A rail transit line integration scheme optimization decision system and method

Abstract

The application discloses a kind of rail transit line fusion scheme optimization decision system and method, to solve the problem of strong subjectivity in line fusion decision in prior art, lack of multidimensional quantitative trade-off.The system includes data integration module, multidimensional analysis module, fusion decision module, scheme evaluation and deduction module;The application quantifies the key elements of passenger flow, capacity, engineering, cost and other multidimensional into a unified analysis framework by integrating passenger flow prediction, engineering simulation, multi-objective optimization algorithm, introduces Cross-line Efficiency Index (CEI) as the core decision index, determines the index weight by combining AHP-entropy weight method, realizes the quantitative comparison and selection and dynamic optimization of the two types of fusion schemes of cross-line operation and transfer connection.The application can convert traditional experience decision into scientific quantitative decision, shorten the scheme comparison period, improve the decision adaptability and risk controllability, and provide reliable technical support for line fusion scheme decision in urban rail transit network planning stage.

Description

Technical Field

[0001] This invention relates to the field of rail transit technology, and in particular to a decision-making system and method for optimizing rail transit line integration schemes. Background Technology

[0002] As urban rail transit networks become increasingly complex, the effective integration of new lines with existing networks has become crucial for improving overall network efficiency. Current technologies often rely on qualitative analysis or single indicators for line integration decisions, lacking a systematic consideration and dynamic optimization of multiple dimensions such as passenger demand, operational capacity, engineering costs, and economic benefits.

[0003] Existing technologies disclose a cloud-based integrated command and dispatch system that utilizes real-time traffic data and road information, combined with node adjacency relationships and planned paths, to extract traffic-road topology features and planned path features. By fusing these features, a dispatch path classification feature is obtained, and finally, a classifier is used to determine the rationality of the current planned path. Existing technologies also disclose a dynamic passenger flow control method for subway stations based on the AnyLogic platform. This method involves setting passenger flow control scenario information; generating a subway station layout map based on the passenger flow control scenario information; dynamically generating a multi-agent pedestrian logic map based on the subway station layout map, with the multi-agent pedestrian logic map corresponding to the passenger flow diversion route; and conducting simulations in the dynamic passenger flow control system based on the passenger flow diversion route to verify and evaluate the passenger flow diversion effect under different passenger flow control scenarios.

[0004] However, existing technologies mostly focus on real-time adjustments during the operational phase. In the network planning and scheme design phases, there is a lack of auxiliary decision-making tools that can evaluate the full lifecycle benefits and costs of different integration modes (such as cross-line operation and transfer optimization) from the source. Therefore, there is an urgent need for a comprehensive solution that can quantitatively evaluate, dynamically optimize, and support decision-making during the planning phase. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art, and proposes a decision-making system and method for optimizing rail transit line integration schemes.

[0006] In a first aspect, embodiments of the present invention provide a rail transit line fusion scheme optimization decision-making system, comprising: a data integration module, a multi-dimensional analysis module, a fusion decision-making module, and a scheme evaluation and deduction module; wherein:

[0007] The data integration module is used to access multi-source data, clean and standardize the multi-source data, and form an analysis dataset with a unified spatiotemporal benchmark.

[0008] The multidimensional analysis module is used to calculate indicators of various dimensions in parallel. It uses TransCAD to predict passenger flow, uses AnyLogic to perform simulation, and outputs quantitative scores for each dimension.

[0009] The fusion decision module includes a weight calculation submodule and a CEI calculation submodule. The weight calculation submodule is used to determine the comprehensive weight of each dimension, and the CEI calculation submodule calculates the cross-line benefit index through a preset formula, and outputs the quantitative comparison results of different fusion schemes and the recommended scheme.

[0010] The scheme evaluation and simulation module is used for sensitivity analysis and case verification. It observes the stability of the scheme by adjusting key parameters, verifies and corrects model parameters by combining historical case inversion, and outputs a sensitivity analysis report.

[0011] Furthermore, the multi-source data includes at least passenger flow gains, cross-line time savings, maintenance cost reductions, engineering costs, operation and maintenance costs, and local line time increases.

