Rail transit cooperative scheduling method and device, electronic equipment and storage medium

Abstract

The invention relates to a rail transit cooperative scheduling method and device, electronic equipment and a storage medium, a rail transit road network comprising at least one line is determined, each line comprises at least two stations, and an operation interval is formed between every two stations. Constraint conditions corresponding to the rail transit road network and a passenger flow demand matrix describing passenger flow demand uncertainty are determined, and a plurality of elements included in the passenger flow demand matrix conform to the same distribution. And solving an optimal global transport capacity attribute set of the rail transit road network according to the constraint condition and the passenger flow demand matrix, and then determining a scheduling scheme corresponding to the optimal global transport capacity attribute set as a target scheduling scheme of the rail transit road network for representing a scheduling strategy of each station in the rail transit road network. According to the method, collaborative optimization of regional rail transit traffic flow and passenger flow can be realized under the condition of passenger flow demand uncertainty by determining the passenger flow demand matrix, and an optimal collaborative scheduling scheme can be accurately and flexibly obtained.

Description

Technical Field

[0001] The present disclosure relates to the field of rail transit, and in particular to a rail transit coordinated scheduling method, device, electronic device and storage medium. Background Art

[0002] In the current context of socio-economic development, regional rail transit systems play a vital role. Compared with single-mode transportation systems, regional rail transit systems are larger in scale, more complex, and come with more significant safety risks. With the continuous changes in population structure and consumer demand characteristics, people's travel requirements are becoming increasingly diverse and challenging. However, the existing regional rail transit system is gradually unable to meet these demands. In order to better meet this challenge, we need to further improve the performance of regional rail transit system optimization methods.

[0003] In the actual operation of rail transit, passenger flow demand is a major condition factor that needs to be considered. The actual passenger flow demand is an uncertain variable before it occurs. Most studies on rail transit system optimization do not consider the uncertainty of passenger flow demand. Since only certain historical passenger flow demand data are used, the solution obtained by the optimization model that does not consider uncertainty may lead to a decline in optimization performance or even cause risks in application scenarios where there is uncertainty in actual passenger flow. Summary of the invention

[0004] In view of this, the present disclosure proposes a rail transit collaborative scheduling method, device, electronic device and storage medium, aiming to accurately determine a scheduling strategy for optimizing the performance of a rail transit system.

[0005] According to a first aspect of the present disclosure, a rail transit coordinated scheduling method is provided, the method comprising:

[0006] Determine a rail transit network including at least one line, each of the lines including at least two stations, and an operating interval between every two stations;

[0007] Determining constraint conditions corresponding to the rail transit network and a passenger flow demand matrix describing the uncertainty of passenger flow demand, wherein multiple elements included in the passenger flow demand matrix conform to the same distribution;

[0008] Solving the optimal global transport capacity attribute set of the rail transit network according to the constraint conditions and the passenger flow demand matrix;

[0009] The scheduling scheme corresponding to the optimal global transport capacity attribute set is determined as the target scheduling scheme of the rail transit network, and the target scheduling scheme is used to characterize the scheduling strategy of each of the stations in the rail transit network.

[0010] In a possible implementation, the determining of the constraint conditions corresponding to the rail transit network and the passenger flow demand matrix describing the uncertainty of the passenger flow demand includes:

[0011] Determining preset constraints of the rail transit network;

[0012] Determining a target distribution according to a plurality of historical passenger flow demands corresponding to the rail transit network;

[0013] Sampling is performed based on the target distribution, and a passenger flow demand matrix describing the uncertainty of the passenger flow demand is determined according to the sampling results.

[0014] In a possible implementation, solving the optimal global transport capacity attribute set of the rail transit network according to the constraint conditions and the passenger flow demand matrix includes:

[0015] Determine a plurality of initial dispatching plans, wherein the dispatching plans include the number of lines, departure intervals, and the number of vehicles between every two stations;

[0016] Performing random passenger flow allocation on all lines based on the passenger flow demand matrix to obtain a passenger flow demand sub-matrix between every two stations in the rail transit network;

[0017] Calculating a plurality of transport capacity risks corresponding to each of the scheduling schemes according to each of the passenger flow demand sub-matrices and a preset robust optimization mathematical model;

[0018] For each of the scheduling schemes, determining at least one global capacity attribute according to a plurality of capacity risks calculated from each of the passenger flow demand sub-matrices;

[0019] The optimization is performed with the goal of optimizing the corresponding global transport capacity attributes to obtain the optimal global transport capacity attribute set.

