Multi-mode combined transportation alternative path and transportation scheme generation method based on road network node interruption

Abstract

The invention provides a multimodal transport alternative path and transport scheme generation method based on road network node interruption. The method comprises the following steps: acquiring road network interruption information; if the road network interruption information is updated, for each original path, judging whether the original path comprises the road network interruption information or not; if yes, marking a break point in the original path, constructing an upstream candidate loading and unloading point set and a downstream candidate loading and unloading point set based on a first constraint condition, and selecting a loading and unloading node from each of the upstream candidate loading and unloading point set and the downstream candidate loading and unloading point set by adopting an upper-layer model by taking the minimum duration as a first objective function; inputting the selected first loading and unloading node and the selected second loading and unloading node into a lower-layer model, wherein the lower-layer model adopts a genetic algorithm to construct a multimodal transport replacement path set; and when the transportation unit travels to one end of the path, determining a final path based on the consumed time of each path, and constructing a multimodal transportation scheme.

Description

Technical Field

[0001] This invention relates to the field of multimodal transport technology, and in particular to a method for generating alternative multimodal transport routes and transport schemes based on road network node interruptions. Background Technology

[0002] As a crucial link in ensuring the supply of goods, material transportation faces numerous challenges in the real world, such as natural disasters, accidents, or aging infrastructure, leading to problems like damaged routes or reduced capacity, thus preventing the completion of transportation tasks according to the original plan. Given the typically time-sensitive nature of material transportation tasks, there is an urgent need to quickly develop feasible alternative transportation solutions when faced with constraints in terms of routes, resources, and manpower. When established material transportation routes are damaged or their capacity is reduced, due to the potentially unique characteristics of the goods to be transported, different types of goods may have significantly different applicable transportation modes. In such cases, a single transportation mode is often insufficient to meet the complex transportation tasks and may not be the optimal alternative transportation solution. Therefore, it is necessary to consider generating alternative multimodal transport collaborative transportation solutions through the coordinated operation of multiple transportation modes.

[0003] However, existing multimodal transport route planning methods are mostly designed for normal transport scenarios and do not fully consider the emergency adjustment needs after route interruptions. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method for generating alternative routes and transportation schemes for multimodal transport based on road network node interruptions, in order to eliminate or improve one or more defects existing in the prior art.

[0005] One aspect of the present invention provides a method for generating alternative multimodal transport routes and transport schemes based on road network node interruptions, the method comprising the following steps: Obtain road network interruption information, which includes interruption nodes and interruption arcs; mark the road network interruption information from the original road network data, which includes a set of nodes and a set of arcs, and the set of nodes includes loading and unloading nodes and non-loading and unloading nodes; If the road network interruption information is updated, then for each original path, determine whether the original path includes road network interruption information; If so, then mark the interruption point in the original path, and construct an upstream candidate loading and unloading point set and a downstream candidate loading and unloading point set based on the first constraint condition. With minimizing the time as the first objective function, the upper-level model selects one loading and unloading node from each of the upstream candidate loading and unloading point set and the downstream candidate loading and unloading point set. The first loading / unloading node selected from the upstream candidate loading / unloading point set and the second loading / unloading node selected from the downstream candidate loading / unloading point set are input into the lower-level model. The lower-level model constructs an initial path set based on the first loading / unloading node and the second loading / unloading node. The lower-level model uses a genetic algorithm to calculate the path fitness and performs crossover and mutation iterations. Finally, it selects a preset number of paths with higher fitness to construct a multimodal transport alternative path set. The set of alternative multimodal transport routes includes multiple routes that are identical at both ends. When a transport unit travels to one end of a route, the time consumed by the transport unit on each route is calculated based on the transport conditions of the transport unit. The final route is determined based on the consumption time, and a multimodal transport plan is constructed.

[0006] The above-mentioned approach can be divided into two parts: the first part is the generation of multimodal transport routes, and the second part is the generation of multimodal transport schemes. After data input, a set of alternative multimodal transport routes is first formed according to the multimodal transport route generation method. Based on this, a candidate set of multimodal transport schemes is constructed as the data input for the multimodal transport scheme generation method. Finally, the final multimodal transport routes and transport schemes are formed according to the transport scheme generation method. In the event of an interruption, the emergency adjustment needs after the route is interrupted can be fully considered, ensuring the efficiency and adaptability of route adjustments when an interruption occurs.

[0007] In some embodiments of the present invention, in the step of determining whether the original path includes road network interruption information if the road network interruption information is updated, if the original path does not include road network interruption information, then the original path is not processed.

[0008] In some embodiments of the present invention, the step of constructing the upstream candidate loading / unloading point set and the downstream candidate loading / unloading point set based on the first constraint condition includes: For each loading and unloading node globally, determine whether the material type of the loading and unloading node matches the material being transported, and construct the matching loading and unloading nodes as the first set of loading and unloading points; Calculate the shortest path between each loading / unloading node in the first set of loading / unloading points and the interruption point. Filter the loading / unloading nodes based on the distance of the shortest path to obtain the second set of loading / unloading points. Based on the extension direction of the original path interruption point, the loading and unloading nodes in the second loading and unloading point set are divided into the upstream candidate loading and unloading point set and the downstream candidate loading and unloading point set.

[0009] In some embodiments of the present invention, in the step of selecting one loading / unloading node from each of the upstream and downstream candidate loading / unloading point sets using the upper-level model with the minimization of time as the first objective function, one loading / unloading node from each of the upstream and downstream candidate loading / unloading point sets is selected to form a node pair. The time of each node pair is calculated. The loading / unloading node in the upstream candidate loading / unloading point set corresponding to the node pair with the minimum time is taken as the first loading / unloading node, and the loading / unloading node in the downstream candidate loading / unloading point set corresponding to the node pair with the minimum time is taken as the second loading / unloading node.

