Urban air traffic vertical take-off and landing field site selection optimization method based on demand maximization

Abstract

The invention relates to the technical field of traffic engineering, and discloses an urban air traffic vertical take-off and landing field site selection optimization method based on demand maximization, and the method comprises the following steps: S1, obtaining and preprocessing online car-hailing original data; s2, dividing grids for the research area range, and taking the grid center as an alternative address of a vertical take-off and landing field; s3, screening out online car-hailing travels which are impossible to turn to urban air traffic; s4, constructing a vertical take-off and landing field site selection model based on potential demand coverage maximization; s5, outputting the selected vertical take-off and landing field address and the corresponding urban air traffic travel demand; and S6, outputting the current optimal site selection and demand as a final solution of the model. According to the method, under the condition of the specific number of vertical takeoff and landing fields, the vertical takeoff and landing field addresses which enable potential requirements of urban air traffic to achieve maximum coverage are selected, an effective solution is provided for development of the urban air traffic, and construction of a more efficient, convenient and environment-friendly urban traffic system is facilitated.

Description

Technical Field

[0001] The invention relates to the technical field of traffic engineering, and in particular to a method for optimizing the site selection of a vertical take-off and landing field for urban air traffic based on demand maximization. Background Art

[0002] In recent years, a new transportation mode called "Urban Air Mobility (UAM)" has gradually emerged in the public eye: an air passenger transport system based on electric vertical take-off and landing aircraft in urban scenes. This mode upgrades the existing urban transportation structure from two-dimensional to three-dimensional, and it can share part of the ground transportation. At the same time, the electrification of transportation tools has less negative impact on the environment.

[0003] The operation of urban air traffic depends on the vertical take-off and landing site, which is responsible for the take-off and landing of aircraft and the boarding and disembarking of passengers. Travelers must reach the vertical take-off and landing site if they want to travel. Therefore, the layout of the vertical take-off and landing site is crucial to meeting the needs of travelers. Most travelers are relatively unfamiliar with urban air traffic as a mode of transportation. The initial layout of the vertical take-off and landing site is crucial to opening up the market for this mode of transportation and affecting the subsequent promotion of urban air traffic. However, existing vertical take-off and landing site site selection research rarely builds models with the goal of maximizing demand coverage. At the same time, the coverage of the vertical take-off and landing site is rarely considered, which may result in the allocation of too far demands to the vertical take-off and landing site, affecting the rationality of the site selection.

[0004] To this end, we propose an optimization method for the location selection of vertical take-off and landing sites for urban air traffic based on demand maximization. Summary of the invention

[0005] The present invention mainly solves the technical problems existing in the above-mentioned prior art and provides a method for optimizing the site selection of urban air traffic vertical take-off and landing sites based on demand maximization.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions: 1. A method for optimizing the site selection of a vertical take-off and landing field for urban air traffic based on demand maximization, characterized in that it comprises the following steps:

[0007] S1: Obtain the original data of online ride-hailing and perform preprocessing;

[0008] S2: Divide the study area into grids, and use the center of the grid as the candidate address for the vertical take-off and landing site;

[0009] S3: Based on the conditions for switching from online ride-hailing to urban air transportation, filter out online ride-hailing trips that are unlikely to switch to urban air transportation;

[0010] S4: Construct a vertical take-off and landing site selection model based on maximizing potential demand coverage;

[0011] S5: Solve the model using variable neighborhood search, and finally output the selected vertical take-off and landing site address and the corresponding urban air traffic travel demand;

[0012] S6: Set the stopping rule of the algorithm and output the current optimal location and demand as the final solution of the model.

[0013] As a further limitation of the above scheme, the preprocessing step in S1 includes deleting records with missing travel time, retaining only travel data with a travel distance exceeding 10 kilometers, and deleting duplicate travel data.

[0014] As a further limitation of the above scheme, the grid in S2 is divided into grids with a side length of 1 km, and the center of the grid is the alternative address of the vertical take-off and landing field.

