Passenger bus transfer method, device and equipment under subway operation interruption

Abstract

The present application relates to the field of bus scheduling and path optimization, and provides a passenger bus transfer method, device and equipment under subway operation interruption, which comprises the following steps: constructing a passenger transfer demand distribution randomness model based on historical data, wherein the historical data is passenger transfer demand data; determining a passenger evacuation strategy according to the fault site and the passenger transfer demand distribution randomness model; determining a candidate bus line set based on the passenger evacuation strategy; constructing a bus transfer model based on the candidate bus line set, and determining the minimization of total delay as the optimization target of the bus transfer model; the total delay is determined according to the delay of ordinary passengers, the delay of passengers who fail to successfully board and the delay of stranded passengers; solving the optimization target based on a genetic algorithm, and outputting an optimal solution. The present application solves the defect of low scheduling efficiency of the traditional single-line transfer method in the prior art, improves the evacuation efficiency, and reduces passenger delay.

Description

Technical Field

[0001] This invention relates to the field of public transport scheduling and route optimization technology, and in particular to a method, apparatus and equipment for passenger bus transfer when subway operation is interrupted. Background Technology

[0002] In recent years, with the acceleration of urbanization, urban public transportation systems have become increasingly complex, becoming an indispensable part of citizens' daily travel. As the backbone of the urban public transportation network, the subway is favored by many passengers for its efficiency and convenience. However, subway hubs, as intersections of multiple rail transit lines, directly affect the stability and efficiency of the entire urban public transportation network. Unfortunately, frequent emergencies in urban public transportation, especially malfunctions at subway hubs, have significantly impacted network performance, causing large numbers of passengers to be stranded and experiencing difficulties transferring, further exacerbating traffic congestion.

[0003] In addressing the issue of subway hub station disruptions, bus connections are widely used as an effective evacuation method in actual operations. Currently, research on bus connections mainly focuses on optimizing connections after ordinary subway stations and single subway lines experience malfunctions. These studies have effectively alleviated traffic pressure around affected stations by designing connecting bus routes, optimizing passenger allocation, and adjusting bus departure schedules.

[0004] However, existing technologies face new challenges in the complex scenario of subway hub disruptions. Subway hub disruptions not only lead to large numbers of passengers being stranded, but also result in more diverse and complex passenger transfer needs. Because hubs connect to numerous lines, and passengers' destinations may span the entire city, traditional single-line connection methods are insufficient to meet the demand. Summary of the Invention

[0005] This invention provides a method, device, and equipment for passenger bus connection during subway operation interruption, which solves the problem of low scheduling efficiency in the traditional single-line connection method in the prior art, improves evacuation efficiency, and reduces passenger delays.

[0006] This invention provides a method for passenger bus transfer during subway operation interruption, comprising the following steps:

[0007] A stochastic model of passenger transfer demand distribution is constructed based on historical data, where the historical data refers to passenger transfer demand data.

[0008] The passenger evacuation strategy is determined based on the faulty stations and the stochastic model of passenger transfer demand distribution;

[0009] A set of candidate bus routes is determined based on the passenger evacuation strategy.

[0010] A bus connection model is constructed based on the candidate bus route set, and the optimization objective of the bus connection model is determined to minimize the total delay; the total delay is determined based on the delay of ordinary passengers, the delay of passengers who failed to board the bus, and the delay of stranded passengers.

[0011] The optimization objective is solved using a genetic algorithm, and the optimal solution is output.

[0012] According to the present invention, a passenger bus transfer method under subway operation interruption is provided. The step of determining a candidate bus route set based on the passenger evacuation strategy specifically includes: determining the transfer start station and transfer destination station based on the passenger evacuation strategy; selecting all bus stops within a preset radius for each transfer start station and each transfer destination station to form a candidate station set for transfer; constructing a candidate bus route set based on preset rules and the candidate station set; the preset rule is that when a bus route passes through bus stops within both the preset radius of the transfer start station and the preset radius of the transfer destination station, then the bus route is selected as a candidate bus route for transfer.

[0013] According to the present invention, a method for passenger bus connection during subway operation interruption includes the following steps: constructing a bus connection model based on the candidate bus route set and determining minimizing the total delay as the optimization objective of the bus connection model. Specifically, this includes: determining the bus connection model and its model set, model variables, and model parameters based on the candidate bus route set; determining the delays of ordinary passengers, passengers who failed to board, and stranded passengers based on the model set, model variables, and model parameters; determining the total delay based on the delays of ordinary passengers, passengers who failed to board, and stranded passengers; and minimizing the total delay as the optimization objective to obtain the optimization objective function.

