Traffic organization method and system based on urban traffic transfer efficiency evaluation

Abstract

The invention provides a traffic organization method and system based on urban traffic transfer efficiency evaluation, and the method comprises the steps: building a building model based on a pre-obtained urban traffic building space, setting an initial traffic organization scheme of traffic nodes, simulating the movement behaviors of passengers at each traffic node through a social force model, and carrying out the simulation of the passenger movement behaviors at each traffic node; obtaining passenger flow simulation data; setting an initial weight for the factor set based on the evaluation model to obtain a comment set; and dynamically adjusting the initial weight of the factor set based on the passenger flow simulation data and the evaluation model to obtain the target weight of the factor set, calculating the comprehensive association degree in combination with the comment set, and obtaining the urban traffic transfer efficiency evaluation result according to the comprehensive association degree to adjust the initial traffic organization scheme. And obtaining a target traffic organization scheme to organize urban traffic. According to the method, different passenger flow scenes are simulated to dynamically adjust the weight of the evaluation index, the urban traffic transfer efficiency level under the multi-factor interaction influence is quantified, and the reliability of a traffic organization scheme is improved.

Description

Technical Field

[0001] This invention relates to the field of urban transportation transfer organization technology, and in particular to a traffic organization method and system based on urban transportation transfer efficiency evaluation. Background Technology

[0002] In rail transit, security checks are commonly implemented at stations. At integrated transportation hubs connecting railways and urban rail transit, repeated security checks can affect passenger travel efficiency. Although some integrated transportation hubs have adopted a mutual recognition mechanism for security checks, there is no reliable transfer efficiency evaluation system to assess the impact of traffic organization schemes on passenger travel efficiency. Therefore, it is difficult to judge whether the adopted traffic organization schemes contribute to improving traffic transfer efficiency. It is evident that how to provide reliable traffic organization schemes through a reliable transfer efficiency evaluation system has become a technical problem that needs to be solved.

[0003] Currently, existing technologies mainly utilize methods such as the Delphi method, grey relational analysis, DEA method, analytic hierarchy process (AHP), and fuzzy comprehensive evaluation method to evaluate the efficiency of one-way security checks for transportation transfers. Based on the evaluation results, traffic organization schemes are then adjusted. However, existing schemes primarily employ static weighting mechanisms when evaluating transportation transfer efficiency, making it difficult to dynamically reflect passenger flow characteristics and changes in the state of transportation facilities. Furthermore, existing schemes lack analysis of the comprehensive impact of multiple factors on passenger travel, resulting in poor reliability of traffic organization schemes. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a traffic organization method and system based on urban traffic transfer efficiency evaluation. This method enables dynamic adjustment of evaluation index weights in simulated different passenger flow scenarios, quantifies the urban traffic transfer efficiency level under the influence of multiple factors, and improves the reliability of traffic organization schemes.

[0005] To achieve the above objectives, embodiments of the present invention provide a traffic organization method based on urban traffic transfer efficiency evaluation, comprising: constructing an architectural model based on pre-acquired urban traffic architectural space and setting an initial traffic organization scheme for traffic nodes according to the architectural model; simulating passenger movement behavior at each traffic node using a pre-constructed social force model based on the initial traffic organization scheme to obtain passenger flow simulation data; setting initial weights for a pre-acquired set of factors based on a preset evaluation model to obtain a set of comments; dynamically adjusting the initial weights of the pre-acquired set of factors based on the passenger flow simulation data and the preset evaluation model to obtain target weights for the pre-acquired set of factors; calculating a comprehensive correlation degree based on the set of comments and the target weights and obtaining an urban traffic transfer efficiency evaluation result based on the comprehensive correlation degree; adjusting the initial traffic organization scheme based on the urban traffic transfer efficiency evaluation result and outputting a target traffic organization scheme to organize urban traffic.

[0006] This invention proposes a traffic organization method based on urban traffic transfer efficiency evaluation. An initial traffic organization scheme is set at traffic nodes in an urban traffic architectural model. Then, a social force model is used to simulate passenger movement behavior based on the initial traffic organization scheme to obtain passenger flow simulation data. This data is then used to simulate different traffic nodes, allowing for the acquisition of passenger flow data under different scenarios. An initial weight is set for a set of factors using a preset evaluation model to construct a comment set. The initial weights of the factor set are then dynamically adjusted using passenger flow simulation data combined with the preset evaluation model, enabling the evaluation results to reflect the real-time operational status of the traffic hub. The correlation between the comment set and the adjusted target weights of the factor set is then evaluated to obtain the urban traffic transfer efficiency evaluation results. An iterative optimization method is used to optimize the traffic organization scheme, thereby achieving dynamic adjustment of evaluation index weights in different passenger flow scenarios. The correlation between the comment set and the target weights quantifies the urban traffic transfer efficiency level under the influence of multiple factors, improving the reliability of the traffic organization scheme.

[0007] Furthermore, based on the pre-acquired urban traffic building space, an architectural model is constructed and an initial traffic organization scheme for traffic nodes is set according to the architectural model, including: acquiring urban traffic building space based on map data of the target city; constructing an architectural model based on the urban traffic building space and setting several traffic nodes in the architectural model; and setting a corresponding initial traffic organization scheme based on each traffic node.

[0008] In the above scheme, urban transportation building space is topologically extracted from real map data, and then the urban transportation building space is constructed into a building model that is highly consistent with the actual situation, providing a reliable data foundation for subsequent data simulation. At the same time, several traffic nodes are set in the building model and corresponding initial traffic organization schemes are configured, so that different passenger flow scenarios can be simulated, no longer limited to a single passenger flow scenario, providing a reliable data foundation for subsequent data simulation and transfer efficiency evaluation, and helping to improve the reliability of traffic organization schemes.