[0015] As a further limitation of the above scheme, the online car-hailing trips screened out in S3 are based on the condition that the value of the time saved by urban air traffic is greater than the extra fees paid.

[0016] As a further limitation of the above scheme, the vertical take-off and landing site selection model in S4 takes each trip as a modeling object and considers the actual travel time and cost in different travel periods.

[0017] As a further limitation of the above solution, the variable neighborhood search method in S5 includes exchanging one address neighborhood structure, searching surrounding neighborhood structures, and exchanging two address neighborhood structures.

[0018] As a further limitation of the above scheme, the stopping rule of the algorithm in S6 is to stop the algorithm when the variable neighborhood descent process fails to find a better solution for five consecutive times.

[0019] The present invention provides an optimization method for site selection of urban air traffic vertical take-off and landing sites based on demand maximization. It has the following beneficial effects:

[0020] Maximizing demand coverage: By optimizing the site selection model, it is possible to maximize the coverage of potential urban air traffic travel needs under the condition of a specific number of vertical take-off and landing sites, thereby improving traffic efficiency.

[0021] Scientific and reasonable site selection decision: It fully considers the impact of different travel time periods and actual travel costs, and can provide a scientific basis for policy makers and urban planners to optimize the site selection of vertical take-off and landing sites.

[0022] Reduce resource waste: By screening out trips that are unlikely to be diverted to air traffic, the complexity of subsequent calculations and resource waste are reduced, making the model run more efficiently.

[0023] Promote the development of urban air traffic: It provides a practical solution for the initial promotion of urban air traffic, helps to increase public awareness and acceptance of new modes of transportation, and promotes the implementation of relevant policies.

[0024] Environmentally friendly transportation mode: The use of electric vertical take-off and landing aircraft helps to reduce the environmental impact of urban transportation and is in line with the concept of sustainable development.

[0025] By constructing a vertical take-off and landing field site selection optimization model, the present invention can select the vertical take-off and landing field addresses that maximize the coverage of the potential demand for urban air traffic under the condition of a specific number of vertical take-off and landing fields, thereby providing an effective solution for the development of urban air traffic and helping to build a more efficient, convenient and environmentally friendly urban transportation system. DETAILED DESCRIPTION

[0026] The method for optimizing the location of a vertical take-off and landing site for urban air traffic based on demand maximization includes the following steps:

[0027] S1. Obtain the original data of online car-hailing services and perform preprocessing;

[0028] In an optional embodiment of the present invention, step S1 specifically includes:

[0029] The missing travel time field in the online car-hailing trip record cannot be used for subsequent research, so the trips with missing travel time in the original data are first deleted. Existing research shows that compared with urban ground transportation, urban air transportation has more advantages in long-distance travel, so the present invention only uses travel data with a travel distance of more than 10 kilometers for subsequent research. Duplicate travel data is invalid data, so it needs to be deleted.

[0030] S2. Divide the research area into grids, and use the center of the grid as the candidate address of the vertical take-off and landing site;

[0031] In an optional embodiment of the present invention, step S2 specifically includes:

[0032] The scope of the study area is set according to the concentrated area of ​​online ride-hailing travel, and only the travel data with both the starting and ending points within the study area are used for subsequent research. The study area is divided into grids with a side length of 1km, and the center of the grid is the alternative address of the vertical take-off and landing site.

[0033] S3. Based on the conditions for switching from online ride-hailing to urban air transportation, screen out online ride-hailing trips that are unlikely to switch to urban air transportation;

[0034] In an optional embodiment of the present invention, step S3 specifically includes:

[0035] Since the present invention sets certain conversion conditions for the trip from online car-hailing to urban air traffic, that is, only when the time value saved by urban air traffic compared with online car-hailing is greater than the extra fee paid by online car-hailing, online car-hailing will turn to urban air traffic and become potential urban air traffic. When the location of the vertical take-off and landing field is determined, and various parameters are determined, it can be determined whether the online car-hailing trip can be turned to urban air traffic and become a potential urban air traffic trip.

