A low-carbon multimodal transport network design method considering shipper route selection

Abstract

The application provides a low-carbon multimodal transport network design method considering consignor path selection, comprising: obtaining freight demand data, multimodal transport network topology, parameters of each transport mode, network node transfer time and cost; obtaining consignor preference data covering all origin-destination points in the planning period, brand effect, utility coefficient of transport time and transport cost, low-carbon subsidy data; based on the obtained data, constructing freight flow batch transport constraints, low-carbon subsidy constraints, consignor path selection constraints and multimodal transport network constraints respectively; establishing an objective function for the purpose of pursuing lower carbon emission cost on the premise of not paying a large amount of low-carbon subsidies; under the constraints, using a particle swarm optimization algorithm embedded with CPLEX to solve the objective function to obtain a low-carbon multimodal transport network design scheme. The method can accurately depict the consignor path selection behavior and realize the dual improvement of transport efficiency and environmental benefit.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of multimodal transport of goods, and particularly relates to a low-carbon multimodal transport network design method considering path selection of consignors. BACKGROUND

[0002] As the core carrier of the low-carbon freight transport system, the multimodal transport network has covered the main freight transport hubs in China, and the annual freight transport scale accounts for an important proportion of the comprehensive transport system. The transport efficiency depends on the intelligent decision of the consignor path selection behavior, the transport mode collaborative optimization and the low-carbon subsidy strategy. The consignor path selection needs to comprehensively consider the heterogeneous demands such as transport time, cost and brand preference; the transport mode collaborative optimization involves the matching of transport capacity and the connection of paths of various transport modes such as highway, railway and waterway; and the low-carbon subsidy strategy needs to dynamically balance the financial expenditure and the carbon emission cost control. The existing design methods mostly use static optimization models, cannot effectively integrate the dynamic subsidy mechanism, and lack the precise depiction of the behavior preferences of consignors and the differences between transport batches, resulting in a significant gap between the actual utilization rate of low-carbon transport modes and the expected target, and the highway transport occupies a dominant position in the total carbon emissions, which seriously restricts the collaborative improvement of the multimodal transport network in service quality and emission reduction target.

[0003] Among the existing multimodal transport related invention patents, a multimodal transport composite rail transport system (CN202310123456.7) and an unmanned multimodal transport vehicle and transport system (CN201910871877.0) provide a multimodal transport infrastructure construction method; a route network planning management system suitable for road engineering design (CN202310345678.9) and a continuity traffic network design method based on SUE (CN201810263236.2) provide path planning and network optimization methods; a train working diagram and path selection optimization method based on a multi-granularity space-time network (CN202111312514.7) and a freight network task deployment method and system (CN202311308405.7) provide transport organization and resource deployment methods, but an intelligent multimodal transport collaborative system integrating path planning, operation optimization and low-carbon target has not yet been formed. The existing technologies independently optimize specific links, lack global collaboration of the whole chain of multimodal transport, and result in limited transport efficiency improvement and poor carbon emission control effect.

[0004] Current multimodal transport network designs suffer from the following technical deficiencies: First, existing route selection models fail to fully consider shippers' heterogeneous preferences for transport time and freight costs, resulting in insufficient utilization of low-carbon transport modes. Second, at the implementation level of low-carbon policies, traditional subsidy strategies (such as fixed-amount subsidies or volume-based subsidies) have significant limitations: on the one hand, fixed subsidies are difficult to adapt to cost differences over different transport distances; on the other hand, volume-based subsidies easily induce irrational transport behaviors such as "group buying." Traditional low-carbon subsidy strategies lack a dynamic adaptation mechanism with transport distance and cargo flow direction, making it difficult to effectively guide medium- and long-distance freight transport towards low-carbon modes. Third, existing network design methods do not integrate the characteristics of batch transport of cargo flow with multimodal transport collaborative optimization, leading to deviations in transport time calculations and imbalances in resource allocation. These problems severely restrict the improvement of the low-carbon efficiency and operational efficiency of multimodal transport systems. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide a low-carbon multimodal transport network design method that takes into account the shipper's route selection, which can accurately characterize the shipper's route selection behavior and achieve a dual improvement in transport efficiency and environmental benefits.