[0009] Furthermore, based on the initial traffic organization plan, the passenger movement behavior at each traffic node is simulated using a pre-constructed social force model to obtain passenger flow simulation data. This includes: acquiring passenger movement behavior parameters and passenger random behavior deviations; calculating the sum of passenger movement behavior forces using the passenger behavior parameters; constructing a social force model based on passenger random behavior deviations and the sum of passenger movement behavior forces; and executing the initial traffic organization plan at each traffic node, simulating passenger movement behavior using the social force model to obtain passenger flow simulation data.

[0010] In the above scheme, a social force model is constructed by considering both passenger-specific factors and external influences. This model can accurately simulate the impact of passenger behavior on passenger flow, providing a reliable data foundation for subsequent evaluation of transfer efficiency and helping to improve the reliability of traffic organization schemes.

[0011] Furthermore, based on the preset evaluation model, initial weights are set for the pre-acquired factor set to obtain a comment set, including: selecting several ride efficiency evaluation indicators to construct a factor set; constructing triples based on the preset evaluation model, the factor set, and the values ​​of each indicator in the factor set, wherein the values ​​of each indicator are obtained from passenger flow simulation data; setting initial weights for each ride efficiency evaluation indicator in the factor set based on the triples and the preset analysis method; and mapping the initial weights of each ride efficiency evaluation indicator in the factor set to the corresponding preset transfer efficiency level to obtain a comment set.

[0012] In the above scheme, multi-dimensional indicators are selected to construct a factor set, and then a triplet is constructed by combining a preset evaluation model and the values ​​of each indicator. Then, a preset analysis method is used to set initial weights for each indicator in the factor set. Through weight mapping, a comment set is constructed, which transforms the subjective evaluation problem into an objective quantitative problem of each factor. This provides a reliable data foundation for the evaluation of subsequent indicators and helps to improve the reliability of the traffic organization scheme.

[0013] Furthermore, based on passenger flow simulation data and a preset evaluation model, the initial weights of the pre-acquired factor set are dynamically adjusted to obtain the target weights of the pre-acquired factor set. This includes: inputting passenger flow simulation data into the preset evaluation model to obtain real-time passenger flow density; and dynamically adjusting the initial weights of each ride efficiency evaluation indicator in the factor set based on the real-time passenger flow density and the preset passenger flow density correction coefficient to obtain the target weights of each ride efficiency evaluation indicator in the factor set.

[0014] In the above scheme, a dynamic weight correction mechanism is adopted to adjust the initial weights of each indicator so that the evaluation of each indicator is more in line with the real passenger flow scenario. This allows for different weight evaluation standards for different passenger flow scenarios, avoiding the problem that static weights cannot reflect the dynamic passenger flow characteristics and changes in facility operation status. It enables the simulation of different passenger flow scenarios to dynamically adjust the weights of evaluation indicators, which helps to quantify the urban transportation transfer efficiency level under the influence of multiple factors through the correlation between the comment set and the target weights, thereby improving the reliability of the traffic organization scheme.

[0015] Furthermore, based on the comment set and target weights, a comprehensive correlation degree is calculated, and the evaluation results of urban transportation transfer efficiency are obtained based on the comprehensive correlation degree. This includes: constructing an initial correlation degree matrix based on each ride efficiency evaluation index and comment set in the factor set through a preset correlation function; improving the initial correlation degree matrix based on a preset distance algorithm to obtain a target correlation degree matrix; and calculating the comprehensive correlation degree based on the target correlation degree matrix and target weights, and obtaining the evaluation results of urban transportation transfer efficiency based on the comprehensive correlation degree.

[0016] In the above scheme, the correlation between evaluation indicators is considered, a correlation matrix is ​​constructed, and the correlation matrix is ​​improved by a preset distance algorithm to obtain a target correlation matrix. This makes the coupling between the indicators higher and closer to the real correlation between the indicators. Finally, by calculating the comprehensive correlation between the target weights and the target correlation matrix, the evaluation results of urban traffic transfer efficiency are obtained. This enables the simulation of different passenger flow scenarios to dynamically adjust the weights of evaluation indicators. The correlation between the comment set and the target weights is used to quantify the level of urban traffic transfer efficiency under the influence of multiple factors, thereby improving the reliability of the traffic organization scheme.

[0017] Furthermore, based on the evaluation results of urban traffic transfer efficiency, the initial traffic organization scheme is adjusted, and the target traffic organization scheme is output to organize urban traffic. This includes: inputting the evaluation results of urban traffic transfer efficiency into the building model, and adjusting the initial traffic organization scheme of each traffic node according to the evaluation results to obtain the traffic organization scheme adjustment result; inputting the traffic organization scheme adjustment result into the social force model and the preset evaluation model to evaluate urban traffic transfer efficiency until the evaluation results of urban traffic transfer efficiency meet the preset evaluation requirements to obtain the target urban traffic transfer efficiency evaluation result; inputting the target urban traffic transfer efficiency evaluation result into the building model, and outputting the target traffic organization scheme to organize urban traffic.

[0018] In the above scheme, after each evaluation of urban traffic transfer efficiency, a feedback optimization mechanism is introduced to iteratively optimize the evaluation results until the results meet the preset evaluation requirements. This indicates that the current evaluation results meet the needs of the traffic nodes, and the current iterative traffic organization scheme is output as the target traffic organization scheme to organize urban traffic. This allows for the dynamic adjustment of evaluation index weights in simulated different passenger flow scenarios. By quantifying the urban traffic transfer efficiency level under the influence of multiple factors through the correlation between the evaluation set and the target weights, the reliability of the traffic organization scheme is improved.