[0036] For each online ride-hailing trip, we can first assume that the vertical take-off and landing field is set at the location that is most conducive to urban air traffic, and determine whether the online ride-hailing trip can be turned to urban air traffic at this time. If the online ride-hailing trip cannot be turned to urban air traffic at the optimal vertical take-off and landing field location, then the online ride-hailing trip cannot be turned to urban air traffic at any other vertical take-off and landing field layout location. These trips that cannot be turned to urban air traffic are screened out to reduce the computational complexity of subsequent models.

[0037] The conditions under which it is impossible to switch from online ride-hailing to urban air are deduced below.

[0038] The conditions for switching from online ride-hailing to urban air mobility are:

[0039] f UAM -f ground <γ(t ground -t UAM )

[0040] where f UAM is the travel cost of urban air traffic, f ground is the travel cost of online car-hailing, γ is the time value coefficient, t ground is the online car-hailing travel time, t UAM is the travel time of urban air traffic. The calculation formula of average time value coefficient is:

[0041]

[0042] Where: income is the annual per capita disposable income, which can be obtained by querying the statistical yearbook of the city where the online car-hailing trip is located, h work is the annual working hours, calculated based on 8 hours of work per day and two days off on weekends. Since different modes of travel have different time value coefficients, the urban air traffic refers to the time value coefficient of taxis at 1.3, so the time value coefficient calculation formula in this study is:

[0043]

[0044] Expanding the travel time and cost of urban air traffic, the conversion conditions can be changed to:

[0045] fareperkm air dist -groung fare <

[0046] γ(ground time -(access time +air time +egress time +2transfer time ))

[0047] f perkm is the cost per kilometer of air travel, d air is the distance of air travel, t transfer The transit time spent at the vertical take-off and landing field, air time The travel time for urban air transportation, access time and egress time They are the time required to access the starting vertical take-off and landing field and the time required to access the terminal vertical take-off and landing field, and the calculation formulas for the three are:

[0048]

[0049]

[0050]

[0051] Among them, v air is the speed of the vertical take-off and landing vehicle, v walk is the walking speed, the conversion condition formula can be transformed into:

[0052]

[0053] 2.γ.transfer time -groung fare -γ·ground time <0

[0054] When the above equation is true, the trip will be transferred from online ride-hailing to urban air transportation, that is, only when the left part of the equation is less than 0, the conversion condition can be met. If the left part reaches the minimum, it still cannot be less than 0, which means that it cannot be less than 0 under any other circumstances. At this time, this trip can never be transferred from online ride-hailing to urban air transportation. Therefore, if you want to filter out online ride-hailing trips that cannot be transferred to urban air transportation, you only need to filter out the trips with the minimum value of the left part greater than zero.

[0055] S4. Construct a vertical take-off and landing site selection model based on maximizing potential demand coverage;

[0056] In an optional embodiment of the present invention, step S4 specifically includes:

[0057] Due to the time-varying phenomenon of urban road network conditions, roads are more congested during peak travel periods and smoother during off-peak travel periods. Therefore, even for trips with the same starting and ending points, different travel periods will lead to different travel times and costs, and travel time and cost are important factors affecting travelers' travel choices. Therefore, when modeling, the present invention takes each trip as the modeling object, uses the actual travel time and cost for each trip, and can take into account the different situations of travel at different time periods.