[0012] Furthermore, the various dimensions of indicators include passenger flow indicators, capacity indicators, and engineering indicators; among which, passenger flow indicators include passenger flow gain, time savings when crossing lines, and time increase on the local line; capacity indicators include the impact of crossing lines on the tracking interval and turnaround capacity of existing lines, and time savings when crossing lines; engineering indicators include quantified geological risks, demolition difficulty, and environmental impact.

[0013] Furthermore, the weight calculation submodule is used to determine the comprehensive weight of each dimension. The specific calculation methods include: using the analytic hierarchy process (AHP) to construct a judgment matrix to allocate weights to each quantitative indicator, and combining the entropy weight method to integrate subjective and objective weights, thereby improving the scientific nature of weight allocation.

[0014] Furthermore, the CEI calculation submodule calculates the cross-line benefit index using a preset formula, which is:

[0015]

[0016] in, To increase passenger flow, For passenger flow weight, To save time across lines, For cross-line weighting, To reduce maintenance costs, For maintenance weight, For project costs, As the weight of the project, For maintenance costs, For operation and maintenance weight, Add time to this line, Increase the weight of this line.

[0017] Furthermore, the fusion decision model must satisfy the following constraints during operation:

[0018] Line capacity constraint: The proportion of cross-line train insertions ≤ the line's remaining capacity threshold;

[0019] Project implementation constraints: The construction risk level is controllable, and the civil engineering of the connecting line is feasible;

[0020] Signal compatibility constraints: Signal systems between cross-line lines must be compatible with each other.

[0021] Furthermore, the sensitivity analysis simulates the stability of the scheme under different scenarios by adjusting the passenger flow change parameters, and identifies risk points such as passenger flow falling short of expectations and overestimation of engineering risks in advance.

[0022] Furthermore, a rail transit line fusion scheme optimization decision system also includes a visualization decision module. The visualization decision module is used to graphically display the analysis results in the form of passenger flow heat map, time isochronous circle, scheme comparison radar chart, CEI threshold decision chart, etc., and supports interactive parameter adjustment and real-time scheme comparison.

[0023] Secondly, this invention also discloses a method for optimizing and deciding on integrated rail transit line schemes, comprising:

[0024] For scenario definition and data input, clarify the needs for traffic line integration, input basic data of relevant areas into the data integration module, and automatically generate two basic schemes: "cross-line operation" and "transfer connection".

[0025] Quantify multi-dimensional indicators. For each plan, call the multi-dimensional analysis module to calculate its quantitative values ​​in multiple dimensions such as passenger flow, capacity, engineering, cost and efficiency.

[0026] Dynamic weighting and comprehensive decision-making are performed. The integrated decision-making module determines the current decision weight based on the analytic hierarchy process and the entropy weight method, calculates the CEI value of each option, and outputs the recommended option based on the CEI decision threshold.

[0027] The module performs sensitivity analysis and generates reports. The scheme evaluation and simulation module conducts key parameter perturbation tests on the recommended schemes, assesses risks, and generates a complete decision report that includes scheme comparison, reasons for recommendation, and risk warnings.

[0028] Thirdly, the present invention also discloses an electronic device, characterized in that it comprises:

[0029] One or more processors;

[0030] Memory, used to store one or more programs;

[0031] When the one or more programs are executed by the one or more processors, the one or more processors implement the decision method as described in claim 9.

[0032] This invention provides a system and method for optimizing and deciding on integrated rail transit line schemes. The system includes: a data integration module for accessing multi-source data, cleaning and standardizing the multi-source data to form an analysis dataset with a unified spatiotemporal benchmark; a multi-dimensional analysis module for parallel calculation of indicators in various dimensions, using TransCAD for passenger flow prediction and AnyLogic for simulation, outputting quantitative scores for each dimension; an integrated decision-making module, including a weight calculation submodule and a CEI calculation submodule. The weight calculation submodule determines the comprehensive weight of each dimension, and the CEI calculation submodule calculates the cross-line benefit index using a preset formula, outputting quantitative comparison results of different integrated schemes and recommended schemes; and a scheme evaluation and deduction module for sensitivity analysis and case verification. It observes the stability of schemes by adjusting key parameters, verifies and corrects model parameters using historical case inversion, and outputs a sensitivity analysis report. This invention can transform traditional experience-based decision-making into scientific quantitative decision-making, shorten the scheme selection cycle, improve decision adaptability and risk controllability, and provide reliable technical support for line integration scheme decisions in the urban rail transit network planning stage.