[0019] This invention also provides a traffic organization system based on urban traffic transfer efficiency evaluation, comprising: an initial traffic organization scheme acquisition module, a passenger flow simulation data acquisition module, a comment set construction module, a weight adjustment module, an evaluation result output module, and a traffic organization module. The initial traffic organization scheme acquisition module is used to construct a building model based on pre-acquired urban traffic building space and set an initial traffic organization scheme for traffic nodes according to the building model. The passenger flow simulation data acquisition module is used to simulate passenger movement behavior at each traffic node using a pre-constructed social force model based on the initial traffic organization scheme to obtain passenger flow simulation data. The comment set construction module is used to set initial weights for a pre-acquired factor set based on a preset evaluation model to obtain a comment set. The weight adjustment module is used to dynamically adjust the initial weights of the pre-acquired factor set based on passenger flow simulation data and the preset evaluation model to obtain the target weights of the pre-acquired factor set. The evaluation result output module is used to calculate the comprehensive correlation degree based on the comment set and the target weights and obtain the urban traffic transfer efficiency evaluation result based on the comprehensive correlation degree. The traffic organization module is used to adjust the initial traffic organization scheme based on the urban traffic transfer efficiency evaluation result and output a target traffic organization scheme to organize urban traffic.

[0020] This invention proposes a traffic organization system based on urban traffic transfer efficiency evaluation. An initial traffic organization scheme is set at traffic nodes in an urban traffic building space model. Then, a social force model is used to simulate passenger movement behavior based on the initial traffic organization scheme to obtain passenger flow simulation data. This data is used to simulate different traffic nodes and obtain passenger flow data under different scenarios. An initial weight is set for a set of factors using a preset evaluation model to construct a comment set. The initial weights of the factor set are dynamically adjusted using passenger flow simulation data combined with the preset evaluation model, so that the evaluation results can reflect the real-time operating status of the traffic hub. The correlation between the comment set and the adjusted target weights of the factor set is then evaluated to obtain the urban traffic transfer efficiency evaluation results. An iterative optimization method is used to optimize the traffic organization scheme, thereby achieving dynamic adjustment of evaluation index weights in different passenger flow scenarios. The correlation between the comment set and the target weights quantifies the urban traffic transfer efficiency level under the influence of multiple factors, improving the reliability of the traffic organization scheme.

[0021] Furthermore, the initial traffic organization scheme acquisition module is used to construct an architectural model based on the pre-acquired urban traffic architectural space and set the initial traffic organization scheme for traffic nodes according to the architectural model. It includes: an urban traffic architectural space acquisition unit, a traffic node setting unit, and an initial traffic organization scheme setting unit. The urban traffic architectural space acquisition unit is used to acquire urban traffic architectural space based on the map data of the target city. The traffic node setting unit is used to construct an architectural model based on the urban traffic architectural space and set several traffic nodes in the architectural model. The initial traffic organization scheme setting unit is used to set the corresponding initial traffic organization scheme based on each traffic node.

[0022] Furthermore, the passenger flow simulation data acquisition module is used to simulate passenger movement behavior at each traffic node based on the initial traffic organization plan and through a pre-built social force model to obtain passenger flow simulation data. This module includes: a passenger movement behavior data acquisition unit, a social force model construction unit, and a simulation unit. The passenger movement behavior data acquisition unit acquires passenger movement behavior parameters and passenger random behavior deviations, and calculates the sum of the resultant forces of passenger movement behavior based on these parameters. The social force model construction unit constructs a social force model based on passenger random behavior deviations and the sum of the resultant forces of passenger movement behavior. The simulation unit executes the initial traffic organization plan at each traffic node and simulates passenger movement behavior through the social force model to obtain passenger flow simulation data. Attached Figure Description

[0023] Figure 1 A flowchart illustrating the steps of a traffic organization method based on urban traffic transfer efficiency evaluation, provided in a certain embodiment of the present invention; Figure 2 This is a schematic diagram of the module structure of a traffic organization system based on urban traffic transfer efficiency evaluation, provided for one embodiment of the present invention. Detailed Implementation

[0024] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Example 1 See Figure 1 , Figure 1 This is a flowchart illustrating the steps of a traffic organization method based on urban traffic transfer efficiency evaluation, provided in one embodiment of the present invention. Figure 1As shown in the figure, this embodiment of the invention proposes a traffic organization method based on urban traffic transfer efficiency evaluation, including steps 101 to 106, each step of which is as follows: Step 101: Based on the pre-acquired urban traffic building space, construct a building model and set the initial traffic organization scheme for traffic nodes according to the building model; Step 102: Based on the initial traffic organization plan, simulate the passenger movement behavior at each traffic node using a pre-constructed social force model to obtain passenger flow simulation data; Step 103: Based on the preset evaluation model, set initial weights for the pre-acquired factor set to obtain the comment set; Step 104: Based on passenger flow simulation data and a preset evaluation model, dynamically adjust the initial weights of the pre-acquired factor set to obtain the target weights of the pre-acquired factor set. Step 105: Based on the comment set and target weights, calculate the comprehensive correlation degree and obtain the evaluation results of urban transportation transfer efficiency based on the comprehensive correlation degree; Step 106: Adjust the initial traffic organization plan based on the evaluation results of urban traffic transfer efficiency, and output the target traffic organization plan to organize urban traffic.

[0026] One specific implementation method, in this embodiment, is explained using the adjustment of security check measures as a traffic organization scheme. First, an architectural model is constructed based on the urban rail transit building space. Each traffic node is identified within the architectural model, and the current security check measures at each traffic node are set as the initial traffic organization scheme. Traffic nodes are generally passenger transfer stations. Then, the social force model in Anylogic software is used to perform micro-simulation of passenger movement behavior, outputting real-time passenger flow simulation data. This passenger flow simulation data simulates passenger walking behavior, considering both the passenger's own actions and external influences. Next, in a preset evaluation model, several evaluation indicators are selected as a factor set. Initial weights are set for the factor set, and a comment set is constructed. In this embodiment, the preset evaluation model... The model can be interpreted using the matter-element extension evaluation model. The initial weights represent the parameter thresholds of each indicator. The evaluation set constructed based on these parameter thresholds is used to classify the urban transportation transfer efficiency into levels. The levels in the evaluation set include: low, average, high, and high. Then, real-time passenger flow simulation data is input into the preset evaluation model. First, the initial weights of the factor set are dynamically adjusted to adapt to different passenger flow scenarios and provide corresponding target weights. Then, the evaluation set and target weights are used to calculate the comprehensive correlation between the target weights and the levels classified in the evaluation set, thus obtaining the evaluation result of urban transportation transfer efficiency. Finally, based on the evaluation result of urban transportation transfer efficiency, the initial traffic organization scheme of each traffic node is adjusted accordingly to generate the target traffic organization scheme for each traffic node, so as to adjust the security check measures of each traffic node.

