Train scheduling optimization method in subway network

Abstract

The invention discloses a train scheduling optimization method in a subway network, and belongs to the technical field of big data processing. The scheduling optimization method comprises the following steps: step 1, constructing a virtual model for highly simulating a new line; 2, formulating a preliminary train marshalling plan, a running chart and a parking strategy according to the expected transport capacity of the new line, and fusing the predicted passenger flow data of the new line to simulate an operation environment; 3, based on the virtual model, carrying out analogue simulation on various train dispatching schemes for a new line, and quantitatively calculating operation indexes under different dispatching combinations; 4, constructing a multi-target optimization algorithm model, and automatically searching an optimal scheduling scheme under the balance of each target; step 5, carrying out deep analysis and verification on the searched optimal scheduling scheme, and emphatically evaluating the connection stability of the new and old lines and the transport capacity distribution rationality of the new lines; and step 6, applying the verified optimal scheduling scheme to the actual operation of the new line, and performing continuous optimization and adjustment.

Description

Technical Field

[0001] The present application relates to the field of big data processing technology, and more specifically, to a method for optimizing train scheduling in a subway network. Background Art

[0002] Under the background of rapid urban development, subway system has been widely used and promoted as an efficient and environmentally friendly public transportation mode. However, with the continuous expansion of urban scale and population growth, subway operation faces increasingly complex challenges, including but not limited to improving operational efficiency, improving passenger service quality and controlling energy consumption.

[0003] At present, subway train dispatching is one of the core links to ensure the normal operation of subway operations. Traditional train dispatching methods are often based on experience and rules, lacking pertinence and flexibility, and difficult to adapt to the ever-changing operating environment. Therefore, seeking an efficient train dispatching optimization method in the subway network has become an urgent problem to be solved.

[0004] Although some optimization methods for subway train scheduling have emerged in the prior art, they often have certain limitations. For example, some methods lack comprehensive consideration of multiple indicators and cannot comprehensively measure the pros and cons of scheduling plans; other methods are difficult to achieve efficient scheduling plan generation and continuous optimization in practical applications, resulting in unsatisfactory scheduling results.

[0005] Therefore, this patent proposes a train dispatch optimization method in the subway network, aiming to overcome the various deficiencies of the existing technology and achieve efficient, stable and intelligent subway operation. This method can comprehensively consider multiple aspects such as train operation efficiency, passenger service quality, energy consumption, etc. through key steps such as building virtual models, simulation, and multi-objective optimization algorithms, so as to achieve the purpose of optimizing dispatching plans, improving operational efficiency, and improving passenger experience. Summary of the invention

[0006] In order to overcome a series of defects in the prior art, the purpose of this application is to provide a train scheduling optimization method in a subway network for the above-mentioned problems, comprising the following steps:

[0007] Step 1, construct a virtual model that highly simulates the new line;

[0008] Step 2: Develop a preliminary train formation plan, operation diagram, and parking strategy based on the expected capacity of the new line, and incorporate the predicted passenger flow data of the new line to simulate the operating environment;

[0009] Step 3: Conduct simulations of various train dispatching schemes for the new line based on the virtual model, quantify the operating indicators under different dispatching combinations, and comprehensively evaluate the performance of various dispatching combinations;

[0010] Step 4: Build a multi-objective optimization algorithm model, assign appropriate weights to the new line's driving efficiency, service quality, energy consumption control, and seamless connection with the original line, and automatically search for the optimal scheduling solution under the trade-off of various objectives;

[0011] Step 5: Conduct in-depth analysis and verification of the optimal dispatching plan found, focusing on evaluating the smooth connection between the new and old lines and the rationality of the capacity allocation of the new line to ensure the feasibility and stability of the plan in actual operation;

[0012] Step 6: Apply the verified optimal scheduling plan to the actual operation of the new line and conduct continuous optimization and adjustment to ensure efficient connection between the new and old lines and overall optimized operation.

[0013] Furthermore, step 1 includes the following steps:

[0014] Collect and analyze relevant information about the new line, fully grasp the specific location of the new stations, platform layout, and station entrance and exit distribution information; at the same time, clearly understand the direction, track, line length and curve radius parameters of the new line;

[0015] Based on the collected data, a 3D digital model of the new line is established using 3D modeling software;

[0016] Seamlessly integrate the 3D digital model of the new line with the digital model of the original line to form an overall 3D virtual model, accurately describing the connection points and conversion scenarios between the new and old lines;

[0017] Various signal systems required for the new line, including train operation control systems, integrated monitoring systems and communication systems, are introduced into the overall three-dimensional virtual model, and simulation debugging is carried out to ensure that various signal systems operate normally in the virtual environment.

[0018] Furthermore, step 2 includes the following steps:

[0019] Comprehensively analyze the design capacity of the new line, including the maximum design operating speed, railway bureau organization and train interval parameters, so as to estimate the maximum number of train operation plans per hour;

[0020] Develop a preliminary train formation plan based on expected passenger flow peak periods;

[0021] Prepare the train operation diagram based on the determined train formation plan and taking into account the line length and station distance of the new line; at the same time, determine the parking time of trains at different stations and formulate a reasonable parking strategy;

[0022] Fully collect and analyze the population distribution and transportation hub conditions along the new line, and use big data modeling to scientifically predict the spatial and temporal distribution pattern of future passenger flow on the new line. Obtain peak passenger flow at different time periods and different stations;

[0023] The predicted passenger flow data of the new line is integrated into the virtual model to simulate the operation of the train under different passenger flow intensities. Through repeated debugging, the train formation plan, operation diagram and parking strategy are optimized to fully adapt to potential passenger flow demand and improve the operating efficiency of the new line.

