Multi-system rail transit hub streamline optimization method and device

Abstract

The embodiment of the invention provides a multi-system rail transit junction streamline optimization method and device, and the method comprises the steps: carrying out the three-dimensional modeling of a multi-system rail transit junction, and obtaining a transportation junction model; dividing the transportation junction model into a plurality of digital blocks according to geographic information, facility layout information, passenger flow data and operation data of the multi-system rail transportation junction; according to passenger flow and logistics demands among different digital blocks, determining a connection relationship among the different digital blocks; according to the method, a streamline path is determined based on a connection relationship between different digital blocks, a streamline operation condition is simulated, a preset optimization target is taken as a target function, the streamline path is optimized, an optimized streamline is obtained, and the operation efficiency of the hub is effectively improved through division of the digital blocks in the hub and optimization of streamline organizations and stream nodes between the blocks; the travel time of passengers is shortened, and the passenger satisfaction is improved.

Description

Technical Field

[0001] The present application relates to the field of rail transit technology, and in particular, to a method and device for optimizing streamlines in a multi-system rail transit hub. Background Art

[0002] With the acceleration of urbanization, multi-system rail transit integrated hubs are playing an increasingly important role in urban construction. As the core of the urban transportation network, rail transit hubs face challenges such as large passenger flow and complex transfers.

[0003] However, existing rail transit hubs often have the following problems during the planning and design process: first, unreasonable block division leads to chaotic passenger and logistics flows within the hub; second, the flow line organization between blocks is unscientific, resulting in long travel times for passengers; third, the flow node setting is not perfect, affecting the overall operation efficiency of the hub. Summary of the invention

[0004] The embodiments of the present application provide a method, apparatus, computer equipment and storage medium for optimizing streamlines in a multi-standard rail transit hub.

[0005] A first aspect of an embodiment of the present application provides a multi-system rail transit hub streamline optimization method, comprising:

[0006] Carry out 3D modeling of multi-system rail transit hubs to obtain a transportation hub model;

[0007] Divide the transportation hub model into multiple digital blocks based on the geographical information, facility layout information, passenger flow data and operation data of the multi-system rail transportation hub;

[0008] Determine the connection relationship between different digital blocks according to the passenger flow and logistics needs between different digital blocks;

[0009] Based on the connection relationship between different digital blocks, the streamline path is determined, and the operation of the streamline is simulated. The preset optimization target is used as the objective function to optimize the streamline path and obtain the optimized streamline.

[0010] In an optional embodiment of the present application, the streamline path is determined based on the connection relationship between different digital blocks, and the streamline operation is simulated, and the streamline path is optimized with the minimum passenger travel time as the objective function to obtain the optimized streamline, including:

[0011] Use graph theory to represent each digital block and the connection paths between different digital blocks, and build a streamline network model;

[0012] Based on the shortest path algorithm, determine the initial streamline path in the streamline network model;

[0013] Use social force model or cellular automation to conduct preliminary simulation of the initial streamline path to evaluate the feasibility of the initial streamline path;

[0014] Taking minimizing passenger travel time, maximizing traffic capacity, and minimizing congestion as the optimization goal, the initial streamline path is optimized using genetic algorithm, particle swarm optimization, or simulated annealing algorithm to obtain the optimized streamline.

[0015] The optimized streamline is simulated and verified using a social force model or a cellular automaton, and the steps of optimizing the streamline are performed according to the simulation results until the simulation results of the optimized streamline meet the preset requirements.

[0016] In an optional embodiment of the present application, the method further includes:

[0017] Set the crowd characteristic parameters, spatial layout parameters, flow rule parameters, simulation control parameters and performance evaluation parameters in the streamline simulation process. Among them, the crowd characteristic parameters include crowd composition, crowd speed and crowd density, the spatial layout parameters include map accuracy, facility size and obstacles, the flow rule parameters include behavior rules, interaction rules and safety distance, the simulation control parameters include time step, simulation duration and initial conditions, and the performance evaluation parameters include capacity, transfer time and congestion level.