[0006] This application provides a method for designing a low-carbon multimodal transport network that takes into account shippers' route choices, including:

[0007] S1. Obtain freight demand data, multimodal transport network topology, parameters of each mode of transport, and transit time and cost of network nodes; wherein, the modes of transport include road, rail, and waterway;

[0008] S2. Obtain data on shippers' preferences, brand effect, utility coefficients of transportation time and transportation costs, and low-carbon subsidy data for all origin and destination points within the planning period;

[0009] S3. Based on the data obtained from S1 and S2, construct constraints for batch transportation of goods, low-carbon subsidies, shipper route selection, and multimodal transport network, respectively.

[0010] S4. Establish an objective function with the aim of achieving lower carbon emission costs without paying large amounts of low-carbon subsidies.

[0011] S5. Under the constraints of S3, the objective function of S4 is solved using a particle swarm optimization algorithm with embedded CPLEX to obtain a low-carbon multimodal transport network design scheme.

[0012] Furthermore, the freight demand data includes: freight turnover between origin and destination, freight type, and transportation requirements;

[0013] The multimodal transport network topology is used to characterize the connectivity between network nodes and transport segments; wherein, the network nodes include: hub stations and ports;

[0014] The parameters for each mode of transportation include: transportation route distance, time, cost, carbon emission factor, and single-trip transportation capacity.

[0015] Furthermore, S3 specifically includes:

[0016] S31. To balance the differences in single-trip transport capacity among road, rail, and waterway transportation modes, the following constraints are established for batch-based freight transport:

[0017] ;

[0018] ;

[0019] ;

[0020] In the formula, Indicates goods Total demand Indicates different modes of transportation In the transportation section The shipping batches, This indicates rounding up, used to ensure that all goods can be transported. Indicates by each mode of transport single transport capacity, This indicates the cargo volume of the last transport, which may not be fully loaded. For the collection of transportation modes, For the aggregation of freight demand, A collection of network nodes, transportation segments .

[0021] Furthermore, S3 specifically includes:

[0022] S32. To encourage shippers to choose low-carbon rail transport methods, a strategy combining fixed subsidies and mileage-variable subsidies is adopted, and the low-carbon subsidy constraints are constructed as follows:

[0023] ;

[0024] ;

[0025] In the formula, This refers to a fixed low-carbon subsidy for railway transportation. This represents the subsidy for changes in unit distance in railway transportation. Indicates railway transportation on the transportation section driving distance, It refers to railway transportation.

[0026] Furthermore, S3 specifically includes:

[0027] S33. Based on the Logit model, and taking into account both the transportation time preference in step S31 and the transportation cost preference in S32, the shipper's route selection constraints are constructed as follows:

[0028] ;

[0029] ;

[0030] In the formula, This represents the shipper's fixed utility in a multimodal transport route. Indicates goods on a multimodal transport route The shipper's utility coefficient for transportation costs. Indicates goods on a multimodal transport route The shipper's utility coefficient for transit time. and Representing goods The transportation costs and transit time of multimodal transport. This represents the shipper's stochastic utility regarding transit time and transit costs. This indicates the proportion of multimodal transport route selection under the shipper's non-inertial preference, taking into account the impact of transportation costs and transit time. This represents the set of all possible paths in the market.

[0031] Furthermore, S3 specifically includes:

[0032] S34. To ensure the conservation of cargo flow in the transportation network and avoid resource idleness or congestion, multimodal transport network constraints are established, specifically including:

[0033] S341, Settings , Representative goods In the transportation section Choose transportation method Otherwise, it is 0;

[0034] and settings , Representative goods At network nodes From the mode of transportation Transform into Otherwise, it is 0;

[0035] Among them, set These represent the modes of transportation: road, rail, and waterway, respectively.

[0036] S342. By constraining the inflow of goods to equal the outflow at all network nodes except the origin and destination points, the following network node inflow-outflow balance constraints are constructed:

[0037] ;

[0038] S343. By restricting that any batch of goods can choose at most one mode of transport on each path, and that any batch of goods can switch to at most one mode of transport at any network node, the uniqueness constraint of transport mode is constructed as follows:

[0039] ;

[0040] ;

[0041] S344. Construct the following path continuity constraints:

[0042] ;

[0043] .

[0044] Furthermore, S4 specifically includes:

[0045] S41. In order to promote multimodal transport and reduce carbon emission costs by leveraging low-carbon subsidies for railway transportation, the objective function is constructed as follows:

[0046] ;

[0047] In the formula, Indicates goods Low-carbon subsidies for rail transport; The unit cost representing carbon emissions Indicates goods Carbon emissions from multimodal transport. Indicates goods Carbon emissions in the market pathway.

[0048] Furthermore, S5 specifically includes:

[0049] S51, Fixed low-carbon subsidies Unit distance change subsidy The proportion of routes selected for each batch of goods The positions of the particles are assigned to construct a particle swarm.

