Urban rail transit opening plan optimization method, device, equipment and medium

Abstract

The application provides a kind of urban rail transit opening plan optimization method, device, equipment and medium, it is related to urban rail transit transport organization technical field, comprising: obtaining first information;Based on transmission efficiency matrix, construct node importance calculation model, actual passenger flow and physical network are regarded as the input value of node importance calculation model, node importance data are calculated, and the importance data and line importance data are calculated according to node importance data;According to node importance data, importance data and line importance data result, the optimization target is calculated;According to train operation data, the constraint condition is calculated, and the line network transfer coordination optimization model is constructed according to the constraint condition and the optimization target;The opening scheme is obtained by solving line network transfer coordination optimization model based on genetic algorithm.The application can effectively improve the adaptability of each level and passenger flow demand, and coordinate the connection state of each operation line in the network.

Description

Technical Field

[0001] This invention relates to the field of urban rail transit organization technology, and more specifically, to a method, apparatus, equipment, and medium for optimizing urban rail transit planning. Background Technology

[0002] In the networked operation mode of urban rail transit, the network transportation plan, as the core of the entire urban rail transit network organization, directly affects the operational efficiency and service quality of rail transit. Existing research on network transportation plan optimization mostly focuses on the macro-level, neglecting the differences in importance among various elements within the network. It typically uses mean-based indicators for optimization, ignoring the priority handling of optimization objects, thus hindering the effectiveness of the optimization process. Therefore, a method for optimizing urban rail transit operation plans based on node importance is needed. This method ranks the elements at the node, section, and line levels of the urban rail transit network according to their differences, avoiding the drawbacks of using mean-based indicators in the research process. It improves the priority of key nodes, sections, and lines in management and optimization, thereby achieving reasonable optimization of the urban rail transit plan. Summary of the Invention

[0003] The purpose of this invention is to provide a method, apparatus, equipment, and medium for optimizing urban rail transit planning, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0004] Firstly, this application provides a method for optimizing urban rail transit operation plans, including:

[0005] Obtain first information, which includes actual passenger flow and urban rail transit network information. The network information includes the physical network of urban rail transit and train operation data. The physical network includes the nodes, sections and lines that constitute urban rail transit and their corresponding connection relationships.

[0006] A node importance calculation model is constructed based on the transmission efficiency matrix. The actual passenger flow and the physical network are used as input values ​​for the node importance calculation model to calculate node importance data. Based on the node importance data, importance data and line importance data are calculated.

[0007] The optimization objective is calculated based on the node importance data, the importance data, and the line importance data. The optimization objective includes optimization objects at three levels: nodes, sections, and lines within the urban rail transit physical network.

[0008] The constraints are calculated based on the train operation data, and a network transfer coordination optimization model is constructed based on the constraints and the optimization objective.

[0009] The operation plan is obtained by solving the network transfer coordination optimization model based on the genetic algorithm. The operation plan includes the train arrival and departure timetables of different trains in the physical network at each transfer station.

[0010] Secondly, this application also provides an optimization device for urban rail transit operation plans, comprising:

[0011] The acquisition module is used to acquire first information, which includes actual passenger flow and urban rail transit network information. The network information includes the physical network of urban rail transit and train operation data. The physical network includes the nodes, sections and lines that constitute urban rail transit and their corresponding connection relationships.

[0012] The processing module constructs a node importance calculation model based on the transmission efficiency matrix, uses the actual passenger flow and the physical network as input values ​​for the node importance calculation model, calculates node importance data, and calculates importance data and line importance data based on the node importance data.

[0013] The analysis module is used to calculate the optimization target based on the node importance data, the importance data, and the line importance data. The optimization target includes optimization objects at three levels: nodes, sections, and lines within the urban rail transit physical network.

[0014] The calculation module is used to calculate the constraints based on the train operation data, and to construct a network transfer coordination optimization model based on the constraints and the optimization objective.

[0015] The output module uses a genetic algorithm to solve the network transfer coordination optimization model to obtain the operation plan, which includes the train arrival and departure timetables of different trains in the physical network at each transfer station.

[0016] Thirdly, this application also provides an optimization device for urban rail transit operation plans, comprising:

[0017] Memory, used to store computer programs;

[0018] A processor is used to implement the steps of the method for optimizing the urban rail transit operation plan when executing the computer program.

[0019] Fourthly, this application also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described optimization method based on urban rail transit operation plans.

