Civil aviation flight time configuration method for popular travel

Abstract

The application discloses a civil aviation flight schedule configuration method for popular travel, which comprises the following steps: firstly, a multi-dimensional weight system for popular travel is determined, and the weight of each flight in the passenger type, airline type, route type and time type dimensions is calculated according to the flight schedule application data; then, the contribution degree of each flight to popular travel is evaluated by comprehensively considering the weight of each dimension, and the weight of each flight in the flight schedule offset is determined; next, a civil aviation flight schedule configuration model for popular travel is established, aiming at minimizing the total weight of the time offset and constraining the same; finally, the established flight schedule model is solved by using a Gurobi solver in a Python environment to obtain a civil aviation flight schedule table for popular travel. The application establishes a civil aviation flight schedule configuration model for popular travel, balances the popular travel demand, configuration efficiency and fairness, and improves the operability of the civil aviation flight schedule configuration.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of civil aviation flight schedule configuration, and particularly relates to a civil aviation flight schedule configuration method for mass travel. BACKGROUND

[0002] The optimization of civil aviation flight schedule configuration is of great significance for improving flight schedule utilization efficiency, promoting normal and orderly flight operation, and ensuring fair participation of air carriers in market competition. In recent years, China has been committed to promoting the massification of aviation services and continuously densifying airport network construction to establish a network of routes that is accessible, smooth, economical and efficient. At the same time, China actively develops regional airlines, promotes effective connection of trunk and regional flights, and promotes differentiated development of low-cost airlines, and fully implements the basic aviation service plan to ensure that the route network in old, young, border and poor areas is basically accessible. However, the existing civil aviation flight schedule configuration method has certain limitations in meeting the mass travel strategy. The current method fails to fully consider the demand of mass travel, resulting in that the flight schedule configuration cannot fully encourage the application of flight schedule by regional airlines and low-cost airlines that adapt to the demand of mass travel. Therefore, there is an urgent need for a new flight schedule configuration method that takes the mass travel strategy into account and can better meet market demand and promote the sustainable development of the civil aviation industry. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a civil aviation flight schedule configuration method for mass travel to solve the problem that the existing schedule configuration model does not fully consider the mass travel strategy. A multi-dimensional weight system for mass travel is determined to quantify the influencing factors of mass travel from four dimensions of passenger type, airline type, route type and time type. According to the flight schedule application data, the weight of each flight in the flight schedule offset is calculated. A flight schedule configuration model for mass travel is established to minimize the total weight of the time offset as the main target, to optimize the arrangement of the flight schedule, to meet the demand of mass travel, and to improve the efficiency and fairness of the configuration. In order to solve the established flight schedule model, the Gurobi solver is used to solve in the Python environment to provide a flight schedule configuration scheme that meets the requirements of mass travel, improves the efficiency and fairness of the configuration.

[0004] The present application adopts the following technical solutions to solve the above technical problems:

[0005] A civil aviation flight schedule configuration method for mass travel, comprising the following steps:

[0006] Step 1), a multi-dimensional weight system facing mass travel is determined, the multi-dimensional weight system includes four dimensions of passenger type, airline type, route type and time type; according to flight time application data, the weight of each flight in the passenger type, airline type, route type and time type dimensions is calculated;

[0007] Step 2), the contribution degree of each flight to mass travel is evaluated by synthesizing the weight of each dimension, and the weight of each flight in the flight time offset is determined;

[0008] Step 3), a civil aviation flight time configuration model facing mass travel is established, aiming at minimizing the total weight of time offset, and the civil aviation flight time configuration model is constrained through constraint conditions;

[0009] Step 4), the established flight time model is solved by using Gurobi solver in Python environment, and the civil aviation flight schedule facing mass travel is obtained.

