Urban vertical take-off and landing field network site selection method coupled with population flow

Abstract

The invention discloses a population flow coupled urban vertical take-off and landing field network site selection method, and belongs to the field of urban air traffic infrastructure planning. Multi-source geographic space big data are fused, and a vertical take-off and landing field candidate point set conforming to urban built-up area environmental constraints is constructed; constructing a population flow coupled low-altitude aircraft vertical take-off and landing field network location model CPF-VNLM, taking the candidate point set as a starting and destination vertical take-off and landing field of the model, and determining an objective function, a multi-constraint condition and a binary decision variable which take maximum air-ground combined travel cost saving as a core; designing a model reduction pruning strategy; and solving the model by combining an integer programming solver to obtain an optimal layout scheme of the vertical take-off and landing field network. According to the invention, through coupling the population space-time distribution data and the urban physical space constraint, the vertical take-off and landing field network layout is optimized, and the operation efficiency and the service quality of the urban air traffic system are significantly improved.

Description

Technical Field

[0001] This application relates to the field of urban air traffic infrastructure planning, and in particular to a method for selecting urban vertical take-off and landing field networks that couples population flow. Background Technology

[0002] Urban Air Mobility (UAM) is emerging as a transformative solution for improving urban transportation, offering advantages such as vertical takeoff and landing (VTOL), overcoming geographical barriers, and serving short-distance travel within cities. With the continued implementation of supporting policies for the low-altitude economy in China, it will significantly improve the convenience of travel for residents of large cities. UAM utilizes vertical takeoff and landing (VTOL) aircraft and electric vertical takeoff and landing (eVTOL) aircraft to provide new on-demand and public transportation services in the urban air, applicable to passenger transport, delivery, emergency services, and freight transport. Current UAM research focuses on eVTOL aircraft technology, intelligent airspace and traffic management, VTOL field and infrastructure planning, and low-altitude intelligent network communication and network planning. With the acceleration of urbanization and the implementation of low-altitude economic policies, Urban Air Mobility (UAM) has become a new solution for alleviating ground traffic congestion and improving travel efficiency.

[0003] As the core infrastructure of the UAM (Universal Air Traffic Management) system, the location of vertical take-off and landing (VTOL) fields directly determines the service coverage, operational efficiency, and user acceptance of air traffic. However, existing VTOL field location research faces three major challenges: First, limited by the urban built-up area environment and the complexity of urban population flow, current VTOL field network location research mainly focuses on coverage location models. Some studies focus on traffic simulation of air-ground and multimodal transport, deviating from the basic characteristics of real urban resident travel flows. This makes it difficult for network layouts to meet the actual needs of travelers in different locations, restricting the integration and operation of UAM with existing transportation systems. Second, as a typical geographic big data-driven modeling approach, VTOL field network location modeling research coupling urban population flow and multiple air-ground transportation modes still faces challenges such as massive data volume, high-dimensional and complex data structures, and significant model computation bottlenecks, greatly restricting the large-scale VTOL field network location calculation and application in real urban environments and under real traffic modes. Third, the complex urban built-up area environment and urban population flow have an extremely important impact on low-altitude transportation networks, but the collaborative relationship and influence process of multiple elements such as air, ground, and people in complex urban environments have not yet been clarified.

[0004] Therefore, there is an urgent need for a vertical take-off and landing field network location method that couples real population OD flow data, overcomes the bottleneck of massive OD flow calculation, and optimizes air-ground intermodal collaboration, in order to solve the three major technical bottlenecks in the existing technology: distorted demand representation, difficulty in model solving, and insufficient collaboration. Summary of the Invention

[0005] To address the challenges of distorted travel demand representation, difficulty in model solving, and insufficient coordination in the selection of urban vertical take-off and landing (VTOL) sites, this application discloses a method for selecting urban VTOL network sites that couples population flow, effectively solving the problem of mismatch between VTOL network layout and actual demand.

