Urban area express line distribution optimization method based on operation organization requirements

Abstract

The invention discloses an urban area express line distribution optimization method based on operation organization requirements, and belongs to the technical field of rail transit. Comprising the following steps: S1, based on a large and small intersection and fast and slow vehicle combination mode, limiting the number of intersection layers into two layers, providing a combination operation strategy of fast and slow vehicles of a large intersection and slow vehicles of a small intersection, and optimizing an overpass line and turning line configuration scheme; and S2, measuring the influence of the wiring design and operation scheme on the passengers based on the construction operation cost and the generalized travel cost of the passengers. And S3, a bilevel planning model is established based on S1 and S2, a bilevel planning framework comprising an upper layer model and a lower layer model is constructed with the positions and forms of the overpass line and the turn-back line as decision variables, the upper layer model takes the lowest construction and operation cost as a target function, and the lower layer model takes the lowest passenger generalized travel cost as a target function. And S4, optimizing line wiring configuration by adopting a hybrid particle swarm optimization algorithm, solving a periodic event plan by combining an iterative approximation algorithm and CPLEX, and solving an optimal solution through a collaborative feedback mechanism. According to the method, the problem of collaborative optimization of the generalized travel cost and the operation construction cost of urban express line passengers is solved.

Description

Technical Field

[0001] The present invention relates to a method for optimizing wiring of a metropolitan area express line based on operational organization requirements, and belongs to the technical field of rail transportation. Background Art

[0002] With China's economic prosperity, urban spatial patterns have shifted. To alleviate population and functional pressures in central urban areas, many major cities have established new districts in their peripheries, leading to increased commuting demand. Rail transit, with its advantages of high capacity, high speed, punctuality, and environmental friendliness, has become the primary means of meeting this growing commuting demand. Consequently, some cities have begun developing urban and suburban high-speed rail systems as core public transportation systems to drive regional development.

[0003] Research on urban rail transit optimization, both domestically and internationally, has limitations. Regarding operational organization, while multi-objective optimization frameworks have been developed around coordinated scheduling of fast and slow trains and multi-route combination models, these models are often based on static passenger flow assumptions, making them incapable of coping with sudden passenger flow shocks. Furthermore, research results primarily focus on single-line scenarios, lacking coordinated scheduling solutions for networked operations. Furthermore, insufficient research examines the dynamic matching of skip-stop strategies with overtaking nodes, leading to imbalances between efficiency and safety. Regarding line design, despite proposals for innovative layouts such as multi-track coordination and a single island with four lines, and the use of cost-benefit models to optimize resource allocation, these approaches rely excessively on fixed timetable assumptions, making them incapable of adapting to dynamic scheduling needs. Furthermore, the dynamic feedback mechanisms underlying passenger transfer behavior are neglected, resulting in limited results in optimizing passenger flow distribution. Furthermore, the lack of validation of line adaptability in complex engineering environments has led to a significant disconnect between theory and practice. This disconnect between these two types of research further constrains overall system performance improvements, necessitating the development of a methodological framework for the coordinated optimization of operations and line design.

[0004] The hybrid particle swarm-iterative approximation collaborative optimization algorithm is a multi-objective collaborative optimization algorithm based on a two-layer feedback mechanism. Its core difference from the traditional heuristic algorithm is that it realizes the synchronous optimization of wiring schemes and train scheduling through a two-way dynamic interactive channel, rather than relying on a single-layer static model solution. The present invention adopts a hybrid particle swarm-iterative approximation collaborative optimization algorithm to deal with dual-objective collaborative optimization problems. Through the global search capability of the hybrid particle swarm algorithm and the local optimization characteristics of the iterative approximation algorithm, it automatically generates a non-dominated solution set, avoiding the subjective bias caused by manual weighting. Combined with the CPLEX solver's precise analytical ability for periodic event planning problems, the algorithm improves the convergence speed by more than 40% in complex network scenarios, and improves the solution set stability under dynamic disturbances by 25%, effectively overcoming the limitations of traditional methods in dynamic constraints and multi-objective trade-offs. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for optimizing the wiring of urban express lines in a metropolitan area based on the needs of operational organizations, so as to achieve the coordinated optimization of the generalized travel expenses of passengers along the entire line and the operating and construction expenses while meeting the actual operational needs.