[0010] As a further optimization scheme of the civil aviation flight time configuration method facing mass travel, the calculation formula of the weight of each flight in the passenger type dimension in step 1) is: Wherein, Indicates the passenger type dimension weight of flight m; Indicates the number of economy class seats of flight m; M indicates a flight set indexed by m;

[0011] The calculation formula of the weight of each flight in the airline type dimension is: Wherein, Indicates the airline type dimension weight of flight m; a m Indicates the airline company to which flight m belongs; A LCC Indicates a set of low-cost airlines; A FSC Indicates a set of full-service airlines;

[0012] The calculation formula of the weight of each flight in the route type dimension is: Wherein, Indicates the route type dimension weight of flight m; Indicates whether the route of flight m' is r, if yes, Otherwise Similarly, if the route of flight m' is the route r of flight m m , Otherwise |D m' |Indicates the number of days of flight m'; R is a route set indexed by r;

[0013] The calculation formula of the weight of each flight in the time type dimension is: Wherein, denotes the time type dimension weight of flight m; T h is the set of hours, indexed by t h is the index; is the average takeoff and landing times of the flight m in the hour t h is the average takeoff and landing times of the flight m in the hour of the season.

[0014] As a further optimization scheme of the civil aviation flight time configuration method for mass travel of the present application, the weight of the flight in the flight time offset in step 2) where ω m denotes the weight of flight m in the flight time offset; α, β, γ respectively denote the preset weight coefficients of passenger type dimension weight, airline type dimension weight, and route type dimension weight, α, β, γ, α+β+γ∈[0,1].

[0015] As a further optimization scheme of the civil aviation flight time configuration method for mass travel of the present application, the civil aviation flight time configuration model in step 3) includes objectives Z1 and Z2, objective Z1 is to minimize the weighted time offset total, and objective Z2 is to minimize the Gini coefficient of the flight time configuration;

[0016] The calculation formula of objective Z1 is as follows:

[0017]

[0018] where T={0,1,...,n-1} denotes the set of time, n time in a day, indexed by t; τ m denotes the time of flight m application; is a 0-1 variable, if flight m is assigned to time t, then otherwise |D m |denotes the number of days of flight m;

[0019] The calculation formula of objective Z2 is as follows:

[0020]

[0021] where ρ a denotes the fairness measure of airline a in time configuration, u denotes the weighted time offset total; u a denotes the sum of the weighted time offset of airline a; |M| denotes the total number of flights, |M a |denotes the number of flights of airline a; A denotes the set of all airlines, indexed by a; |A| denotes the number of airlines. ​

[0022] As a further optimization scheme of the civil aviation flight schedule configuration method for mass travel of the present application, the civil aviation flight schedule configuration model is constrained by the following constraint conditions in step 3): uniqueness constraint, airport capacity constraint, transit time constraint and maximum time offset constraint;

[0023] The uniqueness constraint is used to ensure that each flight can only be assigned one time, and the expression is

[0024] The airport capacity constraint is used to specify that the take-off and landing demand cannot exceed the rolling approach capacity, departure capacity and total capacity of the airport, and the expression is d∈D, where K={Arr, Dep, Total} is a set of flight operation types, including three operation types, Arr is approach, Dep is departure, and Total is total, indexed by k; M k represents a set of all flights of operation type k within the airport group; T s is a set of time intervals of s; D is a set of seasonal operation days, indexed by d; represents the maximum number of flights of operation type k allowed in day d and time interval s; represents whether flight m operates on day d, if yes, then otherwise

[0025] The transit time constraint is used to specify the minimum and maximum transit times of the immediately preceding and following flights, and the expression is where F is a set of immediately preceding and following flight time pairs, indexed by (m, m'); t min,F , t max,F represent the minimum and maximum transit times of the immediately preceding and following flights, respectively;

[0026] The maximum time offset constraint is used to specify that the time offset of each flight cannot exceed the set maximum time offset, and the expression is where f max represents the set maximum time offset.

[0027] As a further optimization scheme of the civil aviation flight schedule configuration method for mass travel of the present application, the detailed steps of step 4) are as follows:

[0028] Step 4.1), the objective function is input into the Gurobi solver by the setObjectiveN method, with the main goal of minimizing the weighted time offset total and the secondary goal of minimizing the Gini coefficient of flight schedule configuration;

[0029] Step 4.2), the constraint condition is input into the Gurobi solver through the "addConstr" method;

[0030] Step 4.3), the program is run to solve the model to obtain the civil aviation flight schedule for mass travel.