[0014] According to the present invention, a method for passenger bus connection during subway operation interruption is provided. The method involves solving the optimization objective based on a genetic algorithm and outputting the optimal solution. Specifically, the method includes: defining the basic parameters of the genetic algorithm; randomly generating an initial population containing multiple individuals, each individual representing a bus scheduling scheme; calculating the fitness value of each individual based on the optimization objective function; iterating the current population based on the fitness value to form a new population until a preset iteration termination condition is met; and returning the individual with the highest fitness value as the optimal solution for the bus scheduling scheme.

[0015] According to the present invention, a method for connecting passengers with public transportation during subway operation interruptions includes iteratively forming a new population based on fitness values. Specifically, this method comprises: selecting individuals with fitness values ​​higher than a preset threshold from the current population as parents; performing crossover operations on the selected parent individuals to generate new offspring individuals; performing mutation operations on some of the new offspring individuals; checking whether the generated individuals satisfy the constraints after the crossover and mutation operations; recalculating the fitness value of each individual based on the individuals that satisfy the constraints; and selecting the individual with the highest fitness value to enter the next generation, forming a new population.

[0016] According to the present invention, a passenger bus transfer method under subway operation interruption is provided. The step of determining the passenger evacuation strategy based on the faulty station and the randomness model of the passenger transfer demand distribution specifically includes: identifying the hub station in the bus network that has experienced the fault as the faulty station; analyzing the passenger transfer demand of the faulty station; and determining a multi-destination passenger evacuation strategy based on the passenger transfer demand.

[0017] According to the present invention, a passenger bus connection method under subway operation interruption is provided. Before constructing the bus connection model, the connection characteristics of candidate bus routes are analyzed. Based on the analysis results, when a bus route's vehicles are within the dispatch range of two evacuation stations, one of the two evacuation stations is selected as the connection starting station of the bus route according to the actual running distance.

[0018] The present invention also provides a public transport connection device based on passenger transfer needs, comprising the following modules:

[0019] The demand distribution model construction module is used to construct a stochastic model of passenger transfer demand distribution based on historical data, wherein the historical data is passenger transfer demand data;

[0020] The passenger evacuation strategy determination module is used to determine the passenger evacuation strategy based on the faulty station and the randomness model of the passenger transfer demand distribution;

[0021] A bus connection model construction module is used to determine a set of candidate bus routes based on the passenger evacuation strategy; construct a bus connection model based on the set of candidate bus routes, and determine minimizing the total delay as the optimization objective of the bus connection model; the total delay is determined based on the delay of ordinary passengers, the delay of passengers who failed to board the bus, and the delay of stranded passengers.

[0022] The solution module is used to solve the optimization objective based on a genetic algorithm and output the optimal solution.

[0023] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the passenger bus transfer method under subway operation interruption as described above.

[0024] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the passenger bus transfer method under subway operation interruption as described above.

[0025] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the passenger bus transfer method under subway operation interruption as described above.

[0026] This invention provides a passenger bus connection method, apparatus, and equipment for subway operation interruptions, which offers the following advantages: By constructing a stochastic model of passenger transfer demand distribution based on historical data, it is possible to more accurately predict and grasp passenger transfer demand after subway hub station disruptions, thereby formulating more reasonable evacuation strategies and reducing passenger waiting time and overall delays. Through passenger evacuation strategies, passenger transport pressure at the affected station and its adjacent stations is effectively alleviated, improving passenger evacuation efficiency and safety. Simultaneously, through detailed analysis and classification of candidate bus routes, existing bus resources can be allocated and utilized more rationally, reducing connection costs and improving transportation efficiency. With minimizing the total delay for both regular and stranded passengers as the optimization objective, a bus connection model is constructed and solved using a genetic algorithm, yielding the optimal bus scheduling scheme, which can minimize passenger delay time and improve the overall efficiency of bus connection services. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0028] Figure 1 This is a flowchart illustrating the passenger bus transfer method provided by the present invention during subway operation interruption.

[0029] Figure 2 This is a schematic diagram of the multi-destination evacuation of stranded passengers provided by the present invention.

[0030] Figure 3 This is a schematic diagram of the analysis of bus connection route types provided by the present invention.