[0020] In a possible implementation, the robust optimization mathematical model is:

[0021] The superscript n is used to represent the nth scene. represents the proportion of passenger flow demand assigned to the mth route from station i to station j to the passenger flow demand of all routes from station i to station j, t l Characterizes the departure interval of line l, n l Characterizes the number of vehicles on route 1, ω k Characterize the capacity risk consequences of the operation interval k, Characterizes the passenger flow demand vector of the operating interval k after passenger flow allocation, b k Characterizes the transportation capacity of the operating interval k, ω p Characterize the capacity risk consequences of station p, Characterizes the passenger flow demand vector of station p after passenger flow allocation, C p represents the throughput of station p, and f(·) represents the capacity risk probability function, which is used to map the passenger flow demand load to the possibility of capacity risk occurrence.

[0022] In a possible implementation, for each of the scheduling schemes, determining at least one global capacity attribute according to the multiple capacity risks calculated from each of the passenger flow demand sub-matrices includes:

[0023] For each of the scheduling schemes, at least one of a capacity risk mean, a variance and a one-sided deviation integral is calculated as a global capacity attribute according to the multiple capacity risks calculated from each of the passenger flow demand sub-matrices.

[0024] In a possible implementation, the optimization is performed with the goal of optimizing the corresponding global transport capacity attributes to obtain an optimal global transport capacity attribute set, including:

[0025] Through genetic algorithm, optimization is performed with the goal of optimizing the corresponding global transport capacity attributes, and the optimal solution is obtained in which each global transport capacity attribute is minimized;

[0026] An optimal global transport capacity attribute set is determined according to each global transport capacity attribute corresponding to the optimal solution.

[0027] In a possible implementation, the constraint condition includes:

[0028] All passengers travel along the recommended routes and passenger demand exceeds capacity, which will not cause long-term congestion.

[0029] According to a second aspect of the present disclosure, a rail transit coordinated scheduling device is provided, the device comprising:

[0030] A network determination module, used to determine a rail transit network including at least one line, each of which includes at least two stations, and an operating interval is between every two stations;

[0031] An information determination module, used to determine the constraint conditions corresponding to the rail transit network and a passenger flow demand matrix describing the uncertainty of passenger flow demand, wherein the multiple elements included in the passenger flow demand matrix conform to the same distribution;

[0032] A target optimization module, used to solve the optimal global transport capacity attribute set of the rail transit network according to the constraint conditions and the passenger flow demand matrix;

[0033] A scheme determination module is used to determine the scheduling scheme corresponding to the optimal global transport capacity attribute set as the target scheduling scheme of the rail transit network, and the target scheduling scheme is used to characterize the scheduling strategy of each station in the rail transit network.

[0034] In a possible implementation, the road network determination module is further used to:

[0035] Determining preset constraints of the rail transit network;

[0036] Determining a target distribution according to a plurality of historical passenger flow demands corresponding to the rail transit network;

[0037] Sampling is performed based on the target distribution, and a passenger flow demand matrix describing the uncertainty of the passenger flow demand is determined according to the sampling results.

[0038] In a possible implementation, the target optimization module is further used to:

[0039] Determine a plurality of initial dispatching plans, wherein the dispatching plans include the number of lines, departure intervals, and the number of vehicles between every two stations;

[0040] Performing random passenger flow allocation on all lines based on the passenger flow demand matrix to obtain a passenger flow demand sub-matrix between every two stations in the rail transit network;

[0041] Calculating a plurality of transport capacity risks corresponding to each of the scheduling schemes according to each of the passenger flow demand sub-matrices and a preset robust optimization mathematical model;

[0042] For each of the scheduling schemes, determining at least one global capacity attribute according to a plurality of capacity risks calculated from each of the passenger flow demand sub-matrices;

[0043] The optimization is performed with the goal of optimizing the corresponding global transport capacity attributes to obtain the optimal global transport capacity attribute set.

[0044] In a possible implementation, the robust optimization mathematical model is:

[0045] The superscript n is used to represent the nth scene. represents the proportion of passenger flow demand assigned to the mth route from station i to station j to the passenger flow demand of all routes from station i to station j, t l Characterizes the departure interval of line l, n l Characterizes the number of vehicles on route l, ω k Characterize the capacity risk consequences of the operation interval k, Characterizes the passenger flow demand vector of the operating interval k after passenger flow allocation, b k Characterizes the transportation capacity of the operating interval k, ω p Characterize the capacity risk consequences of station p, represents the passenger flow demand vector of station p after passenger flow allocation, C prepresents the throughput of station p, and f(·) represents the capacity risk probability function, which is used to map the passenger demand load to the possibility of capacity risk occurrence.