[0031] Compared with the prior art, the above technical scheme has the following technical effects:

[0032] 1. Improve the scientificity and comprehensiveness of mass travel evaluation. Evaluate the contribution of flights to mass travel from four dimensions of passenger type, airline type, route type and time type. By comprehensively considering the influencing factors of mass travel, the promotion of flights to mass travel is more comprehensively and scientifically evaluated;

[0033] 2. Balance the efficiency and fairness of mass travel strategy and time configuration. Considering the evaluation weight of mass travel in each dimension, ensure that the arrangement of flights meets the demand of mass travel while improving the efficiency and fairness of time configuration;

[0034] 3. Efficient and accurate time configuration solution. The model is solved by Gurobi solver in Python environment, the code structure is simple and clear and the execution efficiency is high, and accurate time configuration results can be quickly obtained. The efficient solving process will help the time coordinator to quickly generate scientific and effective flight time configuration scheme, improve the accuracy and operability of time configuration. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 is a flowchart of the method of the present application. DETAILED DESCRIPTION

[0036] The technical solutions of the present application will be further described in detail below in combination with the drawings:

[0037] The present application can be implemented in many different forms, and should not be considered limited to the embodiments described herein. On the contrary, these embodiments are provided to make the present disclosure thorough and complete, and to fully convey the scope of the present application to those skilled in the art. In the drawings, the components are enlarged for clarity.

[0038] As Figure 1 shown, the present application discloses a civil aviation flight time configuration method for mass travel, comprising the following steps:

[0039] Step 1), determine the multi-dimension weight system for mass travel, which includes four dimensions of passenger type, airline type, route type and time type; calculate the weight of each flight in passenger type, airline type, route type and time type dimensions according to flight time application data. Specifically, it includes the following steps:

[0040] Step 1.1), determine the passenger type dimension weight. Mass travel strategy encourages economy class passengers to travel. When configuring flight times, determine the weight according to the number of economy class seats carried by the flight. The more economy class seats, the greater the weight given to the flight to reduce the possibility of time deviation. The weight of the passenger type dimension is calculated as follows:

[0041]

[0042] Wherein, represents the passenger type dimension weight of flight m; represents the number of economy class seats of flight m; M represents the flight set indexed by m.

[0043] Step 1.2), determine the airline type dimension weight. Mass travel strategy encourages low-cost airlines to develop. Therefore, when configuring times, determine the weight according to the type of airline the flight belongs to, and try to meet the time application submitted by low-cost airlines as much as possible. The weight of the airline type dimension is calculated as follows:

[0044]

[0045] Wherein, represents the airline type dimension weight of flight m; a m represents the airline to which flight m belongs; A LCC represents the set of low-cost airlines; A FSC represents the set of full-service airlines.

[0046] Step 1.3), determine the route type dimension weight. Mass travel strategy is committed to the development of regional aviation and the effective connection of trunk and regional flights. Therefore, when configuring times, give greater weight to routes with fewer flights (usually regional flights) and prioritize meeting the time configuration needs of these flights. The weight of the route type dimension is calculated as follows:

[0047]

[0048] Wherein, represents the route type dimension weight of flight m; represents whether the route of flight m' is r, if yes, Otherwise Similarly, if the route of flight m' is the route r of flight mm Then Else |D m' | represents the number of operating days of flight m'; R is the set of routes, indexed by r. The route type dimension considers the proportion of the number of operating flights of route r where flight m is located to the number of operating flights of the route with the largest number of operating flights. The smaller the proportion, the greater the weight of the route type dimension.

[0049] Step 1.4), determine the time-of-day dimension weight. Applications at peak hours can lead to congestion and delays, which is not conducive to improving the travel experience of the public. In order to punish applications at peak hours and encourage airlines to apply for non-peak hours, the weight of the time-of-day application is determined according to the average number of takeoffs and landings per hour within the season. The calculation of the time-of-day weight is as follows:

[0050]

[0051] wherein, represents the time-of-day dimension weight of flight m; T h is the set of hours, indexed by t h ; is the average number of takeoffs and landings per hour of the season for hour t h ; is the average number of takeoffs and landings per hour of the season for the hour where flight m is located.