[0027] This invention proposes a traffic organization method based on urban traffic transfer efficiency evaluation. An initial traffic organization scheme is set at traffic nodes in an urban traffic architectural model. Then, a social force model is used to simulate passenger movement behavior based on the initial traffic organization scheme to obtain passenger flow simulation data. This data is then used to simulate different traffic nodes, allowing for the acquisition of passenger flow data under different scenarios. An initial weight is set for a set of factors using a preset evaluation model to construct a comment set. The initial weights of the factor set are then dynamically adjusted using passenger flow simulation data combined with the preset evaluation model, enabling the evaluation results to reflect the real-time operational status of the traffic hub. The correlation between the comment set and the adjusted target weights of the factor set is then evaluated to obtain the urban traffic transfer efficiency evaluation results. An iterative optimization method is used to optimize the traffic organization scheme, thereby achieving dynamic adjustment of evaluation index weights in different passenger flow scenarios. The correlation between the comment set and the target weights quantifies the urban traffic transfer efficiency level under the influence of multiple factors, improving the reliability of the traffic organization scheme.

[0028] A preferred embodiment involves constructing an architectural model based on pre-acquired urban traffic architectural space and setting an initial traffic organization scheme for traffic nodes according to the architectural model, including: acquiring urban traffic architectural space based on map data of the target city; constructing an architectural model based on the urban traffic architectural space and setting several traffic nodes in the architectural model; and setting a corresponding initial traffic organization scheme based on each traffic node.

[0029] One preferred implementation involves identifying the corresponding urban transportation building space based on the city map data of the target city. This space includes urban rail transit buildings and various transportation station buildings. Then, the actual corresponding urban transportation building space is constructed into a simulation model, and the corresponding traffic nodes are marked in this model. Finally, the current security check measures at each traffic node are set as the initial traffic organization scheme. It is worth noting that the construction process of the building model can be implemented using existing technologies.

[0030] In the above scheme, urban transportation building space is topologically extracted from real map data, and then the urban transportation building space is constructed into a building model that is highly consistent with the actual situation, providing a reliable data foundation for subsequent data simulation. At the same time, several traffic nodes are set in the building model and corresponding initial traffic organization schemes are configured, so that different passenger flow scenarios can be simulated, no longer limited to a single passenger flow scenario, providing a reliable data foundation for subsequent data simulation and transfer efficiency evaluation, and helping to improve the reliability of traffic organization schemes.

[0031] A preferred approach involves simulating passenger movement behavior at each traffic node based on an initial traffic organization scheme, using a pre-constructed social force model to obtain passenger flow simulation data. This includes: acquiring passenger movement behavior parameters and passenger random behavior deviations; calculating the sum of passenger movement behavior forces based on the passenger behavior parameters; constructing a social force model based on the passenger random behavior deviations and the sum of passenger movement behavior forces; and executing the initial traffic organization scheme at each traffic node, simulating passenger movement behavior using the social force model to obtain passenger flow simulation data.

[0032] One preferred implementation method, in this embodiment, studies passenger flow under one-way security check-free conditions. Due to the large passenger volume of railway hubs, the numerous and complex passenger flow lines, and the varying behaviors of different individuals, all of which affect transfer efficiency to varying degrees, the social force model in Anylogic software can be used to perform microscopic simulation of passenger movement behavior and output passenger flow simulation data in real time. The construction process of the social force model is as follows: First, passenger movement behavior parameters are obtained. Since the influence of the passenger's own behavior and external factors on walking behavior is considered when simulating passenger walking behavior, the passenger movement behavior parameters include: passenger mass, passenger walking speed, passenger desired direction, and radius of pedestrians or objects, etc. Based on the passenger movement parameters, the total resultant force acting on the passenger is further calculated. Finally, the social force model is constructed by combining passenger random behavior deviations. The basic equation of the social force model is as follows: ; ; In the formula, For passenger quality; For passenger walking speed; For the direction passengers expect; The radius of the pedestrian or object; and For the radius of the passenger, The radius of a boundary, wall, or other obstacle in the environment. The radius of other targets; The sum of the net forces acting on the passengers; Bias caused by random behavior; Driven by the passengers themselves; For passengers and The forces between them; The forces between a passenger and a boundary, wall, or other obstacle in the environment; For other goals The attraction effect on passengers. As can be seen from the social force model equations, passengers' walking process is mainly affected by the combined influence of their own driving force, the force between themselves and other passengers, the force between themselves and obstacles in the environment, and the attraction effect of the target. Therefore, the social force model can be used to realistically simulate the walking behavior of passengers.

[0033] Finally, by executing the corresponding initial traffic organization plan at each traffic node, the passenger movement behavior is simulated using the constructed social force model to obtain passenger flow simulation data. In a specific interpretation, passenger flow simulation data can be obtained by deploying sensors in the social force model to monitor indicators such as the number of people queuing for security checks and the density of the station hall in real time. For example, a sensor can be set up in the enclosed area at the south end of a traffic node to collect and output queuing data every 30 seconds.

[0034] In the above scheme, a social force model is constructed by considering both passenger-specific factors and external influences. This model can accurately simulate the impact of passenger behavior on passenger flow, providing a reliable data foundation for subsequent evaluation of transfer efficiency and helping to improve the reliability of traffic organization schemes.