[0031] Figure 4 This is a schematic diagram of the structure of the public transport connection device based on passenger transfer needs provided by the present invention.

[0032] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0034] This invention addresses the scenario of subway hub station disruptions by proposing a multi-destination bus connection strategy that considers the stochastic optimization of passenger transfer needs. This strategy leverages bus connections to restore the original station's transfer and distribution functions. After a subway hub station disruption, the distribution of passenger transfer needs is stochastically modeled. Existing bus routes are categorized based on their distance from bus stops to connection points, and a "one-stop advance evacuation" strategy is proposed. Fine-tuning of existing bus routes is then implemented to ensure passenger connectivity. With the optimization objective of minimizing the total delay for both regular and stranded passengers, a multi-destination transfer bus connection and route optimization model is constructed to solve for the bus route operation adjustment scheme.

[0035] The following is combined with Figures 1-5 The embodiments of the present invention are described in detail.

[0036] Figure 1 This is one of the flowcharts illustrating the passenger bus connection method under subway operation interruption provided by the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0037] S110. Construct a stochastic model of passenger transfer demand distribution based on historical data, where historical data refers to passenger transfer demand data.

[0038] Specifically, the randomness of passenger transfer demand distribution is modeled.

[0039] Following a malfunction at a subway hub, passenger transfer demand exhibits uncertainty and dynamism, varying across different time periods and dates. Therefore, passenger transfer demand should be introduced as a random variable during model construction. Based on historical data, this study investigates the distribution characteristics of passenger transfer demand, completing a stochastic model of its distribution to account for the coupling relationship between connecting services and passenger demand under random and dynamic passenger demand conditions.

[0040] S120. Determine passenger evacuation strategies based on the stochastic model of the distribution of faulty stations and passenger transfer demand.

[0041] According to the present invention, a passenger bus connection method under subway operation interruption is provided, which determines the passenger evacuation strategy based on the randomness model of the distribution of faulty stations and passenger transfer demand. Specifically, it includes: identifying the hub station in the bus network that has experienced a fault as the faulty station; analyzing the passenger transfer demand at the faulty station; and determining the passenger evacuation strategy for multiple destinations based on the passenger transfer demand.

[0042] Specifically, passenger evacuation strategies mainly include the following two types.

[0043] (1) Passengers were evacuated to multiple destinations.

[0044] Hub stations serve as transfer points for passengers. When a fault occurs at a hub station, compared to regular subway lines, in addition to considering the large influx of passengers, the diversity of passenger destinations should also be taken into account. For example... Figure 2 As shown, passengers departing from station A2 may originally plan to go to station B5 or transfer at station B5 to station B1 to B4. Therefore, the multi-destination transfer of passengers needs to be considered in the bus connection model.

[0045] (2) Passengers should be evacuated one stop in advance.

[0046] When a subway hub experiences a malfunction, not only are passengers stranded at the affected station, but the disruption also causes large numbers of passengers to gather at adjacent stations on the affected line, creating significant passenger pressure and potential safety hazards. To improve passenger evacuation efficiency and reduce overall delays, this invention proposes an "early evacuation one station" strategy. For example... Figure 2 As shown, the station failure occurred at transfer hub B5. Due to the failure, lines from B5 to A2, B1, B2, B3, and B4 were all affected, and the subway operation mode was adjusted to interval operation. This study focuses on the transfer direction of passengers from station A2 to stations B1-B5. Station A2, as the starting point for passenger transfer, experienced a large number of stranded passengers. Therefore, station A1 was also considered as an evacuation station to alleviate passenger pressure at station A2. Furthermore, the shuttle vehicles in this invention are sourced from existing bus routes. Adding a shuttle starting station during the fine-tuning of routes allows for more efficient utilization of different types of bus lines, thereby reducing shuttle costs and improving transportation efficiency.

[0047] S130. Determine the set of candidate bus routes based on passenger evacuation strategies.

[0048] According to the present invention, a passenger bus transfer method under subway operation interruption is provided, which determines a set of candidate bus routes based on passenger evacuation strategies. Specifically, it includes: determining the transfer start station (hereinafter referred to as the start station or origin) and the transfer destination station (hereinafter referred to as the destination station or destination) based on the passenger evacuation strategy; selecting all bus stops within a preset radius for each transfer start station and each transfer destination station to form a set of candidate stations for transfer; constructing a set of candidate bus routes based on preset rules and the set of candidate stations; the preset rule is that when a bus route passes through bus stops within both the preset radius of the transfer start station and the preset radius of the transfer destination station, then the bus route is selected as a candidate bus route for transfer.