[0010] In some embodiments of the present invention, in the step of constructing an initial path set based on the first loading and unloading node and the second loading and unloading node in the lower-level model, calculating the path fitness using a genetic algorithm and performing crossover and mutation iterations, and finally selecting a preset number of paths with higher fitness to construct a multimodal transport alternative path set, the initial path set is constructed based on the first loading and unloading node and the second loading and unloading node using a path search algorithm, with the objective functions being minimizing transport time and maximizing safety probability.

[0011] In some embodiments of the present invention, in the step of calculating path fitness using a genetic algorithm in the lower-level model, the transportation time and safety probability of the candidate paths are calculated respectively, and the path fitness is calculated based on the transportation time and safety probability.

[0012] In some embodiments of the present invention, the steps of calculating the transportation time and safety probability of candidate routes respectively include: Calculate the arc betweenness centrality, geographical factor influence factor, and weather factor influence factor for each arc segment of the candidate path; The arc segment safety probability is calculated based on the arc segment betweenness centrality, geographical factor influence factor, and weather factor influence factor for each arc segment. The safety probability of a candidate path is calculated based on the safety probability of each arc segment of the candidate path.

[0013] In some embodiments of the present invention, in the step of calculating the arc segment safety probability based on the arc segment betweenness centrality, geographical factor influence factor, and weather factor influence factor for each arc segment, the arc segment betweenness centrality, geographical factor influence factor, and weather factor influence factor are calculated using the following formula: in, Describes the betweenness centrality of an arc segment. Indicates the arc segments traversed in the candidate path The number of candidate paths, This represents the total number of candidate paths; The geographical factor influencing factors are calculated using the following formula: in, Represents arc segment Weighting coefficients for terrain complexity; Represents arc segment The complexity of the terrain, Represents arc segment Geographical factors influencing factors; The weather impact factor is calculated using the following formula: in, Represents arc segment Weather factors influencing terrain complexity The weighting coefficients representing meteorological conditions. Indicates comprehensive meteorological conditions; The product of the arc segment's betweenness centrality, geographical factors, and weather factors is used as the arc segment's safety probability.

[0014] In some embodiments of the present invention, in the step of calculating the safety probability of a candidate path based on the arc safety probability of each arc segment of the candidate path, the average value of the arc safety probabilities in the candidate path is calculated as the safety probability of the candidate path.

[0015] A second aspect of the present invention also provides a system for generating alternative routes and transportation schemes for multimodal transport based on network node interruptions. The system includes a computer device, which includes a processor and a memory. The memory stores computer instructions, and the processor executes the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps of the method described above.

[0016] A third aspect of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned method for generating alternative routes and transport schemes for multimodal transport based on network node interruptions.

[0017] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the text, or may be learned by practice of the invention. The objects and other advantages of the invention will become apparent from the description and the accompanying drawings.

[0018] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0019] The accompanying drawings, which are provided to further illustrate the invention and form part of this application, are not intended to limit the scope of the invention.

[0020] Figure 1 This is a schematic diagram of one embodiment of the method for generating alternative routes and transportation schemes for multimodal transport based on road network node interruptions according to the present invention; Figure 2 This is a schematic diagram of the overall processing flow of this solution; Figure 3 This is the genetic algorithm crossover operation flow of one implementation of this scheme; Figure 4 This is a schematic diagram of the micro-evolutionary submodule algorithm in this scheme; Figure 5 This is a schematic diagram of the multimodal transport route set for the case scenario of this solution; Figure 6 This is a schematic diagram of the optimal transportation route and transportation scheme for freight trains in the example scenario of this solution. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0022] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0023] Existing technologies lack reliable theoretical and model support for multimodal transport routes and transportation schemes. This makes it difficult to achieve seamless integration and optimal resource allocation between various modes of transport during actual cargo transportation, thus affecting overall transportation efficiency and hindering the full realization of the advantages of multimodal transport to generate optimal alternative transportation solutions. Furthermore, considering the diversity of goods, there are various limitations regarding the available transport vehicles, accessible routes, and available warehousing facilities, further increasing the complexity of generating transportation schemes.

[0024] The key technical points of this invention lie in multimodal transport alternative route planning and transportation scheme generation. Existing technical backgrounds and methods mainly include: (1) Multimodal transport route planning Multi-objective multimodal transport route planning problems can be divided into route planning problems under deterministic scenarios and route planning problems under uncertain scenarios. Currently, there is considerable research both domestically and internationally on multimodal transport route planning problems under deterministic scenarios. The methods employed are based on operations research theory, constructing mathematical optimization models for the transport scenario. The objective functions considered mainly include transport cost, transport time, safety risk, and carbon emissions. The solution algorithms used are divided into exact algorithms (such as Dijkstra's algorithm) and heuristic algorithms (such as genetic algorithms). Existing technologies indicate that planning the transport routes of goods must consider the transport requirements of different goods. For example, for goods with high time value requirements, the path with the shortest transport time should be sought, and time cost should be given a larger weight when constructing the objective function. For goods with high safety requirements, the path with the lowest transport risk should be sought, and safety risk should be given a larger weight when constructing the objective function.

[0025] (2) Generation of multimodal transport solutions Existing literature research focuses on the rapid delivery of goods in emergency situations, as well as international rail container transport and sea-rail intermodal transport on the China-Europe Railway Express. The optimization objectives of related studies focus on minimizing transportation costs, transportation time, and carbon emissions.

[0026] (3) Solution algorithm design In multimodal transport route planning, Zhou Jinlong et al. constructed a multimodal transport route optimization model with time window constraints, aiming to minimize transportation costs, carbon emissions, and transport time. They also designed a multi-stage, multi-objective evolutionary algorithm to solve the model. For generating multimodal transport schemes, solutions are generally found using commercial solvers or heuristic algorithms. For small-scale cases, commercial solvers are typically used; for common large-scale problems, heuristic algorithms are mainly employed.

[0027] Existing methods for planning alternative transportation routes and generating solutions when the capacity of a given transportation route is limited have three main problems.