[0046] In a possible implementation, the target optimization module is further used to:

[0047] For each of the scheduling schemes, at least one of a capacity risk mean, a variance and a one-sided deviation integral is calculated as a global capacity attribute according to the multiple capacity risks calculated from each of the passenger flow demand sub-matrices.

[0048] In a possible implementation, the target optimization module is further used to:

[0049] Through genetic algorithm, optimization is performed with the corresponding global transport capacity attributes being optimal, and the optimal solution is obtained in which each global transport capacity attribute is the minimum;

[0050] An optimal global transport capacity attribute set is determined according to each global transport capacity attribute corresponding to the optimal solution.

[0051] In a possible implementation, the constraint condition includes:

[0052] All passengers travel along the recommended routes and passenger demand exceeds capacity, which will not cause long-term congestion.

[0053] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0054] According to a fourth aspect of the present disclosure, a non-volatile computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions implement the above method when executed by a processor.

[0055] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0056] In an embodiment of the present disclosure, a rail transit network including at least one line is determined, each line includes at least two stations, and an operating range is between every two stations. The constraints corresponding to the rail transit network and the passenger flow demand matrix describing the uncertainty of the passenger flow are determined, and the multiple elements included in the passenger flow demand matrix conform to the same distribution. The optimal global transport capacity attribute set of the rail transit network is solved according to the constraints and the passenger flow demand matrix, and then the scheduling scheme corresponding to the optimal global transport capacity attribute set is determined as the target scheduling scheme of the rail transit network, which is used to characterize the scheduling strategy of each station in the rail transit network. The present disclosure can realize the collaborative optimization problem of regional rail transit vehicle flow and passenger flow under the condition of uncertainty of passenger flow demand by determining the passenger flow demand matrix, and accurately and flexibly obtain the optimal collaborative scheduling scheme.

[0057] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0059] Figure 1 A flow chart showing a rail transit coordinated scheduling method according to an embodiment of the present disclosure is shown;

[0060] Figure 2 A schematic diagram showing a rail transit network according to an embodiment of the present disclosure;

[0061] Figure 3 A schematic diagram showing a rail transit coordinated scheduling device according to an embodiment of the present disclosure;

[0062] Figure 4 A schematic diagram showing an electronic device according to an embodiment of the present disclosure;

[0063] Figure 5 A schematic diagram showing another electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0064] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0065] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0066] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0067] The rail transit collaborative scheduling method of the embodiment of the present disclosure can be executed by an electronic device such as a terminal device or a server. Among them, the terminal device can be any fixed or mobile terminal such as a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be a single server or a server cluster composed of multiple servers. Any electronic device can implement the rail transit collaborative scheduling method of the embodiment of the present disclosure by calling the computer-readable instructions stored in the memory by the processor.

[0068] Figure 1 A flow chart of a rail transit coordinated scheduling method according to an embodiment of the present disclosure is shown. Figure 1 As shown, the rail transit coordinated scheduling method of the embodiment of the present disclosure may include the following steps S10-S40.

[0069] Step S10: Determine a rail transit network including at least one line.

[0070] In a possible implementation, the rail transit network for which the transport capacity risk needs to be predicted is determined by an electronic device. The rail transit network may be a network for rail vehicles such as a railway network or a subway network, which includes at least one line (the line here refers to the ride path), each line also includes at least two stations, and there is a corresponding vehicle operation interval between every two adjacent stations. Exemplarily, in the case where the rail transit network is a subway network, it may include subway Line 1 and Line 2. Further, subway Line 1 may include subway stations 1, 2, 3, and 4 arranged in sequence, wherein there are corresponding operation intervals between subway stations 1 and 2, subway stations 2 and 3, and subway stations 3 and 4. Subway Line 2 may include subway stations 5, 3, and 6 arranged in sequence, wherein there are corresponding operation intervals between subway stations 5 and 3, and subway stations 3 and 6, and at least one line may be formed with any subway station as the starting point and another subway station as the end point.

[0071] Figure 2 A schematic diagram of a rail transit network according to an embodiment of the present disclosure is shown. Figure 2As shown, in the case where the embodiment of the present disclosure requires coordinated scheduling of the Chongqing Metro, the rail transit network determined by the electronic device is the subway network of Chongqing, which includes multiple stations. Any two stations can form at least one line as the starting point and the end point. In other words, from a starting station to a terminal station, each riding path can constitute a line. If there are multiple riding paths, multiple lines are formed, and an operating range is formed between two adjacent stations in each line.