[0006] To solve the above technical problems, the present invention provides a method for optimizing the wiring of metropolitan express lines based on the needs of operating organizations. A two-layer planning model is constructed for the wiring optimization of metropolitan express lines, including the following steps:

[0007] S1. Based on the combination of large and small routes and fast and slow trains, the number of route layers is limited to two, and a combined operation strategy of fast and slow trains on large routes and slow trains on small routes is proposed. At the same time, the configuration scheme of passing lanes and return lines is optimized;

[0008] S2. Measure the impact of wiring design and operation plans on passengers based on construction and operation costs and passengers' generalized travel expenses.

[0009] S3. Establish a two-level planning model based on S1 and S2: Using the location and form of the crossing line and the return line as decision variables, construct a two-level planning architecture consisting of an upper-level model and a lower-level model, where the upper-level model takes the lowest construction and operation cost as the objective function, and the lower-level model takes the lowest generalized travel cost of passengers as the objective function.

[0010] S4. A hybrid particle swarm optimization algorithm is used to optimize line wiring configuration, and an iterative approximation algorithm is combined with CPLEX to solve periodic event planning, and the optimal solution is obtained through a collaborative feedback mechanism.

[0011] Furthermore, S1 specifically includes the following steps:

[0012] S101. The research object is a two-way urban rail transit line. The station is divided into up and down platforms. The train departs from the starting station 1, passes through various stations, arrives at the turnaround station n, and then continues to run through the down platform.

[0013] S102: Clearly stipulate that express trains on major routes must stop at turnaround stations on minor routes, and that express trains can overtake other trains at local stations using the passing lane. Express trains are only allowed to be overtaken once at the same-direction passing station.

[0014] S103. Clearly restrict passenger transfers. Changing from fast to slow trains must be done at the last express station, and changing from slow trains to fast trains must be done at the first express station. Transfers between slow trains on large and small routes are prohibited.

[0015] Furthermore, S2 specifically includes the following steps:

[0016] S201. From the perspective of the urban express line operator, the operating and construction costs mainly include initial construction costs and vehicle base costs. The details are as follows:

[0017] C OC =C IC+C V

[0018] Where: C OC The construction and operation costs of the entire line, C IC is the initial construction cost, C V As for the vehicle base cost, the present invention selects the B2 type six-car marshaling train, and the vehicle base cost is 40 million yuan / year.

[0019] The initial construction costs are as follows:

[0020] C IC =C S +C Tu +C Tr

[0021] Where: C S C is the related cost of shield tunneling; Tu The cost of laying the track; C Tr Costs related to turnouts;

[0022] C S The relevant costs of shield tunneling are as follows:

[0023]

[0024] Where: x s is the shield coefficient; L O is a collection of common line sections; L T is a collection of special line sections; o is the length of the normal section;

[0025] C Tu The track laying costs are as follows:

[0026]

[0027] Where: x Tu is the track laying coefficient; a is the location of the starting station of the small circuit; b is the location of the terminal station of the small circuit;

[0028] C Tr The relevant costs for turnouts are as follows:

[0029]

[0030] Where: x Tr,l is the single turnout coefficient; x Tr,s is the intersection turnout coefficient; l c is the length of the single turnout section; l d is the length of the intersection turnout section;

[0031] S202. Passenger travel expenses generally include fare, interval operation fee, and transfer fee, as follows:

[0032] C ij,p =C ij,p,F +C ij，p,S +C ij,p,T

[0033] Where: C ij,p，F is the fare, C ij,p,S is the interval operation cost, C ij,p,T Transfer fee

[0034] C ij,p,F The fare is as follows:

[0035] C ij,p,F =ρW i,j

[0036] Where: ρ is the fare rate, w i,j is the travel distance of passenger i to j.