[0018] In an optional embodiment of the present application, the method further includes:

[0019] Analyze the passenger flow and logistics characteristics at the flow nodes and determine the flow node types;

[0020] Optimize flow node layout according to flow node type;

[0021] Predict passenger flow and logistics demand at flow nodes and dynamically adjust the settings of flow nodes.

[0022] In an optional embodiment of the present application, analyzing the passenger flow and logistics characteristics at the flow node to determine the flow node type includes:

[0023] Collect historical passenger flow data, logistics data, and facility usage data at flow nodes;

[0024] Analyzing the passenger flow characteristics of the flow node according to the historical passenger flow data at the flow node, wherein the passenger flow characteristics include at least one of passenger flow volume, passenger flow direction, passenger flow speed and passenger flow density;

[0025] Analyzing logistics characteristics of the flow node according to historical logistics data at the flow node, wherein the logistics characteristics include at least one of cargo flow, flow direction, and type of transportation tool;

[0026] Evaluate the layout, facility configuration, traffic capacity and safety hazards of current flow nodes based on the facility usage data at the flow nodes;

[0027] The flow node type is determined based on the passenger flow characteristics, logistics characteristics, layout, facility configuration, traffic capacity and safety hazards of the flow node, where the flow node types include transfer nodes, security check nodes, service nodes and emergency evacuation nodes.

[0028] In an optional embodiment of the present application, optimizing the flow node layout according to the flow node type includes:

[0029] Plan the spatial layout of flow nodes according to the flow node type, adjust the facilities of flow nodes, and ensure the safe design of flow nodes.

[0030] In an optional embodiment of the present application, predicting passenger flow and logistics demand at a flow node and dynamically adjusting the setting of the flow node includes:

[0031] After preprocessing and time series analysis of the hub’s geographic information, facility layout, and passenger flow data, the pre-trained passenger flow prediction model or logistics prediction model is input to output passenger flow and logistics demand at the flow nodes;

[0032] According to the passenger flow and logistics demands at the flow nodes, the facility layout and operation strategy of the flow nodes are dynamically adjusted to cope with changes in passenger flow or logistics in different periods of time.

[0033] A second aspect of the embodiment of the present application provides a multi-standard rail transit hub streamline optimization device, including:

[0034] Modeling module, used to perform three-dimensional modeling of multi-system rail transit hubs to obtain a transportation hub model;

[0035] A partitioning module is used to divide the transportation hub model into multiple digital blocks according to the geographical information, facility layout information, passenger flow data and operation data of the multi-system rail transportation hub;

[0036] A determination module, used to determine the connection relationship between different digital blocks according to the passenger flow and logistics requirements between different digital blocks;

[0037] The simulation module is used to determine the streamline path based on the connection relationship between different digital blocks, and simulate the operation of the streamline. It uses the preset optimization target as the objective function to optimize the streamline path and obtain the optimized streamline.

[0038] A third aspect of an embodiment of the present application provides a computer device, comprising: a memory and a processor, the memory storing a computer program, and the processor implementing the steps of any of the above multi-standard rail transit hub streamline optimization methods when executing the computer program.

[0039] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program implements the steps of any of the above methods for optimizing streamlines in a multi-standard rail transit hub.

[0040] The above technical solution provided by the embodiment of the present application has at least some or all of the following advantages compared with the prior art:

[0041] The method for optimizing streamlines in a multi-mode rail transit hub described in the embodiment of the present application performs three-dimensional modeling of the multi-mode rail transit hub to obtain a transportation hub model; divides the transportation hub model into multiple digital blocks according to the geographic information, facility layout information, passenger flow data, and operation data of the multi-mode rail transit hub; determines the connection relationship between different digital blocks according to the passenger flow and logistics requirements between different digital blocks; determines the streamline path based on the connection relationship between different digital blocks, and simulates the operation of the streamline, optimizes the streamline path with a preset optimization target as the objective function, and obtains the optimized streamline, and effectively improves the operation efficiency of the hub, reduces the travel time of passengers, and improves passenger satisfaction by dividing the digital blocks within the hub, organizing the streamlines between blocks, and optimizing the flow nodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0043] Figure 1 A flowchart of a multi-system rail transit hub streamline optimization method provided in one embodiment of the present application;