[0035] A preferred approach involves setting initial weights for a pre-acquired set of factors based on a preset evaluation model to obtain a set of comments. This includes: selecting several ride efficiency evaluation indicators to construct a factor set; constructing triples based on the preset evaluation model, the factor set, and the values ​​of each indicator in the factor set, wherein the values ​​of each indicator are obtained from passenger flow simulation data; setting initial weights for each ride efficiency evaluation indicator in the factor set based on the triples and a preset analysis method; and mapping the initial weights of each ride efficiency evaluation indicator in the factor set to corresponding preset transfer efficiency levels to obtain the set of comments.

[0036] In one preferred implementation scheme, to comprehensively evaluate passenger transfer efficiency, several indicators reflecting the time spent by passengers during transfers and the perceived quality of station services are selected as evaluation indicators, referencing existing relevant design specifications and research results. A factor set is constructed, and in this embodiment, the evaluation indicators include: the number of people queuing for security checks, the number of people queuing at turnstiles, the number of people in the station hall, and transfer time. The number of people queuing for security checks reflects the direct impact of different traffic organization schemes, as differences in age and gender lead to variations in the time passengers spend passing through security checks; therefore, the number of people queuing for security checks can be used as one of the evaluation indicators of passenger efficiency. The number of people queuing at turnstiles reflects the time taken to pass through turnstiles under different traffic organization schemes. Whether the capacity can meet the demand for transfer passengers is important, since all passengers, regardless of whether they have passed through subway security, need to enter the station through the turnstiles. Therefore, the number of people queuing at the turnstiles can be used as one of the indicators for evaluating travel efficiency. The number of people in the station hall reflects the degree of congestion at the evaluated transportation node and judges the impact of passenger flow on the station hall under different traffic organization schemes. Since the number of passengers in the enclosed area of ​​the subway entrance affects transfer efficiency, the number of people in the station hall can be used as one of the indicators for evaluating travel efficiency. Transfer time includes: railway transfer time and transfer time with other modes of transportation. It is used to reflect the passenger transfer time. Excluding subjective factors, under appropriate train intervals and other transfer conditions, it is the shortest time that the transfer behavior can be completed according to the transfer process. It is an indicator for evaluating time efficiency. Then, based on the constructed factor set, the preset evaluation model, and the values ​​of each indicator in the factor set, a triplet is constructed, the specific expression of which is as follows: ; In the formula, N is the evaluation object of the preset evaluation model, namely transfer efficiency, C is the ride efficiency evaluation index, and V is the index value.

[0037] Then, combine the existing Analytic Hierarchy Process (AHP) to determine the initial weights. Initial weights were set for each ride efficiency evaluation index in the tripartite group. The initial weights are then mapped to the corresponding preset transfer efficiency levels to obtain a set of comments, the format of which is shown in Table 1. Table 1 Collection of Comments In Table 1, the preset transfer efficiency levels are divided into low, average, high and high. A rating set framework is constructed based on the triplet, and the initial weight of each indicator in the triplet is represented as the corresponding judgment threshold. The transfer efficiency level is mapped according to the actual value of each indicator in the passenger flow simulation data.

[0038] In the above scheme, multi-dimensional indicators are selected to construct a factor set, and then a triplet is constructed by combining a preset evaluation model and the values ​​of each indicator. Then, a preset analysis method is used to set initial weights for each indicator in the factor set. Through weight mapping, a comment set is constructed, which transforms the subjective evaluation problem into an objective quantitative problem of each factor. This provides a reliable data foundation for the evaluation of subsequent indicators and helps to improve the reliability of the traffic organization scheme.

[0039] A preferred approach involves dynamically adjusting the initial weights of a pre-acquired factor set based on passenger flow simulation data and a preset evaluation model to obtain the target weights of the pre-acquired factor set. This includes: inputting passenger flow simulation data into the preset evaluation model to obtain real-time passenger flow density; and dynamically adjusting the initial weights of each ride efficiency evaluation indicator in the factor set based on the real-time passenger flow density and a preset passenger flow density correction coefficient to obtain the target weights of each ride efficiency evaluation indicator in the factor set.

[0040] One preferred implementation involves inputting passenger flow simulation data into a preset evaluation model to obtain real-time passenger flow density. Combined with the preset passenger flow density correction coefficient Among them, the preset passenger flow density correction coefficient Based on historical data calibration, typically set to 0.2, the initial weights of each ride efficiency evaluation indicator are dynamically adjusted to obtain the target weights for each indicator. The weight adjustment formula is as follows: ; In the formula, These are the initial weights; This is a preset passenger flow density correction factor; The maximum allowable passenger flow density is used as a normalization benchmark in the dynamic adjustment calculation of weights. Its value is set based on the design capacity of the transportation building space or the safety operation standard, and is usually determined by a combination of station structure, passage width, safety regulations and historical passenger flow data.

[0041] One specific example is that when the simulation detects that the number of people queuing for security check exceeds 20, it automatically triggers the preset evaluation model to update the input passenger flow simulation data and automatically adjusts the initial weights dynamically according to the current simulation scenario. For example, during peak hours, the transfer time threshold for the "higher" efficiency level is relaxed from 200 seconds to 220 seconds.

[0042] In the above scheme, a dynamic weight correction mechanism is adopted to adjust the initial weights of each indicator so that the evaluation of each indicator is more in line with the real passenger flow scenario. This allows for different weight evaluation standards for different passenger flow scenarios, avoiding the problem that static weights cannot reflect the dynamic passenger flow characteristics and changes in facility operation status. It enables the simulation of different passenger flow scenarios to dynamically adjust the weights of evaluation indicators, which helps to quantify the urban transportation transfer efficiency level under the influence of multiple factors through the correlation between the comment set and the target weights, thereby improving the reliability of the traffic organization scheme.