[0049] Specifically, an analysis of bus route types.

[0050] When constructing the candidate bus route set, a radius range r is given and used as a threshold for the possible detour distance (straight-line distance) for bus connections. In this invention, r is set to 2km. All bus stops within 2km of the originating and destination subway stations are screened. If a bus route passes through stops that are included in both the originating and destination subway station sets, then the route is considered a candidate connection route. The candidate bus route set is constructed according to this rule.

[0051] According to the present invention, a passenger bus connection method under subway operation interruption is provided. Before constructing the bus connection model, the connection characteristics of candidate bus routes are analyzed. Based on the analysis results, when a bus route's vehicles are within the dispatch range of two evacuation stations, one of the two evacuation stations is selected as the connection starting station of the bus route according to the actual running distance.

[0052] Specifically, before constructing the connection model, the characteristics of candidate bus routes need further analysis. This analysis considers the number of connection start and destination stations along the original bus routes. To accommodate early evacuation, this invention includes two connection start stations and multiple connection destination stations. When a bus on a route is within the dispatch range of two evacuation stations, one of the two evacuation stations is selected as the connection start station based on the actual travel distance. In other words, within a single operating cycle, a bus will not travel to both connection start stations to pick up passengers. The specific types of bus routes are analyzed as follows: Figure 3 As shown in Table 1.

[0053] Table 1 Analysis of Bus Connection Route Types

[0054]

[0055] The connection rules for different types of lines are as follows:

[0056] When a bus route is of type 1, type 2, or type 3, it must take a detour through the starting point to use the route as a connecting route.

[0057] When the bus route is of type 2 or type 5, the original route does not pass through the connecting starting station and the destination station. When the number of destination stations is 1, it means that the route does not pass through the destination station. After the connecting route vehicle is activated, it must pass through the destination station. When the number of destination stations is 2, it means that the route only passes through one of the destination stations. After the connecting route vehicle is activated, it can choose whether to detour to the other destination station.

[0058] When the bus route is of type 1 or type 4, passengers need to choose at least one destination to detour.

[0059] After a bus picks up passengers at the starting station, it must take them to their destination. This means that passengers need to choose in advance whether to take a detour to their destination before being picked up at the starting station.

[0060] S140. Construct a bus connection model based on the candidate bus route set, and determine minimizing the total delay as the optimization objective of the bus connection model.

[0061] Total delays are determined based on delays for regular passengers, delays for passengers who did not board, and delays for stranded passengers.

[0062] According to the present invention, a passenger bus connection method under subway operation interruption is provided. A bus connection model is constructed based on a candidate bus route set, and the optimization objective of the bus connection model is determined to minimize the total delay. Specifically, the method includes: determining the bus connection model and its model set, model variables, and model parameters based on the candidate bus route set; determining the delays of ordinary passengers, passengers who failed to board, and stranded passengers based on the model set, model variables, and model parameters; determining the total delay based on the delays of ordinary passengers, passengers who failed to board, and stranded passengers; and obtaining the optimization objective function by minimizing the total delay. The process of constructing the bus connection model is described in detail below.

[0063] (1) Model assumptions

[0064] The following assumptions are made during the construction of the bus connection model: a. Buses prioritize picking up ordinary passengers at the connection starting station; b. The capacity of the connection parking spaces and the delays caused by multiple buses arriving / stopping at the same time are not considered; c. When the hub station malfunctions, stranded passengers will spontaneously go to the corresponding bridging bus area to wait.

[0065] Tables 2.1, 2.2, and 2.3 below show the symbolic representations and definitions of the bus connection model set, variables, and parameters, respectively.