[0033] Compared with the prior art, the present invention has the following advantages:

[0034] 1. Scientific decision-making: Transforming traditional experience-based decision-making into scientific decision-making based on multi-dimensional quantitative data and optimization algorithms, thereby reducing subjective bias;

[0035] 2. Efficiency Improvement: The solution selection cycle is significantly shortened through automated data processing, model calculation, and report generation;

[0036] 3. Dynamic adaptability: The model parameters and weights can be adjusted according to the city's development stage and policy orientation, exhibiting good adaptability and scalability;

[0037] 4. Controllable risks: Sensitivity analysis can help identify key risks in advance (such as lower-than-expected passenger flow or overestimation of engineering risks) and support the development of contingency plans. Attached Figure Description

[0038] Figure 1 This is a structural diagram of a rail transit line fusion scheme optimization decision system provided in an embodiment of the present invention;

[0039] Figure 2 A flowchart illustrating an optimization decision-making method for rail transit line integration schemes provided in an embodiment of the present invention;

[0040] Figure 3This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0041] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0042] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0043] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0044] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0045] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0046] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0047] To address at least one of the technical problems existing in the aforementioned related technologies, the present invention provides a decision-making system for optimizing rail transit line fusion schemes, such as... Figure 1 It includes: a data integration module, a multidimensional analysis module, a fusion decision-making module, and a solution evaluation and simulation module; among which:

[0048] The data integration module is used to access multi-source data, clean and standardize the multi-source data, and form an analysis dataset with a unified spatiotemporal benchmark; wherein, the multi-source data includes at least passenger flow gains, cross-line time savings, maintenance cost reduction, engineering costs, operation and maintenance costs, and local line time increases.

[0049] The data integration module, serving as the core data support for the rail transit line fusion scheme optimization decision-making system, bears the heavy responsibility of aggregating, governing, and standardizing multi-source heterogeneous data. Its core function is to break down data silos and provide high-quality, highly consistent foundational data for subsequent multi-dimensional analysis and decision-making calculations. This module, through pre-defined standardized data interfaces and flexible adaptation mechanisms, widely accesses multi-source data from various business scenarios such as urban rail transit planning, operation, and engineering construction. The data types covered include both core indicator data directly related to the benefits and costs of the fusion scheme and auxiliary data supporting indicator quantification. The core data should at least cover passenger flow gains (i.e., the increased passenger volume after line integration, including peak and off-peak passenger flow growth data, cross-regional commuter passenger flow increments, etc.), cross-line time savings (referring to the reduced travel time for passengers under the cross-line operation mode compared to the transfer mode, including sub-indicators such as the reduction in direct travel time and the reduction in transfer waiting time), and maintenance cost reduction (i.e. the reduction in equipment maintenance and repair costs achieved through resource sharing, centralized maintenance, etc. after network-integrated operation, including data on cost savings in various professional maintenance aspects such as vehicles, signaling systems, and track facilities). The project costs (including the entire process of investment data such as preliminary survey and design fees, civil construction fees, equipment procurement and installation fees, etc. for projects such as cross-line connecting line construction, transfer station renovation, and adaptation and upgrading of existing lines), operation and maintenance costs (referring to the daily operation and maintenance expenditures added after the implementation of the integration solution, including long-term periodic expenditures such as cross-line train scheduling costs, new equipment operation and maintenance costs, and personnel allocation costs), and the increased time on this line (i.e. the increased travel time caused by the impact of cross-line trains on the original train operation order on this line after they are inserted into the existing line, including detailed data such as waiting time and station adjustment time).

[0050] The multidimensional analysis module is used for parallel calculation of indicators across various dimensions. It performs passenger flow prediction by calling TransCAD and simulation by AnyLogic, outputting quantitative scores for each dimension. In this embodiment, the indicators across various dimensions include passenger flow indicators, capacity indicators, and engineering indicators. Passenger flow indicators include passenger flow gain, time savings when crossing lines, and time increase on the local line. Capacity indicators include the impact of crossing lines on the tracking interval and turnaround capacity of existing lines, and time savings when crossing lines. Engineering indicators include quantitative geological risks, relocation difficulty, and environmental impact.

