined strategy evaluation index.

[0308] Specifically, to determine passenger flow scheduling plans more flexibly, after displaying the first passenger flow scheduling plan, a third input (e.g., such as...) can be received. Figure 4 As shown, the third input can be received through a custom strategy control. The second passenger flow scheduling plan can be received by the user. The second passenger flow scheduling plan can then be evaluated based on the scheduling plan evaluation index to obtain the fourth evaluation result. The second passenger flow scheduling plan and the fourth evaluation result can then be displayed together.

[0309] Optionally, the third input can be used to adjust the first passenger flow scheduling scheme to determine the second passenger flow scheduling scheme.

[0310] Therefore, by receiving the third input, a user-defined second passenger flow scheduling plan can be determined, and then the second passenger flow scheduling plan and the fourth evaluation result can be displayed together, enabling a more flexible determination of the passenger flow scheduling plan.

[0311] Optionally, according to the processing method for rail transit passenger flow scheduling provided by the present invention, the passenger flow prediction data includes: OD passenger flow prediction data, station entry passenger flow prediction data, and cross-sectional passenger flow prediction data, and further includes, before receiving the third input:

[0312] Receive a fourth input, which is used to instruct the acquisition of passenger flow forecast data;

[0313] In response to the fourth input, the OD passenger flow prediction data, the station entry passenger flow prediction data, and the cross-sectional passenger flow prediction data are displayed.

[0314] The OD passenger flow prediction data is obtained based on historical OD passenger flow data, historical inbound passenger flow data, weather forecast data, and a first deep learning model. The inbound passenger flow prediction data is obtained based on the historical inbound passenger flow data, the weather forecast data, and a second deep learning model. The cross-sectional passenger flow prediction data is obtained based on the OD passenger flow prediction data by allocating OD passenger flow to trains.

[0315] Specifically, to facilitate dispatchers in reviewing passenger flow forecast data before determining the second passenger flow scheduling plan, the system can receive a fourth input to determine if the user intends to obtain passenger flow forecast data. In response to this fourth input, it can obtain OD passenger flow forecast data based on historical OD passenger flow data, historical inbound passenger flow data, weather forecast data, and a first deep learning model. It can also obtain inbound passenger flow forecast data based on historical inbound passenger flow data, weather forecast data, and a second deep learning model. Furthermore, based on the OD passenger flow forecast data, it can obtain cross-sectional passenger flow forecast data by allocating OD passenger flow to trains. Finally, it can display the OD passenger flow forecast data, inbound passenger flow forecast data, and cross-sectional passenger flow forecast data.

[0316] Optionally, Figure 8 This is a schematic diagram of the passenger flow prediction interface provided by the present invention, as shown below. Figure 8 As shown, passenger flow forecast data includes OD passenger flow forecast data, inbound passenger flow forecast data, and cross-sectional passenger flow forecast data, such as... Figure 8 As shown, the leftmost button is the indicator selection button, and the right side is the corresponding indicator display interface. The dispatcher can switch the indicator selection button on the left to view the prediction of different indicators.

[0317] Optionally, Figure 9 This is a schematic diagram of the passenger flow prediction process provided by the present invention, such as... Figure 9As shown, OD prediction uses OD volume, station entry volume, and weather data as its foundation. Input data includes OD volume for the same period in the previous several weeks (e.g., 4 weeks), OD volume within the previous hour in 15-minute increments, operating day category, operating time period category, OD station category, OD station passenger flow in and out of the station, and weather category. Operating day and operating time period categories are clustered based on passenger flow characteristics. Operating days are typically divided into three categories: Monday to Thursday, Friday, and weekend. Clustering of passenger flow characteristics for each operating day yields the operating time period category. Stations are categorized into residential, working, mixed residential / working, hub, and shopping / entertainment types, determined based on station location and passenger flow characteristics. Weather is categorized based on factors such as temperature, air quality, and rainfall / snowfall. A model is constructed using deep learning techniques such as LSTM and channel attention mechanisms to perform multi-factor OD prediction.

[0318] Optionally, such as Figure 9 As shown, the inbound passenger flow prediction uses the inbound passenger flow of the same period in the previous several weeks (e.g., 4 weeks), the inbound passenger flow in the previous hour with a granularity of 15 minutes, the operating day category, the operating time period category, the station category, and the weather category as inputs to build a deep learning model for inbound passenger flow prediction.