[0044] Figure 2 A schematic diagram of digital block division provided for one embodiment of the present application;

[0045] Figure 3 A schematic diagram of the organization of flow lines between digital blocks provided in one embodiment of the present application;

[0046] Figure 4 A schematic diagram of a device structure provided for an embodiment of the present application;

[0047] Figure 5 A schematic diagram of the computer device structure provided for one embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the technical solutions and advantages in the embodiments of the present application more clearly understood, the exemplary embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than an exhaustive list of all the embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0049] See also Figure 1 The multi-standard rail transit hub streamline optimization method provided in the embodiment of the present application includes the following steps 100 to 400:

[0050] Step 100: Perform three-dimensional modeling on the multi-system rail transit hub to obtain a transportation hub model;

[0051] Step 200: Divide the transportation hub model into multiple digital blocks according to the geographic information, facility layout information, passenger flow data and operation data of the multi-system rail transportation hub;

[0052] Step 300: Determine the connection relationship between different digital blocks according to the passenger flow and logistics requirements between different digital blocks;

[0053] Step 400: Based on the connection relationship between different digital blocks, the streamline path is determined, and the operation of the streamline is simulated. The preset optimization target is used as the objective function to optimize the streamline path and obtain the optimized streamline.

[0054] In an optional embodiment of the present application, the digitized block in step 200 is as follows: Figure 2 As shown, the streamlines in step 400 are as follows Figure 3 As shown, passengers enter the hub through the main entrance and first arrive at the security check area. After passing the security check, passengers enter the transfer hall, which has entrances to different modes of transportation, including subways, buses, taxis, and bicycle parking areas. Each entrance to each mode of transportation is connected to the corresponding exit for passengers to leave. The commercial service area is located inside the hub and can be visited by passengers when transferring or waiting. The parking area provides convenience for car passengers, who can leave the hub through the main exit after picking up their cars. In case of an emergency, all areas should have quick access to emergency evacuation areas.

[0055] In an optional embodiment of the present application, in step 100, the three-dimensional modeling of the multi-system rail transit hub to obtain the transportation hub model includes:

[0056] The hub was modeled in 3D using Geographic Information System and Building Information Model.

[0057] In an optional embodiment of the present application, in step 200, the transportation hub model is divided into a plurality of digital blocks according to the geographical information, facility layout information, passenger flow data and operation data of the multi-standard rail transportation hub, including:

[0058] Collect geographic information, facility layout, passenger flow data, operation data, etc. of multi-system rail transit hubs;

[0059] Preprocess the geographic information, facility layout, passenger flow data, and operation data of multi-standard rail transit hubs, including data cleaning, normalization, and formatting;

[0060] Determine different functional areas within the hub based on the pre-processed geographic information, facility layout, passenger flow data, and operation data of the multi-system rail transit hub;

[0061] According to the characteristics of different functional areas within the hub, the transportation hub model is divided into multiple digital blocks, where each block has the following attributes: block number; block function; block area; facility layout within the block; and connection relationship between blocks.

[0062] In an optional embodiment of the present application, after preprocessing the geographic information, facility layout, passenger flow data, operation data, etc. of the multi-system rail transit hub, the method includes:

[0063] Extract pre-processed passenger flow data of multi-standard rail transit hubs;

[0064] Construct a passenger flow data sensitivity scoring model to evaluate the sensitivity of passenger flow data and classify it, and encrypt sensitive data based on the classification results;

[0065] The exchanged data is decrypted and stored to store passenger flow data, and a visual interface is built to display passenger flow data in real time.