[0037] Section operation cost C ij,p,S It is related to the length and comfort of the ride. When evaluating the value of time, it is necessary to consider the broad travel costs, among which the purpose of travel and personal income level are key factors, as follows:

[0038]

[0039] Where: μ is the time value coefficient; t r is the interval running time; Y(f e ) is the in-car comfort function; d s Waiting time at station s; f e is the actual number of passengers in the train compartment; b e Rated number of seats on the train, n e is the maximum passenger capacity of the train;

[0040] Transfer fee C ij,p,T It is mainly related to the transfer waiting time and the number of transfers. This invention only considers one transfer, so β is 1, as follows:

[0041]

[0042] Where: ɑ is the weight coefficient of the passenger's generalized travel expenses; W s,w The transfer waiting time at station s; m is the number of transfers; β is the weight coefficient of the enterprise's construction and operation costs;

[0043] Furthermore, S3 specifically includes the following steps:

[0044] S301. Based on the operational characteristics of urban rail express lines and existing research, make assumptions;

[0045] S302, taking the position and form of the crossing line and the return line as decision variables;

[0046] S303. During the wiring scheme optimization phase, the primary goal of the upper-level model is to reduce initial construction investment and long-term operating costs by rationally configuring passing and return lines. The lower-level model, based on wiring conditions, optimizes the passenger travel experience, reducing time costs and transfer burdens. The goal is to collaboratively optimize construction and operational efficiency and passenger service, as follows:

[0047] MinZ1=C OC

[0048] MinZ2=C ij,p

[0049] Furthermore, S4 specifically includes the following steps:

[0050] S401. A hybrid particle swarm algorithm is used to solve the upper-level model and encode the crossing and return line configurations. Particle positions are determined by the crossing line configuration, the ratio of fast and slow trains, and the stop plan. The crossing line configuration is encoded using two bits, and the fast and slow train stop plan is encoded using 0 / 1. The fitness function integrates the objective function and penalty function constraints, and updates discrete variables using a sigmoid function to convert the speed.

[0051] S402: Initialize the particle swarm and randomly generate particle positions and velocities within the feasible solution range. Calculate the initial fitness value and perform constraint corrections based on the underlying model. The underlying model uses an iterative approximation algorithm and the CPLEX solver to ultimately form the initial optimized population.

[0052] S403: Perform an iterative optimization process: Update particle velocities and positions, introduce inertia weights and learning factors to optimize search performance, calculate fitness values, and dynamically adjust the individual optimal solution pbest and the global optimal solution gbest. The lower-level model gradually adjusts the upper and lower bounds through periodic time interval search, solves using CPLEX, and feeds the optimal solution back to the upper-level model for collaborative optimization.

[0053] S404. When the maximum number of iterations is reached or the solution becomes stable and the fitness change is lower than the set threshold, the algorithm is terminated and the global optimal solution is output as the final wiring plan and train operation plan.

[0054] Furthermore, S301 makes assumptions based on the operational characteristics of urban rail transit lines and existing research, specifically including the following steps:

[0055] (1) The research object is the urban rail transit line;

[0056] (2) Line characteristics: a combination of large and small routes, with intermediate stations equipped for turning back;

[0057] (3) The long route does not affect the turnaround time of the short route. The turnaround time of each station is unified. Express trains must stop at the turnaround station of the short route.

[0058] (4) When fast and slow trains pass through a station, the passing line is set at the slow train station to reduce the loss of line capacity;

[0059] (5) In order to reduce the waiting time for slow trains, only one overtaking is allowed at the same-direction overtaking station;

[0060] (6) The number of transfers per passenger shall not exceed one. When changing from an express train to a slow train, the transfer shall be made at the last express station; when changing from a slow train to an express train, the transfer shall be made at the first express station. Transfers between slow trains on large and small routes shall not be considered.

[0061] (7) There are no passengers who board the vehicle in the opposite direction or are stranded.

[0062] Furthermore, S302 uses the position and form of the crossing line and the return line as decision variables, and specifically includes the following steps:

[0063] 1) Train stop selection constraints: Constraints on the first and last stations. Express trains must pass through at least one intermediate station without stopping. The same train must stop in the same manner at the upstream and downstream platforms of the station to ensure that two types of trains are operated periodically.

[0064]

[0065] 2) Turnaround station location constraint: The turnaround station must be located between the starting and ending stations of the route;

[0066] 1≤a<b≤N

[0067] A station can only be equipped with one wiring type. When a turnaround line is required at an intermediate station and there is a demand for overtaking, a horizontal through-type turnaround line can be selected to meet the overtaking demand.

[0068]

[0069] 3) Overtaking line configuration constraints: When configuring overtaking lines, the key issue is to select stations suitable for overtaking line configuration. To ensure efficient operation of the line, overtaking lines should not be configured at the first or last station. At the same time, the overtaking line configuration of the up and down platforms at the same station should be consistent to ensure smooth train operation.