[0024] Furthermore, the maximum number of train operation plans per hour is estimated by the following formula: Among them, T train T is the train interval parameter, which refers to the total time required for a train to depart from the starting station and return to the starting position, expressed in minutes; interval The minimum safe time interval compiled for the railway bureau refers to the minimum safe time interval between trains, expressed in minutes; N is the number of train operation plan formations, which refers to the maximum number of train operations that can be arranged within an hour.

[0025] Further, step 3 includes the following steps:

[0026] Design a variety of train dispatching solutions based on actual operational needs, including different operation diagrams, marshaling plans and parking strategy combinations;

[0027] Import various dispatching plans into the virtual model, set the corresponding vehicle operation parameters, signal system logic and passenger model;

[0028] Run simulations to monitor and record various operational indicators in real time, including train punctuality, passenger load factor, operating costs and energy consumption indicators;

[0029] For the connection with the original line, focus on observing the transfer efficiency of the new and old lines and the consistency of arrival and departure times, and consider whether it is necessary to adjust the parking time at the connecting station;

[0030] Analyze the capacity distribution of the new line, analyze the changes in passenger volume in different sections, and assess whether the capacity is reasonably allocated, and whether there is severe congestion or idle resources;

[0031] Through data comparison, the comprehensive operational performance scores under each scheduling scheme are quantitatively calculated, and the performance of different scheduling combinations is compared in multiple dimensions;

[0032] Based on the simulation results, the various parameters of the scheduling plan are optimized to form several alternative scheduling combinations with better comprehensive operating performance, providing a decision-making basis for the final determination of the scheduling plan.

[0033] Furthermore, the calculation formula of the comprehensive operational performance score Scoretotal is: Scoretotal = w1×On+w2×Lp+w3×Cefficiency-w4×Econsumption+w5×Spassenger+w6×Etransfer, where On is the train punctuality rate, which reflects the degree of punctuality of the train running according to the plan, expressed as a percentage; Lp is the average passenger load factor, which indicates the average full load of the train, expressed as a percentage; Cefficiency is the operating cost efficiency, which is measured by the operating cost per unit passenger mileage. The lower the cost, the higher the efficiency. The higher the value, the lower the efficiency. Econsumption is the energy consumption efficiency, which is the energy consumption per unit mileage. The lower the energy consumption, the higher the efficiency. Therefore, a negative sign is used to indicate it as a deduction in the formula. Spassenger is the passenger satisfaction, which is obtained through passenger surveys or model predictions. It reflects the passenger's satisfaction with the service and is expressed as a percentage. Etransfer is the convenience of transfer, which measures the efficiency and experience of passenger transfers and is expressed as a percentage through a comprehensive evaluation of transfer time, convenience score, etc. W1, W2, W3, W4, W5, and W6 are the weights of each operating indicator, satisfying w1+w2+w3+w4+w5+w6=1.

[0034] Further, step 4 includes the following steps:

[0035] Define the objective function of scheduling optimization, including train punctuality, passenger load factor, energy consumption level and transfer time indicators, and assign different weight coefficients according to their importance;

[0036] Determine the decision variables and their ranges, including train departure time, stop time, speed curve and train formation plan;

[0037] Construct a mathematical model between the objective function and the decision variables to describe the relationship constraints between them;

[0038] Select a multi-objective optimization algorithm for efficient search of the optimal solution;

[0039] The optimization algorithm is embedded in the simulation model, and various operational indicator data fed back by the simulation are used to guide the algorithm to iteratively search for the optimal solution in the solution space;

[0040] During the algorithm iteration process, the changing trends of each objective function are monitored in real time, and the objective weights are adjusted dynamically to meet the optimization preferences at different stages;

[0041] When the optimization algorithm converges, it outputs several groups of scheduling schemes that perform well under the trade-offs of various objectives, and presents the characteristics of each scheme through data analysis and visualization.

[0042] Manually analyze the pros and cons of each candidate scheduling plan, combine actual operational needs and preferences, and ultimately determine the optimal scheduling implementation plan for the new line.

[0043] Further, step 5 includes the following steps:

[0044] Import all parameters of the optimal dispatching solution into the virtual model, including the operation diagram, marshaling plan and parking strategy;

[0045] Simulate operations for days or even weeks, paying special attention to peak hours, to test the stability of the solution under various conditions;

[0046] Focus on analyzing the connection points between the new and old lines, check the consistency indicators of transfer time and arrival and departure times, and evaluate the smoothness of transfer between the new and old lines and the passenger experience;

[0047] Conduct a detailed assessment of the capacity allocation for the new route, analyze the passenger load of each section, and find out whether there are sections and time periods with persistent congestion or idle resources;

[0048] Review the plan from the perspective of train operation, evaluate train running intervals and punctuality indicators, and verify whether the impact of the scheduling plan on the existing lines is within the controllable range;

[0049] Predict and evaluate energy consumption and operating costs to ensure the economic feasibility of the scheduling plan;

[0050] If potential risk points are found, relevant parameters will be optimized in a timely manner and the scheduling plan will be improved in a cyclical manner.

[0051] Furthermore, the smoothness TSI of the transfer between the new and old lines is calculated by the following formula: TSI = SD wait / M wait , where SD wait is the standard deviation of the transfer waiting time, M wait is the average transfer waiting time;

[0052] The consistency index ADCI of arrival and departure time is calculated by the following formula:

[0053]

[0054] Among them, A i and D j are the times of the i-th arrival and j-th departure, respectively. and is the average of the arrival and departure times, and m and p are the number of arrivals and departures, respectively.