[0319] Optionally, such as Figure 9 As shown, cross-sectional passenger flow prediction is based on passenger flow origin-destination (OD). It searches for effective paths of OD, then uses big data and traditional Logit models to model passenger path selection, assigns OD to each path, and then assigns passenger flow to trains according to the relationship between passenger flow of each OD and the arrival and departure times of trains at each station, finally obtaining the cross-sectional passenger flow.

[0320] Optionally, Figure 10 This is a schematic diagram of the passenger flow monitoring interface provided by the present invention, as shown below. Figure 10 As shown, based on data provided by the database, information such as occupancy rate, cross-sectional passenger flow, inbound and outbound passenger flow, transfer passenger flow, passenger transport service status, and vehicle utilization status are displayed visually. Figure 10 As shown, the leftmost button is the indicator selection button, and the right side is the corresponding indicator display interface. Dispatchers can switch the indicator selection button on the left to monitor different indicator conditions.

[0321] Therefore, by receiving the fourth input, OD passenger flow forecast data, inbound passenger flow forecast data, and cross-sectional passenger flow forecast data can be displayed, which makes it convenient for dispatchers to check the passenger flow forecast data before determining the second passenger flow scheduling plan, thereby improving the efficiency of custom passenger flow scheduling plans.

[0322] The rail transit passenger flow scheduling method provided by this invention, by receiving a first input, can determine that the user indicates the need to obtain passenger flow early warning data. In response to the first input, the method can display the passenger flow early warning data, which represents the relationship between passenger flow forecast data and an early warning threshold. This allows dispatchers to determine whether the passenger flow forecast data is within the range defined by the early warning threshold. If the passenger flow forecast data is outside the range defined by the early warning threshold, it indicates that a large passenger flow will occur in a future period. Then, by receiving a second input, the method can determine that the user indicates the need to obtain a first passenger flow scheduling plan. In response to the second input, based on the first capacity data and the passenger flow forecast data, the method obtains the first passenger flow scheduling plan by solving an optimization model. The method can then display the first passenger flow scheduling plan, allowing dispatchers to perform passenger flow scheduling in advance based on this plan. This enables efficient determination of passenger flow scheduling plans for large passenger flows.

[0323] The processing apparatus for passenger flow scheduling of rail transit provided by the present invention will be described below. The processing apparatus for passenger flow scheduling of rail transit described below can be referred to in correspondence with the processing method for passenger flow scheduling of rail transit described above.

[0324] Figure 11 This is one of the structural schematic diagrams of the rail transit passenger flow scheduling processing system provided by the present invention, such as... Figure 11 As shown, the system includes a first input module 1101, a first response module 1102, a second input module 1103, and a second response module 1104, wherein:

[0325] The first input module 1101 is used to receive a first input, which is used to indicate the acquisition of passenger flow early warning data;

[0326] The first response module 1102 is used to respond to the first input and display the passenger flow warning data, wherein the passenger flow warning data is used to represent the magnitude relationship between the passenger flow prediction data and the warning threshold.

[0327] The second input module 1103 is used to receive a second input, which is used to indicate the acquisition of the first passenger flow scheduling scheme.

[0328] The second response module 1104 is used to respond to the second input and display the first passenger flow scheduling scheme;

[0329] The first passenger flow scheduling scheme is obtained by solving an optimization model based on the first transport capacity data and the passenger flow prediction data.

[0330] The rail transit passenger flow scheduling processing system provided by this invention can receive a first input to determine if a user instructs to obtain passenger flow early warning data. In response to this first input, the system can display the passenger flow early warning data, which represents the relationship between passenger flow forecast data and an early warning threshold. This allows dispatchers to determine whether the passenger flow forecast data is within the range defined by the early warning threshold. If the passenger flow forecast data is outside the range defined by the early warning threshold, it indicates that a large passenger flow is expected in a future period. The system can then receive a second input to determine if a user instructs to obtain a first passenger flow scheduling plan. In response to this second input, the system can obtain the first passenger flow scheduling plan by solving an optimization model based on the first capacity data and the passenger flow forecast data. The system can then display the first passenger flow scheduling plan, allowing dispatchers to plan passenger flow in advance based on this plan. This enables efficient determination of passenger flow scheduling plans for large passenger flows.