[0051] Specifically, the multidimensional analysis module, as the core analysis engine of the rail transit line integration scheme optimization decision-making system, undertakes the key responsibilities of parallel calculation, precise quantification, and in-depth analysis of multidimensional indicators. Its core value lies in transforming scattered business indicators into standardized quantitative data that can directly support decision-making, providing a comprehensive and objective analytical basis for the integration decision-making model. Based on a distributed parallel computing architecture, this module can efficiently and collaboratively process indicator calculation tasks across three core dimensions: passenger flow, capacity, and engineering. By integrating professional simulation tools and quantitative analysis algorithms, it achieves a systematic evaluation of key indicators throughout the entire lifecycle of different integration schemes (cross-line operation, transfer connections, etc.), ultimately outputting standardized quantitative scores and indicator analysis reports for each dimension.

[0052] In the specific indicator calculation process, the module calls professional tools and algorithms through customized interfaces to ensure the accuracy and industry adaptability of the calculation results: For passenger flow dimension indicators, the module calls TransCAD traffic planning software to perform multi-scenario passenger flow prediction. Combining regional population size, employment distribution, travel characteristics, existing network passenger flow data and the route of the integration scheme, it accurately calculates passenger flow gains (including sub-indicators such as all-day passenger flow gains, peak hour passenger flow gains, and cross-regional directional passenger flow gains, quantifying the improvement effect of the integration scheme on passenger volume), cross-line time savings (by comparing the total travel time of the cross-line direct mode and the transfer mode, the savings in commuting time, waiting time, and transfer travel time are calculated separately to accurately reflect the value of time efficiency improvement), and local line added time (simulating the impact of cross-line train insertion on the train operation order of the existing line, quantifying the additional running time of local line trains due to passing, station adjustment, etc., and objectively evaluating the potential impact of the scheme on the local line's operating efficiency).

[0053] For capability-related indicators, the module constructs a line operation simulation model by calling the AnyLogic system simulation platform. This model recreates core scenarios such as the signal system characteristics, train timetable, and station operation procedures of existing lines, focusing on analyzing the impact of cross-line operations on the capacity of existing lines. On the one hand, by simulating the line operation status under different cross-line train insertion ratios, the module quantifies the impact of cross-line operations on the tracking interval of existing lines (e.g., calculating the extended tracking interval time and the percentage decrease in line throughput capacity), assessing whether the line capacity redundancy meets the requirements of cross-line operations. On the other hand, focusing on key nodes such as terminal stations and transfer stations, the module simulates and analyzes the impact of cross-line train turnaround operations on the turnaround capacity of existing trains, outputting quantitative results such as the change in turnaround efficiency and the frequency of turnaround operation conflicts. Simultaneously, the module further verifies the authenticity and stability of time savings achieved through cross-line operations by combining simulation data, ensuring the accuracy and reliability of time efficiency indicators.

[0054] The fusion decision module includes a weight calculation submodule and a CEI calculation submodule. The weight calculation submodule is used to determine the comprehensive weight of each dimension, and the CEI calculation submodule calculates the cross-line benefit index through a preset formula, and outputs the quantitative comparison results of different fusion schemes and the recommended scheme.

[0055] In this embodiment, the weight calculation submodule is used to determine the comprehensive weight of each dimension. The specific calculation method includes: using the analytic hierarchy process (AHP) to construct a judgment matrix to allocate weights to each quantitative indicator, and combining the entropy weight method to achieve the fusion of subjective weights and objective weights, thereby improving the scientific nature of the weight allocation.