[0050] S52. Determine whether the iteration condition has been met;

[0051] If so, execute S53 and output the current highest fitness and optimal multimodal transport network design scheme;

[0052] If not, then execute S54, decode the particle position, and calculate the low-carbon subsidy. and multimodal transport volume ;

[0053] S55. Calculate the particle swarm fitness and the multimodal transport network design scheme corresponding to the current particle position through the embedded CPLEX.

[0054] S56. Determine whether the highest fitness has been achieved;

[0055] If so, execute S57, update the highest fitness, update the particle velocity within the limits, and then execute S58;

[0056] If not, execute S58, update the particle position, increment the iteration count by 1, and return to S52.

[0057] Furthermore, S55 specifically includes:

[0058] S551. Obtain the route selection ratio for each batch of goods. Low-carbon subsidies and multimodal transport volume The Logit model was used to calculate the transportation time and carbon emission cost of each mode of transport on each transport segment.

[0059] S552. Transform the constraints in S3, except for the multimodal transport network constraints, into a linear model, and use the embedded CPLEX to determine whether the constraints are met, and calculate the objective function.

[0060] S553. Recalculate the route selection ratio for each batch of goods using the Logit model. The deviation value is used as the particle swarm fitness, and the output is determined by a fixed low-carbon subsidy. Unit distance change subsidy The jointly determined subsidy plan, and the method of transportation With transportation routes The jointly determined multimodal transport scheme is used to obtain a multimodal transport network design scheme.

[0061] The low-carbon multimodal transport network design method provided in this application, which considers shippers' route choices, optimizes the multimodal transport network design by integrating shippers' route preferences and low-carbon goals. This significantly reduces carbon emissions during transportation while improving logistics efficiency. The method comprehensively considers the connection and cost of different modes of transport, providing shippers with diversified and low-carbon route options to meet their dual needs for economic efficiency and environmental protection. Attached Figure Description

[0062] Figure 1 A flowchart is shown below illustrating the low-carbon multimodal transport network design method that takes into account shippers' route choices, as provided in an embodiment of this application.

[0063] Figure 2 This application provides a flowchart illustrating the steps involved in obtaining a low-carbon multimodal transport network design scheme according to an embodiment of the present application.

[0064] Figure 3 This paper presents a flowchart illustrating the steps of solving a multimodal transport network design scheme based on embedded CPLEX, as provided in an embodiment of this application.

[0065] Figure 4 This illustration shows a diagram illustrating the shipper route selection and proportion provided in an embodiment of this application. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this technical solution clearer, the following detailed description, in conjunction with specific embodiments, further illustrates this technical solution. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of this technical solution.

[0067] Example 1

[0068] Please see as follows Figure 1 The flowchart shown illustrates a low-carbon multimodal transport network design method that considers shipper route selection. Figure 1 As shown, the method includes:

[0069] S1. Obtain freight demand data, multimodal transport network topology, parameters of each mode of transport, and transit time and cost at network nodes. The modes of transport include road, rail, and waterway.

[0070] Specifically, the freight demand data includes: freight turnover between origin and destination, freight type and transportation requirements; the multimodal transport network topology is used to characterize the connectivity of each network node and transport segment; wherein, the network nodes include: hub stations and ports; the parameters of each transport mode include: transport segment distance, time, cost, carbon emission factor and single transport capacity.

[0071] S2. Obtain data on shippers' preferences, brand effect, utility coefficients of transportation time and transportation costs, and low-carbon subsidy data for all origin and destination points within the planning period.

[0072] In this step, the utility coefficients of brand effect, transportation time and transportation cost can be obtained through questionnaires or historical order queries. Low carbon subsidy data refers to railway fixed subsidies, mileage subsidy rates and carbon emission limits.

[0073] S3. Based on the data obtained from S1 and S2, construct constraints for batch transportation of goods, low-carbon subsidies, shipper route selection, and multimodal transport network, respectively.

[0074] In this step, the constraints of batch transportation of goods are constructed to ensure capacity matching, the constraints of low-carbon subsidies are constructed to quantify environmental costs, the constraints of shippers' route selection are constructed to reflect shippers' preferences, and the constraints of multimodal transport network are constructed to ensure transportation continuity. The decision variables in these steps can limit the feasible solution space.