[0055] Further, step 6 includes the following steps:

[0056] Before the new line is officially put into operation, all parameters of the optimal dispatching plan will be sent to the actual operation system to ensure that train formation, operation diagram and parking strategy can be smoothly implemented;

[0057] After the new line is put into operation, a complete operation data collection and monitoring mechanism will be established to track the train operation status, passenger flow distribution and energy consumption indicators in real time;

[0058] Regularly analyze actual operation data and evaluate the actual implementation effect of the optimal dispatching plan, including driving efficiency, service quality, energy consumption control and connection stability, and identify possible deficiencies in the plan;

[0059] Continuously optimize the parameters of the scheduling plan based on the problems found and new operational needs;

[0060] Incorporate the latest passenger flow forecast data to assess changes in capacity demand over the next period of time and adjust train capacity and dispatch arrangements for new lines accordingly;

[0061] As for the connection with the original line, we will continue to pay attention to the transfer data, promptly identify potential congestion risks and adjust the parking time at the connecting station to ensure seamless and efficient connection between the new and old lines;

[0062] Establish a closed-loop mechanism for optimizing the dispatching plan, analyze the data feedback from operational practices, and regularly optimize and adjust the parameters of the dispatching plan;

[0063] As time goes by, through continuous data feedback and scheduling optimization, the scheduling plan will become more in line with actual operations, achieving overall optimal operation of new and old lines.

[0064] Compared with the prior art, the beneficial effects of this application are:

[0065] In this application, efficient operation and management of the new line is achieved through virtual model construction, simulation, multi-objective optimization algorithm and other steps; by comprehensively considering multiple indicators such as train punctuality, passenger load factor, energy consumption control, passenger satisfaction, etc., through optimizing the scheduling plan and continuous adjustment, efficient connection and overall optimized operation of the new and old lines are achieved; through continuous data feedback and scheduling optimization, the scheduling plan is ensured to be in line with actual operational needs, and stable, efficient and comfortable operation of the subway transportation system is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a flow chart of a method for optimizing train scheduling in a subway network disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical scheme and advantages of the implementation of the present invention clearer, the technical scheme in the embodiment of the present invention will be described in more detail below in conjunction with the drawings in the embodiment of the present invention. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of the present invention, not all of the embodiments.

[0068] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0069] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0070] like Figure 1 As shown, a method for optimizing train scheduling in a subway network includes the following steps:

[0071] Step 1: Build a virtual model that highly simulates the new line. The main purpose of this step is to create a virtual environment that accurately simulates the new line, including line structure, station locations, traffic flow, etc., so that subsequent steps can be simulated and optimized. By building a virtual model, we can better understand the operating characteristics of the new line and provide an accurate data basis for subsequent steps. The establishment of a virtual model helps to identify potential problems and bottlenecks, and provides an important reference for scheduling optimization.

[0072] Step 2: According to the expected transport capacity of the new line, formulate a preliminary train formation plan, operation diagram and parking strategy, and integrate the predicted passenger flow data of the new line to simulate the operation environment. By integrating passenger flow data, the operation demand can be evaluated more accurately, providing a more reasonable basis for subsequent scheduling plans. This step also helps to test the feasibility and effect of the preliminary plan in a simulated environment, providing a reference for subsequent simulations.

[0073] Step 3: Based on the virtual model, simulate various train dispatching schemes for the new line, quantify the operating indicators under different dispatching combinations, and comprehensively evaluate the performance of various dispatching combinations. Through quantitative evaluation, the advantages and disadvantages of different dispatching combinations can be objectively compared, providing a basis for selecting the best solution. Simulation can also help discover potential problems and optimization space, and provide feedback for subsequent optimization algorithms;

[0074] Step 4: Build a multi-objective optimization algorithm model, assign appropriate weights to the new line's driving efficiency, service quality, energy consumption control, and seamless connection with the original line, and automatically search for the optimal scheduling solution under the trade-off of various objectives. Through multi-objective optimization, the contradictions between various indicators can be balanced, and an optimal scheduling solution that comprehensively considers all aspects of the needs can be found. The application of automatic search optimization algorithms can effectively improve the search efficiency of scheduling solutions and accelerate the process of finding the optimal solution.

[0075] Step 5: Conduct in-depth analysis and verification of the optimal dispatching scheme found, focusing on evaluating the smooth connection between the new and old lines and the rationality of the capacity allocation of the new line to ensure the feasibility and stability of the scheme in actual operation. Through in-depth analysis and verification, possible problems and risks of the optimal dispatching scheme can be discovered, and the scheme can be further optimized to ensure its feasibility and stability in actual operation. The verification process can also provide reference for actual operation and guide subsequent dispatching execution and management;

[0076] Step 6: Apply the verified optimal scheduling plan to the actual operation of the new line and continuously optimize and adjust it to ensure efficient connection between the new and old lines and overall optimized operation. The purpose of this step is to continuously improve the operation plan of the new line to adapt it to changes in the actual operating environment and achieve long-term stable and efficient operation.

[0077] Furthermore, step 1 includes the following steps:

[0078] Collect and analyze relevant information about the new line, fully grasp the specific location of the new station, platform layout, and station entrance and exit distribution information; at the same time, clearly understand the direction of the new line, line length and curve radius parameters. Through the analysis of the station layout, the functional requirements of each station can be determined, such as transfer facilities, emergency exits, etc., to provide basic data for the subsequent model establishment;

[0079] Based on the collected data, a 3D digital model of the new line is built using 3D modeling software. The purpose of this step is to ensure a clear understanding of the basic geometric features of the new line, providing a basis for subsequent model building and signal system layout;

[0080] The 3D digital model of the new line is seamlessly integrated with the digital model of the original line to form an overall 3D virtual model, accurately describing the connection points and conversion scenarios between the new and old lines. Through seamless integration, key parts such as the connection points and conversion scenarios between the new and old lines can be accurately described, providing a real and reliable operating environment for subsequent simulations;

[0081] Various signal systems required for the new line, including train operation control systems, integrated monitoring systems and communication systems, are imported into the overall three-dimensional virtual model, and simulation debugging is carried out to ensure the normal operation of various signal systems in the virtual environment. Through simulation debugging, potential problems and risks can be discovered, and adjustments and optimizations can be made in a timely manner to ensure the reliability and stability of the signal system in actual operation.