[0020] The beneficial effects of this invention are as follows:

[0021] This invention constructs an urban rail node importance calculation model based on the transmission efficiency matrix, and differentiates and sorts the elements in the three levels of network nodes, sections, and lines, thereby improving the priority of key nodes, sections, and lines in management and optimization, effectively enhancing the adaptability of each level to passenger flow demand, and coordinating the connection status of each operating line in the network.

[0022] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the method for optimizing urban rail transit operation plans as described in this embodiment of the invention;

[0025] Figure 2 This is a schematic diagram of the structure of the urban rail transit operation plan optimization device described in this embodiment of the invention;

[0026] Figure 3 This is a schematic diagram of the optimized equipment structure for the urban rail transit operation plan described in this embodiment of the invention.

[0027] The diagram is labeled as follows: 1. Acquisition module; 2. Processing module; 21. First calculation unit; 22. Second calculation unit; 23. First processing unit; 24. Second processing unit; 25. Third processing unit; 26. Fourth processing unit; 3. Analysis module; 31. Fifth processing unit; 32. Third calculation unit; 33. Fourth calculation unit; 4. Calculation module; 41. Fifth calculation unit; 42. Sixth calculation unit; 421. Seventh calculation unit; 422. Eighth calculation unit; 423. Ninth calculation unit; 43. Sixth processing unit; 5. Output module; 800. Optimization equipment for urban rail transit operation plans; 801. Processor; 802. Memory; 803. Multimedia component; 804. I / O interface; 805. Communication component. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present 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 the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0029] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0030] Example 1:

[0031] This embodiment provides a method for optimizing urban rail transit operation plans.

[0032] See Figure 1 The figure shows that the method includes steps S100, S200, S300, S400 and S500.

[0033] Step S100: Obtain first information, which includes actual passenger flow and urban rail transit network information. The network information includes the physical network of urban rail transit and train operation data. The physical network includes the nodes, sections, and lines that constitute urban rail transit and their corresponding connection relationships.

[0034] Understandably, this step involves uploading and storing the first piece of information. By using the network information of urban rail transit, one can intuitively understand the nodes, sections, and lines of urban rail transit within a given time period, as well as their corresponding connections, and obtain relevant train operation data and network structure parameters.

[0035] Step S200: Construct a node importance calculation model based on the transmission efficiency matrix. Use the actual passenger flow and the physical network as input values ​​to the node importance calculation model to calculate node importance data. Then, calculate the overall importance data and line importance data based on the node importance data. It should be noted that step S200 includes steps S210, S220, S230, S240, S250, and S260.

[0036] Step S210: Calculate the information transmission efficiency matrix based on the number of effective paths between any two nodes in the network information, the actual passenger flow, the propagation impedance, and the preset information transmission efficiency mathematical model. The propagation impedance is the travel time cost corresponding to the optimal path between the two nodes.

[0037] Understandably, this step proposes a method for calculating the transmission efficiency value between nodes to measure the mutual contribution between nodes in the rail transit system. Then, an information transmission efficiency matrix is ​​constructed based on the calculated transmission efficiency values. The information transmission efficiency value between nodes in the urban rail transit physical network is defined from three aspects: the number of effective paths between two nodes, propagation impedance, and passenger flow. The calculation formula is as follows:

[0038]

[0039]

[0040] Where I is the set of nodes, i and j are node indices, and P ij Let q be the transmission efficiency value between node i and node j. ij Let N be the passenger flow between node i and node j. ij B represents the valid path data between node i and node j. ij Let K be the propagation impedance value between node i and node j, k' be the optimal path between node i and node j, and K be the propagation impedance value between node i and node j. ij Let i be the set of travel paths between node i and node j. Let $\frac{i}{j}$ be the travel cost corresponding to the optimal path between node $i$ and node $j$.

[0041] Step S220: Obtain the evaluation index parameters of each node in at least two dimensions based on the network information, and normalize all the evaluation index parameters corresponding to each node to obtain the node attribute index matrix.

[0042] It is understandable that in this step, the calculation of node importance considers not only the factors affecting the node's own importance but also the mutual influence of the importance of other nodes within the network. Introducing a transmission efficiency matrix between nodes to measure their mutual influence can improve the objective accuracy of identifying key nodes in urban rail transit networks. Preferably, in this embodiment, four evaluation indicators—selectivity, connection strength, node betweenness, and weighted node betweenness—are used to measure the importance of a node. All attribute indicators are normalized to obtain a node indicator matrix.