[0070] r 1,0 =r n,0 =1

[0071]

[0072] In addition, in order to meet the needs of express trains to pass through without stopping, passing lines should be set up at slow train stations to optimize the operating efficiency of the entire transportation system. Through these measures, we can build a more reasonable and efficient public transportation network.

[0073]

[0074] 4) Train load factor constraint: To ensure the passenger service level, it is necessary to control the carriage congestion and train service level. For any section on the rail transit line, the train load factor must not exceed the specified maximum value. The constraints are as follows;

[0075]

[0076] 5) Train stop time constraints: The train stop time should be within the minimum and maximum allowed stop times, and should be adjusted based on whether there is overtaking and the type of overtaking line configured at the station;

[0077]

[0078] 6) Train tracking interval constraints: To ensure safe and efficient train tracking, a minimum time interval must be set appropriately. When fast and slow trains run on the same track, the tracking modes vary, and the train tracking interval must be strictly constrained.

[0079]

[0080] 7) Train turnaround time constraint: The train turnaround time represents the complete operation time for the train to complete the turnaround operation through the turnaround line and switch from the upward direction to the downward direction. The train turnaround time should also comply with the upper and lower limit constraints.

[0081]

[0082] It can be seen from the above technical solutions that an embodiment of the present invention provides a method for optimizing the wiring of urban express lines in a metropolitan area based on the needs of operational organizations. By constructing a two-layer planning model, the combined operation strategy of large-route fast and slow trains and small-route slow trains is combined with the configuration optimization of passing lines and return lines, thereby realizing the coordinated optimization of construction and operation costs and passengers' generalized travel expenses. A hybrid particle swarm algorithm and an iterative approximation algorithm are jointly solved, and the collaborative feedback mechanism effectively improves the solution efficiency and the reliability of the global optimal solution. Through refined designs such as passing line position constraints, transfer behavior restrictions and full load rate control, the passenger travel experience is significantly improved while reducing the construction and operation costs of the enterprise. The improved hybrid particle swarm algorithm can still quickly converge to a stable solution under complex constraints, providing scientific decision-making support for the efficient operation of the urban express line. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The accompanying drawings are only provided for reference and illustration and are not intended to limit the present invention.

[0084] Figure 1 This is a schematic diagram of the steps of a method for optimizing wiring of a metropolitan area express line based on the needs of an operating organization according to the present invention;

[0085] Figure 2 This is a schematic diagram of a two-way urban rail transit line of the present invention;

[0086] Figure 3 This is a schematic diagram of passenger travel selection according to the present invention;

[0087] Figure 4 Schematic diagram of the double-layer planning model of the present invention;

[0088] Figure 5 This is a flow chart of the hybrid particle swarm-iterative approximation collaborative optimization algorithm of the present invention;

[0089] Figure 6 This is a schematic diagram of the optimal operation plan for Nanjing Metro Line S1 during the morning rush hour according to the present invention;

[0090] Figure 7 This is the optimized wiring diagram of Nanjing Metro Line S1 of the present invention;

[0091] Figure 8 This is the optimal driving diagram for the morning rush hour of Nanjing Metro Line S1 according to the present invention; DETAILED DESCRIPTION

[0092] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below with reference to specific illustrations.

[0093] The present invention considers a method for optimizing the wiring of metropolitan express lines based on the needs of the operating organization, and constructs a two-layer planning model for the wiring optimization of the metropolitan express lines. Figure 1 As shown, the following steps are included:

[0094] S1. Based on the combination of large and small routes and fast and slow trains, the number of route layers is limited to two, and a combined operation strategy of fast and slow trains on large routes and slow trains on small routes is proposed. At the same time, the configuration scheme of passing lanes and return lines is optimized;

[0095] S2. Measure the impact of wiring design and operation plans on passengers based on construction and operation costs and passengers' generalized travel expenses.

[0096] S3. Establish a two-level planning model based on S1 and S2: Using the location and form of the crossing line and the return line as decision variables, construct a two-level planning architecture consisting of an upper-level model and a lower-level model, where the upper-level model takes the lowest construction and operation cost as the objective function, and the lower-level model takes the lowest generalized travel cost of passengers as the objective function.