[0052] Step 2), integrate the weights of various dimensions to evaluate the contribution of flights to public travel and determine the weight of each flight in flight time offset. Each flight involves the weights of four dimensions: passenger type, airline type, route type, and time-of-day application type. By weighted summing the weights of these four dimensions, the weight of each flight in flight time offset is calculated. The greater the value of the weight, the higher the importance of the flight in time offset, and accordingly, the flight is less likely to occur time offset. The calculation formula of the weight is as follows:

[0053]

[0054] wherein, ω m represents the weight of flight m in flight time offset; α, β, γ respectively represent the weight coefficients of the passenger type dimension weight, the airline type dimension weight, and the route type dimension weight, and α, β, γ, (α+β+γ) ∈ [0,1]. These weight coefficients are used to adjust the relative importance of each dimension weight in the weighted sum. It is easy to know that the above conditions can guarantee ω m ∈ [0,1].

[0055] Step 3), a civil aviation flight schedule configuration model for mass travel is established, aiming to minimize the total weighted time deviation and ensure the rationality and feasibility of flight schedule through appropriate constraints. The model includes two objectives, objective Z1 is to minimize the total weighted time deviation, and objective Z2 is to minimize the Gini coefficient of flight schedule configuration.

[0056] The calculation formula of objective Z1 is as follows:

[0057]

[0058] Where Z1 represents the total weighted time deviation; T = {0, 1,..., n-1} represents the set of time, n time points in a day, indexed by t; τ m represents the time point applied by flight m; is a binary variable, if flight m is assigned to time t, Otherwise |D m | represents the number of days of flight m. The so-called flight time deviation is the absolute value of the difference between the time point applied and the assigned time point, and the weighted time deviation is multiplied by the weight of the flight in the time deviation.

[0059] The calculation formula of objective Z2 is as follows:

[0060]

[0061] Where Z2 represents the Gini coefficient of flight schedule configuration, ρ a represents the fairness measure of airline a in time configuration; |A| represents the number of airlines. The smaller the Gini coefficient, the more fair the time configuration. The fairness measure value of the airline is calculated as follows:

[0062]

[0063] Where u represents the total weighted time deviation; u a represents the sum of the weighted time deviation of airline a; |M represents the total number of flights, |M a | represents the number of flights of airline a; A is the set of all airlines, indexed by a.

[0064] The constraints of the model mainly include uniqueness constraint, airport capacity constraint, transit time constraint and maximum time deviation constraint.

[0065] The specific constraints are as follows:

[0066] The uniqueness constraint ensures that each flight can only be assigned one time point, expressed as:

[0067]

[0068] Airport capacity constraints specify that the demand of arrivals and departures cannot exceed the rolling arrival capacity, departure capacity and total capacity of the airport, expressed as:

[0069]

[0070] where K = {Arr, Dep, Total} is the set of flight operation types, including three operation types, Arr for arrivals, Dep for departures, and Total for the sum, indexed by k; M k denotes the set of all flights of operation type k within the airport cluster; T s is the set of time intervals of s time periods; D is the set of seasonal operation days, indexed by d; denotes the maximum number of flights of operation type k allowed in day d and time interval s; denotes whether flight m operates in day d, if yes, then otherwise

[0071] The transit time constraints specify the minimum and maximum transit time between two consecutive flights, expressed as:

[0072]

[0073] where F is the set of pairs of consecutive flight times, indexed by (m, m'); t min,F and t max,F denote the minimum and maximum transit time between two consecutive flights, respectively.

[0074] The maximum time offset constraint specifies that the time offset of each flight cannot exceed the set maximum time offset, expressed as:

[0075]

[0076] where f max denotes the set maximum time offset.