[0066] In an optional embodiment of the present application, pre-processed passenger flow data is extracted by presetting an autoencoder network model.

[0067] In an optional embodiment of the present application, the construction of a passenger flow data sensitivity scoring model to evaluate the sensitivity of passenger flow data and classify it refers to collecting historical passenger flow data and extracting historical feature vectors, calculating the mean of the historical feature vectors and setting them as the benchmark data vector;

[0068] Collect passenger flow data in real time and extract real-time feature vectors. Use the K-means clustering analysis algorithm to cluster the real-time feature vectors, and select the center point of each cluster as the reference data vector.

[0069] Combining the RBF kernel function with the integral, the cumulative similarity A(x) between the benchmark data vector and the passenger flow data feature vector is calculated. The formula is:

[0070]

[0071] Where x is the passenger flow data feature vector, x0 is the benchmark data vector, and x i is the historical feature vector of the i-th passenger flow data;

[0072] Perform logarithmic transformation B(x) on the accumulated similarity A, the formula is:

[0073] B(x)=log(1+A(x));

[0074] The hyperbolic tangent function is introduced to smooth the accumulated similarity of the reference feature vector to obtain the accumulated similarity C(x) after smoothing. The formula is:

[0075]

[0076] Where M is the number of reference eigenvectors, x j is the jth reference eigenvector;

[0077] A sensitivity scoring model is constructed to evaluate the sensitivity score S(x) of the passenger flow data feature vector. The formula is:

[0078]

[0079] The sensitivity score of historical passenger flow data is collected to set an evaluation threshold, and the sensitivity score of the passenger flow data feature vector is compared with the evaluation threshold. If the sensitivity score of the passenger flow data feature vector is greater than and equal to the evaluation threshold, it is judged as sensitive data. If the sensitivity score of the passenger flow data feature vector is less than the evaluation threshold, it is judged as ordinary data.

[0080] In an optional embodiment of the present application, encrypting the sensitive data of the passenger flow data based on the classification result refers to randomly generating a key K using a random number generator;

[0081] Use exponential function and sine function to perform nonlinear transformation on sensitive data to obtain the nonlinear transformation result H(x′), the formula is:

[0082]

[0083] Where x′ is sensitive data;

[0084] The sensitive data is transformed nonlinearly using logarithmic function and smooth inverse tangent function to obtain the multi-level nonlinear transformation result E(x′), the formula is:

[0085] E(x′)=tan -1 (x′ 2 k+log(x'+k));

[0086] Construct the encryption formula:

[0087] S(x′)=H(x′)+E(x′);

[0088] Bring sensitive data into the encryption formula for encryption.

[0089] In an optional embodiment of the present application, decrypting the exchanged data to store the passenger flow data refers to receiving the transmitted encrypted data and the corresponding key K;

[0090] Use the decryption formula to decrypt the received data and obtain the decrypted sensitive data O(S). The formula is:

[0091]

[0092] The collected application system information and sensitive data generated by the analysis are stored in the database, the application system information and sensitive data generated by the analysis in the database are backed up in the cloud, and the backup data are regularly checked for integrity.

[0093] In an optional embodiment of the present application, in step 400, the streamline path is determined based on the connection relationship between different digital blocks, and the streamline operation is simulated, and the streamline path is optimized with the minimum passenger travel time as the objective function to obtain the optimized streamline, including:

[0094] Use graph theory to represent each digital block and the connection paths between different digital blocks, and build a streamline network model;

[0095] Based on the shortest path algorithm, determine the initial streamline path in the streamline network model;

[0096] Use social force model or cellular automation to conduct preliminary simulation of the initial streamline path to evaluate the feasibility of the initial streamline path;

[0097] Taking minimizing passenger travel time, maximizing traffic capacity, and minimizing congestion as the optimization goal, the initial streamline path is optimized using genetic algorithm, particle swarm optimization, or simulated annealing algorithm to obtain the optimized streamline.