[0075] S3 specifically includes:

[0076] S31. To balance the differences in single-trip transport capacity among road, rail, and waterway transportation modes, the following constraints are established for batch-based freight transport:

[0077] ;

[0078] ;

[0079] ;

[0080] In the formula, Indicates goods Total demand Indicates different modes of transportation In the transportation section The shipping batches, This indicates rounding up, used to ensure that all goods can be transported. Indicates by each mode of transport single transport capacity, This indicates the cargo volume of the last transport, which may not be fully loaded. For the collection of transportation modes, For the aggregation of freight demand, A collection of network nodes, transportation segments .

[0081] Specifically, S3 includes:

[0082] S32. To encourage shippers to choose low-carbon rail transport methods, a strategy combining fixed subsidies and mileage-variable subsidies is adopted, and the low-carbon subsidy constraints are constructed as follows:

[0083] ;

[0084] ;

[0085] In the formula, This refers to a fixed low-carbon subsidy for railway transportation. This represents the subsidy for changes in unit distance in railway transportation. Indicates railway transportation on the transportation section driving distance, It refers to railway transportation.

[0086] Specifically, S3 includes:

[0087] S33. Based on the Logit model, and taking into account both the transportation time preference in step S31 and the transportation cost preference in S32, the shipper's route selection constraints are constructed as follows:

[0088] ;

[0089] ;

[0090] In the formula, This represents the shipper's fixed utility in a multimodal transport route. Indicates goods on a multimodal transport route The shipper's utility coefficient for transportation costs. Indicates goods on a multimodal transport route The shipper's utility coefficient for transit time. and Representing goods The transportation costs and transit time of multimodal transport. This represents the shipper's stochastic utility regarding transit time and transit costs. This indicates the proportion of multimodal transport route selection under the shipper's non-inertial preference, taking into account the impact of transportation costs and transit time. This represents the set of all possible paths in the market.

[0091] Specifically, S3 includes:

[0092] S34. To ensure the conservation of cargo flow in the transportation network and avoid resource idleness or congestion, multimodal transport network constraints are established, specifically including:

[0093] S341, Settings , Representative goods In the transportation section Choose transportation method Otherwise, it is 0;

[0094] and settings , Representative goods At network nodes From the mode of transportation Transform into Otherwise, it is 0;

[0095] Among them, set These represent the modes of transportation: road, rail, and waterway, respectively.

[0096] S342. By constraining the inflow of goods to equal the outflow at all network nodes except the origin and destination points, the following network node inflow-outflow balance constraints are constructed:

[0097] ;

[0098] S343. By restricting that any batch of goods can choose at most one mode of transport on each path, and that any batch of goods can switch to at most one mode of transport at any network node, the uniqueness constraint of transport mode is constructed as follows:

[0099] ;

[0100] ;

[0101] S344. Construct the following path continuity constraints:

[0102] ;

[0103] .

[0104] S4. Establish an objective function with the aim of achieving lower carbon emission costs without paying large amounts of low-carbon subsidies.

[0105] S4 specifically includes:

[0106] S41. In order to promote multimodal transport and reduce carbon emission costs by leveraging low-carbon subsidies for railway transportation, the objective function is constructed as follows: ;

[0107] In the formula, Indicates goods Low-carbon subsidies for rail transport; The unit cost representing carbon emissions Indicates goods Carbon emissions from multimodal transport. Indicates goods Carbon emissions in the market pathway.

[0108] S5. Under the constraints of S3, the objective function of S4 is solved using a particle swarm optimization algorithm with embedded CPLEX to obtain a low-carbon multimodal transport network design scheme.

[0109] In this step, due to the decision variables of this application (such as fixed low-carbon subsidies) Unit distance change subsidy and the proportion of routes selected for each batch of goods This includes continuous decision variables and 0 / 1 decision variables (such as...). and Therefore, the objective function belongs to a mixed-integer programming model. Since the Logit model is a complex nonlinear function, it can be specifically classified as a mixed-integer nonlinear programming model. Such problems are commonly solved using particle swarm optimization (PSO) algorithms. To address this, this application designs a PSO algorithm framework embedding CPLEX.

[0110] This application embeds CPLEX into a particle swarm optimization algorithm, which is used to determine the fixed low-carbon subsidy in carpet subsidies. Unit distance change subsidy It also overcomes the nonlinear solution challenge of nested Logit models; while CPLEX is used to determine the mode of transport in multimodal transport schemes. With transportation routes It is used to evaluate the particle fitness of the particle swarm optimization algorithm.

