Low-carbon multimodal transport network design method considering shipper path selection

Abstract

The invention provides a low-carbon multimodal transport network design method considering shipper path selection. The method comprises the following steps: acquiring freight demand data, a multimodal transport network topology structure, parameters of each transport mode, and network node transit time and cost; obtaining all origin-destination shipper preference data, brand effect, utility coefficient of transportation time and transportation cost and low-carbon subsidy data in the coverage planning period; based on the acquired data, respectively constructing a goods flow batch transportation constraint, a low-carbon subsidy constraint, a shipper path selection constraint and a multimodal transport network constraint; a target function is established with the purpose of pursuing low carbon emission cost on the premise of not paying a large amount of low-carbon subsidy; and under the constraint, a CPLEX-embedded particle swarm optimization algorithm is used to solve the objective function so as to obtain a low-carbon multimodal transport network design scheme. According to the method, the path selection behavior of the shipper can be accurately described, and dual improvement of transportation efficiency and environmental benefits is realized.

Description

Technical Field

[0001] This application belongs to the field of multimodal transport technology, and in particular relates to a low-carbon multimodal transport network design method that takes into account the shipper's route selection. Background Technology

[0002] As the core carrier of the low-carbon freight system, the multimodal transport network has covered major freight hubs nationwide, accounting for a significant proportion of the annual freight volume in the comprehensive transportation system. Its transport efficiency depends on intelligent decision-making regarding shippers' route selection behavior, coordinated optimization of transport modes, and low-carbon subsidy strategies. Shippers' route selection requires comprehensive consideration of heterogeneous demands such as transport time, cost, and brand preference; coordinated optimization of transport modes involves capacity matching and route connection among various modes such as road, rail, and waterway; and low-carbon subsidy strategies require a dynamic balance between fiscal expenditure and carbon emission cost control. Existing design methods mostly employ static optimization models, failing to effectively integrate dynamic subsidy mechanisms and lacking precise characterization of shippers' behavioral preferences and differences in transport batches. This results in a significant gap between the actual utilization rate of low-carbon transport modes and expected targets, with road transport dominating total carbon emissions, severely restricting the coordinated improvement of multimodal transport networks in service quality and emission reduction targets.

[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 methods for constructing multimodal transport infrastructure; a route network planning and management system suitable for road engineering design (CN202310345678.9) and a continuous traffic network design method based on SUE (CN201810263236.2) provide methods for route planning and network optimization; a train timetable and route selection optimization method based on multi-granularity spatiotemporal network (CN202111312514.7) and a freight network task allocation method and system (CN202311308405.7) provide methods for transport organization and resource allocation, but an intelligent multimodal transport collaborative system integrating route planning, operation optimization and low-carbon goals has not yet been formed. Existing technologies optimize specific links independently, lacking global coordination across the entire multimodal transport chain, resulting in limited improvements in transport efficiency and poor carbon emission control.

[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, leading to 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, resulting in 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] In the formula, D b This represents the total demand for goods b∈B. This represents the transport batches of different modes of transport k∈K on transport segments i,j∈N. This indicates rounding up, used to ensure that all goods can be transported. This represents the single transport capacity for each mode of transport, k∈K. Let K represent the cargo volume of the last transport trip, which may not be fully loaded. Here, K is the set of transport modes, B is the set of freight demand, N is the set of network nodes, and transport segments ij∈N.

[0020] Furthermore, S3 specifically includes:

[0021] 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:

[0022]

[0023] Z0,Z1≥0

[0024] In the formula, Z0 represents the fixed low-carbon subsidy for railway transportation, and Z1 represents the variable subsidy per unit distance for railway transportation. k1 represents the distance traveled by the railway in the transport segment ij, and k2 represents the railway transport.

[0025] Furthermore, S3 specifically includes:

[0026] 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:

[0027]

[0028] In the formula, This represents the shipper's fixed utility in a multimodal transport route. This represents the utility coefficient of the shipper of goods b on the transportation costs along the multimodal transport route. Let P represent the shipper's utility coefficient for transit time on cargo b along a multimodal transport route. b and T b ε represents the multimodal transport cost and transport time for cargo b, respectively. pt This represents the shipper's stochastic utility regarding transit time and transit costs. M represents the proportion of multimodal transport route selection under the shipper's non-inertial preference, taking into account the impact of transportation costs and transportation time. b This represents the set of all possible paths in the market.

[0029] Furthermore, S3 specifically includes:

[0030] 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:

[0031] S341, Settings This indicates that cargo b selects transportation mode k on transportation segment ij; otherwise, it is 0.

[0032] and settings This indicates that goods b change from transportation mode r to k at network node i; otherwise, it is 0.

[0033] Where the set k∈{rd,rl,wt} represents the modes of transportation: road, rail, and waterway, respectively.