[0098] The optimized streamline is simulated and verified using a social force model or a cellular automaton, and the steps of optimizing the streamline are performed according to the simulation results until the simulation results of the optimized streamline meet the preset requirements.

[0099] In an optional embodiment of the present application, the method further includes:

[0100] Set the crowd characteristic parameters, spatial layout parameters, flow rule parameters, simulation control parameters and performance evaluation parameters in the streamline simulation process. Among them, the crowd characteristic parameters include crowd composition, crowd speed and crowd density, the spatial layout parameters include map accuracy, facility size and obstacles, the flow rule parameters include behavior rules, interaction rules and safety distance, the simulation control parameters include time step, simulation duration and initial conditions, and the performance evaluation parameters include capacity, transfer time and congestion level.

[0101] In an optional embodiment of the present application, crowd composition: according to the actual situation of the hub, set the proportion of different types of people, such as commuters, tourists, business people, the elderly, the disabled, etc. Crowd speed: set different average walking speeds for different types of people, such as the average speed of adults is 1.2-1.5 meters per second, and the elderly may be 0.8-1.0 meters per second. Crowd density: set the maximum and recommended density of the crowd in different areas, such as the recommended density of 5-7 people per square meter in the transfer channel. Map accuracy: provide an accurate floor plan of the hub, including all channels, stairs, escalators, exits, etc. Facility size: set the size of all facilities in detail, such as channel width, stair step height, escalator length, etc. Obstacles: mark all fixed and temporary obstacles on the simulation map, such as kiosks, rest areas, luggage storage, etc. Behavioral rules: set basic rules for crowd behavior in walking, queuing, transfers, etc., such as obstacle avoidance, choosing the shortest path, following traffic instructions, etc. Interaction rules: simulate the interaction between people, such as avoidance, following, and behavioral changes during congestion. Safety distance: Set the safe distance that people should maintain while walking to avoid collisions. Time step: Set the simulation time step, usually 0.1-0.5 seconds, to simulate the real flow of people. Simulation duration: Set the simulation duration according to the time period that needs to be simulated, such as simulating the morning rush hour may take 1-2 hours. Initial conditions: Set the initial distribution of the crowd, such as uniform distribution at the hub entrance or setting a specific initial density based on historical data. Capacity: Evaluate the maximum capacity of different areas to determine the bottleneck location. Transfer time: Calculate the average transfer time from one block to another. Crowding level: Evaluate the congestion level of different areas through simulation results and identify congestion hotspots.

[0102] In an optional embodiment of the present application, the method further includes:

[0103] Analyze the passenger flow and logistics characteristics at the flow nodes and determine the flow node types;

[0104] According to the flow node type, optimize the flow node layout and improve the flow node capacity;

[0105] Predict passenger flow and logistics demand at flow nodes and dynamically adjust the settings of flow nodes.

[0106] In an optional embodiment of the present application, analyzing the passenger flow and logistics characteristics at the flow node to determine the flow node type includes:

[0107] Collect historical passenger flow data, logistics data and facility usage data at flow nodes, wherein the data includes data during peak hours and non-peak hours;

[0108] Analyzing the passenger flow characteristics of the flow node according to the historical passenger flow data at the flow node, wherein the passenger flow characteristics include at least one of passenger flow volume, passenger flow direction, passenger flow speed and passenger flow density;

[0109] Analyzing logistics characteristics of the flow node according to historical logistics data at the flow node, wherein the logistics characteristics include at least one of cargo flow, flow direction, and type of transportation tool;

[0110] Evaluate the layout, facility configuration, traffic capacity and safety hazards of current flow nodes based on the facility usage data at the flow nodes;

[0111] The flow node type is determined based on the passenger flow characteristics, logistics characteristics, layout, facility configuration, traffic capacity and safety hazards of the flow node, where the flow node types include transfer nodes, security check nodes, service nodes and emergency evacuation nodes.