[0082] Furthermore, step 2 includes the following steps:

[0083] Comprehensively analyze the design capacity of the new line, including the design maximum operating speed, railway bureau organization and train interval parameters to determine the design capacity of the new line, so as to estimate the maximum number of train operation plans per hour, providing important reference for the formulation of subsequent train formation plans;

[0084] Based on the expected peak passenger flow period, formulate a preliminary train formation plan to ensure that the transportation needs of passengers can be met during the high passenger flow period. The preliminary train formation plan should take into account the needs of passenger flow concentration areas and transfer stations to achieve rapid transportation and smooth transfer of passengers;

[0085] Prepare a train operation diagram based on the determined train formation plan and taking into account the line length and station distance of the new line. At the same time, determine the stop time of trains at different stations and formulate a reasonable stop strategy to ensure that the stop time of trains at each station fully meets the passengers' boarding and alighting needs.

[0086] Fully collect and analyze the population distribution and transportation hub conditions along the new line, and use big data modeling to scientifically predict the spatial and temporal distribution of future passenger flow on the new line. Obtain peak passenger flow at different time periods and stations to provide a basis for the optimization of subsequent train formation plans and operation diagrams;

[0087] The predicted passenger flow data of the new line is integrated into the virtual model to simulate the operation of the train under different passenger flow intensities. Through repeated debugging, the train formation plan, operation diagram and parking strategy are optimized to fully adapt to potential passenger flow demand and improve the operating efficiency of the new line.

[0088] Furthermore, the maximum number of train operation plans per hour is estimated by the following formula: Among them, T train T is the train interval parameter, which refers to the total time required for a train to depart from the starting station and return to the starting position, expressed in minutes; interval The minimum safe time interval compiled for the railway bureau refers to the minimum safe time interval between trains, expressed in minutes; N is the number of train operation plan formations, which refers to the maximum number of train operations that can be arranged within an hour.

[0089] Further, step 3 includes the following steps:

[0090] According to actual operation needs, design a variety of different train dispatching schemes to meet different transportation needs and passenger flow conditions. Each dispatching scheme should take into account the rationality of the operation diagram, the effectiveness of the train formation plan, and the feasibility of the parking strategy to achieve high efficiency and stability of operation;

[0091] Import the designed various scheduling schemes into the virtual model, set the corresponding vehicle operation parameters (such as speed, acceleration, etc.), signal system logic (such as signal lights, turnout control logic, etc.) and passenger model (such as passenger entry and exit behavior rules);

[0092] Setting perfect parameters and models can ensure the accuracy and authenticity of the simulation and provide a reliable basis for subsequent simulations;

[0093] Conduct operation simulation in the virtual model, monitor the operation of the train in real time, and record the data of various operation indicators, including train punctuality, passenger load factor, operation cost, and energy consumption indicators. By monitoring and recording these indicators, the operation effects of various scheduling plans can be evaluated;

[0094] Pay special attention to the connection between the new and old lines, focus on observing the transfer efficiency and the consistency of arrival and departure times, and evaluate whether the parking time at the connecting station needs to be adjusted to ensure the smoothness of the connection. Optimizing the parking time at the connecting station can effectively reduce the waiting time for passengers to transfer and improve the travel experience of passengers;

[0095] Analyze the changes in passenger volume in different sections of the new line to assess whether the capacity is reasonably allocated and whether there is severe congestion or idle resources; adjust the capacity distribution based on the analysis results to optimize the passenger travel experience and improve the efficiency and economy of operations;

[0096] Quantify the operating indicators under various scheduling schemes to form a comprehensive operating performance score for multi-dimensional comparative analysis. Through data comparison, evaluate the performance of various scheduling combinations and provide a basis for subsequent scheme optimization and decision-making;

[0097] According to the simulation results and comparative analysis, various dispatching parameters are optimized to form several alternative dispatching combinations with better comprehensive operating performance. These alternative dispatching combinations can provide a decision-making basis for the final determination of the dispatching plan to ensure the efficient operation and service quality of the new line.

[0098] Furthermore, the calculation formula of the comprehensive operational performance score Scoretotal is: Scoretotal = w1×On+w2×Lp+w3×Cefficiency-w4×Econsumption+w5×Spassenger+w6×Etransfer, where On is the train punctuality rate, which reflects the degree of punctuality of the train running according to the plan, expressed as a percentage; Lp is the average passenger load factor, which indicates the average full load of the train, expressed as a percentage; Cefficiency is the operating cost efficiency, which is measured by the operating cost per unit passenger mileage. The lower the cost, the higher the efficiency. The higher the value, the lower the efficiency. Econsumption is the energy consumption efficiency, which is the energy consumption per unit mileage. The lower the energy consumption, the higher the efficiency. Therefore, a negative sign is used to indicate it as a deduction in the formula. Spassenger is the passenger satisfaction, which is obtained through passenger surveys or model predictions. It reflects the passenger's satisfaction with the service and is expressed as a percentage. Etransfer is the convenience of transfer, which measures the efficiency and experience of passenger transfers and is expressed as a percentage through a comprehensive evaluation of transfer time, convenience score, etc. W1, W2, W3, W4, W5, and W6 are the weights of each operating indicator, satisfying w1+w2+w3+w4+w5+w6=1.