[0028] First, existing multimodal transport route planning models rarely consider the issue of route adjustment under road network disruptions, and also fail to adequately account for the requirements of different transport goods on road segment grades and transport capacity, resulting in insufficient foresight regarding potential risks and consequences. Second, in constructing models for generating alternative transport solutions, existing methods often fail to construct actual connection processes in their models when dealing with the connections between different modes of transport in transport solutions, instead using average transfer times as a substitute. This can lead to transport solutions that are not feasible in practice. Finally, because multimodal transport requires the construction of complex transport networks encompassing multiple modes of transport, the models constructed by existing methods result in massive computational scales for multimodal transport route planning and transport solution generation. Existing methods do not employ special handling to reduce the complexity of such large-scale problems, thus their solution algorithms often suffer from high computational complexity, limiting their application effectiveness in real-world scenarios and failing to meet the demands for efficiency and flexibility.

[0029] To address the problems existing in the construction of optimization models using existing methods, this invention aims to construct a multimodal transport route planning model by comprehensively considering the requirements of interruption scenarios and the demands of materials on transport route levels and transport capacity. Simultaneously, an improved genetic algorithm is designed to generate a multimodal transport route set. Furthermore, by refining the transfer links between different modes of transportation in the model, a simplified problem-solving algorithm is designed, thereby improving the solution efficiency and ensuring the practical feasibility of the optimized transport scheme. In summary, this solution constructs a suitable multimodal transport route planning model and transport scheme generation model based on existing road network conditions, and designs a corresponding optimization algorithm to ensure the accessibility and speed of material transport under multimodal transport. This has become a key problem urgently needing to be solved in the current field of material transport. This solution provides a method for generating multimodal transport routes and transport schemes under conditions of limited capacity of given transport routes, which has high practical significance.

[0030] like Figure 1 As shown, this invention proposes a method for generating alternative multimodal transport routes and transport schemes based on road network node interruptions. The steps of the method include: Step S100: Obtain road network interruption information, which includes interruption nodes and interruption arcs. Mark the road network interruption information from the original road network data. The original road network data includes a set of nodes and a set of arcs. The set of nodes includes loading and unloading nodes and non-loading and unloading nodes. Step S200: If the road network interruption information is updated, then for each original path, determine whether the original path includes road network interruption information; In the specific implementation process, in the step of determining whether the original path includes road network interruption information, it is determined whether the original path includes interruption nodes or interruption arcs in the road network interruption information.

[0031] If so, then step S310 is adopted to mark the interruption points in the original path, and construct the upstream candidate loading and unloading point set and the downstream candidate loading and unloading point set based on the first constraint condition. The first objective function is to minimize the time. The upper-level model is used to select one loading and unloading node from the upstream candidate loading and unloading point set and the downstream candidate loading and unloading point set. Step S400: The first loading and unloading node selected from the upstream candidate loading and unloading point set and the second loading and unloading node selected from the downstream candidate loading and unloading point set are input into the lower-level model. The lower-level model constructs an initial path set based on the first loading and unloading node and the second loading and unloading node. The lower-level model uses a genetic algorithm to calculate the path fitness and performs crossover and mutation iterations. Finally, a preset number of paths with higher fitness are selected to construct a multimodal transport alternative path set. Step S500: The set of alternative multimodal transport routes includes multiple routes that are identical at both ends. When a transport unit travels to one end of a route, the time consumed by the transport unit on each route is calculated based on the transport conditions of the transport unit. The final route is determined based on the consumption time, and a multimodal transport plan is constructed.

[0032] The transport unit can be a train, a car, or an airplane, etc.

[0033] The transportation conditions include actual transportation conditions and time restrictions. For example, if the transportation conditions of goods in a train are restricted to land and air routes, then routes involving sea routes need to be eliminated.

[0034] In the specific implementation process, this scheme matches the corresponding path for the transportation unit based on the transportation conditions of the transportation unit. For each transportation unit, considering the actual transportation conditions and transportation restrictions, it matches an alternative path from the set of multimodal transport alternative paths for each train, and further refines the arrival and departure times of each node of the train on the alternative path to form a final feasible actual transportation scheme.

[0035] Specifically, the speed of each transport unit in the preset route (train, airplane, or ship) is used to calculate the time consumed for each route based on the length of the route.

[0036] In practical implementation, the transportation plan includes trains, automobiles, and airplanes, and can be represented as shown in Table 7 below: Table 7 Using the above approach, the technical solution can be divided into two parts, such as... Figure 2As shown, the first part is the generation of multimodal transport routes, and the second part is the generation of multimodal transport schemes. After completing the data input, a set of alternative multimodal transport routes is first formed according to the multimodal transport route generation method; based on this, a set of candidate multimodal transport schemes is constructed as the data input for the multimodal transport scheme generation method; finally, the final multimodal transport routes and transport schemes are formed according to the transport scheme generation method. In the event of an interruption, the system can fully consider the emergency adjustment needs after the route is interrupted, ensuring the efficiency and adaptability of route adjustments when an interruption occurs.

[0037] In some embodiments of this invention, the scheme considers the design of a transportation route adjustment model under the condition of route interruption, aiming at minimizing transportation time and maximizing safety, and constructs a two-layer optimization model for multimodal transport routes. The upper-layer model is a loading and unloading point selection model that considers the capacity limitations of loading and unloading points, while the lower-layer model optimizes the multimodal transport route based on the loading and unloading point locations input from the upper-layer model, considering transportation condition constraints. The definitions of relevant symbols in the model are shown in Table 1.

[0038] Table 1 In some embodiments of the present invention, in the step of determining whether the original path includes road network interruption information if the road network interruption information is updated, if the original path does not include road network interruption information, then the original path is not processed.