[0072] Step S20: Determine the constraints corresponding to the rail transit network and the passenger flow demand matrix describing the uncertainty of passenger flow demand

[0073] In a possible implementation, after determining the rail transit network, the electronic device can determine the constraints of the rail transit network and the passenger flow demand matrix describing the uncertainty of the passenger flow demand, so as to further optimize the calculation according to the constraints and the passenger flow demand matrix to obtain a solution for scheduling the rail transit network. Among them, the constraints are used to constrain the entire optimization calculation process, so that the entire process of determining the target scheduling solution is carried out under the constraints. The passenger flow demand matrix is ​​used to characterize the total passenger flow demand of all lines between every two stations in the rail transit network, that is, in an application scenario, there are the number of passengers who need to travel from a certain starting station to a certain terminal station. Optionally, each element in the passenger flow demand matrix represents the passenger flow demand between the corresponding starting station and the terminal station in an application scenario, and the multiple elements included in the passenger flow demand matrix conform to the same distribution. Among them, the "application scenario" can be determined by influencing factors such as time, holidays, and weather, that is, an application scenario can correspond to a time period, a weather or another influencing factor, or a combination thereof. Taking the application scenario of different time periods as an example, if the rail transit network includes Station 1, Station 2, Station 3, and Station 4, then a certain point in the passenger flow demand matrix of the rail transit network represents the number of passengers from the starting station (for example, Station 1) to the terminal station (for example, Station 3) during the time period corresponding to the point, where the number of passengers includes the total number of passengers on various different travel routes from Station 1 to Station 3.

[0074] Optionally, the constraints of the rail transit network of the embodiment of the present disclosure can be used for simulation modeling under the constraints, so as to perform optimization calculations through simulation modeling to obtain the optimal solution that can schedule the rail transit network. Among them, the constraints may include that all passengers travel along the recommended route, that is, when passengers travel from any station to other stations according to the corresponding route after passenger flow allocation, the passenger flow demand exceeds the capacity and does not cause long-term congestion (that is, the congestion time is lower than the preset threshold), etc. At the same time, the constraints may also include conditions for constraining the optimization method, such as the first-level risk is the most important and the basis for multi-level cascade risk analysis. The constraints may also include only considering the first-level risk of the rail transit network during the optimization process, without considering the multi-level cascade risk. Among them, the first-level risk may include the station capacity risk corresponding to each station and the interval capacity risk between every two stations, wherein the capacity risk represents the risk that the passenger flow demand exceeds the traffic operation capacity.

[0075] Furthermore, the constraints and passenger flow demands of the rail transit network can be preset, or the constraints are preset, and the passenger flow demand matrix is ​​determined based on historical data. That is, the electronic device can directly determine the preset constraints of the rail transit network, and then determine the target distribution based on multiple historical passenger flow demands corresponding to the rail transit network, and then perform sampling based on the target distribution, and determine the corresponding passenger flow demand matrix based on the sampling results. Exemplarily, the electronic device can first obtain the passenger flow demand of the rail transit network multiple times according to preset rules, such as obtaining the passenger flow demand once every hour within the historical 24 hours to obtain multiple historical passenger flow demands. After obtaining the corresponding multiple historical passenger flow demands, the electronic device generates a corresponding target distribution based on the multiple historical passenger flow demands, such as fitting the historical passenger flow demand into the target distribution, and randomly sampling a preset number of passenger flow demands from the target distribution to form the passenger flow demand matrix corresponding to the entire rail transit network. Its three dimensions represent different application scenarios, different starting stations O and different terminal stations D, where different application scenarios can be represented by different time periods, different weather conditions or other influencing factors, or a combination of influencing factors. That is, each tuple in the passenger flow demand matrix corresponds to the passenger flow demand from a certain starting station to a certain terminal station in a certain application scenario. Furthermore, the passenger flow demand submatrix corresponding to at least one line from any station to another station in the rail transit network can be determined by splitting the passenger flow demand matrix corresponding to the rail transit network. This includes the passenger flow demands corresponding to n scenarios of various routes (travel paths) from station i to station j.

[0076] Step S30: solving the optimal global transport capacity attribute set of the rail transit network according to the constraint conditions and the passenger flow demand matrix.