[0058] The set of online ride-hailing trips input into the model is denoted as P, the number of online ride-hailing trips is n1, and any online ride-hailing trip p i ∈P,i∈1,2,…,n1; the starting point set of all online car-hailing trips is denoted as N, and the number of points in the set is n1. i ∈N,i∈1,2,…,n1; the destination set of all online car-hailing trips is denoted as M, and the number of points in the set is n1. The destination d of any online car-hailing trip is i ∈M,i∈1,2,…,n1; any trip p in the set of online car-hailing trips i ∈P,i∈1,2,…,n1 contains the starting and ending information of the trip o i ∈N,d i ∈M; the set of candidate addresses for the vertical take-off and landing field is represented by L, the number of candidate addresses is n2, and any candidate address l of the vertical take-off and landing field i ∈L,i∈1,2,…,n2. There are 5 decision variables involved in the model, namely: l k , Z p , These five decision variables are all 0-1 variables:

[0059] y k When 1, it indicates the alternative address l k ∈L, k∈1,2,…,n2 is selected as the location of the vertical take-off and landing field;

[0060] Z p When it is 1, it means that trip p uses online car-hailing;

[0061] When it is 1, it means that trip p uses urban air transportation and passes through the starting vertical take-off and landing field and the terminal vertical take-off and landing field respectively k, m∈L;

[0062] When it is 1, it means that trip p uses urban air transportation and the starting point i is assigned to the vertical take-off and landing site k;

[0063] When it is 1, it means that trip p uses urban air transportation and the destination j is assigned to the vertical take-off and landing airport m.

[0064] The objective function included in the vertical take-off and landing site selection model constructed by the present invention is as follows:

[0065] S41. Build a model with the optimization goal of maximizing demand coverage:

[0066]

[0067] It indicates that trip p uses urban air transportation, and the starting point of the trip is assigned to the vertical take-off and landing field k, and the end point of the trip is assigned to the vertical take-off and landing field m; P represents the set of online car-hailing trips; L represents the set of alternative addresses of vertical take-off and landing fields.

[0068] S42. The number of vertical take-off and landing sites is u, which is an input value of the model and can be formulated according to actual needs:

[0069]

[0070] l k It is 1 when the candidate address k is selected as the vertical take-off and landing field, otherwise it is 0.

[0071] S43. Travel can only be by online car-hailing or urban air transportation:

[0072]

[0073] Z p It is 1 when trip p chooses to take an online car-hailing service, otherwise it is 0.

[0074] S44. If urban air transportation is used, the starting point and destination of the trip can only be assigned to the address selected as the vertical take-off and landing site:

[0075]

[0076] Indicates that trip p uses urban air transportation, and the starting point of the trip is allocated to the vertical take-off and landing site k; Indicates that trip p uses urban air transportation and the trip destination is assigned to the vertical take-off and landing site m

[0077] S45. If urban air transportation is used, the starting point and the end point of the trip can only be assigned to one vertical take-off and landing field:

[0078]

[0079] S46. If urban air transportation is chosen, the decision variables and The assigned vertical take-off and landing field must be consistent with The assigned starting and ending vertical take-off and landing fields are the same:

[0080]

[0081] S47. If urban air transportation is used, the vertical take-off and landing field assigned to the starting and ending points must be within the coverage area of ​​the vertical take-off and landing field:

[0082]

[0083] in Need to meet:

[0084]

[0085] Where, d ik and d jm They respectively represent the distance from the starting point of the trip to the starting vertical take-off and landing field and the distance from the ending vertical take-off and landing field to the trip destination; d is the coverage area of ​​the vertical take-off and landing field.

[0086] S48. Online ride-hailing will switch to urban air transportation only when the time value saved by urban air transportation is greater than the extra cost paid by online ride-hailing:

[0087]

[0088] γ is the travel time value of the traveler; is the travel time when trip p uses online ride-hailing; is the travel cost when taking online car-hailing service for trip p; t km represents the duration of the air flight phase when trip p uses urban air transportation; c km represents the cost of the air flight phase when trip p uses urban air transportation; t w represents the transfer time at the vertical take-off and landing field when trip p uses urban air transportation; t ik represents the time from the starting point of the trip to the starting vertical take-off and landing field when trip p uses urban air transportation; t jm It represents the time from the terminal vertical take-off and landing field to the destination when trip p uses urban air transportation.