[0056] Specifically, in the subjective weight determination stage, a multi-level judgment matrix is ​​constructed based on the core logic of the Analytic Hierarchy Process (AHP): First, "comprehensive optimization of line integration schemes" is defined as the target layer; passenger flow, capacity, engineering, and cost are defined as the criterion layer; and specific quantitative indicators such as passenger flow gain, cross-line time savings, geological risks, and engineering costs are defined as the scheme layer, thus establishing a clear hierarchical structure of indicators. Subsequently, a team of experts in rail transit planning and design, operation management, and engineering construction is invited to conduct pairwise comparisons and scoring of the relative importance of each indicator within the same level, forming a standardized judgment matrix. By calculating the maximum eigenvalue and corresponding eigenvector of the judgment matrix, the initial allocation of subjective weights is completed, and a consistency check is performed. If the check result does not meet the consistency requirements (CR < 0.1), the results are fed back to the expert team for adjustment and optimization of the judgment matrix until the consistency conditions are met, ensuring that the subjective weight allocation conforms to industry professional understanding and actual decision-making needs.

[0057] In the objective weight calculation stage, leveraging the objective weighting characteristics of the entropy weighting method, and based on the standardized analysis dataset output by the data integration module and the multidimensional analysis module, the information entropy value of each quantitative indicator is calculated to reflect the dispersion and effective information content of the data: the smaller the information entropy of an indicator, the greater the difference between different fusion schemes, the richer the decision-making information it carries, and the higher its corresponding objective weight; conversely, the larger the information entropy, the lower the indicator's decision contribution, and the smaller its objective weight. Specifically, the quantitative data of each indicator is first normalized to eliminate the influence of differences in different dimensions and numerical ranges. Then, the information entropy and difference coefficient of each indicator are calculated. Finally, the objective weight of each indicator is determined based on the proportion of the difference coefficient, achieving weight allocation entirely based on data distribution characteristics and avoiding excessive interference from subjective preferences in the weighting results.

[0058] In this embodiment, the Cross-Line Benefit Index (CEI) is introduced as a core decision indicator. The Cross-Line Benefit Index (CEI) is the core decision indicator of the rail transit line integration scheme optimization decision system. Its core value lies in the quantitative integration of the full life cycle benefits and costs of the two integration schemes of cross-line operation and transfer connection. By constructing a "benefit-cost" ratio model, it provides an intuitive and comparable quantitative basis for the scientific selection of line integration mode, and solves the technical problem that it is difficult to systematically weigh multi-dimensional factors in traditional decision-making.

[0059] The CEI (Convergence for Integrated Transportation) is essentially a quantitative indicator of the relative value of the comprehensive benefits and costs of an integrated transportation solution. It integrates disparate benefit and cost indicators to form a unified decision-making threshold. Its core logic is that when the comprehensive benefits of cross-line operation significantly outweigh its comprehensive costs, the model is worth implementing; conversely, a transfer-connection model should be prioritized, or further weighed in conjunction with urban strategic needs. This index not only covers the direct economic and operational benefits after the solution's implementation but also fully considers the costs throughout the entire process, including engineering construction and long-term operation and maintenance, achieving a quantitative assessment of the entire lifecycle of the integrated transportation solution.

[0060] In this embodiment, the CEI calculation submodule calculates the cross-line benefit index using a preset formula, which is:

[0061]

[0062] in, To increase passenger flow, For passenger flow weight, To save time across lines, For cross-line weighting, To reduce maintenance costs, For maintenance weight, For project costs, As the weight of the project, is the operation and maintenance cost, is the operation and maintenance weight, is the time added to this line, is the weight added to this line.

[0063] In this embodiment, constraint conditions are also proposed to achieve cross-line operation. The

[0064] constraint conditions must meet the following prerequisite conditions:

[0065] (1) Line capacity: The input of the cross-line train insertion ratio data integration module for the relevant area ≤ the remaining line capacity threshold of the relevant area input by the data integration module;

[0066] (2) The construction risk level is controllable, and the civil engineering of the connecting line is feasible;

[0067] (3) The signal systems between cross-line routes are mutually compatible.