[0331] Optionally, Figure 12 This is the second structural schematic diagram of the rail transit passenger flow scheduling processing system provided by the present invention, as shown below. Figure 12 As shown, the data layer includes the line topology, the number of trains in service, the number of reserve trains, specific data for each line, the operation plan for each line, passenger flow entering stations, passenger flow exiting stations, and transfer passenger flow. The line topology is used for network passenger flow monitoring and OD (Original-Destination) passenger flow allocation; the number of trains in service and the number of reserve trains are used for train utilization monitoring and passenger flow scheduling strategy formulation; the specific data for each line refers to the interval travel time, station dwell time, and other data for each section.

[0332] Data such as interval length, turnaround capacity of each station, and throughput capacity of each station; the operation plan of each line refers to the departure interval at different times, which is mainly used for the formulation of passenger flow scheduling strategies; the passenger flow in the station, the passenger flow out of the station, and the passenger flow for transfer are used for passenger flow monitoring and prediction. When a certain indicator exceeds the threshold, an alarm and warning are triggered, and passenger flow scheduling strategies are formulated based on these indicators.

[0333] like Figure 12 As shown, the functional layer includes three modules: passenger flow monitoring and forecasting, passenger flow alarm and warning, and decision execution and feedback. The passenger flow monitoring and forecasting module primarily monitors occupancy rate, passenger flow at the zero-section, passenger flow entering and exiting the station, transfer passenger flow, passenger flow statistics, and vehicle utilization.

[0334] Information such as passenger flow conditions, as well as statistical indicators such as the top 10 stations in terms of inbound passenger flow, outbound passenger flow, transfer passenger flow, and branch line passenger volume, are collected. Short-term origin-destination (OD) passenger flow, short-term inbound passenger flow, and short-term cross-sectional passenger flow are also predicted. Passenger flow alerts are issued.

[0335] The early warning module mainly combines the ongoing train operation plan with the capacity and passenger flow of each line to monitor passenger flow entering and leaving the station, transfer passenger flow, cross-sectional load factor, and platform capacity exceeding the critical threshold.

[0336] The system provides early warnings and timely alerts regarding capacity utilization. The decision-making, execution, and feedback module primarily formulates passenger flow scheduling strategies for situations with large passenger flows, including three types: capacity allocation strategies, coordinated flow restriction strategies, and combined strategies. It outputs evaluation indicators such as train occupancy rate and train travel distance. After determining the strategy to be adopted, it is reported to the general dispatcher, who then issues the strategy to management personnel at all levels.

[0337] 0 as Figure 12 As shown, the operation terminal layer implements human-computer interaction functions, including a response module and

[0338] Input module. Dispatchers use the response module to view road network conditions, such as passenger flow at each station (inbound, outbound, transfer, occupancy rate, and statistical indicators); and to view alarms and warnings, such as passenger flow at stations (inbound, outbound, transfer, and station capacity).

[0339] Check if the utilization rate exceeds the threshold; review passenger flow scheduling strategies and various evaluation indicators, including capacity allocation strategies, coordinated flow restriction strategies, and combined strategies. Dispatchers select passengers through the input module.

[0340] The flow scheduling strategy is then reported to the central dispatcher.

[0341] Figure 13 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 13 As shown, the electronic device may include: a processor 1310, a communication interface 1320, a memory 1330, and a communication bus 1340, wherein the processor 1310, the communication interface 1320, and the memory 1330 communicate with each other via the communication bus 1340. The processor 1310 can call logical instructions in the memory 1330 to execute a rail transit passenger flow scheduling processing method, which includes:

[0342] Receive a first input, which is used to instruct the acquisition of passenger flow early warning data;

[0343] In response to the first input, the passenger flow warning data is displayed, which is used to represent the magnitude relationship between the passenger flow prediction data and the warning threshold.

[0344] Receive a second input, which is used to indicate the acquisition of a first passenger flow scheduling plan;

[0345] In response to the second input, the first passenger flow scheduling plan is displayed;

[0346] The first passenger flow scheduling scheme is obtained by solving an optimization model based on the first transport capacity data and the passenger flow prediction data.

[0347] Furthermore, the logical instructions in the aforementioned memory 1330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0348] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program that can be stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer is able to execute the rail transit passenger flow scheduling processing method provided by the above methods, the method comprising:

[0349] Receive a first input, which is used to instruct the acquisition of passenger flow early warning data;

[0350] In response to the first input, the passenger flow warning data is displayed, which is used to represent the magnitude relationship between the passenger flow prediction data and the warning threshold.