[0077] In a possible implementation, after determining the constraint conditions and the passenger flow demand matrix, the electronic device can perform simulation modeling based on the constraint conditions and the passenger flow demand matrix to obtain a problem model, which can represent the relationship between the passenger flow demand matrix, the scheduling scheme, and the global transport capacity attribute set, and obtain the optimal global transport capacity attribute set of the rail transit network by optimizing the problem model. Among them, there is no restriction on the establishment method and specific form of the problem model. Optionally, the process of optimizing the electronic device can include: determining an initial scheduling scheme (such as the initial solution of the genetic algorithm described later), each scheduling scheme includes the number of lines, departure intervals and number of vehicles between every two stations, the number of vehicles can be the number of vehicles departing from the starting station within a preset time period, and the departure interval can be the departure interval of a preset route in the rail transit network. For example, when the rail transit network is a subway network, the departure interval can include the departure interval of each preset route such as Subway Line 1, Subway Line 2, and Subway Line 3. Based on the passenger flow demand matrix, random passenger flow allocation is performed on different lines to obtain a passenger flow demand submatrix between every two stations in the rail transit network. The “random allocation” here means randomly allocating the total passenger flow demand from station i to station j in the passenger flow demand matrix to various routes (i.e. different riding routes) between station i and station j.

[0078] Next, multiple capacity risks corresponding to each scheduling scheme can be calculated based on each passenger flow demand sub-matrix and the preset robust optimization mathematical model. Among them, multiple capacity risks correspond to different application scenarios, that is, a scheduling scheme determines a capacity risk in an application scenario. For each scheduling scheme, at least one global capacity attribute is determined based on the multiple capacity risks calculated according to the corresponding passenger flow demand sub-matrix, and then the corresponding global capacity attributes are optimized with the goal of being optimal, that is, the scheduling scheme is optimized to obtain the optimal global capacity attribute set. Among them, the global capacity attribute of a scheduling scheme can characterize the overall capacity risk of the scheduling scheme in various application scenarios.

[0079] Optionally, after determining the passenger flow demand submatrix corresponding to each two stations i and j, the electronic device can also determine the passenger flow demand vector corresponding to each station in each line between the two stations according to the passenger flow demand submatrix. And the passenger flow demand vector corresponding to each operating interval Furthermore, the passenger flow demand vectors corresponding to the station and the operating interval can be respectively expressed by the formula and formula Calculated. Where n represents the nth scenario, p and k represent the serial numbers of the station and the operating range respectively, i represents the starting station, j represents the ending station, m represents the line number from the starting station i to the ending station j, S represents the number of stations included in the rail network traffic, represents the proportion of the passenger flow demand assigned to the mth route from station 1 to station j to the passenger flow demand of all routes from station i to station j, Used to indicate whether station p is the mth route from station i to station j If it is the starting station, terminal station or transfer station, it takes 1, otherwise it takes 0. Used to represent the mth route from station i to station j Whether the operation interval k has been passed, if yes, it is 1, otherwise it is 0.

[0080] Optionally, the robust optimization mathematical model for calculating multiple transport capacity risks corresponding to a scheduling scheme in the embodiment of the present disclosure (i.e., an example of the above problem model) can be: This scheduling scheme corresponds to a scheduling scheme for line l from station i to station j (i.e., the departure interval t of line l is l , the number of vehicles n l ), where the departure interval of line l is t l It can be expressed by the departure interval of the starting station i, the number of vehicles n l It can be represented by the number of vehicles at the starting station i, where the superscript n is used to represent the nth scenario. represents the proportion of passenger flow demand assigned to the mth route from station i to station j to the passenger flow demand of all routes from station i to station j, t l Characterizes the departure interval of line l, n l The number of vehicles representing route 1 satisfies Tl represents the one-way running time of line l, ω k Characterize the capacity risk consequences of the operation interval k, Characterizes the passenger flow demand vector of the operating interval k after passenger flow allocation, b k The transport capacity of the operating interval k can be characterized by the formula Determine, where λ l The number of vehicles preset for route 1, ξ 1k Indicates whether interval k is on line 1, if yes, it is 1, otherwise it is 0, ω p Characterize the capacity risk consequences of station p, Characterizes the passenger flow demand vector of station p after passenger flow allocation, C p represents the throughput of station p, and f(·) represents the capacity risk probability function, which is used to map the passenger flow demand load to the possibility of capacity risk. k ,ω p It can be set according to actual situation.