[0089] S49. All decision variables are 0-1 variables:

[0090]

[0091] S5. Solve the model using variable neighborhood search, and finally output the selected vertical take-off and landing site address and the corresponding urban air traffic travel demand;

[0092] In an optional embodiment of the present invention, S5 specifically includes:

[0093] S51, generating initial site selection and initial demand;

[0094] Assume that the set of selected vertical take-off and landing site addresses is H, and the required number is D, H can be expressed as: H = [l1,h2,…,h u-1 ,h u ], where u is the target number of vertical take-off and landing sites, which is a pre-set input value. For online car-hailing data, the longitude and latitude of its starting and ending points are known, from which the actual distance between its starting and ending points and each alternative address can be calculated and whether it is within the coverage of the vertical take-off and landing site can be determined. Based on this, this study counts the number of online car-hailing starting and ending points contained in each vertical take-off and landing site alternative address within its coverage range, and takes the first u vertical take-off and landing sites with the largest number as the initial vertical take-off and landing sites of the input model. After the initial vertical take-off and landing site is selected, the potential urban air traffic trips covered by it can be calculated, which is used as the initial travel demand quantity.

[0095] S52, variable neighborhood descent: input the initial solution into the variable neighborhood descent process, and if a solution better than the current optimal solution is obtained in the variable neighborhood descent process, the current global optimal solution is updated;

[0096] Variable neighborhood descent is based on several designed neighborhood structures, which can realize alternating search in multiple neighborhoods. The neighborhood structures designed in this study include: exchange one address neighborhood structure, exchange two address neighborhood structure, and surrounding search neighborhood structure. Among them: (a) exchange one address neighborhood structure replaces a vertical take-off and landing field in the current solution with one of the remaining candidate addresses; (b) exchange two address neighborhood structure selects two vertical take-off and landing fields in the current solution to replace with two addresses in the remaining candidate addresses; (c) surrounding search neighborhood structure replaces a vertical take-off and landing field in the current solution with one of the remaining candidate addresses around the address.

[0097] First, the initial vertical take-off and landing field and the corresponding demand of the input variable neighborhood descent process are given as the current optimal solution. The neighborhood structure is arranged in the order of exchanging one address neighborhood structure, surrounding search neighborhood structure, and exchanging two address neighborhood structures, and the search starts from the first neighborhood structure. First, the exchange one address neighborhood structure is searched. If a solution better than the current optimal solution is found in the neighborhood structure, the current optimal solution is updated and the search continues in the neighborhood structure; if a better solution is not found in the neighborhood, the surrounding search neighborhood structure is searched. If a solution better than the current optimal solution is found in the surrounding search neighborhood structure, the current optimal solution is updated and the search is returned to the exchange one address neighborhood structure; if a better solution is not found in the neighborhood, the exchange two address neighborhood structure is searched. If a better solution than the current optimal solution is found in the exchanged two address neighborhood structures, the current optimal solution is updated and the search is returned to the exchanged one address neighborhood structure; if a better solution is not found in the neighborhood, the neighborhood descent process is terminated and the current optimal solution is returned.

[0098] S53, perturbation: performing a perturbation operation on the optimal solution obtained in the variable domain descent process, and inputting the perturbed solution into the next variable domain descent process;

[0099] The perturbation process is to transform the current optimal solution of the model obtained through variable neighborhood descent, and its purpose is to avoid falling into the local optimum. In this algorithm, the perturbation process is designed to exchange two locations, randomly select two locations in the current optimal location and two locations in the remaining candidate locations for replacement, and then use the replaced location points and corresponding demands as the input for the next round of variable neighborhood descent.

[0100] S54: The algorithm ends when the algorithm stopping rule is reached.

[0101] The stopping rule of this algorithm is set as follows: when the variable domain descent process fails to find a better solution for five consecutive times, the algorithm is stopped and the current optimal location and demand are output as the final solution of the model.