[0112] In an optional embodiment of the present application, optimizing the flow node layout according to the flow node type includes:

[0113] Plan the spatial layout of flow nodes according to the flow node type, adjust the facilities of flow nodes, and ensure the safe design of flow nodes.

[0114] In this application, according to the type and function of the flow nodes, the spatial layout of the flow nodes is replanned, the location of the facilities and the use of space are optimized, and the facilities of the flow nodes are increased or adjusted, such as adding escalators, widening passages, adding rest areas, etc., so as to improve the traffic efficiency and passenger comfort, and ensure the safe design of the flow nodes, including the reasonable layout of emergency exits and the unobstructed evacuation routes.

[0115] In an optional embodiment of the present application, the method further includes:

[0116] Establishing a mathematical model of the traffic capacity of a flow node, wherein the model includes a simulation of passenger flow, a simulation of facility operation, etc.;

[0117] Use computer simulation technology to simulate the operation of flow nodes under different passenger flows and test traffic capacity;

[0118] According to the simulation results, the parameters of the flow nodes, such as channel width, facility operating speed, safety distance, etc., are adjusted to improve the traffic capacity.

[0119] In an optional embodiment of the present application, predicting passenger flow and logistics demand at a flow node and dynamically adjusting the setting of the flow node includes:

[0120] After preprocessing and time series analysis of the hub’s geographic information, facility layout, and passenger flow data, the pre-trained passenger flow prediction model or logistics prediction model is input to output passenger flow and logistics demand at the flow nodes;

[0121] According to the passenger flow and logistics demands at the flow nodes, the facility layout and operation strategy of the flow nodes are dynamically adjusted to cope with changes in passenger flow or logistics in different periods of time.

[0122] In an optional embodiment of the present application, the method further includes:

[0123] Apply the optimized digital blocks, streamlines and flow nodes to actual projects, collect actual passenger flow data, and continuously optimize the optimized digital blocks, streamlines and flow nodes.

[0124] In an optional embodiment of the present application, the method further includes:

[0125] Provide passengers with real-time transfer information and navigation services through terminals such as mobile applications or information display screens, and study the hub's emergency evacuation strategies to ensure that passengers can be evacuated quickly and safely in an emergency.

[0126] Taking the reconstruction project of a large multi-system rail transit integrated hub as an example, the multi-system rail transit hub streamline optimization method provided in this application is applied. The specific steps are as follows:

[0127] The first step is to build a digital block model

[0128] (1) Block division: The hub is divided into five main digital blocks, namely: Zone A (subway transfer area), Zone B (intercity railway waiting area), Zone C (bus transfer area), Zone D (commercial service area) and Zone E (parking lot area).

[0129] (2) Block attributes: The attributes of each block are shown in Table 1 below:

[0130] Table 1

[0131]

[0132] The second step is to optimize the streamline organization between blocks.

[0133] (1) Streamline design: Based on the connection relationship between blocks, the following streamline paths are designed:

[0134] From Area A to Area B: Two main transfer passages are set up, equipped with escalators and stairs;

[0135] From Area A to Area C: a passage leading directly to the bus docking area is set up;

[0136] Area A to Area D: connected through the atrium of the commercial area, with multiple shopping channels;

[0137] Area B to Area C: connected by a corridor equipped with a moving walkway;

[0138] Area B to Area D: connected through the west side passage of the commercial area.

[0139] (2) Streamline simulation: Use computer simulation software to simulate the flow of people during peak hours. According to the simulation results, adjust the width of the streamline, add transfer nodes, and optimize the streamline organization.

[0140] Step 3: Optimize stream node settings

[0141] (1) Flow node layout: Set flow nodes at the following key locations:

[0142] Transfer node between Area A and Area B: add two sets of escalators to improve transfer efficiency;

[0143] Area C bus station: set up multiple waiting shelters to realize the parking of buses of different routes in different areas;

[0144] Commercial entrance in Zone D: Install intelligent security gate to improve security efficiency.