[0351] Receive a second input, which is used to indicate the acquisition of a first passenger flow scheduling plan;

[0352] In response to the second input, the first passenger flow scheduling plan is displayed;

[0353] The first passenger flow scheduling scheme is obtained by solving an optimization model based on the first transport capacity data and the passenger flow prediction data.

[0354] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for processing rail transit passenger flow scheduling provided by the above methods, the method comprising:

[0355] Receive a first input, which is used to instruct the acquisition of passenger flow early warning data;

[0356] In response to the first input, the passenger flow warning data is displayed, which is used to represent the magnitude relationship between the passenger flow prediction data and the warning threshold.

[0357] Receive a second input, which is used to indicate the acquisition of a first passenger flow scheduling plan;

[0358] In response to the second input, the first passenger flow scheduling plan is displayed;

[0359] The first passenger flow scheduling scheme is obtained by solving an optimization model based on the first transport capacity data and the passenger flow prediction data.

[0360] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0361] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0362] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for handling passenger flow scheduling in rail transit, characterized in that, include: Receive a first input, which is used to instruct the acquisition of passenger flow early warning data; In response to the first input, the passenger flow warning data is displayed, which is used to represent the magnitude relationship between the passenger flow prediction data and the warning threshold. Receive a second input, which is used to indicate the acquisition of a first passenger flow scheduling plan; In response to the second input, the first passenger flow scheduling plan is displayed; The first passenger flow scheduling scheme is obtained by solving an optimization model based on the first transport capacity data and the passenger flow prediction data; The passenger flow forecast data includes first passenger flow data and second passenger flow data; The first passenger flow scheduling scheme is obtained by solving the optimization model, including: Obtain passenger flow scheduling time periods; When the passenger flow scheduling period is the current midday period, in each first scheduling cycle of the passenger flow scheduling period, based on the first transport capacity data and the first passenger flow data of the first scheduling cycle, the first optimization model is solved to determine the train frequency increase scheme and the first flow restriction scheme for the first scheduling cycle; Alternatively, if the passenger flow scheduling period is a subsequent midday period, in each second scheduling cycle within the passenger flow scheduling period, based on the first capacity data and the second passenger flow data of the second scheduling cycle, a second optimization model is solved to determine the second flow restriction scheme and at least one operation scheme for the second scheduling cycle; The duration of the first scheduling cycle is shorter than the duration of the second scheduling cycle.

2. The method for handling passenger flow scheduling in rail transit according to claim 1, characterized in that, The first passenger flow scheduling scheme includes capacity allocation strategy data, flow restriction strategy data, and combined strategy data. The combined strategy data includes the capacity allocation strategy data and the coordinated flow restriction strategy data. Receiving the second input includes: Receive the first sub-input, which is used to determine the passenger flow scheduling period; Receive a second sub-input, which is used to determine the target strategy display method among multiple strategy display methods; The response to the second input, displaying the first passenger flow scheduling plan, includes: In response to the first sub-input and the second sub-input, the first passenger flow scheduling scheme is displayed based on the target strategy data display method; The multiple strategy data display methods include: a first display method for displaying the capacity allocation strategy data, a second display method for displaying the flow restriction strategy data, and a third display method for displaying the combined strategy data; The optimization model includes a capacity optimization sub-model and a flow restriction optimization sub-model. The capacity allocation strategy data is obtained by solving the capacity optimization sub-model based on the passenger flow scheduling period, the first capacity data, and the passenger flow forecast data. The flow restriction strategy data is obtained by solving the flow restriction optimization sub-model based on the passenger flow scheduling period, the first capacity data, and the passenger flow forecast data. The coordinated flow restriction strategy data is obtained by solving the flow restriction optimization sub-model based on the passenger flow scheduling period, the second capacity data, and the passenger flow forecast data. The second capacity data is obtained by adjusting the first capacity data based on the capacity allocation strategy data.