[0039] In some embodiments of the present invention, the step of constructing the upstream candidate loading / unloading point set and the downstream candidate loading / unloading point set based on the first constraint condition includes: For each loading and unloading node globally, determine whether the material type of the loading and unloading node matches the material being transported, and construct the matching loading and unloading nodes as the first set of loading and unloading points; Calculate the shortest path between each loading / unloading node in the first set of loading / unloading points and the interruption point. Filter the loading / unloading nodes based on the distance of the shortest path to obtain the second set of loading / unloading points. Based on the extension direction of the original path interruption point, the loading and unloading nodes in the second loading and unloading point set are divided into the upstream candidate loading and unloading point set and the downstream candidate loading and unloading point set.

[0040] In some embodiments of the present invention, in the step of selecting one loading / unloading node from each of the upstream and downstream candidate loading / unloading point sets using the upper-level model with the minimization of time as the first objective function, one loading / unloading node from each of the upstream and downstream candidate loading / unloading point sets is selected to form a node pair. The time of each node pair is calculated. The loading / unloading node in the upstream candidate loading / unloading point set corresponding to the node pair with the minimum time is taken as the first loading / unloading node, and the loading / unloading node in the downstream candidate loading / unloading point set corresponding to the node pair with the minimum time is taken as the second loading / unloading node.

[0041] In the specific implementation process, in the step of minimizing the duration as the first objective function, the node can travel at a preset speed in the shortest path constructed by the node pair, and the estimated travel time is taken as the duration corresponding to the node pair.

[0042] In some embodiments of the present invention, in the step of constructing an initial path set based on the first loading and unloading node and the second loading and unloading node in the lower-level model, calculating the path fitness using a genetic algorithm and performing crossover and mutation iterations, and finally selecting a preset number of paths with higher fitness to construct a multimodal transport alternative path set, the initial path set is constructed based on the first loading and unloading node and the second loading and unloading node using a path search algorithm, with the objective functions being minimizing transport time and maximizing safety probability.

[0043] In some embodiments of the present invention, the reciprocal of the transportation time is calculated, and the transportation time and safety probability of the reciprocal are normalized, and the sum of the two values ​​is calculated as the path fitness.

[0044] In some embodiments of the present invention, in the calculation of transportation time, the length of each arc segment and the means of transportation used are calculated, and the time consumed by the arc segment is calculated based on the length of the arc and the speed set by the corresponding means of transportation. The total time is used as the transportation time, and the arc segment is the road segment between two adjacent nodes in the candidate path.

[0045] In some embodiments of the present invention, during the construction of the initial path set, (1) Determination of transit nodes: Based on the solution results of the multimodal transport route planning method, the selected transit nodes before the route interruption point are determined. The candidate transfer node positions are the nodes in the upstream candidate loading / unloading point set, and the selected nodes after the line interruption point. The candidate transit node positions are the nodes in the set of downstream candidate loading and unloading points.

[0046] (2) For all the multimodal transport routes obtained by the multimodal transport route planning method, filter out all multimodal transport routes that do not meet the transshipment requirements based on the transport restrictions of the transported goods on the highway section.

[0047] (3) For the section in front of the line break point One alternative relay node, and the one behind the line break point. For each of the remaining multimodal transport routes, the shortest path between any two transit nodes is found, forming a candidate set of multimodal transport solutions. This candidate set contains... One alternative route.

[0048] In some embodiments of the present invention, in the step of calculating path fitness using a genetic algorithm in the lower-level model, the transportation time and safety probability of the candidate paths are calculated respectively, and the path fitness is calculated based on the transportation time and safety probability.

[0049] In some embodiments of the present invention, the steps of calculating the transportation time and safety probability of candidate routes respectively include: Calculate the arc betweenness centrality, geographical factor influence factor, and weather factor influence factor for each arc segment of the candidate path; The arc segment safety probability is calculated based on the arc segment betweenness centrality, geographical factor influence factor, and weather factor influence factor for each arc segment. The safety probability of a candidate path is calculated based on the safety probability of each arc segment of the candidate path.

[0050] In the specific implementation process, when determining the safety of multimodal transport routes, it is necessary to consider the betweenness centrality of the arc segment, topography and geographical factors, weather and environmental conditions. The following formula is used to calculate the betweenness centrality of the arc segment, the influence factor of geographical factors, and the influence factor of weather.

[0051] In some embodiments of the present invention, in the step of calculating the arc segment safety probability based on the arc segment betweenness centrality, geographical factor influence factor, and weather factor influence factor for each arc segment, the arc segment betweenness centrality, geographical factor influence factor, and weather factor influence factor are calculated using the following formula: in, Describes the betweenness centrality of an arc segment. Indicates the arc segments traversed in the candidate path The number of candidate paths, This represents the total number of candidate paths; The geographical factor influencing factors are calculated using the following formula: in, Represents arc segment Weighting coefficients for terrain complexity; Represents arc segment The complexity of the terrain, Represents arc segment Geographical factors influencing factors; The weather impact factor is calculated using the following formula: in, Represents arc segment Weather factors influencing terrain complexity The weighting coefficients representing meteorological conditions. Indicates comprehensive meteorological conditions; The product of the arc segment's betweenness centrality, geographical factors, and weather factors is used as the arc segment's safety probability.

[0052] In some embodiments of the present invention, in the step of calculating the safety probability of a candidate path based on the arc safety probability of each arc segment of the candidate path, the average value of the arc safety probabilities in the candidate path is calculated as the safety probability of the candidate path.

[0053] In some embodiments of the present invention, the upper-level model performs loading / unloading point selection. Distance constraints: Node constraints: Directional constraints: Decision variable constraints: The objective function aims to minimize the number of loading and unloading points, thereby improving the efficiency of path planning calculations in the lower-level model. Distance constraints ensure that the distance between selected loading / unloading points and breakpoints in the transportation route meets specific limitations. Node constraints ensure that selected nodes have the capability to handle the loading and unloading of specific materials. At that time, the node has the ability to load and unload materials. In the directional constraints, the preceding and succeeding loading and unloading points of the interruption point must each contain at least one point.