[0099] Further, step 4 includes the following steps:

[0100] Define the objective function of scheduling optimization, including train punctuality, passenger load factor, energy consumption level and transfer time indicators, and assign different weight coefficients according to their importance;

[0101] Determine the decision variables and their ranges, including train departure time, stop time, speed curve and train formation plan;

[0102] Construct a mathematical model between the objective function and the decision variables to describe the relationship constraints between them;

[0103] Select a multi-objective optimization algorithm for efficient search of the optimal solution;

[0104] The optimization algorithm is embedded in the simulation model, and various operational indicator data fed back by the simulation are used to guide the algorithm to iteratively search for the optimal solution in the solution space;

[0105] During the algorithm iteration process, the changing trends of each objective function are monitored in real time, and the objective weights are adjusted dynamically to meet the optimization preferences at different stages;

[0106] When the optimization algorithm converges, it outputs several groups of scheduling schemes that perform well under the trade-offs of various objectives, and presents the characteristics of each scheme through data analysis and visualization.

[0107] Manually analyze the pros and cons of each candidate scheduling plan, combine actual operational needs and preferences, and ultimately determine the optimal scheduling implementation plan for the new line.

[0108] In this embodiment, it is crucial to clarify the objective function of scheduling optimization. These objective functions cover multiple key indicators such as train punctuality, passenger load, energy consumption level and transfer time. Each indicator represents the performance requirements of different aspects of the rail transit system, so different weight coefficients need to be assigned according to their importance. For example, the train punctuality is crucial to the travel experience of passengers, so its weight coefficient may be relatively high; while the energy consumption level is directly related to the operating cost, which is also a factor that cannot be ignored. Next, determining the decision variables and their value ranges is the basis for building an optimization model. Decision variables include train departure time, parking time, driving speed curve and marshaling scheme. These variables not only determine the operating efficiency of the train, but also directly affect the travel experience of passengers and the operating cost of the rail transit system. Therefore, it is necessary to reasonably set the value range of these variables according to the actual situation to ensure the effectiveness and practicality of the optimization model. When constructing a mathematical model between the objective function and the decision variables, it is necessary to describe the relationship constraints between them in detail. These relationship constraints are usually based on the actual operation of the rail transit system, such as the running speed of the train cannot exceed the speed limit, and the departure interval needs to meet certain safety requirements. By constructing such a mathematical model, complex scheduling problems can be transformed into a mathematical optimization problem, so that mathematical tools can be used for efficient solution. In order to efficiently search for the optimal solution, it is necessary to select a suitable multi-objective optimization algorithm. These algorithms are usually based on principles such as heuristic search or genetic algorithm, and can quickly find the optimal solution that satisfies multiple objective functions in the solution space. When selecting an algorithm, factors such as its convergence speed, solution quality and stability need to be considered to ensure the accuracy and reliability of the optimization results. Embedding the optimization algorithm into the simulation model is an important link. Through the simulation model, the actual operation of the rail transit system can be simulated and various operating indicator data can be obtained. These data can be used as feedback for the optimization algorithm to guide the algorithm to iteratively search for the optimal solution in the solution space. Through continuous iteration and optimization, the optimal scheduling solution that satisfies multiple objective functions can be gradually found. During the algorithm iteration process, it is necessary to monitor the changing trend of each objective function in real time. This helps to understand the convergence of the optimization algorithm and the trade-off relationship between different objective functions. According to actual needs, the target weight can be dynamically adjusted to meet the optimization preferences at different stages. For example, in the early stages, more emphasis may be placed on improving train punctuality and passenger load factors; while in the later stages, more attention may be paid to reducing energy consumption levels and optimizing transfer times. When the optimization algorithm converges, several groups of scheduling solutions that perform well under the trade-offs of various objectives will be obtained. In order to more intuitively display the characteristics of these solutions, data analysis and visualization techniques can be used to present the key indicators in the solutions in the form of charts or animations. This helps to have a more comprehensive understanding of the advantages and disadvantages of each solution and provides strong support for the final determination of the optimal implementation plan. Finally, it is necessary to manually analyze the advantages and disadvantages of each candidate scheduling solution.This includes comparing the performance of different schemes in terms of train punctuality, passenger load, energy consumption, and transfer time, and taking into account the actual operational needs and preferences. Through in-depth analysis and discussion, the optimal scheduling implementation plan for the new line can be finally determined, providing strong guarantees for the safety, efficiency, and sustainable development of the rail transit system.

[0109] Further, step 5 includes the following steps:

[0110] Import all parameters of the optimal dispatching solution into the virtual model, including the operation diagram, marshaling plan and parking strategy;

[0111] Simulate operations for days or even weeks, paying special attention to peak hours, to test the stability of the solution under various conditions;

[0112] Focus on analyzing the connection points between the new and old lines, check the consistency indicators of transfer time and arrival and departure times, and evaluate the smoothness of transfer between the new and old lines and the passenger experience;

[0113] Conduct a detailed assessment of the capacity allocation for the new route, analyze the passenger load of each section, and find out whether there are sections and time periods with persistent congestion or idle resources;

[0114] Review the plan from the perspective of train operation, evaluate train running intervals and punctuality indicators, and verify whether the impact of the scheduling plan on the existing lines is within the controllable range;

[0115] Predict and evaluate energy consumption and operating costs to ensure the economic feasibility of the scheduling plan;

[0116] If potential risk points are found, relevant parameters will be optimized in a timely manner and the scheduling plan will be improved in a cyclical manner.