[0043] Step S230: Construct an urban rail node importance calculation model based on the information transmission efficiency matrix, the node attribute index matrix, and the preset feature attribute weights.

[0044] Understandably, the computational model obtained in this step is as follows:

[0045]

[0046] Where i and n are node indices, S i F represents the importance of node i. c Let T' be the transmission efficiency matrix between nodes. c Let w be the node attribute index matrix, w be the weight matrix, ω1 be the node selectivity weight, ω2 be the node connection strength weight, ω3 be the node betweenness weight, ω4 be the weighted node betweenness weight, and P be the node attribute index matrix. 1n P represents the transmission efficiency value between node 1 and node n. 2n P represents the transmission efficiency value between node 2 and node n. n1 P represents the transmission efficiency value between node n and node 1. n2 Let k′ be the transmission efficiency value between node n and node 2. n Let c′ be the degree of selection of node n. n Let s′ be the connection strength of node n. n Let x' be the node betweenness of node n. n Let n be the weighted node betweenness of node n.

[0047] Step S240: Construct an urban rail transit section importance calculation model based on the section passenger flow betweenness and the node influence index. The section passenger flow betweenness is the ratio of the optimal path and the passenger flow of the path corresponding to the section to the total passenger flow on all optimal paths in the urban rail transit physical network. The node influence index is the average node importance of all the nodes connected by the section.

[0048] It is understandable that the interval importance calculation model obtained in this step is as follows:

[0049]

[0050] Among them, S e For interval importance, ω Le L' is the weight of the indicator for the inter-regional passenger flow betweenness. e ω is the normalized value of the inter-regional passenger flow betweenness index; Ie The node influence index I e Indicator weights; I' is the normalized value of the node influence index.

[0051] Step S250: Calculate the interval importance calculation result based on the node importance data and the urban rail transit interval importance calculation model.

[0052] Understandably, in this step, the node importance data is substituted into the importance calculation model to obtain the interval importance index.

[0053] Step S260: Construct an urban rail transit line importance calculation model based on the average importance of nodes and the average importance of intervals on the line in the physical network. Use the interval importance data as the input value of the urban rail transit line importance calculation model to calculate the line importance data. The average importance of intervals is the average of the interval importance data contained in the line.

[0054] It is understandable that the model for calculating the importance of the line in this step is as follows:

[0055]

[0056] Where, ω Si ω represents the node importance weight. Se δ represents the interval importance weight; il δ is the correlation coefficient between node i and line l; el Let be the correlation coefficient between interval e and line l.

[0057] S300: An optimization objective is calculated based on the node importance data, the importance data, and the line importance data. The optimization objective includes optimization objects at three levels: nodes, sections, and lines within the urban rail transit physical network. It should be noted that step S300 includes steps S310, S320, and S330.

[0058] Step S310: Obtain node weights based on the node importance data, and calculate the node optimization target with the aim of maximizing the weighted matching degree value of all nodes in the urban rail transit physical network.

[0059] Understandably, the node optimization objective in this step fully considers the differences in the importance of the research objects, amplifies the influence of key nodes, and increases the priority of key nodes, resulting in reasonable and reliable results. The formula for calculating the node optimization objective is:

[0060]

[0061] Where F1 is the node optimization objective, and i is the node index. Let T'(i) represent the weight coefficient of node i, and T'(i) represent the node matching degree values ​​corresponding to all transfer directions at node i.

[0062] Step S320: Obtain the interval weights based on the interval importance data, and calculate the interval optimization target with the aim of maximizing the weighted matching degree value of all intervals within the urban rail transit physical network.

[0063] It is understandable that the formula for calculating the interval optimization objective in this step is:

[0064]

[0065] Where F2 is the interval optimization objective, Let be the weighting coefficient for interval e; Let be the interval matching degree of line l in the h direction, which is the matching degree of all trains on that line in the transfer section during the study period. This indicates the matching degree of the x-th train on line l in the h direction within interval e.

[0066] Step S330: Obtain the line weights based on the line importance data, and calculate the line optimization target with the aim of maximizing the weighted matching degree value of each line in the urban rail transit physical network.

[0067] It is understandable that the calculation formula for the optimization target of each line in the network in this step is:

[0068]

[0069] Where F3 is the route optimization objective, and ζ(l) is the weight coefficient of route l. Let M(l) be the line matching degree of the x-th train on line l in the h direction, and M(l) represent the capacity matching degree of all trains on line l in both the up and down directions.