[0054] Lower-level model: Multimodal transport route planning Objective function: Calculate transit time: Path selection flow balance constraints: Ensure that no path loops or repeated selection of arc segments occur: Node transportation mode switching constraints: When goods pass through a certain arc segment, only one mode of transportation can be selected: Ensure that the transportation mode of goods does not change continuously at a certain point in time: The selected route for transporting certain goods meets its road grade requirements: When an arc segment is interrupted, the path selection of the interrupted arc segment must be prevented. When an arc segment is not selected into the path, its safety probability is not considered: Decision variable constraints: The objective function aims to minimize transportation time and maximize safety probability. The total time consists of the transit time of goods during all transportation tasks, as well as the loading, transit, and unloading times at various transportation nodes.

[0055] In some embodiments of this invention, the generation of multimodal transport schemes considers the design of a multimodal transport scheme generation model based on the scenario of line interruption. The aim is to solve the problem of how to complete the transport task by avoiding the interruption point through multimodal transport methods when the line is interrupted, with the goal of minimizing the total transport time. A two-layer optimization model for multimodal transport collaborative transport schemes is constructed. The transport scheme generation model considers the characteristics of rail, road, and air transport, imposing restrictions on aspects such as connection time, capacity, and transport limitations between modes of transport. The dynamic operation scheme arrangement model for railway freight trains, based on the origin and destination of each transport demand on the railway segment and the time of demand generation determined by the upper-layer model, arranges the operating lines of each freight train on the existing train timetable and returns elements such as the transport time of the railway segment to the upper-layer model.

[0056] (1) Transportation plan generation model The meanings of the relevant symbols in the model are shown in Table 2.

[0057] Table 2 The transportation plan generation model uses the shortest total running time of the transportation task as its objective function. Constraints for calculating the time consumption of transportation tasks: Multimodal transport route selection constraints: Loading and unloading station capacity constraints: ; Only one train is allowed to load and unload at a time at a node. ; Multimodal transport goods category restrictions and constraints: ; ; Constraints of Aeronautical Discrete Spatiotemporal Networks: Among them, the multimodal transport route selection constraint restricts each transport task to only one route from the multimodal transport route candidate set; the multimodal transport goods category restriction constraint is used to prohibit the use of certain specific modes of transportation in special circumstances; the aviation discrete spatiotemporal network constraint indicates that in air transport, aircraft taking off and landing at the same take-off and landing point must meet the shortest time interval requirement.

[0058] Lower-level model: Dynamic operation plan layout model for railway freight trains The meanings of the relevant symbols in the model are shown in Table 3.

[0059] Table 3 The lower-level model is constructed with the objective function of minimizing the total running time of freight trains and minimizing the adjustments of scheduled trains.

[0060] Flow balance constraints: Safety interval constraints: Loading and unloading line quantity constraints: Delivery deadline constraints: Loading and unloading station constraints: Freight train meeting constraints: Train stop duration constraints: Flow balance constraints ensure the feasibility of train routes in time and space; the number of loading and unloading lines constraints restrict the number of vehicles staying at a station at the same time from exceeding the number of loading and unloading lines at that station; loading and unloading station constraints restrict trains to selecting loading and unloading stations only from the pool of alternative loading and unloading stations; freight train meeting constraints only apply to special freight trains that cannot meet on the tracks.

[0061] In one implementation scheme, the lower-level model uses a genetic algorithm to calculate the path fitness and performs crossover and mutation iterations to finally select a preset number of paths with higher fitness to construct a set of alternative multimodal transport paths. In this scheme, a greedy Dijkstra algorithm is designed to generate an initial feasible solution, and then a genetic algorithm is used to optimize the initial solution to obtain the optimal solution.

[0062] Step 1: Based on Dijkstra's algorithm, which takes into account transportation time and the capacity of arc nodes, allocate paths in the road network according to the transportation task ID.

[0063] Step 2: Use a genetic algorithm to optimize the selected paths for each transportation task in each offspring.

[0064] 1. Initialize the population and randomly assign transportation tasks to the road network to generate multiple individuals.

[0065] 2. Assess fitness and select a subset of individuals using a tournament selection method.

[0066] 3. Two-node partial matching crossover, arc deletion and repair mutation to generate offspring, and supplement the population to the population size.

[0067] 4. Update the population; if the iteration stops, proceed to step 3; otherwise, return to evaluating fitness.

[0068] Step 3: Output the currently known optimal solution.

[0069] Initial feasible solution generation based on the improved Dijkstra algorithm Based on the constraints, Dijkstra's algorithm, which considers transportation time and the capabilities of nodes in the arc segment, is used to generate feasible paths for each transportation task in the road network. Dijkstra's algorithm employs a greedy strategy, starting from the initial point and gradually expanding outwards until the shortest path to all vertices is found.

[0070] Specifically: Initialize the types and quantities of transported materials, all nodes in the diagram, and the Chuangjie node-arc table.

[0071] From the set of nodes for which the shortest path has not yet been determined, select the node closest to the starting point and update the set.

[0072] Iterate through each adjacent node of the node, and determine whether the arc segment satisfies the constraints. If the constraints are satisfied, update the transportation time, arc segment capacity, and node capacity.

[0073] Repeat the set of steps to update the transport time until all nodes have been processed or the priority queue is empty.

[0074] Optimization based on genetic algorithm Chromosome encoding: Chromosomes use variable-length hierarchical integer encoding, with each layer representing a transportation task. Each gene position represents the arc segment selected by the transportation path of the transportation task, and the start and end points of the gene are the start and end points of the transportation task.

[0075] Fitness assessment: Individuals are selected from the population using a tournament selection method to form a mating pool. The fitness of these individuals is compared, and the individual with the highest fitness is selected as the winner. This process is repeated until a sufficient number of individuals are selected for crossover and mutation operations.