[0117] Step 5 plays a vital role in the process of perfecting the scheduling plan. It covers multiple aspects and aims to comprehensively and deeply test and optimize the scheduling plan to ensure that it can achieve the best results in actual operation.

[0118] First, all parameters of the optimal scheduling solution need to be imported into the virtual model. These parameters include but are not limited to the operation diagram, marshaling plan, and parking strategy. The operation diagram is the basis of train operation, which details the train's running time, running range, and stop stations. The marshaling plan determines the train's carriage configuration and passenger capacity, which is the key to ensuring the rationality of train capacity allocation. The parking strategy involves the train's stop time, parking location, and parking method, which is of great significance to improving passenger satisfaction and operational efficiency.

[0119] Next, it is necessary to simulate the operation of several consecutive days or even weeks to test the stability of the scheduling scheme under various working conditions. Especially during peak operating hours, it is necessary to pay close attention to the operation of the trains to check whether there are delays, congestion and other problems. At the same time, it is also necessary to pay attention to the adaptability of the scheme under different weather, passenger flow and other conditions to ensure its reliability in actual operation.

[0120] During the simulation operation, it is also necessary to focus on analyzing the connection stations between the new and old lines. This includes checking whether the transfer time is reasonable, whether the arrival and departure times are consistent, and other indicators. By analyzing these indicators, the smoothness of the transfer between the new and old lines and the passenger experience can be evaluated, thereby identifying potential areas for improvement.

[0121] In addition, a detailed assessment of the capacity allocation of the new line is required. This includes analyzing the passenger load of each section and finding out whether there are sections and time periods with persistent congestion or idle resources. By adjusting the departure intervals of trains, carriage configurations and other measures, capacity allocation can be optimized and operational efficiency can be improved.

[0122] In addition to the above aspects, it is also necessary to review the scheduling plan from the perspective of train operation. This includes evaluating the train running interval and punctuality indicators, and verifying whether the impact of the scheduling plan on the existing lines is within the controllable range. By optimizing the running interval and improving the punctuality rate, the waiting time of passengers can be reduced and the passenger satisfaction can be improved.

[0123] Finally, energy consumption and operating costs need to be predicted and evaluated. This includes analyzing the impact of scheduling plans on energy consumption and operating costs to ensure the economic feasibility of scheduling plans. By adopting energy-saving technologies and optimizing operating strategies, operating costs can be reduced while ensuring service quality.

[0124] During the execution of step 5, if potential risk points are found, relevant parameters need to be optimized in a timely manner to improve the scheduling plan in a cyclical manner. Through continuous simulation, analysis and optimization, the reliability and efficiency of the scheduling plan can be gradually improved, providing strong support for actual operations.

[0125] Furthermore, the smoothness TSI of the transfer between the new and old lines is calculated by the following formula: TSI = SD wait / M wait , where SD wait is the standard deviation of the transfer waiting time, M wait is the average transfer waiting time;

[0126] The consistency index ADCI of arrival and departure time is calculated by the following formula:

[0127]

[0128] Among them, A iand D j are the times of the i-th arrival and j-th departure, respectively. and is the average of the arrival and departure times, and m and p are the number of arrivals and departures, respectively.

[0129] Further, step 6 includes the following steps:

[0130] Before the new line is officially put into operation, all parameters of the optimal dispatching plan will be sent to the actual operation system to ensure that train formation, operation diagram and parking strategy can be smoothly implemented;

[0131] After the new line is put into operation, a complete operation data collection and monitoring mechanism will be established to track the train operation status, passenger flow distribution and energy consumption indicators in real time;

[0132] Regularly analyze actual operation data and evaluate the actual implementation effect of the optimal dispatching plan, including driving efficiency, service quality, energy consumption control and connection stability, and identify possible deficiencies in the plan;

[0133] Continuously optimize the parameters of the scheduling plan based on the problems found and new operational needs;

[0134] Incorporate the latest passenger flow forecast data to assess changes in capacity demand over the next period of time and adjust train capacity and dispatch arrangements for new lines accordingly;

[0135] As for the connection with the original line, we will continue to pay attention to the transfer data, promptly identify potential congestion risks and adjust the parking time at the connecting station to ensure seamless and efficient connection between the new and old lines;

[0136] Establish a closed-loop mechanism for optimizing the dispatching plan, analyze the data feedback from operational practices, and regularly optimize and adjust the parameters of the dispatching plan;

[0137] As time goes by, through continuous data feedback and scheduling optimization, the scheduling plan will become more in line with actual operations, achieving overall optimal operation of new and old lines.

[0138] Step 6 involves a series of detailed operations to ensure that the new line can run efficiently and smoothly before and after formal operation. The following is a detailed description of the steps:

[0139] First, before the new line is officially put into operation, we will accurately send the various parameters of the carefully designed and optimized dispatching plan, including train formation, operation diagram and parking strategy, to the actual operation system. This step is crucial, as it ensures that various parameters can be accurately imported into the system, laying a solid foundation for subsequent smooth operation.

[0140] After the new line is put into operation, we will immediately establish a complete operation data collection and monitoring mechanism. This mechanism can track the train operation status in real time, including speed, location, fault conditions, etc., and can also monitor passenger flow distribution and energy consumption indicators. Through these data, we can fully understand the operation of the new line and provide strong support for subsequent optimization and adjustment.

[0141] Based on data collection and monitoring, we will regularly analyze actual operation data and evaluate the actual implementation effect of the optimal dispatching plan. These evaluation indicators include driving efficiency, service quality, energy consumption control, and smooth connection. Through in-depth analysis of these indicators, we can find possible deficiencies and problems in the dispatching plan and provide direction for subsequent optimization and adjustment.