[0111] Please refer to the flowchart of the steps for obtaining the low-carbon multimodal transport network design scheme shown in Figure 2. As shown in Figure 2, step S5 specifically includes:

[0112] S51, Fixed low-carbon subsidies Unit distance change subsidy The proportion of routes selected for each batch of goods The positions of the particles are assigned to construct a particle swarm.

[0113] S52. Determine whether the iteration conditions have been met.

[0114] If so, execute S53 and output the current highest fitness and optimal multimodal transport network design scheme.

[0115] If not, then execute S54, decode the particle position, and calculate the low-carbon subsidy. and multimodal transport volume ;

[0116] S55. Calculate the particle swarm fitness and the multimodal transport network design scheme corresponding to the current particle position through the embedded CPLEX.

[0117] S56. Determine whether the highest fitness has been achieved;

[0118] If so, execute S57, update the highest fitness, update the particle velocity within the limits, and then execute S58;

[0119] If not, execute S58, update the particle position, increment the iteration count by 1, and return to S52.

[0120] Please refer to Figure 3 for the flowchart of solving multimodal transport network design schemes based on embedded CPLEX. As shown in Figure 3, step S55 specifically includes:

[0121] S551. Obtain the route selection ratio for each batch of goods. Low-carbon subsidies and multimodal transport volume The Logit model was used to calculate the transportation time and carbon emission costs for each mode of transport on each transport route.

[0122] S552. Transform the constraints in S3, except for the multimodal transport network constraints, into a linear model, and use the embedded CPLEX to determine whether the constraints are met, and calculate the objective function.

[0123] S553. Recalculate the route selection ratio for each batch of goods using the Logit model. The deviation value is used as the particle swarm fitness, and the output is determined by a fixed low-carbon subsidy. Unit distance change subsidy The jointly determined subsidy plan, and the method of transportation With transportation routes The jointly determined multimodal transport scheme is used to obtain a multimodal transport network design scheme.

[0124] Example 2:

[0125] The multimodal transport network design described in this application is based on the coordinated optimization of multiple modes of transport such as road, rail, and waterway to construct the optimal freight route from origin to destination.

[0126] As an example, taking freight transportation from Shenyang to Taiyuan, the design process requires constructing a transportation network with 16 hub nodes and configuring three optional modes of transportation—road, rail, and waterway—for each transportation segment. Taking into account factors such as transportation time, cost, and carbon emissions, and combining the shippers' personalized needs for timeliness and cost, a complete low-carbon multimodal transport network design scheme is output. For example: first, transport by rail from Shenyang to Yingkou (enjoying low-carbon subsidies), then by waterway to Tianjin, and finally by road to Taiyuan.

[0127] The shipper route selection described in this application refers to the decision-making process by which a shipper selects a transportation route in a multimodal transport network based on heterogeneous needs such as transportation time and transportation costs.

[0128] As an example, please refer toFigure 4 The diagram shows the shipper's route selection and proportions. (See attached image.) Figure 4 As shown, the shipper's route selection can be decomposed into two levels: the upper level is carrier selection (B1, B2), and the lower level is specific transportation route selection (P1-P5). The selection ratio of each transportation route is determined by the shipper's utility preferences for freight, time, etc., with railways being more attractive due to subsidy strategies.

[0129] The above content is only a preferred embodiment of the present invention. For those skilled in the art, many changes can be made in the specific implementation and application scope based on the ideas of the present invention. As long as these changes do not depart from the concept of the present invention, they all fall within the protection scope of the present invention.