[0077] Step 4), use Gurobi solver to solve the established flight schedule model in Python environment, get the civil aviation flight schedule for mass travel, the specific steps are as follows:

[0078] Step 4.1), input the objective function into the Gurobi solver through the setObjectiveN method, with the main goal of minimizing the weighted total time offset (priority = 0) and the secondary goal of minimizing the Gini coefficient of flight schedule configuration (priority = 1);

[0079] Step 4.2), the constraints are input into the Gurobi solver through the "addConstr" method;

[0080] Step 4.3), the program is run to solve the model, and the civil aviation flight schedule for mass travel is obtained.

[0081] Taking the flight schedule configuration of Shanghai Hongqiao Airport in the summer and autumn of 2019 as an example, flight schedule plan data is used instead of flight schedule application data. The data set includes fields such as flight number, operating airline, operating aircraft type, route, operating date (day of the week), departure airport, arrival airport, departure time, and arrival time, and there are 766 flights with different flight numbers.

[0082] The model parameters are set as follows:

[0083] a) Airport capacity. The airport capacity of Shanghai Hongqiao Airport can be divided into 15min capacity and 60min capacity. The 15min capacity is the rolling capacity, and the total number of flights per 15min cannot exceed 15, with no more than 9 departure and arrival flights. The 60min capacity varies with time, as shown in Table 1.

[0084] Table 1 60min capacity of Shanghai Hongqiao Airport

[0085]

[0086] b) Connecting flight transit time. The minimum transit time is set to 65min, and the maximum transit time is set to 180min.

[0087] c) Maximum allowed time deviation. The maximum allowed time deviation for each flight is set to 30min.

[0088] The specific values of the weight of each dimension of each flight are calculated. The distribution of the values of the dimension weight is shown in Table 2.

[0089] Table 2 Statistics of the values of the dimension weight

[0090]

[0091] Taking the weight coefficient of the four type dimensions as 0.250 (i.e. α = β = γ = 0.250) as an example, the weight of each flight in the flight time deviation is obtained. Among them, the maximum weight value is 0.849, and this flight is the least likely to occur time deviation, and the minimum weight value is 0.003, and this flight is the most likely to occur time deviation.

[0092] The weight of flight time deviation and flight time plan data are taken as model input, the model is solved in Python environment using Gurobi solver, the main goal is to minimize the total weight of time deviation, and the Gini coefficient of flight time configuration fairness is taken as the secondary goal, and the uniqueness constraint, airport capacity constraint, transit time constraint, maximum time deviation constraint are taken as constraint conditions. The results of the flight schedule with minimum total weighted time deviation (considering popular travel) and the flight schedule with minimum total time deviation (not considering popular travel) are shown in Table 3. The flight schedule with minimum total weighted time deviation considers the needs of popular travel, and compared with the flight schedule with minimum total time deviation without considering the weight, the total weighted time deviation is reduced by 4246.060 min in a season. Although the total time deviation needs to be increased by 3100 min, the average daily deviation is only increased by 14.286 min. By increasing the relatively small time deviation, the flight time configuration result for popular travel is achieved.

[0093] Table 3 Comparison of flight schedule results

[0094]

[0095] Those skilled in the art can understand that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and unless defined as such, should not be interpreted in an idealized or overly formal sense.