[0142] We will continue to optimize and adjust the parameters of the dispatching plan in response to the problems found and new operational needs. This includes fine-tuning the train formation, operation diagram, and parking strategy to adapt to changes in actual operations. At the same time, we will also incorporate the latest passenger flow forecast data, evaluate the changes in capacity demand in the future, and adjust the train capacity and dispatching arrangements of the new lines accordingly.

[0143] In addition, we will pay special attention to the connection between the new line and the original line. By continuously paying attention to the transfer data, we can promptly identify the potential congestion and adjust the parking time at the connecting station to ensure seamless and efficient connection between the new and old lines. This will not only help improve the travel experience of passengers, but also effectively improve the operating efficiency of the entire rail transit system.

[0144] In order to achieve continuous optimization and updating of the dispatching plan, we will establish a closed-loop mechanism for dispatching plan optimization. This mechanism uses the comprehensive analysis results of operational practice feedback as input and regularly optimizes and adjusts the parameters of the dispatching plan. In this way, we can continuously adapt to changes in the operating environment and make the dispatching plan more in line with actual operational needs.

[0145] As time goes by, through continuous data feedback and dispatch optimization, our dispatch plan will gradually become more perfect. Ultimately, we will achieve the overall optimal operation of new and old lines, providing passengers with a more convenient and comfortable travel experience.

Claims

1. Train dispatch optimization method in subway network, It is characterized in that The following steps are involved: Step 1, construct a virtual model that highly simulates the new line; Step 2: Develop a preliminary train formation plan, operation diagram, and parking strategy based on the expected capacity of the new line, and incorporate the predicted passenger flow data of the new line to simulate the operating environment; Step 3: Conduct simulations of various train dispatching schemes for the new line based on the virtual model, quantify the operating indicators under different dispatching combinations, and comprehensively evaluate the performance of various dispatching combinations; Step 4: Build a multi-objective optimization algorithm model, assign appropriate weights to the new line's driving efficiency, service quality, energy consumption control, and seamless connection with the original line, and automatically search for the optimal scheduling solution under the trade-off of various objectives; Step 5: Conduct in-depth analysis and verification of the optimal dispatching plan found, focusing on evaluating the smooth connection between the new and old lines and the rationality of the capacity allocation of the new line to ensure the feasibility and stability of the plan in actual operation; Step 6: Apply the verified optimal scheduling plan to the actual operation of the new line and conduct continuous optimization and adjustment to ensure efficient connection between the new and old lines and overall optimized operation.

2. The train dispatching optimization method in the subway network according to claim 1, It is characterized in that Step 1 includes the following steps: Collect and analyze relevant information about the new line, fully grasp the specific location of the new stations, platform layout, and station entrance and exit distribution information; at the same time, clearly understand the direction, track, line length and curve radius parameters of the new line; Based on the collected data, a 3D digital model of the new line is established using 3D modeling software; Seamlessly integrate the 3D digital model of the new line with the digital model of the original line to form an overall 3D virtual model, accurately describing the connection points and conversion scenarios between the new and old lines; Various signal systems required for the new line, including train operation control systems, integrated monitoring systems and communication systems, are introduced into the overall three-dimensional virtual model, and simulation debugging is carried out to ensure that various signal systems operate normally in the virtual environment.

3. The train dispatching optimization method in the subway network according to claim 1, It is characterized in that Step 2 includes the following steps: Comprehensively analyze the design capacity of the new line, including the maximum design operating speed, railway bureau organization and train interval parameters, so as to estimate the maximum number of train operation plans per hour; Develop a preliminary train formation plan based on expected passenger flow peak periods; Prepare the train operation diagram based on the determined train formation plan and taking into account the line length and station distance of the new line; at the same time, determine the parking time of trains at different stations and formulate a reasonable parking strategy; Fully collect and analyze the population distribution and transportation hub conditions along the new line, and use big data modeling to scientifically predict the spatial and temporal distribution pattern of future passenger flow on the new line. Obtain peak passenger flow at different time periods and different stations; The predicted passenger flow data of the new line is integrated into the virtual model to simulate the operation of the train under different passenger flow intensities. Through repeated debugging, the train formation plan, operation diagram and parking strategy are optimized to fully adapt to potential passenger flow demand and improve the operating efficiency of the new line.

4. The train dispatching optimization method in the subway network according to claim 3, It is characterized in that The maximum number of train operation plans per hour is estimated using the following formula: Among them, T train T is the train interval parameter, which refers to the total time required for a train to depart from the starting station and return to the starting position, expressed in minutes; interval The minimum safe time interval compiled for the railway bureau refers to the minimum safe time interval between trains, expressed in minutes; N is the number of train operation plan formations, which refers to the maximum number of train operations that can be arranged within an hour.

5. The train dispatching optimization method in the subway network according to claim 1, It is characterized in that Step 3 includes the following steps: Design a variety of train dispatching solutions based on actual operational needs, including different operation diagrams, marshaling plans and parking strategy combinations; Import various dispatching plans into the virtual model, set the corresponding vehicle operation parameters, signal system logic and passenger model; Run simulations to monitor and record various operational indicators in real time, including train punctuality, passenger load factor, operating costs and energy consumption indicators; For the connection with the original line, focus on observing the transfer efficiency of the new and old lines and the consistency of arrival and departure times, and consider whether it is necessary to adjust the parking time at the connecting station; Analyze the capacity distribution of the new line, analyze the changes in passenger volume in different sections, and assess whether the capacity is reasonably allocated, and whether there is severe congestion or idle resources; Through data comparison, the comprehensive operational performance scores under each scheduling scheme are quantitatively calculated, and the performance of different scheduling combinations is compared in multiple dimensions; Based on the simulation results, the various parameters of the scheduling plan are optimized to form several alternative scheduling combinations with better comprehensive operating performance, providing a decision-making basis for the final determination of the scheduling plan.