[0145] (2) Optimizing traffic flow node capacity: Analyzing historical data through artificial intelligence algorithms, predicting passenger flow demand during peak hours, and dynamically adjusting the layout of facilities at traffic nodes.

[0146] Step 4: Implementation and Feedback

[0147] The optimization scheme was applied to the hub renovation project, and the following data were collected during the implementation process:

[0148] Passenger transfer time;

[0149] Traffic flow in each block;

[0150] Flow node capacity;

[0151] Passenger satisfaction survey.

[0152] The plan is continuously optimized based on the collected data. After implementation, the overall operation efficiency of the hub increased by 20%, the average transfer time of passengers was shortened by 15%, passenger congestion was reduced by 20%, and passenger satisfaction increased to more than 90%. The addition of a safety monitoring system has improved the safety management level of the hub. Real-time monitoring and data analysis help managers better predict and respond to passenger flow peaks and reduce congestion. Clear navigation instructions and convenient self-service have improved the overall travel experience of passengers.

[0153] This application uses advanced integrated digital technology to optimize the block division and streamline layout of a multi-system rail transit integrated hub. Through digital block division, a reasonable layout of functional areas within the hub is achieved; the streamline organization between blocks is optimized, reducing passenger travel time; the flow node setting is improved, and the overall operation efficiency of the hub is improved; computer simulation and artificial intelligence algorithms are used to achieve dynamic optimization of the solution; ultimately the hub operation efficiency is significantly improved and passenger satisfaction is improved.

[0154] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0155] See also Figure 4 An embodiment of the present application provides a multi-standard rail transit hub streamline optimization device 400, including:

[0156] A modeling module 410 is used to perform three-dimensional modeling on a multi-system rail transit hub to obtain a transportation hub model;

[0157] A division module 420 is used to divide the transportation hub model into a plurality of digital blocks according to the geographical information, facility layout information, passenger flow data and operation data of the multi-system rail transportation hub;

[0158] A determination module 430, for determining the connection relationship between different digital blocks according to the passenger flow and logistics requirements between different digital blocks;

[0159] The simulation module 440 is used to determine the streamline path based on the connection relationship between different digital blocks, and simulate the operation of the streamline, optimize the streamline path with a preset optimization target as the objective function, and obtain the optimized streamline.

[0160] For the specific definition of the above-mentioned device 400, please refer to the definition of the streamline optimization method for a multi-standard rail transit hub above, which will not be repeated here. Each module in the above-mentioned device 400 can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0161] In one embodiment, a computer device is provided, the internal structure diagram of which can be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a multi-standard rail transit hub streamline optimization method as described above is implemented. Including: including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, any step in the multi-standard rail transit hub streamline optimization method as described above is implemented.

[0162] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any step in the above-mentioned multi-standard rail transit hub streamline optimization method can be implemented.

[0163] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0164] The present application is described 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 application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0165] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0166] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0167] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0168] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A multi-system rail transit hub streamline optimization method, characterized in that: include: Carry out 3D modeling of multi-system rail transit hubs to obtain a transportation hub model; Divide the transportation hub model into multiple digital blocks based on the geographical information, facility layout information, passenger flow data and operation data of the multi-system rail transportation hub; Determine the connection relationship between different digital blocks according to the passenger flow and logistics needs between different digital blocks; Based on the connection relationship between different digital blocks, the streamline path is determined, and the operation of the streamline is simulated. The preset optimization target is used as the objective function to optimize the streamline path and obtain the optimized streamline.