[0070] S400: Constraints are calculated based on the train operation data, and a network transfer coordination optimization model is constructed based on the constraints and the optimization objective. It should be noted that step S400 includes steps S410, S420, and S430.

[0071] Step S410: Calculate the overall optimization objective based on the optimization objective. The overall optimization objective is the target value under the condition of maximizing the weighted matching degree accumulation value of the three levels of nodes, sections and lines in the urban rail transit physical network.

[0072] Understandably, this step considers the matching of passenger flow and transportation plans under network operation conditions from the perspective of overall network collaborative management, which makes the optimization results more comprehensive, rather than focusing on only one aspect. The formula for calculating the overall optimization objective is:

[0073] F = F1 + F2 + F3

[0074] Where F is the overall optimization objective, F1 is the node optimization objective, F2 is the interval optimization objective, and F1 is the line optimization objective.

[0075] Step S420: Calculate the train time constraints based on the train operation data. The train time constraints include station stopping constraints, train departure time relationship constraints for each line, and time period constraints. It should be noted that step S420 includes steps S421, S422, and S423.

[0076] Step S421: Calculate the stopping constraints of trains on the same line and in the same direction at each transfer station based on the arrival and departure time restrictions that adjacent trains on the same line should meet in the train operation data.

[0077] It is understandable that in this step, the stopping constraints of trains in the q direction of line p at each transfer station are constructed, and the arrival and departure time restrictions of two adjacent trains on the same line should be met.

[0078] Step S422: Calculate the departure time relationship constraints of trains on each line based on the departure time of the first train on all lines and the departure interval of the same direction on the same line in the train operation data.

[0079] It is understandable that in this step, the departure time relationship constraints of trains on each line are constructed. The departure time of any train in the same direction on the line at each transfer station can be determined based on the departure time of the first train on the line within the coordination period.

[0080] Step S423: Calculate the time period constraint based on the departure time of the first train on the line at each transfer station, the departure time of the last train on the line, the departure time of the last train on the line, and the end time of the last train on the line in the train operation data.

[0081] Understandably, in this step, time constraints are established: the departure time of the first train on the line at each transfer station should be greater than the start time of the line, the departure time of the last train on the line should be less than the end time of the line, and the difference between the departure time and the end time of the last train cannot be greater than the train departure interval on the line.

[0082] Preferably, in this embodiment, it is also necessary to construct departure interval constraints, arrival and departure relationship constraints between adjacent transfer stations of trains on the line, and train connection relationships at transfer stations.

[0083] Step S430: Obtain the network transfer coordination optimization model based on the overall optimization objective and the train time constraints.

[0084] It is understandable that in this step, the calculation formula of the overall optimization objective and the constraints are combined to obtain the network transfer coordination optimization model.

[0085] S500. The operation plan is obtained by solving the network transfer coordination optimization model based on the genetic algorithm. The operation plan includes the train arrival and departure timetables of different trains in the physical network at each transfer station.

[0086] It is understandable that, in this step, given the known passenger flow at each transfer station in each direction, a genetic algorithm is used to solve the network transfer coordination optimization model to obtain the arrival and departure timetables of each train on each line when the matching degree at each level of the network is optimal. It should be noted that solving the network transfer coordination optimization model using a genetic algorithm is common knowledge in the field of urban rail transit organization, and will not be elaborated upon in this embodiment.

[0087] Example 2:

[0088] like Figure 2 As shown in the figure, this embodiment provides an optimization device for urban rail transit operation plans, the device comprising:

[0089] The acquisition module 1 is used to acquire first information, which includes actual passenger flow and urban rail transit network information. The network information includes the physical network of urban rail transit and train operation data. The physical network includes the nodes, sections and lines that constitute urban rail transit and their corresponding connection relationships.

[0090] Processing module 2 constructs a node importance calculation model based on the transmission efficiency matrix, uses the actual passenger flow and the physical network as input values ​​for the node importance calculation model, calculates node importance data, and calculates interval importance data and line importance data based on the node importance data.

[0091] Analysis module 3 is used to calculate optimization targets based on the node importance data, the importance data, and the line importance data results. The optimization targets include optimization objects at three levels: nodes, sections, and lines within the urban rail transit physical network.

[0092] The calculation module 4 is used to calculate the constraints based on the train operation data, and to construct a network transfer coordination optimization model based on the constraints and the optimization objective.