[0076] Crossover operations employ two operators: partial matching crossover with two nodes and sequential crossover based on node sharing across multiple task paths. The partial matching crossover with two nodes involves randomly labeling genes with the same endpoint as pre-localization genes and genes with the same starting point as post-localization genes within the same task path. Segments between pre-localization and post-localization genes are swapped. Then, the arc capacity and node swapping capability are checked. For arcs or nodes that do not meet the constraints, the path is replanned using Dijkstra's algorithm with time and node capability constraints to generate offspring chromosomes. The crossover based on node sharing involves randomly selecting two chromosomes. If the two tasks have the same starting or ending point, a shared intermediate node is selected as the crossover point. The path segment from the starting / ending point of one parent to the shared node is copied to the corresponding position in the other parent. After combination, the constraints are also checked for capacity and swapping capability and corrected using Dijkstra's algorithm to obtain offspring chromosomes.

[0077] Mutation Operations: Mutation operators include transport mode mutation and arc deletion / repair mutation. A repair operator, based on the Ruin and Repair concept, is designed to repair the generated new path in different cases. Several gene loci are randomly selected from a chromosome, and the arcs within these loci are modified. The operator checks if there are other transport modes for the node pairs in these loci; if so, they are replaced with other transport modes, otherwise, the original mode remains unchanged. The operator checks if the arc capacity and the transport mode conversion node's capacity are sufficient; if insufficient, the original arc is returned, otherwise, the original mode remains unchanged. Arc deletion / repair mutation can further increase individual diversity. A chromosome and its transport tasks are randomly selected; consecutive arcs are randomly selected and deleted, marking the corresponding parts of the path as "to be repaired"; Dijkstra's algorithm, considering transport time and arc node capacity, is used to replan the path segment from the deletion point, ensuring the path meets all constraints; if the fitness of the repaired path is better than the original path, the repaired path is retained; otherwise, the original path is restored.

[0078] In some implementations of this scheme, an improved micro-evolutionary algorithm based on Lagrange relaxation is employed. To efficiently solve the two-layer optimization model of multimodal transport collaborative transport schemes constructed in the multimodal transport scheme generation method, an improved micro-evolutionary algorithm based on Lagrange relaxation is designed to solve for reasonable transport schemes.

[0079] For the upper-level multimodal transport spatiotemporal route selection model, a micro-evolutionary algorithm is designed to solve for multimodal transport spatiotemporal routes that meet transportation requirements and output a reasonable multimodal transport scheme. Based on the conventional genetic algorithm, the micro-evolutionary algorithm improves the solution efficiency by extracting the dominant gene structure. The specific steps are as follows: Step 1: Chromosome Encoding. Individuals in the population are encoded using real numbers. Each bit on the chromosome represents a transportation demand, and the chromosome length equals the total number of transportation demands. The value of each gene position on the chromosome represents the selection of the corresponding transportation demand within its multimodal transport route candidate set.

[0080] Step 2: Fitness Function Calculation. The optimization objective of the upper-level model is to minimize transportation time. To facilitate the calculation of the micro-evolutionary algorithm, a maximum value M is introduced to construct the fitness function. This establishes a mapping relationship with the objective function based on the characteristics of the micro-evolutionary algorithm, ensuring that the individual's fitness is as high as possible.

[0081] Step 3: Dominant Gene Extraction. The dominant gene structure involves extracting identical genes from individuals with high fitness values ​​in the current generation and passing them on to the next generation, thus accelerating algorithm iteration. Since dominant genes are related to fitness, we analyze chromosome genes with high fitness, count genes with values ​​at the same position exceeding a certain proportion, and add them to the dominant gene set. The selection steps are as follows.

[0082] (1) Sort the individuals in the population according to their fitness values ​​and extract the first specified number of individuals as the elite population; (2) In the loop, elite individuals are continuously selected to form a candidate population. The cumulative probability is calculated based on the individual fitness. A roulette wheel is used to select individuals to join the new population until the size of the new population reaches the set value.

[0083] Step 4: Genetic operations based on dominant gene structure. Genetic operations include selection, crossover, and mutation. Dominant gene structure plays a primary role in the crossover operation; other parts remain consistent with traditional genetic operations. When performing the crossover operation, it is necessary to preserve dominant genes as much as possible; therefore, the crossover operation procedure is designed as follows: Figure 3 As shown.

[0084] Step 5: Population iteration, the algorithm flow of the micro-evolutionary submodule is as follows: Figure 4 As shown.

[0085] The dynamic operation plan layout model for lower-level railway freight trains is solved using the Lagrange relaxation algorithm. First, the complex constraints in the model are relaxed into multiple single-objective optimization sub-problems using the Lagrange relaxation algorithm. For simpler sub-problems, shortest path and greedy algorithms are then used for fast solutions. The results are returned to the upper-level model to participate in the fitness function calculation process of the micro-evolutionary algorithm. The specific steps are as follows: Step 1: First, introduce the Lagrange multipliers. By relaxing the safety interval constraint into the objective function, a new objective function Z is obtained.

[0086] Step 2: Decompose the Lagrange-relaxed model into | F |A single train subproblem. For any subproblem, its objective function is as follows: The calculation is shown.

[0087] Step 3: Solve all subproblems in parallel. Solving each subproblem is equivalent to searching for the shortest time-varying cost path for each train in the spatiotemporal network, and the Lagrange multipliers are updated using the subgradient method.

[0088] Step 4: Return the solution to the upper-level model to participate in the fitness function value calculation.

[0089] The beneficial effect of this scheme is that by constructing a two-layer optimization model that takes into account the three modes of transportation and refines the connection process between the modes of transportation, and designing a hybrid optimization algorithm based on micro-evolutionary algorithm and Lagrange relaxation, the model can be solved quickly, and the real effectiveness of the transportation scheme obtained by the solution is guaranteed.

[0090] Example: (1) Scene setting The simulation scenarios mainly include three parts: traffic conditions in the simulation area, line interruption conditions, and transportation tasks.

[0091] Regional traffic conditions The simulation scenario includes three modes of transportation: rail transport, road transport, and air transport.