[0034] 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:

[0035]

[0036] 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:

[0037]

[0038] S344. Construct the following path continuity constraints:

[0039]

[0040] Furthermore, S4 specifically includes:

[0041] 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:

[0042]

[0043] In the formula, G b This indicates that goods b are eligible for low-carbon subsidies for rail transport, C E E represents the unit cost of carbon emissions. b This indicates the carbon emissions from multimodal transport of cargo b. This represents the carbon emissions of goods b in the market path.

[0044] Furthermore, S5 specifically includes:

[0045] S51, Fixed low-carbon subsidy Z0, unit distance variable subsidy Z1, and route selection ratio for each batch of goods. The positions of the particles are assigned to construct a particle swarm.

[0046] S52. Determine whether the iteration conditions have been met;

[0047] If so, execute S53 and output the current highest fitness and optimal multimodal transport network design scheme;

[0048] If not, then execute S54, decode the particle position, and calculate the low-carbon subsidy G. b and multimodal transport volume D b ;

[0049] S55. Calculate the particle swarm fitness and the multimodal transport network design scheme corresponding to the current particle position through the embedded CPLEX.

[0050] S56. Determine whether the highest fitness has been achieved;

[0051] If so, execute S57, update the highest fitness, update the particle velocity within the limits, and then execute S58;

[0052] If not, execute S58, update the particle position, increment the iteration count by 1, and return to S52.

[0053] Furthermore, S55 specifically includes:

[0054] S551. Obtain the route selection ratio for each batch of goods. Low carbon subsidies G b and multimodal transport volume D b And use the Logit model to calculate the transportation time and carbon emission cost of each mode of transportation on each transportation segment;

[0055] 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.

[0056] 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 subsidy scheme determined by the fixed low-carbon subsidy Z0 and the unit distance variable subsidy Z1 is output, along with the subsidy scheme determined by the mode of transportation. With transportation routes The jointly determined multimodal transport scheme is used to obtain a multimodal transport network design scheme.

[0057] 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

[0058] 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.

[0059] 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.

[0060] 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.

[0061] Figure 4 This illustration shows a diagram illustrating the shipper route selection and proportion provided in an embodiment of this application. Detailed Implementation

[0062] 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.

[0063] Example 1:

[0064] 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:

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] S3 specifically includes:

[0072] 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:

[0073]

[0074] In the formula, D b This represents the total demand for goods b∈B. This represents the transport batches of different modes of transport k∈K on transport segments i,j∈N. This indicates rounding up, used to ensure that all goods can be transported. This represents the single transport capacity for each mode of transport, k∈K. Let K represent the cargo volume of the last transport trip, which may not be fully loaded. Here, K is the set of transport modes, B is the set of freight demand, N is the set of network nodes, and transport segments ij∈N.

[0075] S3 specifically includes:

[0076] 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:

[0077]

[0078] Z0,Z1≥0

[0079] In the formula, Z0 represents the fixed low-carbon subsidy for railway transportation, and Z1 represents the variable subsidy per unit distance for railway transportation. k1 represents the distance traveled by the railway in the transport segment ij, and k2 represents the railway transport.

[0080] S3 specifically includes:

[0081] 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:

[0082]

[0083] In the formula, This represents the shipper's fixed utility in a multimodal transport route. This represents the utility coefficient of the shipper of goods b on the transportation costs along the multimodal transport route. Let P represent the shipper's utility coefficient for transit time on cargo b along a multimodal transport route. b and T b ε represents the multimodal transport cost and transport time for cargo b, respectively. pt This represents the shipper's stochastic utility regarding transit time and transit costs. M represents the proportion of multimodal transport routes chosen by shippers under non-inertial preferences, taking into account the impact of transportation costs and transit time. b This represents the set of all possible paths in the market.

[0084] S3 specifically includes:

[0085] 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:

[0086] S341, Settings This indicates that cargo b selects transportation mode k on transportation segment ij; otherwise, it is 0.

[0087] and settings This indicates that goods b change from transportation mode r to k at network node i; otherwise, it is 0.

[0088] Where the set k∈{rd,rl,wt} represents the modes of transportation: road, rail, and waterway, respectively.

[0089] 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:

[0090]

[0091] 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:

[0092]

[0093] S344. Construct the following path continuity constraints:

[0094]

[0095] S4. Establish an objective function with the aim of achieving lower carbon emission costs without paying large amounts of low-carbon subsidies.

[0096] S4 specifically includes:

[0097] 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:

[0098]

[0099] In the formula, G b This indicates that goods b are eligible for low-carbon subsidies for rail transport, C E E represents the unit cost of carbon emissions. b This indicates the carbon emissions from multimodal transport of cargo b. This represents the carbon emissions of goods b in the market path.

[0100] 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.

[0101] In this step, due to the decision variables of this application (such as the fixed low-carbon subsidy Z0, the unit distance variable subsidy Z1, and the route selection ratio of 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.