[0068] In this embodiment, the cross-line benefit index (CEI) is defined as the core decision threshold. In the formula, the numerator is the benefit item (the time-saving benefit of cross-line direct access input by the data integration module for the relevant area + the passenger flow coverage gain of the data integration module input for the relevant area + the cost reduction of networked operation of the data integration module input for the relevant area), and the denominator is the cost item (the project cost input by the data integration module for the relevant area + the operation and maintenance cost input by the data integration module for the relevant area + the time cost of the increased passenger flow of this line input by the data integration module for the relevant area). The weights of each index are obtained through matrix calculation of the AHP data integration module input for the relevant area. Combining the measurement and analysis of the interconnection cases of each city, the decision criteria are as follows:

[0069] If CEI of the relevant area ≥ CEIk: Priority is given to cross-line operation;

[0070] If CEIh of the relevant area ≤ CEI < CEIk: It is necessary to combine with the urban strategic positioning (such as the key development axis may tend to cross-line);

[0071] If CEI of the relevant area < CEIh: The transfer mode is adopted. Among them, CEIh and CEIk respectively represent the minimum threshold and the maximum threshold.

[0072] In this embodiment, AHP weight calculation assigns weights to the calculation indicators by constructing a judgment matrix, and calculates the weight of each indicator. The specific method includes inputting data on indicators such as passenger flow increase after the operation of cross-line trains, time savings after cross-line operation, network line maintenance cost reduction, increased engineering costs, operation and maintenance costs, and estimated increase in passenger flow on the local line. After the system runs, the initial CEI value for the "full cross-line scheme" is calculated. Based on the threshold, the system recommends "decision-making needs to be combined with the urban development axis" as the preferred option.

[0073] The scheme evaluation and simulation module is used for sensitivity analysis and case verification. It observes the stability of the scheme by adjusting key parameters, verifies and corrects model parameters using historical case studies, and outputs a sensitivity analysis report. In this embodiment, the sensitivity analysis simulates the stability of the scheme under different scenarios by adjusting passenger flow change parameters, identifying potential risks such as lower-than-expected passenger flow and overestimation of engineering risks in advance. For example, the scheme evaluation and simulation module observes the stability of the scheme by adjusting key parameters (such as passenger flow growth ±20%). Then, it performs inverse verification with historical cases, corrects model parameters, and outputs a sensitivity analysis report.

[0074] The scheme evaluation and simulation module, as the core of the rail transit line integrated scheme optimization decision-making system for risk prevention and accuracy calibration, undertakes the key responsibilities of verifying the stability of preliminary decision schemes, iteratively optimizing model parameters, and predicting potential risks. Its core value lies in improving the reliability of decision results and the adaptability of the model through multi-scenario simulation and historical experience review, providing comprehensive risk assessment and data support for the final scheme selection. This module is based on a dual-track verification logic of "sensitivity analysis data integration module input to relevant areas + case verification data integration module input to relevant areas." It focuses on the anti-interference capability of the current scheme under different external condition fluctuations, and also relies on historical practice data to correct model parameters, ensuring that the recommended scheme output by the system not only conforms to the theoretical calculation logic, but also adapts to the complex needs of actual engineering and operation scenarios.

[0075] In the sensitivity analysis phase, the module constructs a multi-dimensional parameter perturbation system. By systematically adjusting key parameters affecting the benefits and costs of the fusion scheme, it simulates the performance changes of the scheme under different external environments and implementation conditions, accurately assessing the stability and robustness of the scheme. Among them, passenger flow change parameters, as the core variable affecting the feasibility of the route fusion scheme, are the focus of sensitivity analysis. Based on potential influencing factors such as regional population growth forecasts, industrial layout adjustments, and changes in transportation policies, the module sets up various passenger flow fluctuation scenarios, including gradient change scenarios such as ±10%, ±20%, and ±30% of the relevant area input by the passenger flow growth data integration module and the relevant area input by the data integration module. It also covers sub-scenarios such as passenger flow peak shift and cross-regional passenger flow ratio adjustment. By dynamically adjusting these parameters, the calculation process of the multi-dimensional analysis module and the fusion decision model is re-triggered, comparing the change range of the cross-line benefit index (CEI), the switching of scheme recommendation results, and the correlation fluctuation patterns of various dimensional indicators under different passenger flow scenarios. In addition to core parameters related to passenger flow changes, sensitivity analysis can also flexibly incorporate key parameters such as fluctuations in engineering costs (e.g., cost changes caused by rising building material prices or adjustments to construction techniques), fluctuations in operation and maintenance costs (e.g., increased labor costs or increased operation and maintenance expenditures due to accelerated equipment aging), and adjustments to engineering risk levels (e.g., geological conditions becoming more complex than expected or demolition difficulties escalating) based on actual decision-making needs. Through methods such as single-parameter independent disturbances and multi-parameter combined disturbances, it can comprehensively simulate the impact of various uncertainties on the solution. Through this analysis process, the module can identify key risks that the plan may face during actual implementation. For example, when passenger flow growth falls short of expectations (e.g., only 80% of the predicted value is achieved), the CEI value of the cross-line operation plan may fall below the decision threshold, causing the plan to change from "feasible" to "infeasible." Or, when the actual level of engineering risk is higher than the calculated value, engineering costs increase significantly, thereby compressing the overall benefit space of the plan. Early identification of such risks can provide decision-makers with a clear direction for developing targeted risk response plans, such as reserving policies for passenger flow cultivation and optimizing engineering construction plans to reduce risks.