[0093] Output module 5 uses a genetic algorithm to solve the network transfer coordination optimization model to obtain the operation plan, which includes the train arrival and departure timetables of different trains in the physical network at each transfer station.

[0094] In one specific embodiment of this disclosure, the processing module 2 includes:

[0095] The first calculation unit 21 is used to calculate the information transmission efficiency matrix based on the number of effective paths between any two nodes in the network information, the actual passenger flow and the propagation impedance and the preset information transmission efficiency mathematical model, wherein the propagation impedance is the travel time cost corresponding to the optimal path between the two nodes.

[0096] The second calculation unit 22 is used to obtain the evaluation index parameters of each node in at least two dimensions based on the network information, and to normalize all the evaluation index parameters corresponding to each node to obtain the node attribute index matrix.

[0097] The first processing unit 23 is used to construct an urban rail node importance calculation model based on the information transmission efficiency matrix, the node attribute index matrix and the preset feature attribute weights.

[0098] The second processing unit 24 is used to obtain node weights based on the node importance data, and to calculate the node optimization target with the aim of maximizing the weighted matching degree value of all nodes in the urban rail transit physical network.

[0099] The third processing unit 25 is used to obtain the interval weight based on the interval importance data, and to calculate the interval optimization target with the aim of maximizing the weighted matching degree value of all intervals in the urban rail transit physical network.

[0100] The fourth processing unit 26 is used to obtain the line weight based on the line importance data, and to calculate the line optimization target with the aim of maximizing the weighted matching degree value of each line in the urban rail transit physical network.

[0101] In one specific embodiment of this disclosure, the analysis module 3 includes:

[0102] The fifth processing unit 31 constructs an urban rail transit section importance calculation model based on the section passenger flow betweenness and the node influence index. The section passenger flow betweenness is the ratio of the optimal path of any section in the urban rail transit physical network and the passenger flow of the path corresponding to the section to the total passenger flow on all optimal paths. The node influence index is the average node importance of all the nodes connected to the section.

[0103] The third calculation unit 32 is used to calculate the interval importance calculation result based on the node importance data and the urban rail transit interval importance calculation model.

[0104] The fourth calculation unit 33 constructs an urban rail transit line importance calculation model based on the average importance of nodes and the average importance of intervals on the line in the physical network. The interval importance data is used as the input value of the urban rail transit line importance calculation model to calculate the line importance data. The average importance of intervals is the average of the interval importance data contained in the line.

[0105] In one specific embodiment of this disclosure, the computing module 4 includes:

[0106] The fifth calculation unit 41 is used to calculate the overall optimization objective based on the optimization objective. The overall optimization objective is the target value under the condition of maximizing the weighted matching degree accumulation value of the three levels of nodes, sections and lines in the urban rail transit physical network.

[0107] The sixth calculation unit 42 is used to calculate the train time constraints based on the train operation data. The train time constraints include station stopping constraints, train departure time relationship constraints on each line, and time period constraints.

[0108] The sixth processing unit 43 is used to obtain a network transfer coordination optimization model based on the overall optimization objective and the train time constraints.

[0109] In one specific embodiment of this disclosure, the sixth computing unit 42 includes:

[0110] The seventh calculation unit 421 is used to calculate the stopping constraints of trains on the same line and in the same direction at each transfer station based on the arrival and departure time restrictions that adjacent trains on the same line should meet in the train operation data.

[0111] The eighth calculation unit 422 is used to calculate the departure time relationship constraints of trains on each line based on the departure time of the first train on all lines and the departure interval of the same direction on the same line in the train operation data.

[0112] The ninth calculation unit 423 is used to calculate time period constraints based on the departure time of the first train on the line at each transfer station, the departure time of the last train on the line, the departure time of the last train on the line, and the end time of the last train on the line in the train operation data.

[0113] Example 3:

[0114] Corresponding to the above method embodiments, this embodiment also provides an optimization device for urban rail transit operation plans. The optimization device for urban rail transit operation plans described below and the optimization method for urban rail transit operation plans described above can be referred to in correspondence.