[0043] A preferred scheme involves calculating a comprehensive correlation degree based on a set of comments and target weights, and obtaining an evaluation result of urban transportation transfer efficiency based on the comprehensive correlation degree. This includes: constructing an initial correlation degree matrix using a preset correlation function based on each ride efficiency evaluation index and the set of comments in a factor set; improving the initial correlation degree matrix using a preset distance algorithm to obtain a target correlation degree matrix; and calculating the comprehensive correlation degree based on the target correlation degree matrix and target weights, and obtaining the evaluation result of urban transportation transfer efficiency based on the comprehensive correlation degree.

[0044] One preferred implementation method involves first calculating the correlation between each ride efficiency evaluation index and the set of comments based on the factor set, using a preset correlation function to construct a correlation matrix. In this embodiment, the preset correlation function can be an extension correlation function, and the correlation matrix is ​​represented as follows: ; In the formula, As an indicator For level j The degree of correlation; m : Indicates the number of factor sets (i.e., evaluation indicators); for example, in this embodiment, the factor set includes four indicators: "number of people queuing for security checks," "number of people queuing for turnstiles," "number of people in the station hall," and "transfer time."m =4; n : Indicates the number of evaluation sets (i.e., evaluation levels); for example, in this embodiment, the evaluation sets divide transfer efficiency into four levels: "lower", "average", "higher", and "higher". n =4.

[0045] Then, Mahalanobis distance is introduced to improve the correlation matrix. The specific process is as follows: construct the index covariance matrix based on passenger flow simulation data. T ,set up Let n be random variables, called matrices, and use them to construct n-dimensional random index variables. C covariance matrix T , is represented as: ; ; In the formula, The amount The covariance is used to calculate the Mahalanobis distance-improved correlation function, resulting in the target correlation matrix, as shown below: ; In the formula, This is the vector of observed values ​​for the indicators to be evaluated, which are the actual values ​​of each passenger flow efficiency evaluation indicator obtained from passenger flow simulation data; for example, transfer time (seconds), number of people in the station hall (people), length of security check line (people), and length of gate line (people). This is the threshold vector of indicators for a certain level in the comment set, that is, the standard value range of each indicator under that level. It is a vector transpose; It is the covariance matrix The inverse matrix is ​​used to eliminate the influence of correlation between indicators when calculating distance.

[0046] Then, combining the target weights of each indicator, the overall correlation degree is calculated, as shown below: ; In this embodiment, the final evaluation level of each indicator is determined according to the principle of maximum correlation, thereby outputting the evaluation result of urban transportation transfer efficiency.

[0047] In the above scheme, the correlation between evaluation indicators is considered, a correlation matrix is ​​constructed, and the correlation matrix is ​​improved by a preset distance algorithm to obtain a target correlation matrix. This makes the coupling between the indicators higher and closer to the real correlation between the indicators. Finally, by calculating the comprehensive correlation between the target weights and the target correlation matrix, the evaluation results of urban traffic transfer efficiency are obtained. This enables the simulation of different passenger flow scenarios to dynamically adjust the weights of evaluation indicators. The correlation between the comment set and the target weights is used to quantify the level of urban traffic transfer efficiency under the influence of multiple factors, thereby improving the reliability of the traffic organization scheme.

[0048] A preferred scheme involves adjusting an initial traffic organization scheme based on urban traffic transfer efficiency evaluation results and outputting a target traffic organization scheme to organize urban traffic. This includes: inputting the urban traffic transfer efficiency evaluation results into a building model and adjusting the initial traffic organization schemes of each traffic node according to the evaluation results to obtain the adjusted traffic organization scheme results; inputting the adjusted traffic organization scheme results into a social force model and a preset evaluation model to evaluate urban traffic transfer efficiency until the evaluation results meet preset evaluation requirements, thus obtaining the target urban traffic transfer efficiency evaluation results; and inputting the target urban traffic transfer efficiency evaluation results into the building model and outputting the target traffic organization scheme to organize urban traffic.

[0049] One preferred implementation involves inputting the urban traffic transfer efficiency evaluation results into the corresponding building model, adjusting the security measures at each traffic node (e.g., adding or removing turnstiles), and then applying the adjusted traffic organization scheme to the social force model for re-evaluation. If the urban traffic transfer efficiency evaluation results output by the preset evaluation model do not meet the preset evaluation requirements, a new round of traffic organization scheme updates is performed until the preset evaluation requirements are met. The preset evaluation requirements can be adjusted adaptively according to each traffic node. Once the traffic organization scheme meets the preset evaluation requirements, the current traffic organization scheme is output as the target traffic organization scheme and applied to actual urban traffic. For example, if the preset evaluation requirement for a certain traffic node is "high," and the urban traffic transfer efficiency evaluation result is "low," the number of turnstiles is automatically increased (e.g., from 4 to 6), and the simulation is repeated until the traffic organization scheme meets the preset evaluation requirements, at which point the corresponding target traffic organization scheme is output.

[0050] In the above scheme, after each evaluation of urban traffic transfer efficiency, a feedback optimization mechanism is introduced to iteratively optimize the evaluation results until the results meet the preset evaluation requirements. This indicates that the current evaluation results meet the needs of the traffic nodes, and the current iterative traffic organization scheme is output as the target traffic organization scheme to organize urban traffic. This allows for the dynamic adjustment of evaluation index weights in simulated different passenger flow scenarios. By quantifying the urban traffic transfer efficiency level under the influence of multiple factors through the correlation between the evaluation set and the target weights, the reliability of the traffic organization scheme is improved.