[0076] In this embodiment, a rail transit data integration module inputs a relevant regional line fusion scheme optimization decision system, which further includes a visualization decision module. The visualization decision module is used to graphically display the analysis results in the form of passenger flow heat map, time isochronous circle, scheme comparison radar chart, CEI threshold decision chart, etc., and supports interactive parameter adjustment and real-time scheme comparison.

[0077] This embodiment provides a decision-making system for optimizing rail transit line integration schemes, including: a data integration module for accessing multi-source data, cleaning and standardizing the multi-source data to form an analysis dataset with a unified spatiotemporal benchmark; a multi-dimensional analysis module for parallel calculation of indicators in various dimensions, using TransCAD for passenger flow prediction and AnyLogic for simulation, outputting quantitative scores for each dimension; an integration decision module, including a weight calculation submodule and a CEI calculation submodule. The weight calculation submodule determines the comprehensive weight of each dimension, and the CEI calculation submodule calculates the cross-line benefit index using a preset formula, outputting quantitative comparison results of different integration schemes and recommended schemes; and a scheme evaluation and deduction module for sensitivity analysis and case verification. It observes the stability of schemes by adjusting key parameters, verifies and corrects model parameters using historical case inversion, and outputs a sensitivity analysis report. This embodiment can transform traditional experience-based decision-making into scientific quantitative decision-making, shorten the scheme selection cycle, improve decision adaptability and risk controllability, and provide reliable technical support for line integration scheme decisions in the urban rail transit network planning stage.

[0078] Based on the same inventive concept, embodiments of the present invention also provide a method for optimizing and deciding on integrated rail transit line schemes, such as... Figure 2 ,include:

[0079] For scenario definition and data input, clarify the needs for traffic line integration, input basic data of relevant areas into the data integration module, and automatically generate two basic schemes: "cross-line operation" and "transfer connection".

[0080] Quantify multi-dimensional indicators. For each plan, call the multi-dimensional analysis module to calculate its quantitative values ​​in multiple dimensions such as passenger flow, capacity, engineering, cost and efficiency.

[0081] Dynamic weighting and comprehensive decision-making are performed. The integrated decision-making module determines the current decision weight based on the analytic hierarchy process and the entropy weight method, calculates the CEI value of each option, and outputs the recommended option based on the CEI decision threshold.

[0082] The module performs sensitivity analysis and generates reports. The scheme evaluation and simulation module conducts key parameter perturbation tests on the recommended schemes, assesses risks, and generates a complete decision report that includes scheme comparison, reasons for recommendation, and risk warnings.

[0083] The specific working methods of the data integration module, multidimensional analysis module, fusion decision module, and scheme evaluation and deduction module have been described in detail in a rail transit line fusion scheme optimization decision system, and will not be repeated here in this embodiment.

[0084] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 3This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement any of the decision-making methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0085] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0086] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0087] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0088] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps in any of the decision-making methods described in the above embodiments. The computer-readable storage medium may be volatile or non-volatile.

[0089] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device executes the above-described decision-making method.