[0115] Figure 3 This is a block diagram illustrating an urban rail transit operation planning optimization device 800 according to an exemplary embodiment. Figure 3 As shown, the urban rail transit operation optimization device 800 may include: a processor 801 and a memory 802. The urban rail transit operation optimization device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0116] The processor 801 controls the overall operation of the urban rail transit operation plan optimization device 800 to complete all or part of the steps in the aforementioned urban rail transit operation plan optimization method. The memory 802 stores various types of data to support the operation of the urban rail transit operation plan optimization device 800. This data may include, for example, instructions for any application or method operating on the urban rail transit operation plan optimization device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using 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 storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the urban rail transit operation optimization device 800 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0117] In an exemplary embodiment, the urban rail transit operation plan optimization 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 execute the above-described urban rail transit operation plan optimization method.

[0118] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-described method for optimizing urban rail transit operation plans. For example, the computer-readable storage medium may be the memory 802 including program instructions, which may be executed by the processor 801 of the urban rail transit operation plan optimization device 800 to complete the above-described method for optimizing urban rail transit operation plans.

[0119] Example 4:

[0120] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below and the method for optimizing urban rail transit operation plans described above can be referred to in correspondence.

[0121] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for optimizing urban rail transit operation plans as described in the above method embodiments.

[0122] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0123] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing urban rail transit operation plans, characterized in that, include: Obtain first information, which includes actual passenger flow and urban rail transit network information. The network information includes the physical network of urban rail transit and train operation data. The physical network includes the nodes, sections and lines that constitute urban rail transit and their corresponding connection relationships. A node importance calculation model is constructed based on the transmission efficiency matrix. The actual passenger flow and the physical network are used as input values ​​for the node importance calculation model to calculate node importance data. Then, interval importance data and line importance data are calculated based on the node importance data. The optimization objective is calculated based on the node importance data, the interval importance data, and the line importance data. The optimization objective includes optimization objects at three levels: nodes, intervals, and lines within the urban rail transit physical network. The constraints are calculated based on the train operation data, and a network transfer coordination optimization model is constructed based on the constraints and the optimization objective. The operation plan is obtained by solving the network transfer coordination optimization model based on the genetic algorithm. The operation plan includes the train arrival and departure timetables of different trains in the physical network at each transfer station. The process includes calculating constraints based on the train operation data, and constructing a network transfer coordination optimization model based on the constraints and the optimization objective, including: The overall optimization objective is calculated based on the optimization objective, which is the target value under the condition of maximizing the weighted matching degree accumulation value of the three levels of nodes, sections and lines in the urban rail transit physical network; Train time constraints are calculated based on the train operation data. These constraints include station stopping constraints, train departure time relationship constraints on each line, and time period constraints. Based on the overall optimization objective and the train time constraints, a network transfer coordination optimization model is obtained.

2. The method for optimizing urban rail transit operation plans according to claim 1, characterized in that, A node importance calculation model is constructed based on the transmission efficiency matrix, including: The information transmission efficiency matrix is ​​calculated based on the number of effective paths between any two nodes in the network information, the actual passenger flow, the propagation impedance, and the preset information transmission efficiency mathematical model. The propagation impedance is the travel time cost corresponding to the optimal path between the two nodes. Based on the network information, the evaluation index parameters of each node in at least two dimensions are obtained, and the node attribute index matrix is ​​obtained by normalizing all the evaluation index parameters corresponding to each node. A model for calculating the importance of urban rail nodes is constructed based on the information transmission efficiency matrix, the node attribute index matrix, and the preset feature attribute weights.

3. The method for optimizing urban rail transit operation plans according to claim 1, characterized in that, Based on the node importance data, interval importance data and line importance data are calculated, including: A model for calculating the importance of urban rail transit sections is constructed based on the interval passenger flow betweenness and the node influence index. The interval passenger flow betweenness is the ratio of the optimal path and the passenger flow of the path corresponding to the interval to the total passenger flow on all optimal paths in the urban rail transit physical network. The node influence index is the average node importance of all nodes connected to the interval. The interval importance calculation results are obtained based on the node importance data and the urban rail transit interval importance calculation model; An urban rail transit line importance calculation model is constructed based on the mean importance of nodes and the mean importance of intervals on the lines in the physical network. The interval importance data is used as the input value of the urban rail transit line importance calculation model to calculate the line importance data. The mean importance of intervals is the mean of the interval importance data contained in the line.

4. The method for optimizing urban rail transit operation plans according to claim 1, characterized in that, The optimization objective is calculated based on the node importance data, the importance data, and the line importance data, including: The node weights are obtained based on the node importance data, and the node optimization target is calculated with the aim of maximizing the weighted matching degree value of all nodes in the urban rail transit physical network. Based on the interval importance data, the interval weights are obtained, and the interval optimization objective is calculated with the aim of maximizing the weighted matching degree value of all intervals within the urban rail transit physical network. Based on the importance data of the lines, the line weights are obtained, and the line optimization target is calculated with the aim of maximizing the weighted matching degree value of each line in the urban rail transit physical network.