[0066]

[0067]

[0068] Table 2.1 Connection Model Set and Set Meaning

[0069] Table 2.2 Connection Model Variables and Their Meanings

[0070]

[0071] Table 2.3 Connection Model Parameters and Parameter Meanings

[0072]

[0073] (2) Optimization Objective

[0074] The objective function for optimizing the public transport connection model is defined as follows:

[0075] minT total =min(T) ordinary +T fail +σT disrupted )

[0076] The objective function is defined as minimizing the total delay T. total The total delay is T, which is the delay for ordinary passengers on the bus. ordinary Delays for passengers who failed to board the train (T) fail (Due to full capacity for stranded passengers) Delays for stranded passengers T disrupted The sum of these values. For ordinary passengers, the delay refers to the increased travel time within the bus following the detour to pick up stranded passengers; for passengers who failed to board, the delay refers to the additional waiting time; and for stranded passengers, the delay is defined as the time spent waiting for connecting vehicles at the interrupted hub. Here, σ is a weighting parameter, ranging from 0 to 1, representing the degree of impact of the delay for stranded passengers.

[0077] Delay for ordinary passengers T ordinary The main cause of the delays is the extra travel time caused by vehicles taking detours. Different types of bus routes will have different delays. For example, there will be no delays for ordinary passengers on type 6 routes. The delays in types 2 and 5 need to be further judged based on the specific situation of the route destination. In types 1 and 4, different connecting destinations will also cause corresponding delays.

[0078]

[0079] Delays for passengers unable to board the train fail The extra waiting time for passengers who cannot board fully booked buses is mainly determined by train departure intervals.

[0080]

[0081] Delays for stranded passengers T disrupted This is the sum of the time all stranded passengers wait for the arrival of a shuttle vehicle, that is, the sum of the waiting time from the occurrence of the breakdown to the passengers being picked up.

[0082]

[0083] (3) Construction of bridging lines

[0084]

[0085] The generation of feeder route pairs is determined by the selection of the origin and destination stations. For example, Type 1 bus routes require a pair of origin-destination feeder routes, generating one or two pairs of feeder routes depending on the number of destinations. Type 2 routes need to consider the specific circumstances of the route to determine the status of the feeder routes. Type 3 routes only require a pair of destination feeder routes. Type 6 routes do not require any new feeder route pairs. Feeder route pairs for Types 1, 2, and 4 cannot overlap.

[0086] (4) Time constraints

[0087]

[0088] D bj =A bj +δ bj

[0089]

[0090] S150. Solve the optimization objective based on the genetic algorithm and output the optimal solution.

[0091] According to the present invention, a passenger bus connection method under subway operation interruption is provided. The method is based on a genetic algorithm to solve the optimization objective and output the optimal solution. Specifically, the method includes: defining the basic parameters of the genetic algorithm; randomly generating an initial population containing multiple individuals, each individual representing a bus scheduling scheme; calculating the fitness value of each individual according to the optimization objective function; iterating the current population to form a new population based on the fitness value until a preset iteration termination condition is met; and returning the individual with the highest fitness value as the optimal solution of the bus scheduling scheme.

[0092] According to the present invention, a method for passenger bus transfer during subway operation interruption involves iteratively forming a new population based on fitness values. Specifically, the method includes: selecting individuals with fitness values ​​higher than a preset threshold from the current population as parents; performing crossover operations on the selected parent individuals to generate new offspring individuals; performing mutation operations on some of the new offspring individuals; checking whether the generated individuals meet the constraints after the crossover and mutation operations; recalculating the fitness value of each individual based on those meeting the constraints; and selecting the individual with the highest fitness value to enter the next generation, forming a new population.

[0093] Specifically, a genetic algorithm is an optimization algorithm that simulates the process of biological evolution and is suitable for solving complex optimization problems. This invention introduces a genetic algorithm for the bus connection problem, aiming to optimize bus scheduling and operation to meet passenger demand and improve system efficiency. The goal of this problem is to optimize connections between multiple bus routes to minimize total passenger delays, while considering other constraints. This problem is essentially a mixed-integer nonlinear programming problem involving multiple interrelated variables.

[0094] (1) Individual representation

[0095] In a genetic algorithm, each individual represents a possible solution. In this problem, each individual contains the following decision variables:

[0096] x b : Indicates the dispatch status of the b-th bus in bus route i.

[0097] u iog and u idg : These represent the connection status of the j-th stop on bus route i (whether to take a detour to pick up stranded passengers). The combination of these variables represents a complete scheduling scheme.

[0098] (2) Fitness function

[0099] The fitness function is used to evaluate the performance of each individual. In this invention, the goal is to minimize the total delay for both regular and stranded passengers.

[0100] (3) Introduction to the overall process of genetic algorithm

[0101] Population initialization: A certain number of individuals are randomly generated to form the initial population. Each individual represents a possible bus scheduling scheme.