[0051] Example 2 See Figure 2 , Figure 2 This is a schematic diagram of the modular structure of a traffic organization system based on urban traffic transfer efficiency evaluation, provided as an embodiment of the present invention. Figure 2 As shown in the figure, this embodiment of the invention also provides a traffic organization system based on urban traffic transfer efficiency evaluation, including: an initial traffic organization scheme acquisition module 201, a passenger flow simulation data acquisition module 202, a comment set construction module 203, a weight adjustment module 204, an evaluation result output module 205, and a traffic organization module 206; the initial traffic organization scheme acquisition module 201 is used to construct an architectural model based on pre-acquired urban traffic architectural space and set an initial traffic organization scheme for traffic nodes according to the architectural model; the passenger flow simulation data acquisition module 202 is used to simulate the passenger movement behavior of each traffic node based on the initial traffic organization scheme through a pre-constructed social force model. The system obtains passenger flow simulation data; the comment set construction module 203 is used to set initial weights for the pre-acquired factor set based on the preset evaluation model to obtain the comment set; the weight adjustment module 204 is used to dynamically adjust the initial weights of the pre-acquired factor set based on the passenger flow simulation data and the preset evaluation model to obtain the target weights of the pre-acquired factor set; the evaluation result output module 205 is used to calculate the comprehensive correlation degree based on the comment set and the target weights and obtain the urban traffic transfer efficiency evaluation result based on the comprehensive correlation degree; the traffic organization module 206 is used to adjust the initial traffic organization plan based on the urban traffic transfer efficiency evaluation result and output the target traffic organization plan to organize urban traffic.

[0052] This invention proposes a traffic organization system based on urban traffic transfer efficiency evaluation. An initial traffic organization scheme is set at traffic nodes in an urban traffic building space model. Then, a social force model is used to simulate passenger movement behavior based on the initial traffic organization scheme to obtain passenger flow simulation data. This data is used to simulate different traffic nodes and obtain passenger flow data under different scenarios. An initial weight is set for a set of factors using a preset evaluation model to construct a comment set. The initial weights of the factor set are dynamically adjusted using passenger flow simulation data combined with the preset evaluation model, so that the evaluation results can reflect the real-time operating status of the traffic hub. The correlation between the comment set and the adjusted target weights of the factor set is then evaluated to obtain the urban traffic transfer efficiency evaluation results. An iterative optimization method is used to optimize the traffic organization scheme, thereby achieving dynamic adjustment of evaluation index weights in different passenger flow scenarios. The correlation between the comment set and the target weights quantifies the urban traffic transfer efficiency level under the influence of multiple factors, improving the reliability of the traffic organization scheme.

[0053] Furthermore, the initial traffic organization scheme acquisition module 201 is used to construct an architectural model based on the pre-acquired urban traffic architectural space and set an initial traffic organization scheme for traffic nodes according to the architectural model. It includes: an urban traffic architectural space acquisition unit 301, a traffic node setting unit 302, and an initial traffic organization scheme setting unit 303. The urban traffic architectural space acquisition unit 301 is used to acquire urban traffic architectural space based on the map data of the target city. The traffic node setting unit 302 is used to construct an architectural model based on the urban traffic architectural space and set several traffic nodes in the architectural model. The initial traffic organization scheme setting unit 303 is used to set the corresponding initial traffic organization scheme based on each traffic node.

[0054] Furthermore, the passenger flow simulation data acquisition module 202 is used to simulate passenger movement behavior at each traffic node based on the initial traffic organization plan and through a pre-constructed social force model to obtain passenger flow simulation data. This module includes: a passenger movement behavior data acquisition unit 401, a social force model construction unit 402, and a simulation unit 403. The passenger movement behavior data acquisition unit 401 is used to acquire passenger movement behavior parameters and passenger random behavior deviations, and to calculate the sum of the resultant forces of passenger movement behavior based on the passenger behavior parameters. The social force model construction unit 402 is used to construct a social force model based on passenger random behavior deviations and the sum of the resultant forces of passenger movement behavior. The simulation unit 403 is used to execute the initial traffic organization plan at each traffic node and simulate passenger movement behavior through the social force model to obtain passenger flow simulation data.

[0055] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0056] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the described specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0057] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

Claims

1. A traffic organization method based on urban traffic transfer efficiency evaluation, characterized in that, Applied to urban transportation, several traffic nodes are set up in the urban transportation system, including: Based on the pre-acquired urban traffic building space, a building model is constructed and an initial traffic organization scheme for traffic nodes is set according to the building model; Based on the initial traffic organization scheme, the passenger movement behavior at each traffic node is simulated using a pre-constructed social force model to obtain passenger flow simulation data. Based on the preset evaluation model, initial weights are set for the pre-acquired factor set to obtain the comment set; Based on the passenger flow simulation data and the preset evaluation model, the initial weights of the pre-acquired factor set are dynamically adjusted to obtain the target weights of the pre-acquired factor set. Based on the set of comments and the target weights, the comprehensive correlation degree is calculated, and the evaluation result of urban transportation transfer efficiency is obtained according to the comprehensive correlation degree. Based on the evaluation results of urban traffic transfer efficiency, the initial traffic organization scheme is adjusted, and the target traffic organization scheme is output to organize urban traffic.

2. The traffic organization method based on urban traffic transfer efficiency evaluation as described in claim 1, characterized in that, The process of constructing a building model based on pre-acquired urban traffic building space and setting an initial traffic organization scheme for traffic nodes according to the building model includes: Based on map data of the target city, obtain urban transportation and building space; A building model is constructed based on the urban transportation building space, and several transportation nodes are set in the building model; Based on each of the aforementioned traffic nodes, a corresponding initial traffic organization scheme is set.

3. The traffic organization method based on urban traffic transfer efficiency evaluation as described in claim 1, characterized in that, Based on the initial traffic organization scheme, passenger movement behavior at each traffic node is simulated using a pre-constructed social force model to obtain passenger flow simulation data, including: Obtain passenger motion behavior parameters and passenger random behavior deviations, and calculate the sum of passenger motion behavior forces using passenger behavior parameters; A social force model is constructed based on the passenger random behavior deviation and the sum of the passenger motion behavior forces. The initial traffic organization scheme is executed at each traffic node, and passenger movement behavior is simulated using the social force model to obtain passenger flow simulation data.