[0081] Furthermore, after calculating the multiple capacity risks corresponding to each scheduling scheme, the electronic device can also calculate at least one of the mean, variance, and unilateral deviation integral of the capacity risk as a global capacity attribute. That is, for each scheduling scheme, at least one of the mean, variance, and unilateral deviation integral of the capacity risk can be calculated based on the multiple capacity risks calculated for each passenger flow demand submatrix as a global capacity attribute. It can be done through the following formula:

[0082] Calculated, where N is the total number of application scenarios.

[0083] variance It can be done through the following formula:

[0084] Calculated.

[0085] The one-sided deviation integral can be expressed as follows:

[0086] Calculated.

[0087] Optionally, after calculating and obtaining at least one global transport capacity attribute, the electronic device optimizes with the goal of optimizing at least one global transport capacity attribute of multiple lines corresponding to each scheduling scheme, and obtains an optimal set of global transport capacity attributes. Exemplarily, in the case where the global transport capacity attribute includes a mean and a variance, the problem model can be optimized with the goal of optimizing the corresponding mean and variance, and obtains an optimal set of global transport capacity attributes. In the case where the global transport capacity attribute includes a unilateral deviation integral and variance, the problem model can be optimized with the goal of optimizing the corresponding unilateral deviation integral and variance, and obtains an optimal set of global transport capacity attributes. Among them, the optimal goal can be the lowest for each global transport capacity attribute.

[0088] Furthermore, the embodiment of the present disclosure can also seek the optimal solution based on a genetic algorithm, that is, the genetic algorithm can be used to optimize the corresponding global transport properties with the goal of being optimal, and obtain the optimal solution in which each global transport property is the minimum. Then determine the optimal global transport property set according to each global transport property corresponding to the optimal solution. Optionally, the genetic algorithm used in the present disclosure can be a non-dominated sorting algorithm NSGA-II, which adopts a fast non-dominated sorting algorithm in non-dominated sorting, and classifies all individuals according to their performance under multiple objective functions to facilitate subsequent selection and crossover operations. At the same time, NSGA-II adopts the concept of crowding distance in diversity maintenance, that is, calculating the distance between individuals in the target space to ensure the diversity between the selected individuals. In terms of individual selection, NSGA-II adopts a tournament selection method, which can better select excellent individuals under the premise of ensuring diversity. In addition, NSGA-II also improves the convergence speed and accuracy of the algorithm by controlling crossover and mutation probabilities and optimizing crossover operations.

[0089] Step S40: Determine the scheduling plan corresponding to the optimal global transport capacity attribute set as the target scheduling plan of the rail transit network.

[0090] In one possible implementation, the electronic device can determine the correspondence between each set of global transport capacity attributes including the corresponding global transport capacity attributes and the scheduling scheme during the optimization process. Therefore, after determining the optimal global transport capacity attribute set, the corresponding scheduling scheme can be determined as the optimal target scheme for the rail transit network, and the target scheduling scheme is used to characterize the scheduling strategy of each station in the rail transit network. Optionally, after determining the target scheduling scheme, each station and section in the rail transit network can be scheduled based on the target scheduling scheme, including adjusting the number of lines, departure intervals and number of vehicles between every two stations. Optionally, based on the passenger flow distribution process, the target scheduling scheme can also include the passenger flow distribution method of the lines between every two stations.

[0091] Based on the above technical features, the disclosed embodiment can use the passenger flow demand matrix that characterizes the passenger flow demand corresponding to multiple scenarios as the input of the optimization model, providing more comprehensive and flexible input data for the optimization model. At the same time, by establishing a robust optimization attribute model and using a genetic algorithm to solve it, it shows stronger robustness under uncertain input conditions. Compared with deterministic models, it can better deal with factors such as system risks, operating costs and passenger satisfaction, and accurately and efficiently obtain the optimal scheduling plan for rail network transportation.

[0092] Figure 3 A schematic diagram of a rail transit coordinated scheduling device according to an embodiment of the present disclosure is shown. Figure 3As shown, the rail transit coordinated scheduling device of the embodiment of the present disclosure may include:

[0093] A network determination module 30 is used to determine a rail transit network including at least one line, each of which includes at least two stations, and an operating interval is between every two stations;

[0094] An information determination module 31, used to determine the constraint conditions corresponding to the rail transit network and a passenger flow demand matrix describing the uncertainty of passenger flow demand, wherein the multiple elements included in the passenger flow demand matrix conform to the same distribution;

[0095] A target optimization module 32, used for solving the optimal global transport capacity attribute set of the rail transit network according to the constraint conditions and the passenger flow demand matrix;

[0096] The scheme determination module 33 is used to determine the scheduling scheme corresponding to the optimal global transport capacity attribute set as the target scheduling scheme of the rail transit network, and the target scheduling scheme is used to characterize the scheduling strategy of each station in the rail transit network.