[0102] Selection operation: Use Tournament Selection to select superior individuals from the population to enter the next generation.

[0103] Crossover: Performing a crossover operation on selected individuals generates new individuals. Crossover simulates gene recombination, generating offspring with characteristics of both parents by exchanging partial genes from the parents.

[0104] Mutation: This involves performing mutation operations on a subset of individuals, altering a portion of their gene values. Mutation helps maintain population diversity and may lead to the discovery of better solutions.

[0105] Constraint checking: After crossover and mutation, check whether the generated individuals satisfy the constraints of the problem. If not, apply penalties or adjust the individuals to satisfy the constraints.

[0106] Fitness assessment: Calculate the fitness value for each individual, which indicates the quality of their solution.

[0107] Selecting the next generation: Select the next generation of individuals based on fitness values, and continue to select, crossover, mutate and evaluate to form a new population.

[0108] Termination condition: The algorithm ends when a certain termination condition is met, such as after a fixed number of iterations (algebras) or when the fitness value reaches a predetermined threshold.

[0109] Output the optimal solution: After the algorithm finishes, return the individual with the highest fitness value as the optimal solution to the bus scheduling problem.

[0110] (4) Overall algorithm flow interpretation

[0111] a. Initialization, i.e. setting parameters: defining parameters such as population size (e.g., n_pop), crossover probability (e.g., cxpb), mutation probability (e.g., mutpb), and maximum number of iterations (e.g., NGEN).

[0112] b. Population initialization, i.e., generating the initial population: creating a population containing multiple individuals (schemes). Each individual is a dictionary containing x. b u iog and u idg The values ​​of variables. The initial population is generated randomly, meaning that the value of each variable is randomly determined within a reasonable range.

[0113] c. Fitness Evaluation: Calculate the fitness value for each individual, indicating the quality of the scheduling plan. The fitness value is calculated based on the objective function of total passenger delay. A higher fitness value (corresponding to a lower total passenger delay) indicates a better plan.

[0114] d. Iterative process: In each generation, the algorithm optimizes the population through selection, crossover, mutation, and evaluation, gradually approaching the optimal solution.

[0115] Selection: Parent individuals are selected from the current population based on their fitness values. The method used is Tournament Selection, which randomly selects several individuals from the population and chooses the individual with the highest fitness value as the parent. This parent individual will be used to generate the next generation of the population.

[0116] Crossover: A crossover operation is performed on selected parent individuals to generate new individuals (offspring). The goal of the crossover operation is to combine the genes (i.e., variable x) of two parent individuals. b u iog and u idg The values ​​of the variables (i.e., the values ​​of the variables) are exchanged between the two parent individuals to produce better offspring. During the crossover process, the variable values ​​of the two parent individuals may be swapped, forming new combinations.

[0117] Mutation: Mutation operations are performed on offspring individuals to introduce diversity. Mutation is achieved by randomly changing the values ​​of the individuals. The probability of mutation is controlled by a set mutation probability value, mutpb. For example, if u idg The current value is 0, and after mutation, it may become 1, and vice versa. Mutation operations can help algorithms escape local optima and explore more solution spaces.

[0118] Constraint checks: After crossover and mutation, check whether the generated individuals satisfy the constraints of the problem. For example, check u iog and u idg Does it satisfy the uniqueness constraint for each site, or x b Is the scheduling reasonable? If an individual does not meet the constraints, penalties or corrections can be imposed.

[0119] Fitness assessment: Calculate the fitness value for each newly generated individual. The fitness value is derived from the objective function. The new fitness value will be used in the selection process for the next generation.

[0120] Population Update: Based on fitness values, select the individual with the highest fitness (corresponding to the lowest total delay) to enter the next generation. The population will continuously optimize to gradually approach the optimal solution. The updated population will continue to undergo selection, crossover, and mutation in the next generation until the required number of iterations is reached or convergence occurs.

[0121] Iteration termination: The algorithm ends when a preset termination condition is met, such as when the number of iterations reaches the maximum value NGEN or when the fitness value of the population no longer changes significantly (i.e., convergence).

[0122] e. Output the optimal solution: After the algorithm finishes, it returns the individual with the highest fitness value as the optimal solution to the bus scheduling problem. This individual contains the optimal x. b u iog and u idg The combined approach can minimize the total delay time for passengers.