6. The train dispatching optimization method in the subway network according to claim 5, It is characterized in that The calculation formula of the comprehensive operational performance score Scoretotal is: Scoretotal = w 1 ×On+w 2 ×Lp+w 3 ×Cefficiency-w 4 ×Econsumption+w 5 ×Spassenger+w 6 ×Etransfer, where On is the train punctuality rate, which reflects the degree of punctuality of the train running according to the plan, expressed as a percentage; Lp is the average passenger load factor, which indicates the average full load of the train, expressed as a percentage; Cefficiency is the operating cost efficiency, which is measured by the operating cost per unit passenger mileage. The lower the cost, the higher the efficiency; Econsumption is the energy consumption efficiency, which is the energy consumption per unit mileage. The lower the energy consumption, the higher the efficiency. Therefore, a negative sign is used to represent it as a deduction item in the formula; Spassenger is passenger satisfaction, which is obtained through passenger surveys or model predictions, reflecting the passenger's satisfaction with the service, expressed as a percentage; Etransfer is the convenience of transfer, which measures the efficiency and experience of passenger transfers, and is comprehensively evaluated through transfer time, convenience score, etc., expressed as a percentage; w 1 , w 2 , w 3 , w 4 , w 5 , w 6 is the weight of each operating indicator, satisfying w 1 +w 2 +w 3 +w 4 +w 5 +w 6 =1.

7. The train dispatching optimization method in the subway network according to claim 1, It is characterized in that Step 4 includes the following steps: Define the objective function of scheduling optimization, including train punctuality, passenger load factor, energy consumption level and transfer time indicators, and assign different weight coefficients according to their importance; Determine the decision variables and their ranges, including train departure time, stop time, speed curve and train formation plan; Construct a mathematical model between the objective function and the decision variables to describe the relationship constraints between them; Select a multi-objective optimization algorithm for efficient search of the optimal solution; The optimization algorithm is embedded in the simulation model, and various operational indicator data fed back by the simulation are used to guide the algorithm to iteratively search for the optimal solution in the solution space; During the algorithm iteration process, the changing trends of each objective function are monitored in real time, and the objective weights are adjusted dynamically to meet the optimization preferences at different stages; When the optimization algorithm converges, it outputs several groups of scheduling schemes that perform well under the trade-offs of various objectives, and presents the characteristics of each scheme through data analysis and visualization. Manually analyze the pros and cons of each candidate scheduling plan, combine actual operational needs and preferences, and ultimately determine the optimal scheduling implementation plan for the new line.

8. The train dispatching optimization method in the subway network according to claim 1, It is characterized in that Step 5 includes the following steps: Import all parameters of the optimal dispatching solution into the virtual model, including the operation diagram, marshaling plan and parking strategy; Simulate operations for days or even weeks, paying special attention to peak hours, to test the stability of the solution under various conditions; Focus on analyzing the connection points between the new and old lines, check the consistency indicators of transfer time and arrival and departure times, and evaluate the smoothness of transfer between the new and old lines and the passenger experience; Conduct a detailed assessment of the capacity allocation for the new route, analyze the passenger load of each section, and find out whether there are sections and time periods with persistent congestion or idle resources; Review the plan from the perspective of train operation, evaluate train running intervals and punctuality indicators, and verify whether the impact of the scheduling plan on the existing lines is within the controllable range; Predict and evaluate energy consumption and operating costs to ensure the economic feasibility of the scheduling plan; If potential risk points are found, relevant parameters will be optimized in a timely manner and the scheduling plan will be improved in a cyclical manner.

9. The train dispatching optimization method in the subway network according to claim 8, It is characterized in that The smoothness of the transfer between the new and old lines, TSI, is calculated by the following formula: TSI = SD wait / M wait , where SD wait is the standard deviation of the transfer waiting time, M wait is the average transfer waiting time; The consistency index ADCI of arrival and departure time is calculated by the following formula: Among them, A i and D j are the times of the i-th arrival and j-th departure, respectively. and is the average of the arrival and departure times, and m and p are the number of arrivals and departures, respectively.

10. The train dispatching optimization method in the subway network according to claim 1, It is characterized in that Step 6 includes the following steps: Before the new line is officially put into operation, all parameters of the optimal dispatching plan will be sent to the actual operation system to ensure that train formation, operation diagram and parking strategy can be smoothly implemented; After the new line is put into operation, a complete operation data collection and monitoring mechanism will be established to track the train operation status, passenger flow distribution and energy consumption indicators in real time; Regularly analyze actual operation data and evaluate the actual implementation effect of the optimal dispatching plan, including driving efficiency, service quality, energy consumption control and connection stability, and identify possible deficiencies in the plan; Continuously optimize the parameters of the scheduling plan based on the problems found and new operational needs; Incorporate the latest passenger flow forecast data to assess changes in capacity demand over the next period of time and adjust train capacity and dispatch arrangements for new lines accordingly; As for the connection with the original line, we will continue to pay attention to the transfer data, promptly identify potential congestion risks and adjust the parking time at the connecting station to ensure seamless and efficient connection between the new and old lines; Establish a closed-loop mechanism for optimizing the dispatching plan, analyze the data feedback from operational practices, and regularly optimize and adjust the parameters of the dispatching plan; As time goes by, through continuous data feedback and scheduling optimization, the scheduling plan will become more in line with actual operations, achieving overall optimal operation of new and old lines.

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