4. The traffic organization method based on urban traffic transfer efficiency evaluation as described in claim 1, characterized in that, The process involves setting initial weights for the pre-acquired factor set based on a preset evaluation model to obtain a set of comments, including: A factor set was constructed by selecting several ride efficiency evaluation indicators; Based on the preset evaluation model, the factor set, and the values ​​of each indicator in the factor set, a triplet is constructed, wherein the values ​​of each indicator are obtained from the passenger flow simulation data. Based on the triplet and the preset analysis method, initial weights are set for each ride efficiency evaluation index in the factor set. The initial weights of each ride efficiency evaluation index in the factor set are mapped to the corresponding preset transfer efficiency levels to obtain a set of comments.

5. A traffic organization method based on urban traffic transfer efficiency evaluation as described in claim 4, characterized in that, Based on the passenger flow simulation data and the preset evaluation model, the initial weights of the pre-acquired factor set are dynamically adjusted to obtain the target weights of the pre-acquired factor set, including: Input the passenger flow simulation data into the preset evaluation model to obtain the real-time passenger flow density; Based on the real-time passenger flow density and the preset passenger flow density correction coefficient, the initial weights of each ride efficiency evaluation index in the factor set are dynamically adjusted to obtain the target weights of each ride efficiency evaluation index in the factor set.

6. The traffic organization method based on urban traffic transfer efficiency evaluation as described in claim 5, characterized in that, Based on the set of comments and the target weights, a comprehensive correlation degree is calculated, and the urban transportation transfer efficiency evaluation result is obtained according to the comprehensive correlation degree, including: Based on the ride efficiency evaluation indicators in the factor set and the comment set, an initial correlation matrix is ​​constructed using a preset correlation function. Based on a preset distance algorithm, the initial correlation matrix is ​​improved to obtain the target correlation matrix; Based on the target correlation matrix and the target weights, the comprehensive correlation is calculated, and the evaluation result of urban transportation transfer efficiency is obtained based on the comprehensive correlation.

7. The traffic organization method based on urban traffic transfer efficiency evaluation as described in claim 1, characterized in that, Based on the urban traffic transfer efficiency evaluation results, the initial traffic organization scheme is adjusted, and a target traffic organization scheme is output to organize urban traffic, including: The urban traffic transfer efficiency evaluation results are input into the building model, and the initial traffic organization scheme of each traffic node is adjusted according to the urban traffic transfer efficiency evaluation results to obtain the traffic organization scheme adjustment results. The results of the traffic organization scheme adjustment are input into the social force model and the preset evaluation model to evaluate the urban traffic transfer efficiency until the urban traffic transfer efficiency evaluation results meet the preset evaluation requirements, and the target city traffic transfer efficiency evaluation results are obtained. The evaluation results of the target city's traffic transfer efficiency are input into the building model, and the target traffic organization scheme is output to organize urban traffic.

8. A traffic organization system based on urban traffic transfer efficiency evaluation, characterized in that, Implementing a traffic organization method based on urban traffic transfer efficiency evaluation as described in any one of claims 1 to 7, comprising: The system includes modules for obtaining initial traffic organization plans, obtaining passenger flow simulation data, constructing a comment set, adjusting weights, outputting evaluation results, and organizing traffic. The initial traffic organization scheme acquisition module is used to construct a building model based on the pre-acquired urban traffic building space and set the initial traffic organization scheme for traffic nodes according to the building model. The passenger flow simulation data acquisition module is used to simulate the passenger movement behavior of each traffic node based on the initial traffic organization scheme and through a pre-constructed social force model to obtain passenger flow simulation data. The comment set construction module is used to set initial weights on the pre-acquired factor set based on a preset evaluation model to obtain the comment set; The weight adjustment module is used to dynamically adjust the initial weights of the pre-acquired factor set based on the passenger flow simulation data and the preset evaluation model, so as to obtain the target weights of the pre-acquired factor set. The evaluation result output module is used to calculate the comprehensive correlation degree based on the comment set and the target weight, and obtain the urban transportation transfer efficiency evaluation result based on the comprehensive correlation degree. The traffic organization module is used to adjust the initial traffic organization scheme based on the urban traffic transfer efficiency evaluation results, and output the target traffic organization scheme to organize urban traffic.

9. A traffic organization system based on urban traffic transfer efficiency evaluation as described in claim 8, characterized in that, The initial traffic organization scheme acquisition module is used to construct a building model based on the pre-acquired urban traffic building space and set the initial traffic organization scheme for traffic nodes according to the building model, including: Urban transportation building space acquisition unit, traffic node setting unit, and initial traffic organization scheme setting unit; The urban transportation building space acquisition unit is used to acquire urban transportation building space based on map data of the target city; The traffic node setting unit is used to construct an architectural model based on the urban traffic architectural space, and to set several traffic nodes in the architectural model; The initial traffic organization scheme setting unit is used to set the corresponding initial traffic organization scheme based on each of the traffic nodes.

10. A traffic organization system based on urban traffic transfer efficiency evaluation as described in claim 8, characterized in that, The passenger flow simulation data acquisition module is used to simulate the passenger movement behavior at each traffic node based on the initial traffic organization scheme and through a pre-constructed social force model to obtain passenger flow simulation data, including: The system comprises a passenger motion behavior data acquisition unit, a social force model construction unit, and a simulation unit. The passenger motion behavior data acquisition unit is used to acquire passenger motion behavior parameters and passenger random behavior deviations, and to calculate the sum of the resultant forces of passenger motion behavior through passenger behavior parameters. The social force model building unit is used to construct a social force model based on the passenger random behavior deviation and the sum of the passenger motion behavior forces. The simulation unit is used to execute the initial traffic organization plan at each traffic node and simulate passenger movement behavior through the social force model to obtain passenger flow simulation data.

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