[0097] S4. A hybrid particle swarm optimization algorithm is used to optimize line wiring configuration, and an iterative approximation algorithm is combined with CPLEX to solve periodic event planning, and the optimal solution is obtained through a collaborative feedback mechanism.

[0098] like Figure 2-Figure 3 As shown,

[0099] S1 specifically includes the following steps:

[0100] like Figure 2 As shown, S101 clearly states that the research object is a two-way line of urban rail transit, and the station is divided into up and down platforms. The train departs from the starting station 1, passes through various stations to reach the turnaround station n, and then continues to run through the down platform;

[0101] like Figure 3 As shown, S102 clearly stipulates that express trains on large routes must stop at the turning station of small routes, and express trains must overtake at the slow train station via the overtaking line. Overtaking stations in the same direction are only allowed to be overtaken once; S103 clearly restricts passenger transfer behavior, and the change from fast to slow trains must be completed at the last express station, and the change from slow trains to fast trains must be completed at the first express station. Transfers between slow trains on large and small routes are prohibited.

[0102] like Figure 4 As shown, S3 specifically includes the following steps:

[0103] S301. Based on the operational characteristics of urban rail express lines and existing research, make assumptions;

[0104] S302, using the position and form of the crossing line and the return line as decision variables, specifically includes the following steps:

[0105] (1) Train stop selection constraints: The first and last station stop constraints require that the express train pass through at least one intermediate station without stopping. The same train stops in the same way at the up and down platforms of the station to ensure that two types of trains are operated periodically.

[0106] (2) Turnaround station location constraint: The turnaround station must be located between the starting and ending stations of the line;

[0107] (3) Overtaking line setting constraints: When configuring overtaking lines, the key issue is to select stations suitable for setting up overtaking lines. In order to ensure the efficient operation of the line, overtaking lines should not be configured at the first and last stations. At the same time, the configuration of overtaking lines on the up and down platforms of the same station should be consistent to ensure the smooth operation of trains;

[0108] (4) Train load factor constraint: To ensure the passenger service level, it is necessary to control the carriage congestion and train service level. For any section on the rail transit line, the train load factor must not exceed the specified maximum value. The constraints are as follows;

[0109] (5) Train stop time constraints: The train stop time should be within the minimum and maximum allowed stop times, and should be adjusted according to whether there is overtaking and the type of overtaking line configured at the station;

[0110] (6) Train tracking interval constraints: To ensure safe and efficient train tracking operation, the shortest time interval must be reasonably set. When fast and slow trains run on the same track, the tracking modes are diverse, and the train tracking interval must be strictly constrained:

[0111] (7) Train turnaround time constraint: The train turnaround time refers to the complete operation time for the train to complete the turnaround operation through the turnaround line and switch from the upward direction to the downward direction. The train turnaround time should also comply with the upper and lower limit constraints.

[0112] S303. During the wiring scheme optimization phase, the primary goal of the upper-level model is to reduce initial construction investment and long-term operating costs by rationally configuring passing and return lines. The lower-level model, based on wiring conditions, optimizes the passenger travel experience, reducing time costs and transfer burdens. The goal is to collaboratively optimize construction and operational efficiency and passenger service, as follows:

[0113] MinZ1=C OC

[0114] MinZ2=C ij,p

[0115] like Figure 5 As shown, S4 specifically includes the following steps:

[0116] S401. A hybrid particle swarm algorithm is used to solve the upper-level model and encode the crossing and return line configurations. Particle positions are determined by the crossing line configuration, the ratio of fast and slow trains, and the stop plan. The crossing line configuration is encoded using two bits, and the fast and slow train stop plan is encoded using 0 / 1. The fitness function integrates the objective function and penalty function constraints, and updates discrete variables using a sigmoid function to convert the speed.

[0117] S402: Initialize the particle swarm and randomly generate particle positions and velocities within the feasible solution range. Calculate the initial fitness value and perform constraint corrections based on the underlying model. The underlying model uses an iterative approximation algorithm and the CPLEX solver to ultimately form the initial optimized population.