[0097] In a possible implementation, the road network determination module 30 is further configured to:

[0098] Determining preset constraints of the rail transit network;

[0099] Determining a target distribution according to a plurality of historical passenger flow demands corresponding to the rail transit network;

[0100] Sampling is performed based on the target distribution, and a passenger flow demand matrix describing the uncertainty of the passenger flow demand is determined according to the sampling results.

[0101] In a possible implementation, the target optimization module 32 is further configured to:

[0102] Determine a plurality of initial dispatching plans, wherein the dispatching plans include the number of lines, departure intervals, and the number of vehicles between every two stations;

[0103] Performing random passenger flow allocation on all lines based on the passenger flow demand matrix to obtain a passenger flow demand sub-matrix between every two stations in the rail transit network;

[0104] Calculating a plurality of transport capacity risks corresponding to each of the scheduling schemes according to each of the passenger flow demand sub-matrices and a preset robust optimization mathematical model;

[0105] For each of the scheduling schemes, determining at least one global capacity attribute according to a plurality of capacity risks calculated from each of the passenger flow demand sub-matrices;

[0106] The optimization is performed with the goal of optimizing the corresponding global transport capacity attributes to obtain the optimal global transport capacity attribute set.

[0107] In a possible implementation, the robust optimization mathematical model is:

[0108] The superscript n is used to represent the nth scene. represents the proportion of passenger flow demand assigned to the mth route from station i to station j to the passenger flow demand of all routes from station i to station j, t l Characterizes the departure interval of line l, n l Characterizes the number of vehicles on route 1, ω k Characterize the capacity risk consequences of the operation interval k, Characterizes the passenger flow demand vector of the operating interval k after passenger flow allocation, b k Characterizes the transportation capacity of the operating interval k, ω p Characterize the capacity risk consequences of station p, Characterizes the passenger flow demand vector of station p after passenger flow allocation, C p represents the throughput of station p, and f(·) represents the capacity risk probability function, which is used to map the passenger flow demand load to the possibility of capacity risk occurrence.

[0109] In a possible implementation, the target optimization module 32 is further configured to:

[0110] For each of the scheduling schemes, at least one of a capacity risk mean, a variance and a one-sided deviation integral is calculated as a global capacity attribute according to the multiple capacity risks calculated from each of the passenger flow demand sub-matrices.

[0111] In a possible implementation, the target optimization module 32 is further configured to:

[0112] Through genetic algorithm, optimization is performed with the goal of optimizing the corresponding global transport capacity attributes, and the optimal solution is obtained in which each global transport capacity attribute is minimized;

[0113] An optimal global transport capacity attribute set is determined according to each global transport capacity attribute corresponding to the optimal solution.

[0114] In a possible implementation, the constraint condition includes:

[0115] All passengers travel along the recommended routes and passenger demand exceeds capacity, which will not cause long-term congestion.

[0116] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0117] The embodiment of the present disclosure also provides a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor. The computer-readable storage medium can be a volatile or non-volatile computer-readable storage medium.

[0118] An embodiment of the present disclosure further proposes an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0119] The embodiments of the present disclosure also provide a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0120] Figure 4 A schematic diagram of an electronic device 800 according to an embodiment of the present disclosure is shown. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0121] Reference Figure 4 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0122] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0123] The memory 804 is configured to store various types of data to support operations on the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0124] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0125] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0126] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0127] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0128] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the electronic device 800, the relative positioning of the components, such as the display and keypad of the electronic device 800, and the sensor assembly 814 can also detect the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0129] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0130] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0131] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions, which can be executed by a processor 820 of an electronic device 800 to perform the above method.

[0132] Figure 5 1 is a schematic diagram showing another electronic device 1900 according to an embodiment of the present disclosure. For example, the electronic device 1900 may be provided as a server or a terminal device. Figure 5, the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0133] The electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

[0134] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.