3. The method for handling passenger flow scheduling in rail transit according to claim 2, characterized in that, The step of displaying the first passenger flow scheduling scheme in response to the first sub-input and the second sub-input, based on the target strategy data display method, includes: When the target strategy data display method is the first display method, the capacity allocation strategy data and the first evaluation result are displayed. The first evaluation result is determined based on the capacity allocation strategy data and the preset capacity allocation strategy evaluation index. Alternatively, if the target strategy data display method is the second display method, the rate limiting strategy data and the second evaluation result are displayed, wherein the second evaluation result is determined based on the rate limiting strategy data and the preset rate limiting strategy evaluation index; Alternatively, if the target strategy data display method is the third display method, the combined strategy data and the third evaluation result are displayed, wherein the third evaluation result is determined based on the combined strategy data and the preset combined strategy evaluation index.

4. The method for handling passenger flow scheduling in rail transit according to claim 3, characterized in that, The multiple strategy data display methods also include a fourth display method for displaying the capacity allocation strategy data, the flow restriction strategy data, and the combined strategy data; The step of displaying the first passenger flow scheduling scheme in response to the first sub-input and the second sub-input, based on the target strategy data display method, includes: When the target strategy data display method is the fourth display method, the capacity allocation strategy data, the flow restriction strategy data, the combined strategy data, and the strategy comparison data are displayed. The strategy comparison data is determined based on the first evaluation result, the second evaluation result, and the third evaluation result.

5. The method for handling passenger flow scheduling in rail transit according to claim 3 or 4, characterized in that, After displaying the first passenger flow scheduling plan in response to the second input, the method further includes: Receive a third input, which is used to determine a second passenger flow scheduling scheme; In response to the third input, the second passenger flow scheduling plan and the fourth evaluation result are displayed; The fourth evaluation result is determined based on the second passenger flow scheduling scheme and the scheduling scheme evaluation index. The scheduling scheme evaluation index is any one of the preset capacity allocation strategy evaluation index, the preset flow restriction strategy evaluation index, and the preset combined strategy evaluation index.

6. The method for handling passenger flow scheduling in rail transit according to claim 5, characterized in that, The passenger flow forecast data includes: OD passenger flow forecast data, inbound passenger flow forecast data, and cross-sectional passenger flow forecast data. Before receiving the third input, it also includes: Receive a fourth input, which is used to instruct the acquisition of passenger flow forecast data; In response to the fourth input, the OD passenger flow prediction data, the station entry passenger flow prediction data, and the cross-sectional passenger flow prediction data are displayed. The OD passenger flow prediction data is obtained based on historical OD passenger flow data, historical inbound passenger flow data, weather forecast data, and a first deep learning model. The inbound passenger flow prediction data is obtained based on the historical inbound passenger flow data, the weather forecast data, and a second deep learning model. The cross-sectional passenger flow prediction data is obtained based on the OD passenger flow prediction data by allocating OD passenger flow to trains.

7. A processing system for passenger flow scheduling in rail transit, characterized in that, include: The first input module is used to receive a first input, which is used to indicate the acquisition of passenger flow early warning data; The first response module is used to respond to the first input and display the passenger flow warning data, wherein the passenger flow warning data is used to represent the magnitude relationship between the passenger flow prediction data and the warning threshold. The second input module is used to receive a second input, which is used to indicate the acquisition of the first passenger flow scheduling plan. The second response module is used to respond to the second input and display the first passenger flow scheduling scheme; The first passenger flow scheduling scheme is obtained by solving an optimization model based on the first transport capacity data and the passenger flow prediction data; The passenger flow forecast data includes first passenger flow data and second passenger flow data; The first passenger flow scheduling scheme is obtained by solving the optimization model, including: Obtain passenger flow scheduling time periods; When the passenger flow scheduling period is the current midday period, in each first scheduling cycle of the passenger flow scheduling period, based on the first transport capacity data and the first passenger flow data of the first scheduling cycle, the first optimization model is solved to determine the train frequency increase scheme and the first flow restriction scheme for the first scheduling cycle; Alternatively, if the passenger flow scheduling period is a subsequent midday period, in each second scheduling cycle within the passenger flow scheduling period, based on the first capacity data and the second passenger flow data of the second scheduling cycle, a second optimization model is solved to determine the second flow restriction scheme and at least one operation scheme for the second scheduling cycle; The duration of the first scheduling cycle is shorter than the duration of the second scheduling cycle.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the rail transit passenger flow scheduling processing method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the rail transit passenger flow scheduling processing method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the rail transit passenger flow scheduling processing method as described in any one of claims 1 to 6.

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