[0118] S403: Perform an iterative optimization process: Update particle velocities and positions, introduce inertia weights and learning factors to optimize search performance, calculate fitness values, and dynamically adjust the individual optimal solution pbest and the global optimal solution gbest. The lower-level model gradually adjusts the upper and lower bounds through periodic time interval search, solves using CPLEX, and feeds the optimal solution back to the upper-level model for collaborative optimization.

[0119] S404. When the maximum number of iterations is reached or the solution becomes stable and the fitness change is lower than the set threshold, the algorithm is terminated and the global optimal solution is output as the final wiring plan and train operation plan.

[0120] The model is verified using Nanjing Metro Line S1:

[0121] Nanjing Metro Line S1 is 37.3 kilometers long and has eight stations. It utilizes six-car B-type drum trains, each with a capacity of 240 passengers, for a total of 1,440 passengers. The train operates at a maximum speed of 100 km / h. After adjustments, an optimal route plan with a large and small alternation of express and slow trains was developed. The specific plan is shown in Table 1. With a 1:2 ratio, express and slow trains alternate, achieving a throughput of 12 pairs per hour.

[0122] Table 1 Morning peak traffic schedule

[0123]

[0124] In the optimized design, the starting station of the short-distance route is Lukou Airport Station, and the turning station is Focheng West Road Station. The express trains to major stations only stop at Nanjing South Station, Xiangyu Road South Station, Focheng West Road Station and Lukou Airport Station. The most superior route is set at Zhengfang Middle Road Station, equipped with a double-island three-line passing line, and trains pass in both directions; Focheng West Road Station is equipped with a horizontal through-type turning line, and Lukou Airport Station and Nanjing South Station are equipped with vertical double turning lines. The schematic diagram of the optimal operation plan for Nanjing Metro Line S1 during the morning rush hour, the optimized wiring diagram for Nanjing Metro Line S1 and the optimal driving operation diagram for Line S1 during the morning rush hour are shown as follows: Figure 6 、 Figure 7 and Figure 8 shown.

[0125] At the same time, a comprehensive comparison and analysis was conducted between the optimal wiring scheme and the original scheme, the pure large and small intersection scheme, and the pure fast and slow train scheme. The specific comparison results are shown in Table 2.

[0126] Table 2 Comparison of different operating organization forms

[0127]

[0128] Comparing various indicators, the construction and operating costs of the combined express and slow train operation scheme for large and small routes were 9.7945 million yuan, with an optimization effect of 16.50%; the passenger travel cost in a broad sense was 13.3171 million yuan, with an optimization effect of 17.35%; and the total system cost was 23.1116 million yuan, with an optimization effect of 16.99%. The optimized scheme effectively meets the passenger flow demand of the urban express line, significantly reduces passenger travel expenses and enterprise operating costs, and improves transportation capacity, with significant optimization results.

Claims

1. A method for optimizing the wiring of metropolitan area express lines based on the needs of operating organizations, characterized by A two-layer planning model is constructed for the optimization of the urban express line distribution, including the following steps: S1. Based on the combination of large and small routes and fast and slow trains, the number of route layers is limited to two, and a combined operation strategy of fast and slow trains on large routes and slow trains on small routes is proposed. At the same time, the configuration scheme of passing lanes and return lines is optimized; S2. Measure the impact of wiring design and operation plans on passengers based on construction and operation costs and passengers' generalized travel expenses. S3. Establish a two-level planning model based on S1 and S2: Using the location and form of the crossing line and the return line as decision variables, construct a two-level planning architecture consisting of an upper-level model and a lower-level model, where the upper-level model takes the lowest construction and operation cost as the objective function, and the lower-level model takes the lowest generalized travel cost of passengers as the objective function. S4. A hybrid particle swarm optimization algorithm is used to optimize line wiring configuration, and an iterative approximation algorithm is combined with CPLEX to solve periodic event planning, and the optimal solution is obtained through a collaborative feedback mechanism.

2. The method for optimizing the wiring of metropolitan area express lines based on the needs of the operating organization according to claim 1 is characterized in that: S1 specifically includes the following steps: S101. The research object is a two-way urban rail transit line. The station is divided into up and down platforms. The train departs from the starting station 1, passes through various stations, arrives at the turnaround station n, and then continues to run through the down platform. S102: Clearly stipulate that express trains on major routes must stop at turnaround stations on minor routes, and that express trains can overtake other trains at local stations using the passing lane. Express trains are only allowed to be overtaken once at the same-direction passing station. S103. Clearly restrict passenger transfers. Changing from fast to slow trains must be done at the last express station, and changing from slow trains to fast trains must be done at the first express station. Transfers between slow trains on large and small routes are prohibited.