[0102] This application embeds CPLEX into a particle swarm optimization algorithm. The particle swarm optimization algorithm is used to decide the fixed low-carbon subsidy Z0 and the unit distance variable subsidy Z1 in carpet subsidies, and overcomes the nonlinear solution problem of the nested Logit model. Meanwhile, CPLEX is used to decide the transportation mode in multimodal transport schemes. With transportation routes It is also used to evaluate the particle fitness of the particle swarm optimization algorithm.

[0103] Please see as follows Figure 2 The flowchart shown illustrates the steps to obtain a low-carbon multimodal transport network design scheme. Figure 2 As shown, S5 specifically includes:

[0104] S51, Fixed low-carbon subsidy Z0, unit distance variable subsidy Z1, and route selection ratio for each batch of goods. The positions of the particles are assigned to construct a particle swarm.

[0105] S52. Determine whether the iteration conditions have been met.

[0106] If so, execute S53 and output the current highest fitness and optimal multimodal transport network design scheme.

[0107] If not, then execute S54, decode the particle position, and calculate the low-carbon subsidy G. b and multimodal transport volume D b ;

[0108] S55. Calculate the particle swarm fitness and the multimodal transport network design scheme corresponding to the current particle position through the embedded CPLEX.

[0109] S56. Determine whether the highest fitness has been achieved;

[0110] If so, execute S57, update the highest fitness, update the particle velocity within the limits, and then execute S58;

[0111] If not, execute S58, update the particle position, increment the iteration count by 1, and return to S52.

[0112] Please see as follows Figure 3 The flowchart shown illustrates the steps involved in solving multimodal transport network design schemes using embedded CPLEX. Figure 3 As shown, S55 specifically includes:

[0113] S551. Obtain the route selection ratio for each batch of goods. Low carbon subsidies G b and multimodal transport volume D b The Logit model was used to calculate the transportation time and carbon emission costs for each mode of transport on each transport route.

[0114] 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.

[0115] 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 subsidy scheme determined by the fixed low-carbon subsidy Z0 and the unit distance variable subsidy Z1 is output, along with the subsidy scheme determined by the mode of transportation. With transportation routes The jointly determined multimodal transport scheme is used to obtain a multimodal transport network design scheme.

[0116] Example 2:

[0117] 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.

[0118] 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.

[0119] The shipper route selection described in this application refers to the behavioral 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.

[0120] As an example, please refer to Figure 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.

[0121] 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.

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, 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, D b This represents the total demand for goods b∈B. This represents the transport batches of different modes of transport k∈K on transport segments i,j∈N. This indicates rounding up, used to ensure that all goods can be transported. This represents the single transport capacity for each mode of transport, k∈K. Let K represent the cargo volume of the last transport trip, which may not be fully loaded. Here, K is the set of transport modes, B is the set of freight demand, N is the set of network nodes, and transport segments ij∈N.

4. The method as described in claim 1, characterized in that, S3 specifically includes: 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: Z0,Z1≥0 In the formula, Z0 represents the fixed low-carbon subsidy for railway transportation, and Z1 represents the variable subsidy per unit distance for railway transportation. k1 represents the distance traveled by the railway in the transport segment ij, and k2 represents the railway transport.

5. The method as described in claim 3, characterized in that, S3 specifically includes: 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. This represents the utility coefficient of the shipper of goods b on the transportation costs along the multimodal transport route. Let P represent the shipper's utility coefficient for transit time on cargo b along a multimodal transport route. b and T b ε represents the multimodal transport cost and transport time for cargo b, respectively. pt This represents the shipper's stochastic utility regarding transit time and transit costs. M represents the proportion of multimodal transport routes chosen by shippers under non-inertial preferences, taking into account the impact of transportation costs and transit time. b This represents the set of all possible paths in the market.

6. The method as described in claim 1, characterized in that, S3 specifically includes: 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 This indicates that cargo b selects transportation mode k on transportation segment ij; otherwise, it is 0. and settings This indicates that goods b change from transportation mode r to k at network node i; otherwise, it is 0. Where the set k∈{rd,rl,wt} represents 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:

7. The method as described in claim 1, characterized in that, 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, G b This indicates that goods b are eligible for low-carbon subsidies for rail transport, C E E represents the unit cost of carbon emissions. b This indicates the carbon emissions from multimodal transport of cargo b. This represents the carbon emissions of goods b in the market path.

8. The method as described in claim 1, characterized in that, S5 specifically includes: S51, Fixed low-carbon subsidy Z0, unit distance variable subsidy Z1, and route selection ratio 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 G. b and multimodal transport volume D b ; 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.

9. The method as described in claim 8, characterized in that, Specifically, S55 includes: S551. Obtain the route selection ratio for each batch of goods. Low carbon subsidies G b and multimodal transport volume D b And use the Logit model to calculate the transportation time and carbon emission cost of each mode of transportation on each transportation 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 subsidy scheme determined by the fixed low-carbon subsidy Z0 and the unit distance variable subsidy Z1 is output, along with the subsidy scheme determined by the mode 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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