[0123] (5) This genetic algorithm optimizes the bus connection scheme through iterative selection, crossover, and mutation. The core of the algorithm lies in designing appropriate individual representations, reasonable fitness functions, and effective constraint handling mechanisms. Through these steps, the algorithm can find an optimal bus scheduling scheme that minimizes the total passenger delay.

[0124] The proposed strategy for bus dispatching and route optimization during subway hub station disruptions effectively improves passenger evacuation efficiency, reduces the total delay for both regular and stranded passengers, and enhances the emergency response capability and resilience of the urban public transportation system by constructing a randomness model of passenger transfer demand and combining it with a genetic algorithm to optimize bus dispatching.

[0125] The following describes the bus transfer device based on passenger transfer needs provided by the present invention. The bus transfer device based on passenger transfer needs described below can be referred to in correspondence with the passenger bus transfer method under subway operation interruption described above.

[0126] like Figure 4 The diagram illustrates a public transport connection device based on passenger transfer demand provided by the present invention, comprising: a demand distribution model construction module 410, used to construct a randomness model of passenger transfer demand distribution based on historical data, wherein the historical data is passenger transfer demand data; a passenger evacuation strategy determination module 420, used to determine a passenger evacuation strategy based on faulty stations and the randomness model of passenger transfer demand distribution; a public transport connection model construction module 430, used to determine a set of candidate bus routes based on the passenger evacuation strategy; construct a public transport connection model based on the set of candidate bus routes, and determine minimizing the total delay as the optimization objective of the public transport connection model; the total delay is determined based on the delay of ordinary passengers, the delay of passengers who failed to board, and the delay of stranded passengers; and a solution module 440, used to solve the optimization objective based on a genetic algorithm and output the optimal solution.

[0127] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 540. The processor 510, communication interface 520, and memory 530 communicate with each other via the communication bus 540. The processor 510 can call logical instructions in the memory 530 to execute a passenger bus connection method under subway operation interruption. This method includes: constructing a stochastic model of passenger transfer demand distribution based on historical data (the historical data being passenger transfer demand data); determining a passenger evacuation strategy based on the faulty station and the stochastic model of passenger transfer demand distribution; determining a set of candidate bus routes based on the passenger evacuation strategy; constructing a bus connection model based on the candidate bus route set, and determining minimizing the total delay as the optimization objective of the bus connection model; the total delay is determined based on the delay of ordinary passengers, the delay of passengers who failed to board, and the delay of stranded passengers; and solving the optimization objective using a genetic algorithm to output the optimal solution.

[0128] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0129] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the passenger bus connection method under subway operation interruption provided by the above methods. The method includes: constructing a stochastic model of passenger transfer demand distribution based on historical data, where the historical data is passenger transfer demand data; determining a passenger evacuation strategy based on the faulty station and the stochastic model of passenger transfer demand distribution; determining a set of candidate bus routes based on the passenger evacuation strategy; constructing a bus connection model based on the set of candidate bus routes, and determining minimizing the total delay as the optimization objective of the bus connection model; the total delay is determined based on the delay of ordinary passengers, the delay of passengers who failed to board, and the delay of stranded passengers; and solving the optimization objective based on a genetic algorithm to output the optimal solution.

[0130] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a passenger bus connection method under subway operation interruption provided by the above methods. The method includes: constructing a stochastic model of passenger transfer demand distribution based on historical data, where the historical data is passenger transfer demand data; determining a passenger evacuation strategy based on the faulty station and the stochastic model of passenger transfer demand distribution; determining a set of candidate bus routes based on the passenger evacuation strategy; constructing a bus connection model based on the set of candidate bus routes, and determining minimizing the total delay as the optimization objective of the bus connection model; the total delay is determined based on the delay of ordinary passengers, the delay of passengers who failed to board, and the delay of stranded passengers; and solving the optimization objective based on a genetic algorithm to output the optimal solution.

[0131] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0132] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for passenger bus transfer during subway operation interruption, characterized in that, include: Based on the distribution characteristics of passenger transfer demand from historical data, a stochastic model of passenger transfer demand distribution is constructed. The historical data is passenger transfer demand data, and the stochastic model of passenger transfer demand distribution considers the coupling relationship between connecting services and passenger demand. The passenger evacuation strategy is determined based on the faulty station and the stochastic model of passenger transfer demand distribution, including: identifying the hub station in the public transport network that has experienced a failure as the faulty station; analyzing the passenger transfer demand at the faulty station; and determining a multi-destination passenger evacuation strategy based on the passenger transfer demand. A set of candidate bus routes is determined based on the passenger evacuation strategy. A bus connection model is constructed based on the candidate bus route set, and the optimization objective of the bus connection model is determined to minimize the total delay; the total delay is determined based on the delay of ordinary passengers, the delay of passengers who failed to board the bus, and the delay of stranded passengers. The optimization objective is solved using a genetic algorithm, and the optimal solution is output.