Claims

1. A method for designing a low-carbon multimodal transport network that considers shippers' route choices, characterized in that, The method includes: S1. Obtain freight demand data, multimodal transport network topology, parameters of each mode of transport, and transit time and cost of network nodes; wherein, the modes of transport include road, rail, and waterway; S2. Obtain data on shippers' preferences, brand effect, utility coefficients of transportation time and transportation costs, and low-carbon subsidy data for all origin and destination points within the planning period; S3. Based on the data obtained from S1 and S2, construct constraints for batch transportation of goods, low-carbon subsidies, shipper route selection, and multimodal transport network, respectively. S4. Establish an objective function with the aim of achieving lower carbon emission costs without paying large amounts of low-carbon subsidies. S5. Under the constraints of S3, the objective function of S4 is solved using a particle swarm optimization algorithm with embedded CPLEX to obtain a low-carbon multimodal transport network design scheme. S3 specifically includes: S31. To balance the differences in single-trip transport capacity among road, rail, and waterway transportation modes, the following constraints are established for batch-based freight transport: ； ； ； In the formula, Indicates goods Total demand , Indicates different modes of transportation In the transportation section The shipping batches, , , This indicates rounding up, used to ensure that all goods can be transported. Indicates by each mode of transport single transport capacity, This indicates the cargo volume of the last transport, which may not be fully loaded. For the collection of transportation modes, For the aggregation of freight demand, A set of network nodes; S32. To encourage shippers to choose low-carbon rail transport methods, a strategy combining fixed subsidies and mileage-variable subsidies is adopted, and the low-carbon subsidy constraints are constructed as follows: ； ； In the formula, This refers to a fixed low-carbon subsidy for railway transportation. This represents the subsidy for changes in unit distance in railway transportation. Indicates railway transportation on the transportation section driving distance, Indicates railway transportation; S33. Based on the Logit model, and taking into account both the transportation time preference in step S31 and the transportation cost preference in S32, the shipper's route selection constraints are constructed as follows: ； ； In the formula, This represents the shipper's fixed utility in a multimodal transport route. Indicates goods on a multimodal transport route The shipper's utility coefficient for transportation costs. Indicates goods on a multimodal transport route The shipper's utility coefficient for transit time. and Representing goods The transportation costs and transit time of multimodal transport. This represents the shipper's stochastic utility regarding transit time and transit costs. This indicates the proportion of multimodal transport route selection under the shipper's non-inertial preference, taking into account the impact of transportation costs and transit time. The set of all possible paths in the market; S34. To ensure the conservation of cargo flow in the transportation network and avoid resource idleness or congestion, multimodal transport network constraints are established, specifically including: S341, Settings , Representative goods In the transportation section Choose transportation method Otherwise, it is 0; and settings , Representative goods At network nodes From the mode of transportation Transform into Otherwise, it is 0; Among them, set These represent the modes of transportation: road, rail, and waterway, respectively. S342. By constraining the inflow of goods to equal the outflow at all network nodes except the origin and destination points, the following network node inflow-outflow balance constraints are constructed: ； S343. By restricting that any batch of goods can choose at most one mode of transport on each path, and that any batch of goods can switch to at most one mode of transport at any network node, the uniqueness constraint of transport mode is constructed as follows: ； ； S344. Construct the following path continuity constraints: ； ； S4 specifically includes: S41. In order to promote multimodal transport and reduce carbon emission costs by leveraging low-carbon subsidies for railway transportation, the objective function is constructed as follows: ； In the formula, Indicates goods Low-carbon subsidies for rail transport; The unit cost representing carbon emissions Indicates goods Carbon emissions from multimodal transport. Indicates goods Carbon emissions in the market pathway; S5 specifically includes: S51, fixing the low-carbon subsidy Unit distance change subsidy The proportion of routes selected for each batch of goods The positions of the particles are assigned to construct a particle swarm. S52. Determine whether the iteration condition has been met; If so, execute S53 and output the current highest fitness and optimal multimodal transport network design scheme; If not, then execute S54, decode the particle position, and calculate the low-carbon subsidy. and multimodal transport volume ; S55. Calculate the particle swarm fitness and the multimodal transport network design scheme corresponding to the current particle position through the embedded CPLEX. S56. Determine whether the highest fitness has been achieved; If so, execute S57, update the highest fitness, update the particle velocity within the limits, and then execute S58; If not, execute S58, update the particle position, increment the iteration count by 1, and return to S52.

2. The method as described in claim 1, characterized in that, The freight demand data includes: freight turnover between origin and destination, freight type, and transportation requirements; The multimodal transport network topology is used to characterize the connectivity between network nodes and transport segments; wherein, the network nodes include: hub stations and ports; The parameters for each mode of transportation include: transportation route distance, time, cost, carbon emission factor, and single-trip transportation capacity.

3. The method as described in claim 1, characterized in that, Specifically, S55 includes: S551. Obtain the route selection ratio for each batch of goods. Low-carbon subsidies and multimodal transport volume The Logit model was used to calculate the transportation time and carbon emission cost of each mode of transport on each transport segment. S552. Transform the constraints in S3, except for the multimodal transport network constraints, into a linear model, and use the embedded CPLEX to determine whether the constraints are met, and calculate the objective function. S553. Recalculate the route selection ratio for each batch of goods using the Logit model. The deviation value is used as the particle swarm fitness, and the output is determined by a fixed low-carbon subsidy. Unit distance change subsidy The jointly determined subsidy plan, and the method of transportation With transportation routes The jointly determined multimodal transport scheme is used to obtain a multimodal transport network design scheme.

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