[0092] The area includes one railway line with 46 stations from S1 to S46; all national and provincial highways along the railway line and connecting roads around the stations; and six available airports within the area.

[0093] In the simulation scenario, the basic transportation conditions for each mode of transport within the region are set as follows: Railway passenger and freight trains: There are ordinary passenger and freight trains operating normally on the railway line. Due to the interruption of the S19~S22 section of the line, all trains needing to pass through this section are suspended. The remaining passenger and freight trains are unaffected and operate normally according to the scheduled routes and times. Railway loading and unloading efficiency: The maximum number of freight trains departing from each station along the railway line per day is 6. At the same time, the shortest loading time for each train at the station is 90 minutes, and the shortest unloading time is 120 minutes.

[0094] Line outage situation In the example scenario, a natural disaster causes a railway line to be interrupted, making normal transportation impossible and preventing the line from being restored in a short period of time. The damaged points, affected railway sections, and selected loading and unloading points are shown in Table 4. The capacity of other railway lines and highways along the railway within the example area is not affected by this natural disaster.

[0095] Table 4 Case Scenario Parameter Settings Transportation task: Consider a batch of supplies, consisting of 6 railway freight cars, that needs to be transported from S1 to S... 46 .

[0096] (2) Generate path To ensure the generation of multimodal transport solutions, it is necessary to first plan multimodal transport routes for the interruption scenario from a multimodal transport perspective, forming a multimodal transport route set. Selecting three loading and unloading points before and after the damaged section can establish a more flexible intermodal transport solution between road and rail, reducing the impact of route interruption on the transport of goods and improving the resilience and reliability of the intermodal transport solution.

[0097] For each pair of loading and unloading points, considering transportation distance, transportation time, and transportation safety as objectives, five road transportation routes are calculated as the path set for that loading and unloading point, representing the shortest transportation distance, shortest transportation time, optimal combination of transportation time and safety, highest transportation safety, and second highest transportation safety. The road transportation routes between each pair of loading and unloading points are solved, resulting in a total of 45 paths, as shown below. Figure 5 As shown.

[0098] Construction of a multimodal transport route alternative set The preliminary calculations yielded 45 road-rail intermodal transport routes and 2 road-air intermodal transport routes. Considering that the transported objects in this scenario do not have special restrictions such as exceeding height or size limits, further optimization of the candidate set can be performed based on the above 45 routes. After optimization, the candidate set contains a total of 11 routes, including 9 road-rail intermodal transport routes and 2 road-air intermodal transport routes. The transport details of all candidate multimodal transport routes are shown in Table 5 (Table 5 unit: km).

[0099] Table 5 Multimodal transport collaborative transportation solutions The example problem was solved using an improved micro-evolutionary algorithm based on Lagrange relaxation. The entire transportation process took 1 day, 17 hours, and 47 minutes.

[0100] All goods were transported using a combined road and rail transport system. Table 6 shows the transport plans for the six freight trains and the arrival and departure times at each node along the entire route. A schematic diagram of the specific transport plans for each freight train is shown below. Figure 6 As shown, this invention can solve for a reasonable multimodal transport scheme for materials, thereby quickly formulating feasible alternative transport schemes when a given material transport route is interrupted due to various unforeseen circumstances.

[0101] Table 6 The key points and protected aspects of this invention are divided into three parts. First, addressing the shortcomings of existing optimization models in multimodal transport, a two-layer multimodal transport route planning model is constructed that comprehensively considers the differences in loading and unloading points during interruption scenarios, the requirements of materials for road segment grades and throughput capacity. Second, a collaborative optimization model for two-layer multimodal transport schemes is constructed by refining the transfer and connection links between different modes of transportation. Third, a path set generation algorithm based on genetic algorithm is designed, and a hybrid optimization algorithm combining micro-evolutionary algorithm and Lagrange relaxation is constructed.

[0102] Compared to existing methods, this invention has three main advantages. First, it considers the differences in loading and unloading points under interruption scenarios and combines the requirements of different transported goods on the capacity and grade of transport routes to construct a two-layer model for planning multimodal transport routes. Second, in the model construction, this invention refines the transfer and connection links between different modes of transportation, thereby making the optimized transport plan truly feasible. Third, in terms of algorithms, this invention addresses the large-scale problems and complex networks of multimodal transport by generating a set of multimodal transport routes based on a genetic algorithm and constructing a hybrid optimization algorithm based on micro-evolutionary algorithms and Lagrange relaxation. This simplifies the problem during the solution process, thereby reducing the difficulty of solving the problem and improving the model's solution efficiency.

[0103] This invention applies route planning algorithms (such as Dijkstra's algorithm and genetic algorithms) to optimize transportation routes in multimodal transport route planning. In addition to these two algorithms, A* algorithm, Floyd algorithm, or ant colony algorithm can also be used, which are also suitable for route generation and optimization and can be flexibly adjusted according to actual application requirements.

[0104] This invention employs a combination of exact and heuristic algorithms (such as the exact solution method Lagrange relaxation algorithm and the heuristic solution method micro-evolutionary algorithm) to solve multimodal transport schemes. For exact solution methods, dynamic programming algorithms and branch-and-cut algorithms can also be used; for heuristic solution methods, genetic algorithms, ant colony algorithms, and particle swarm optimization algorithms can also be used. These algorithms can all solve multimodal transport scheme generation models, but they will differ in solution quality and efficiency.

[0105] This invention also provides a system for generating alternative routes and transportation schemes for multimodal transport based on network node interruptions. The system includes a computer device, which includes a processor and a memory. The memory stores computer instructions, and the processor executes the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps of the method described above.

[0106] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the aforementioned method for generating alternative multimodal transport routes and transport schemes based on network node interruptions. The computer-readable storage medium can be a tangible storage medium, such as random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, floppy disks, hard disks, removable storage disks, CD-ROMs, or any other form of storage medium known in the art.

[0107] Those skilled in the art will understand that the exemplary components, systems, and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Whether implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention. When implemented in hardware, it can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the desired tasks. The programs or code segments can be stored in a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave.