5. An optimization device for urban rail transit operation plans, characterized in that, include: The acquisition module is used to acquire first information, which includes actual passenger flow and urban rail transit network information. The network information includes the physical network of urban rail transit and train operation data. The physical network includes the nodes, sections and lines that constitute urban rail transit and their corresponding connection relationships. The processing module constructs a node importance calculation model based on the transmission efficiency matrix, uses the actual passenger flow and the physical network as input values ​​for the node importance calculation model, calculates node importance data, and calculates interval importance data and line importance data based on the node importance data. The analysis module is used to calculate the optimization target based on the node importance data, the interval importance data, and the line importance data. The optimization target includes optimization objects at three levels: nodes, intervals, and lines within the urban rail transit physical network. The calculation module is used to calculate the constraints based on the train operation data, and to construct a network transfer coordination optimization model based on the constraints and the optimization objective. The output module uses a genetic algorithm to solve the network transfer coordination optimization model to obtain the operation plan, which includes the train arrival and departure timetables of different trains in the physical network at each transfer station. The process includes calculating constraints based on the train operation data, and constructing a network transfer coordination optimization model based on the constraints and the optimization objective, including: The overall optimization objective is calculated based on the optimization objective, which is the target value under the condition of maximizing the weighted matching degree accumulation value of the three levels of nodes, sections and lines in the urban rail transit physical network; Train time constraints are calculated based on the train operation data. These constraints include station stopping constraints, train departure time relationship constraints on each line, and time period constraints. Based on the overall optimization objective and the train time constraints, a network transfer coordination optimization model is obtained.

6. The urban rail transit operation plan optimization device according to claim 5, characterized in that, The processing module includes: The first calculation unit is used to calculate the information transmission efficiency matrix based on the number of effective paths between any two nodes in the network information, the actual passenger flow, the propagation impedance, and a preset information transmission efficiency mathematical model. The propagation impedance is the travel time cost corresponding to the optimal path between the two nodes. The second calculation unit is used to obtain the evaluation index parameters of each node in at least two dimensions based on the network information, and to normalize all the evaluation index parameters corresponding to each node to obtain a node attribute index matrix. The first processing unit is used to construct an urban rail node importance calculation model based on the information transmission efficiency matrix, the node attribute index matrix, and preset feature attribute weights.

7. The urban rail transit operation plan optimization device according to claim 5, characterized in that, The processing module includes: The second processing unit is used to obtain node weights based on the node importance data, and to calculate the node optimization target with the aim of maximizing the weighted matching degree value of all nodes in the urban rail transit physical network. The third processing unit is used to obtain the interval weight based on the interval importance data, and to calculate the interval optimization target with the aim of maximizing the weighted matching degree value of all intervals in the urban rail transit physical network. The fourth processing unit is used to obtain the line weights based on the line importance data, and to calculate the line optimization target with the aim of maximizing the weighted matching degree value of each line in the urban rail transit physical network.

8. The urban rail transit operation plan optimization device according to claim 5, characterized in that, The analysis module includes: The fifth processing unit constructs an urban rail transit section importance calculation model based on the section passenger flow betweenness and the node influence index. The section passenger flow betweenness is the ratio of the optimal path of any section in the urban rail transit physical network and the passenger flow of the path corresponding to the section to the total passenger flow on all optimal paths. The node influence index is the average node importance of all nodes connected to the section. The third calculation unit is used to calculate the interval importance calculation result based on the node importance data and the urban rail transit interval importance calculation model; The fourth calculation unit constructs an urban rail transit line importance calculation model based on the average importance of nodes and the average importance of intervals on the line in the physical network. The interval importance data is used as the input value of the urban rail transit line importance calculation model to calculate the line importance data. The average importance of intervals is the average of the interval importance data contained in the line.

9. An optimization device for urban rail transit operation planning, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the method for optimizing the urban rail transit operation plan as described in any one of claims 1 to 4 when executing the computer program.

10. A medium, characterized in that: The medium stores a computer program that, when executed by a processor, implements the steps of the optimization method for the urban rail transit operation plan as described in any one of claims 1 to 4.

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