2. The method according to claim 1, characterized in that The method of determining the streamline path based on the connection relationship between different digital blocks, simulating the streamline operation, optimizing the streamline path with the minimum passenger travel time as the objective function, and obtaining the optimized streamline includes: Use graph theory to represent each digital block and the connection paths between different digital blocks, and build a streamline network model; Based on the shortest path algorithm, determine the initial streamline path in the streamline network model; Use social force model or cellular automation to conduct preliminary simulation of the initial streamline path to evaluate the feasibility of the initial streamline path; Taking minimizing passenger travel time, maximizing traffic capacity, and minimizing congestion as the optimization goal, the initial streamline path is optimized using genetic algorithm, particle swarm optimization, or simulated annealing algorithm to obtain the optimized streamline. The optimized streamline is simulated and verified using a social force model or a cellular automaton, and the steps of optimizing the streamline are performed according to the simulation results until the simulation results of the optimized streamline meet the preset requirements.

3. The method according to claim 1, characterized in that The method further comprises: Set the crowd characteristic parameters, spatial layout parameters, flow rule parameters, simulation control parameters and performance evaluation parameters in the streamline simulation process. Among them, the crowd characteristic parameters include crowd composition, crowd speed and crowd density, the spatial layout parameters include map accuracy, facility size and obstacles, the flow rule parameters include behavior rules, interaction rules and safety distance, the simulation control parameters include time step, simulation duration and initial conditions, and the performance evaluation parameters include capacity, transfer time and congestion level.

4. The method according to claim 1, characterized in that: The method further comprises: Analyze the passenger flow and logistics characteristics at the flow nodes and determine the flow node types; Optimize flow node layout according to flow node type; Predict passenger flow and logistics demand at flow nodes and dynamically adjust the settings of flow nodes.

5. The method according to claim 4, characterized in that The analyzing the passenger flow and logistics characteristics at the flow node and determining the flow node type includes: Collect historical passenger flow data, logistics data, and facility usage data at flow nodes; Analyzing the passenger flow characteristics of the flow node according to the historical passenger flow data at the flow node, wherein the passenger flow characteristics include at least one of passenger flow volume, passenger flow direction, passenger flow speed and passenger flow density; Analyzing logistics characteristics of the flow node according to historical logistics data at the flow node, wherein the logistics characteristics include at least one of cargo flow, flow direction, and type of transportation tool; Evaluate the layout, facility configuration, traffic capacity and safety hazards of current flow nodes based on the facility usage data at the flow nodes; The flow node type is determined based on the passenger flow characteristics, logistics characteristics, layout, facility configuration, traffic capacity and safety hazards of the flow node, where the flow node types include transfer nodes, security check nodes, service nodes and emergency evacuation nodes.

6. The method according to claim 4, characterized in that The optimizing the flow node layout according to the flow node type includes: Plan the spatial layout of flow nodes according to the flow node type, adjust the facilities of flow nodes, and ensure the safe design of flow nodes.

7. The method according to claim 4, characterized in that The predicting of passenger flow and logistics demand at the flow node and dynamically adjusting the setting of the flow node include: After preprocessing and time series analysis of the hub’s geographic information, facility layout, and passenger flow data, the pre-trained passenger flow prediction model or logistics prediction model is input to output passenger flow and logistics demand at the flow nodes; According to the passenger flow and logistics demands at the flow nodes, the facility layout and operation strategy of the flow nodes are dynamically adjusted to cope with changes in passenger flow or logistics in different periods of time.

8. A multi-system rail transit hub streamline optimization device, characterized in that: include: Modeling module, used to perform three-dimensional modeling of multi-system rail transit hubs to obtain a transportation hub model; A partitioning module is used to divide the transportation hub model into multiple digital blocks according to the geographical information, facility layout information, passenger flow data and operation data of the multi-system rail transportation hub; A determination module is used to determine the connection relationship between different digital blocks according to the passenger flow and logistics requirements between different digital blocks; The simulation module is used to determine the streamline path based on the connection relationship between different digital blocks, and simulate the operation of the streamline. It uses the preset optimization target as the objective function to optimize the streamline path and obtain the optimized streamline.

9. A computer device comprising: It comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the multi-system rail transit hub streamline optimization method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-system rail transit hub streamline optimization method described in any one of claims 1 to 7 are implemented.

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