[0096] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for configuring civil aviation flight schedule for mass travel, characterized in that, The method comprises the following steps: Step 1), determining a multi-dimension weight system for mass travel, the multi-dimension weight system comprising four dimensions of passenger type, airline type, route type and time point type; according to flight time point application data, calculating the weight of each flight in the dimensions of passenger type, airline type, route type and time point type; Step 2), comprehensively evaluating the dimension weight to evaluate the contribution degree of the flight to mass travel, and determining the weight of each flight in flight time point offset; Step 3), establishing a civil aviation flight time point configuration model for mass travel, aiming to minimize the total weight of time point offset, and constraining the civil aviation flight time point configuration model through constraint conditions; The civil aviation flight time point configuration model for mass travel comprises target Z1 and Z2, the target Z1 is to minimize the total weight of time point offset, and the target Z2 is to minimize the Gini coefficient of flight time point configuration; The calculation formula of the target Z1 is as follows: where T = {0, 1,..., n - 1} represents the set of time instants, one day having n time instants, indexed by t; τ m denotes the time instant at which flight m is requested; is a 0-1 variable, if flight m is assigned to time instant t, otherwise |D m | denotes the number of days on which flight m is operated; ω m denotes the weight of flight m in the flight time offset. The calculation formula of the target Z2 is as follows: where ρ a denotes the fairness measure of airline a in the schedule at time t, u denotes the total amount of weighted time shifts; u a denotes the sum of weighted time shifts of airline a; |M| denotes the total number of flights, |M a denotes the number of flights of airline a; A denotes the set of all airlines, indexed by a; |A| denotes the number of airlines; The civil aviation flight time point configuration model is constrained through the following constraint conditions: uniqueness constraint, airport capacity constraint, over-station time constraint and maximum time point offset constraint; The uniqueness constraint is used to ensure that each flight can only be assigned one time, expressed as The airport capacity constraints are used to specify that the demand for arrivals and departures cannot exceed the rolling arrival capacity, the rolling departure capacity and the total capacity of the airport, expressed as where K = {Arr, Dep, Total} is the set of flight operation types, including three operation types, Arr for arrivals, Dep for departures, and Total for the sum, indexed by k; M k denotes the set of all flights of operation type k within the airport cluster; T s is the set of time periods of time interval s; D is the set of operational days of the season, indexed by d; denotes the maximum number of flights of operation type k allowed in day d, time interval s; denotes whether flight m operates in day d, if yes, then otherwise The transit time constraint is used to define the minimum and maximum transit time between two consecutive flights, expressed as where F is a set of pairs of consecutive flight times indexed by (m, m′); t min,F , t max,F represent the minimum and maximum transit time between two consecutive flights, respectively. The maximum time offset constraint is used to stipulate that the time offset of each flight cannot exceed the set maximum time offset, expressed as In the formula, f max represents the set maximum time offset; Step 4), solving the established flight time point model by using a Gurobi solver in a Python environment to obtain a civil aviation flight time table for mass travel.

2. The civil aviation flight schedule configuration method for popular travel according to claim 1, characterized in that, The calculation formula of each flight in step 1) in the passenger type dimension weight is: Wherein, Indicates the passenger type dimension weight of flight m; Indicates the number of economy class seats of flight m; M indicates a flight set, indexed by m; The calculation formula of each flight in the airline type dimension weight is: Wherein, The airline type dimension weight of flight m is represented by a m The airline to which flight m belongs is represented by A LCC The set of low-cost airlines is represented by A FSC The set of full-service airlines is represented by The calculation formula of the flight in the route type dimension weight is: Wherein, represents the route type dimension weight of the flight m; represents whether the route of the flight m' is r, if yes, then Otherwise Similarly, if the route of the flight m' is the route r of the flight m m , Otherwise |D m′ |represents the running days of the flight m'; and R is a route set, indexed by r; The calculation formula of the flight in the time type dimension weight is: Wherein, The time type dimension weight of flight m is represented by T h The hour set is represented by t h Index; The average take-off and landing times of the one-season hour t h The average take-off and landing times of the one-season hour of flight m is represented by T The average take-off and landing times of the one-season hour of flight m is represented by T 3. The civil aviation flight schedule configuration method for popular travel according to claim 2, characterized in that, The weight of the flight in the flight time offset in step 2 wherein ω m denotes the weight of the flight m in the flight time offset; α, β, γ respectively denote preset weight coefficients of passenger type dimension weight, airline type dimension weight, route type dimension weight, α, β, γ, α+β+γ ∈ [0, 1].

4. The civil aviation flight schedule configuration method for popular travel according to claim 3, characterized in that, The detailed steps of the step 4) are as follows: Step 4.1), inputting the objective function into the Gurobi solver through a setObjectiveN method, taking the minimization of the total weight of time point offset as the main target and the minimization of the Gini coefficient of flight time point configuration as the secondary target; Step 4.2), inputting the constraint conditions into the Gurobi solver through an addConstr method; Step 4.3), running the program to solve the model to obtain a civil aviation flight time table for mass travel.

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