[0090] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0091] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0092] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0093] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0094] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0095] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0096] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0097] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0098] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0099] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A decision-making system for optimizing integrated rail transit line schemes, characterized in that, include: The module includes a data integration module, a multidimensional analysis module, a fusion decision-making module, and a solution evaluation and simulation module; among which: The data integration module is used to access multi-source data, clean and standardize the multi-source data, and form an analysis dataset with a unified spatiotemporal benchmark. The multidimensional analysis module is used to calculate indicators of various dimensions in parallel. It uses TransCAD to predict passenger flow, uses AnyLogic to perform simulation, and outputs quantitative scores for each dimension. The fusion decision module includes a weight calculation submodule and a CEI calculation submodule. The weight calculation submodule is used to determine the comprehensive weight of each dimension, and the CEI calculation submodule calculates the cross-line benefit index through a preset formula, and outputs the quantitative comparison results of different fusion schemes and the recommended scheme. The scheme evaluation and simulation module is used for sensitivity analysis and case verification. It observes the stability of the scheme by adjusting key parameters, verifies and corrects model parameters by combining historical case inversion, and outputs a sensitivity analysis report.

2. The decision-making system according to claim 1, characterized in that, The multi-source data includes at least passenger flow gains, time savings from cross-line travel, maintenance cost reductions, engineering costs, operation and maintenance costs, and time increases for the local line.

3. The decision-making system according to claim 1, characterized in that, The indicators in each dimension include passenger flow indicators, capacity indicators, and engineering indicators. Passenger flow indicators include passenger flow gain, time savings when crossing lines, and time increase on the local line. Capacity indicators include the impact of crossing lines on the tracking interval and turnaround capacity of existing lines, and time savings when crossing lines. Engineering indicators include quantified geological risks, demolition difficulty, and environmental impact.

4. The decision-making system according to claim 1, characterized in that, The weight calculation submodule is used to determine the comprehensive weight of each dimension. The specific calculation methods include: using the analytic hierarchy process (AHP) to construct a judgment matrix to assign weights to each quantitative indicator, and combining the entropy weight method to integrate subjective and objective weights, thereby improving the scientific nature of weight allocation.

5. The decision-making system according to claim 1, characterized in that, The CEI calculation submodule calculates the cross-line benefit index using a preset formula, which is: in, To increase passenger flow, For passenger flow weight, To save time across lines, For cross-line weighting, To reduce maintenance costs, For maintenance weight, For project costs, As the weight of the project, For maintenance costs, For operation and maintenance weight, Add time to this line, Increase the weight of this line.

6. The decision-making system according to claim 1, characterized in that, The fusion decision model must satisfy the following constraints during operation: Line capacity constraint: The proportion of cross-line train insertions ≤ the line's remaining capacity threshold; Project implementation constraints: The construction risk level is controllable, and the civil engineering of the connecting line is feasible; Signal compatibility constraints: Signal systems between cross-line lines must be compatible with each other.

7. The decision-making system according to claim 1, characterized in that, The sensitivity analysis simulates the stability of the plan under different scenarios by adjusting the passenger flow change parameters, and identifies risk points such as passenger flow falling short of expectations and overestimation of engineering risks in advance.

8. The decision-making system according to claim 1, characterized in that, It also includes a visualization decision-making module, which is used to graphically display the analysis results in the form of passenger flow heat maps, time isochronous circles, scheme comparison radar charts, CEI threshold decision charts, etc., and supports interactive parameter adjustment and real-time scheme comparison.

9. A method for optimizing and deciding on integrated rail transit line schemes, applied to any of the decision systems in claims 1-8, characterized in that, include: For scenario definition and data input, clarify the needs for traffic line integration, input basic data of relevant areas into the data integration module, and automatically generate two basic schemes: "cross-line operation" and "transfer connection"; Quantify multi-dimensional indicators. For each plan, call the multi-dimensional analysis module to calculate its quantitative values ​​in multiple dimensions such as passenger flow, capacity, engineering, cost and efficiency. Dynamic weighting and comprehensive decision-making are performed. The integrated decision-making module determines the current decision weight based on the analytic hierarchy process and the entropy weight method, and calculates the CEI value of each option. The recommended solution is output based on the CEI decision threshold; The module performs sensitivity analysis and generates reports. The scheme evaluation and simulation module conducts key parameter perturbation tests on the recommended schemes, assesses risks, and generates a complete decision report that includes scheme comparison, reasons for recommendation, and risk warnings.

10. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the decision method as described in claim 9.

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