2. The passenger bus transfer method under subway operation interruption according to claim 1, characterized in that, The process of determining the candidate bus route set based on the passenger evacuation strategy specifically includes: The starting and destination stations for the shuttle service are determined based on the aforementioned passenger evacuation strategy. Select all bus stops within a preset radius for each connecting start station and each connecting destination station to form a candidate station set for connecting routes; A candidate bus route set is constructed based on preset rules and the candidate station set; the preset rule is that when a bus route passes through stations that include both bus stations within a preset radius of the starting station and bus stations within a preset radius of the destination station, then the bus route is considered as a candidate bus route for connection.

3. The passenger bus transfer method under subway operation interruption according to claim 1, characterized in that, The step of constructing a bus connection model based on the candidate bus route set, and determining minimizing the total delay as the optimization objective of the bus connection model, specifically includes: Based on the candidate bus route set, determine the bus connection model and its model set, model variables, and model parameters; The delays for regular passengers, passengers who failed to board, and stranded passengers are determined based on the model set, model variables, and model parameters. The total delay is determined based on the delays of ordinary passengers, the delays of passengers who failed to board, and the delays of stranded passengers; Minimizing the total delay is taken as the optimization objective, resulting in the optimization objective function.

4. The passenger bus transfer method under subway operation interruption according to claim 1, characterized in that, The process of solving the optimization objective based on a genetic algorithm and outputting the optimal solution specifically includes: Define the basic parameters of the genetic algorithm, randomly generate an initial population containing multiple individuals, each individual representing a bus scheduling scheme; The fitness value of each individual is calculated based on the optimization objective function; The current population is iterated based on its fitness value to form a new population until the preset iteration termination condition is met. The individual with the highest fitness value is returned as the optimal solution for the bus dispatching scheme.

5. The passenger bus transfer method under subway operation interruption according to claim 4, characterized in that, The process of iteratively forming a new population based on the fitness value of the current population specifically includes: Select individuals with fitness higher than a preset threshold from the current population as parents, perform crossover on the selected parents, and generate new offspring individuals; Mutation operations are performed on some of the new offspring individuals; After crossover and mutation operations, check whether the generated individuals meet the constraints. The fitness value of each individual is recalculated based on the individuals that meet the constraints. The individual with the highest fitness value is selected to enter the next generation, forming a new population.

6. The passenger bus transfer method under subway operation interruption according to claim 1, characterized in that, Before constructing the public transport connection model, the method also includes: The connection characteristics of the candidate bus routes are analyzed; Based on the analysis results, when a bus route's vehicles are within the dispatch range of two evacuation stations, one of the two evacuation stations will be selected as the connecting start station for the bus route according to the actual operating distance.

7. A passenger bus shuttle device for subway operation interruption, characterized in that, include: The demand distribution model construction module is used to construct a passenger transfer demand distribution stochastic model based on the distribution characteristics of passenger transfer demand in historical data. The historical data is passenger transfer demand data, and the passenger transfer demand distribution stochastic model considers the coupling relationship between connecting services and passenger demand. The passenger evacuation strategy determination module is used to determine the passenger evacuation strategy based on the faulty station and the randomness model of the passenger transfer demand distribution, including determining the hub station in the public transport network that has experienced a fault as the faulty station. Analyze the passenger transfer demand at the faulty station and determine a multi-destination passenger evacuation strategy based on the passenger transfer demand; The bus connection model construction module is used to determine a set of candidate bus routes based on the passenger evacuation strategy. A bus connection model is constructed based on the candidate bus route set, and the optimization objective of the bus connection model is determined to be minimizing the total delay. The total delay is determined based on the delays of ordinary passengers, passengers who failed to board, and passengers who were stranded. The solution module is used to solve the optimization objective based on a genetic algorithm and output the optimal solution.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the passenger bus transfer method under subway operation interruption as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the passenger bus transfer method under the interruption of subway operation as described in any one of claims 1 to 6.

Images