[0135] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0136] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0137] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0138] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0139] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0140] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0141] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0142] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0143] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A rail transit coordinated dispatching method, characterized in that: The method comprises: Determine a rail transit network including at least one line, each of the lines including at least two stations, and an operating interval between every two stations; Determining constraint conditions corresponding to the rail transit network and a passenger flow demand matrix describing the uncertainty of passenger flow demand, wherein multiple elements included in the passenger flow demand matrix conform to the same distribution; Solving the optimal global transport capacity attribute set of the rail transit network according to the constraint conditions and the passenger flow demand matrix; The scheduling scheme corresponding to the optimal global transport capacity attribute set is determined as the target scheduling scheme of the rail transit network, and the target scheduling scheme is used to characterize the scheduling strategy of each of the stations in the rail transit network.

2. The method according to claim 1, characterized in that The determining of the constraint conditions corresponding to the rail transit network and the passenger flow demand matrix describing the uncertainty of passenger flow demand includes: Determining preset constraints of the rail transit network; Determining a target distribution according to a plurality of historical passenger flow demands corresponding to the rail transit network; Sampling is performed based on the target distribution, and a passenger flow demand matrix describing the uncertainty of the passenger flow demand is determined according to the sampling results.

3. The method according to claim 1 or 2, characterized in that: Solving the optimal global transport capacity attribute set of the rail transit network according to the constraint conditions and the passenger flow demand matrix includes: Determine a plurality of initial dispatching plans, wherein the dispatching plans include the number of lines, departure intervals, and the number of vehicles between every two stations; Performing random passenger flow allocation on all lines based on the passenger flow demand matrix to obtain a passenger flow demand sub-matrix between every two stations in the rail transit network; Calculating a plurality of transport capacity risks corresponding to each of the scheduling schemes according to each of the passenger flow demand sub-matrices and a preset robust optimization mathematical model; For each of the scheduling schemes, determining at least one global capacity attribute according to a plurality of capacity risks calculated from each of the passenger flow demand sub-matrices; The optimization is performed with the goal of optimizing the corresponding global transport capacity attributes to obtain the optimal global transport capacity attribute set.

4. The method according to claim 3, characterized in that The robust optimization mathematical model is: The superscript n is used to represent the nth scene. represents the proportion of passenger flow demand assigned to the mth route from station i to station j to the passenger flow demand of all routes from station i to station j, t l Characterizes the departure interval of line I, n l Characterizes the number of vehicles on route l, ω k Characterize the capacity risk consequences of the operation interval k, Characterizes the passenger flow demand vector of the operating interval k after passenger flow allocation, b k Characterizes the transportation capacity of the operating interval k, ω p Characterize the capacity risk consequences of station p, represents the passenger flow demand vector of station p after passenger flow allocation, C p represents the throughput of station p, and f(·) represents the capacity risk probability function, which is used to map the passenger demand load to the possibility of capacity risk occurrence.

5. The method according to claim 3 or 4, characterized in that: For each of the scheduling schemes, determining at least one global capacity attribute according to the multiple capacity risks calculated from each of the passenger flow demand sub-matrices includes: For each of the scheduling schemes, at least one of a capacity risk mean, a variance and a one-sided deviation integral is calculated as a global capacity attribute according to the multiple capacity risks calculated from each of the passenger flow demand sub-matrices.

6. The method according to any one of claims 3 to 5, characterized in that: The optimization is performed with the goal of optimizing the corresponding global transport capacity attributes to obtain the optimal global transport capacity attribute set, including: Through genetic algorithm, optimization is performed with the corresponding global transport capacity attributes being optimal, and the optimal solution is obtained in which each global transport capacity attribute is the minimum; An optimal global transport capacity attribute set is determined according to each global transport capacity attribute corresponding to the optimal solution.

7. The method according to any one of claims 1 to 6, characterized in that: The constraints include: All passengers travel along the recommended routes and passenger demand exceeds capacity, which will not cause long-term congestion.

8. A rail transit coordinated dispatching device, characterized in that: The device comprises: A network determination module, used to determine a rail transit network including at least one line, each of which includes at least two stations, and an operating interval is between every two stations; An information determination module, used to determine the constraint conditions corresponding to the rail transit network and a passenger flow demand matrix describing the uncertainty of passenger flow demand, wherein the multiple elements included in the passenger flow demand matrix conform to the same distribution; A target optimization module, used to solve the optimal global transport capacity attribute set of the rail transit network according to the constraint conditions and the passenger flow demand matrix; A scheme determination module is used to determine the scheduling scheme corresponding to the optimal global transport capacity attribute set as the target scheduling scheme of the rail transit network, and the target scheduling scheme is used to characterize the scheduling strategy of each station in the rail transit network.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the method described in any one of claims 1 to 7 when executing the instructions stored in the memory.

10. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images