[0102] Working principle of the present invention:

[0103] 1. Data preprocessing (S1):

[0104] (1) Collect online ride-hailing travel data, clean it up and filter it, remove records with missing travel time and distance less than 10 kilometers, and ensure the validity of the data.

[0105] (2) Delete duplicate travel data to improve data quality.

[0106] 2. Regional division (S2):

[0107] The research scope is set according to the concentrated area of ​​online ride-hailing travel data, and is divided into grids with a side length of 1 km. The center of the grid is used as the alternative address of the vertical take-off and landing site.

[0108] 3. Screening potential trips (S3):

[0109] (1) Determine the conditions for online ride-hailing trips to switch to urban air transportation and screen out travel records that do not meet the conversion conditions. The specific condition is that travelers will only choose air transportation when the time value saved by urban air transportation compared to online ride-hailing trips is greater than its additional cost.

[0110] (2) Determine the potential conversion of each trip and eliminate trips that cannot be converted to urban air traffic at the optimal take-off and landing field location.

[0111] Model construction (S4):

[0112] (1) Construct a vertical take-off and landing site selection model based on maximizing potential demand coverage, taking into account the time and cost differences in different travel periods, so as to more accurately reflect the actual travel situation.

[0113] (2) Determine the decision variables and construct the objective function to ensure that the model covers multiple factors such as travel choices, location and coverage of vertical take-off and landing sites, etc.

[0114] 3. Model solution (S5):

[0115] (1) The model is solved using a variable neighborhood search algorithm to output the selected vertical take-off and landing site address and the corresponding urban air traffic demand.

[0116] (2) Includes initial site selection and demand generation, variable neighborhood descent, perturbation operations, and setting stopping rules.

[0117] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for optimizing the location of urban air traffic vertical take-off and landing sites based on demand maximization, characterized in that: The following steps are involved: S1: Obtain the original data of online ride-hailing and perform preprocessing; S2: Divide the study area into grids, and use the center of the grid as the candidate address for the vertical take-off and landing site; S3: Based on the conditions for switching from online ride-hailing to urban air transportation, filter out online ride-hailing trips that are unlikely to switch to urban air transportation; S4: Construct a vertical take-off and landing site selection model based on maximizing potential demand coverage; S5: Solve the model using variable neighborhood search, and finally output the selected vertical take-off and landing site address and the corresponding urban air traffic travel demand; S6: Set the stopping rule of the algorithm and output the current optimal location and demand as the final solution of the model.

2. The method for optimizing the site selection of urban air traffic vertical take-off and landing sites based on demand maximization according to claim 1, characterized in that: The preprocessing step in S1 includes deleting records with missing travel time, retaining only travel data with a travel distance exceeding 10 kilometers, and deleting duplicate travel data.

3. The method for optimizing the site selection of urban air traffic vertical take-off and landing sites based on demand maximization according to claim 1, characterized in that: The grid in S2 is divided into grids with a side length of 1 km, and the center of the grid is the candidate address of the vertical take-off and landing field.

4. The method for optimizing the site selection of urban air traffic vertical take-off and landing sites based on demand maximization according to claim 1, characterized in that: The online car-hailing trips screened out in S3 are based on the condition that the time value saved by urban air traffic is greater than the extra fees paid.

5. The method for optimizing the site selection of urban air traffic vertical take-off and landing sites based on demand maximization according to claim 1, characterized in that: The vertical take-off and landing site selection model in S4 takes each trip as the modeling object and considers the actual travel time and cost in different travel periods.

6. The method for optimizing the site selection of urban air traffic vertical take-off and landing sites based on demand maximization according to claim 1, characterized in that: The variable neighborhood search method in S5 includes exchanging one address neighborhood structure, searching surrounding neighborhood structures, and exchanging two address neighborhood structures. As a further limitation of the above scheme, the stopping rule of the algorithm in S6 is to stop the algorithm when the variable neighborhood descent process fails to find a better solution for five consecutive times.

Images