[0108] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0109] In this invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or in place of features of other embodiments.

[0110] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for generating alternative multimodal transport routes and transportation schemes based on road network node interruptions, characterized in that, The steps of the method include: Obtain road network interruption information, which includes interruption nodes and interruption arcs; mark the road network interruption information from the original road network data, which includes a set of nodes and a set of arcs, and the set of nodes includes loading and unloading nodes and non-loading and unloading nodes; If the road network interruption information is updated, then for each original path, determine whether the original path includes road network interruption information; If so, then mark the interruption point in the original path, and construct an upstream candidate loading and unloading point set and a downstream candidate loading and unloading point set based on the first constraint condition. With minimizing the time as the first objective function, the upper-level model selects one loading and unloading node from each of the upstream candidate loading and unloading point set and the downstream candidate loading and unloading point set. The first loading / unloading node selected from the upstream candidate loading / unloading point set and the second loading / unloading node selected from the downstream candidate loading / unloading point set are input into the lower-level model. The lower-level model constructs an initial path set based on the first loading / unloading node and the second loading / unloading node. The lower-level model uses a genetic algorithm to calculate the path fitness and performs crossover and mutation iterations. Finally, it selects a preset number of paths with higher fitness to construct a multimodal transport alternative path set. The set of alternative multimodal transport routes includes multiple routes that are identical at both ends. When a transport unit travels to one end of a route, the time consumed by the transport unit on each route is calculated based on the transport conditions of the transport unit. The final route is determined based on the consumption time, and a multimodal transport plan is constructed.

2. The method for generating alternative multimodal transport routes and transport schemes based on road network node interruptions according to claim 1, characterized in that, If the road network interruption information is updated, then for each original path, in the step of determining whether the original path includes the road network interruption information, if the original path does not include the road network interruption information, then the original path will not be processed.

3. The method for generating alternative multimodal transport routes and transport schemes based on road network node interruptions according to claim 1, characterized in that, The steps for constructing the upstream candidate loading / unloading point set and the downstream candidate loading / unloading point set based on the first constraint condition include: For each loading and unloading node globally, determine whether the material type of the loading and unloading node matches the material being transported, and construct the matching loading and unloading nodes as the first set of loading and unloading points; Calculate the shortest path between each loading / unloading node in the first set of loading / unloading points and the interruption point. Filter the loading / unloading nodes based on the distance of the shortest path to obtain the second set of loading / unloading points. Based on the extension direction of the original path interruption point, the loading and unloading nodes in the second loading and unloading point set are divided into the upstream candidate loading and unloading point set and the downstream candidate loading and unloading point set.

4. The method for generating alternative multimodal transport routes and transport schemes based on road network node interruptions according to claim 1, characterized in that, In the step of selecting one loading / unloading node from each of the upstream and downstream candidate loading / unloading point sets using the upper-level model with minimizing the duration as the first objective function, one loading / unloading node is selected from each of the upstream and downstream candidate loading / unloading point sets to form a node pair. The duration of each node pair is calculated. The loading / unloading node in the upstream candidate loading / unloading point set corresponding to the node pair with the shortest duration is taken as the first loading / unloading node, and the loading / unloading node in the downstream candidate loading / unloading point set corresponding to the node pair with the shortest duration is taken as the second loading / unloading node.

5. The method for generating alternative multimodal transport routes and transport schemes based on road network node interruptions according to claim 1, characterized in that, In the step of calculating path fitness using a genetic algorithm in the lower-level model, the transportation time and safety probability of the candidate paths are calculated respectively, and the path fitness is calculated based on the transportation time and safety probability.

6. The method for generating multimodal transport alternative routes and transport schemes based on road network node interruptions according to any one of claims 1 to 5, characterized in that, The steps for calculating the transit time and safety probability of candidate routes include: Calculate the arc betweenness centrality, geographical factor influence factor, and weather factor influence factor for each arc segment of the candidate path; The arc segment safety probability is calculated based on the arc segment betweenness centrality, geographical factor influence factor, and weather factor influence factor for each arc segment. The safety probability of a candidate path is calculated based on the safety probability of each arc segment of the candidate path.

7. The method for generating multimodal transport alternative routes and transportation schemes based on road network node interruptions according to claim 6, characterized in that, In the step of calculating the arc segment safety probability based on the arc segment betweenness centrality, geographical factor influence factor, and weather influence factor for each arc segment, the following formula is used to calculate the arc segment betweenness centrality, geographical factor influence factor, and weather influence factor: in, Describes the betweenness centrality of an arc segment. Indicates the arc segments traversed in the candidate path The number of candidate paths, This represents the total number of candidate paths; The geographical factor influencing factors are calculated using the following formula: in, Represents arc segment Weighting coefficients for terrain complexity; Represents arc segment The complexity of the terrain, Represents arc segment Geographical factors are influencing factors.

8. The method for generating alternative multimodal transport routes and transport schemes based on road network node interruptions according to claim 6, characterized in that, In the step of calculating the arc segment safety probability based on the arc segment betweenness centrality, geographical factor influence factor, and weather factor influence factor for each arc segment, the weather factor influence factor is calculated using the following formula: in, Represents arc segment Weather influencing factors of terrain complexity The weighting coefficients representing meteorological conditions. This indicates the overall meteorological conditions.

9. The method for generating multimodal transport alternative routes and transportation schemes based on road network node interruptions according to claim 6, characterized in that, In the step of calculating the safety probability of a candidate path based on the safety probability of each arc segment of the candidate path, the average value of the safety probabilities of the arc segments in the candidate path is calculated as the safety probability of the candidate path.

10. A system for generating alternative routes and transportation schemes for multimodal transport based on road network node interruptions, characterized in that, The system includes a computer device, which includes a processor and a memory. The memory stores computer instructions, and the processor executes the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps of the method as described in any one of claims 1 to 9.

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