3. The method for optimizing the wiring of metropolitan area express lines based on the needs of the operating organization according to claim 1, characterized in that: S2 specifically includes the following steps: S201. From the perspective of the urban express line operator, the operating and construction costs mainly include initial construction costs and vehicle base costs. The details are as follows: C OC =C IC +C V S202. Passenger travel expenses generally include fare, interval operation costs, and transfer costs, as follows: C ij，p =C ij,p,F +C ij，p，S +C ij，p，T 。 4. The method for optimizing the wiring of metropolitan area express lines based on the needs of operating organizations according to claim 1, characterized in that: S3 specifically includes the following steps: S301. Based on the operational characteristics of urban rail express lines and existing research, make assumptions; S302, taking the position and form of the crossing line and the return line as decision variables; S303. During the wiring scheme optimization phase, the primary goal of the upper-level model is to reduce initial construction investment and long-term operating costs by rationally configuring passing and return lines. The lower-level model, based on wiring conditions, optimizes the passenger travel experience, reducing time costs and transfer burdens. The goal is to collaboratively optimize construction and operational efficiency and passenger service, as follows: MinZ1=C OC MinZ2=C ij,p 。 5. The method for optimizing the wiring of metropolitan area express lines based on the needs of the operating organization according to claim 1, characterized in that: S4 specifically includes the following steps: S401. A hybrid particle swarm algorithm is used to solve the upper-level model and encode the crossing and return line configurations. Particle positions are determined by the crossing line configuration, the ratio of fast and slow trains, and the stop plan. The crossing line configuration is encoded using two bits, and the fast and slow train stop plan is encoded using 0 / 1. The fitness function integrates the objective function and penalty function constraints, and updates discrete variables using a sigmoid function to convert the speed. S402: Initialize the particle swarm and randomly generate particle positions and velocities within the feasible solution range. Calculate the initial fitness value and perform constraint corrections based on the underlying model. The underlying model uses an iterative approximation algorithm and the CPLEX solver to ultimately form the initial optimized population. S403: Perform an iterative optimization process: Update particle velocities and positions, introduce inertia weights and learning factors to optimize search performance, calculate fitness values, and dynamically adjust the individual optimal solution pbest and the global optimal solution gbest. The lower-level model gradually adjusts the upper and lower bounds through periodic time interval search, solves using CPLEX, and feeds the optimal solution back to the upper-level model for collaborative optimization. S404. When the maximum number of iterations is reached or the solution becomes stable and the fitness change is lower than the set threshold, the algorithm is terminated and the global optimal solution is output as the final wiring plan and train operation plan.

6. The method for optimizing the wiring of metropolitan area express lines based on the needs of the operating organization according to claim 4 is characterized in that: S301 specifically includes the following steps: (1) The research object is the urban rail transit line; (2) Line characteristics: a combination of large and small routes, with intermediate stations equipped for turning back; (3) The long route does not affect the turnaround time of the short route. The turnaround time of each station is unified. Express trains must stop at the turnaround station of the short route. (4) When fast and slow trains pass through a station, the passing line is set at the slow train station to reduce the loss of line capacity; (5) In order to reduce the waiting time for slow trains, only one overtaking is allowed at the same-direction overtaking station; (6) The number of transfers per passenger shall not exceed one. When changing from an express train to a slow train, the transfer shall be made at the last express station; when changing from a slow train to an express train, the transfer shall be made at the first express station. Transfers between slow trains on large and small routes shall not be considered. (7) There are no passengers who board the vehicle in the opposite direction or are stranded.

7. The method for optimizing the wiring of metropolitan area express lines based on the needs of the operating organization according to claim 5, characterized in that: S404 specifically includes the following steps: (1) When the number of iterations exceeds the preset upper limit, or the fitness fluctuation amplitude of multiple generations of solutions is less than the threshold and the distribution is stable, the search process is stopped; (2) Select the global optimal solution with the highest fitness, and finally output the optimal wiring plan and train operation plan to ensure that the operation constraints and efficiency goals are met.

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