[0006] A method for selecting urban vertical take-off and landing field networks coupled with population flow specifically includes the following steps:

[0007] Step 1: Integrate multi-source geospatial big data to construct a set of candidate vertical take-off and landing sites that meet the environmental constraints of urban built-up areas;

[0008] Step 2: Construct a vertical take-off and landing field network location model CPF-VNLM that couples population flow. Use the set of candidate vertical take-off and landing fields obtained in Step 1 as the departure and destination vertical take-off and landing fields of the model. Determine the objective function, multiple constraints and binary decision variables with maximizing the cost savings of air-ground intermodal travel as the core.

[0009] Step 3: Based on the CPF-VNLM low-altitude aircraft vertical take-off and landing field network location model coupled with population flow from Step 2, design a model reduction and pruning strategy;

[0010] Step 4: Based on the model reduction and pruning strategy in Step 3, and combined with an integer programming solver, solve the CPF-VNLM vertical take-off and landing field network location model coupled with population flow to obtain the optimal layout scheme of the vertical take-off and landing field network.

[0011] Furthermore, step 1 specifically includes the following steps:

[0012] Step 1.1: Based on urban basic geographic data, land use planning and airspace constraints, screen candidate areas for configurable vertical take-off and landing fields;

[0013] Step 1.2: Based on the construction standards for vertical take-off and landing sites, extract building areas that meet the area and shape requirements from the candidate areas;

[0014] Step 1.3: Integrate mobile phone signal population spatiotemporal distribution data, extract high-density population flow areas, and perform spatial overlay analysis with the areas selected in Steps 1.1 and 1.2 to select candidate locations that simultaneously meet physical conditions and population demand potential;

[0015] Step 1.4: Analyze the selected candidate locations and construct a set of candidate points for vertical take-off and landing fields.

[0016] Furthermore, step 2 specifically includes the following steps:

[0017] Step 2.1: Taking maximizing the savings in travel costs through air-ground intermodal transportation of urban population flow as the objective, the objective function of the CPF-VNLM vertical take-off and landing field network location model coupled with population flow is as follows: ;

[0018] Among them, the itinerary As a group The flow of travel consists of ground-based inbound shuttle services, air shuttle services, and ground-based outbound shuttle services; the set of services... It is the collection of all OD flow processes;

[0019] For the itinerary The number of people traveling; For the destination's vertical take-off and landing field; This is the departure vertical takeoff and landing field; For the itinerary The original travel time; For the itinerary The value of time; For the itinerary The original cost of transportation; Passengers pass through the departure vertical take-off and landing field Vertical take-off and landing field to destination Flight time in the air; For passengers waiting for the aircraft; For the time of takeoff and landing of the aircraft; For the itinerary From the departure vertical takeoff and landing field to the destination vertical take-off and landing field The cost of air travel; This is a ground-based shuttle service to the station. For the itinerary Using the ground shuttle bus mode at the station Arrival at the departure vertical take-off and landing field Travel time; For the itinerary Using the ground shuttle bus mode at the station Arrival at the departure vertical take-off and landing field The cost of transportation; This is a ground-based shuttle service from the station. For the itinerary Use the exit ground shuttle mode Arrival at the destination vertical take-off and landing field Travel time; For the itinerary Using ground shuttle mode Arrival at the destination vertical take-off and landing field The cost of transportation; For the binary decision variables of the air path, if the journey Select the vertical takeoff and landing field from the departure point. to the destination vertical take-off and landing field ,but The value is 1 if it is 1, otherwise it is 0. For the binary decision variable of connecting to the station, if the journey Using the ground shuttle bus mode at the station Arrival at the departure vertical take-off and landing field ,but The value is 1 if it is 1, otherwise it is 0. For the binary decision variable of exiting the station for connecting to the destination; if the itinerary Use the exit ground shuttle mode Departure from the destination vertical takeoff and landing field ,but The value is 1 if it is 1, otherwise it is 0.

[0020] Step 2.2: Define the constraints of the model, including: number of take-off and landing fields, route uniqueness constraint, station activation constraint, inbound connection matching constraint, outbound connection matching constraint, connection integrity constraint, and variable value constraint.

[0021] Furthermore, the specific constraints include the following:

[0022] Constraint 1: Number of takeoff and landing fields:

[0023] ;

[0024] Constraint 2: Route Uniqueness Constraint:

[0025] ;

[0026] Constraint 3: Site Activation Constraint:

[0027] ;

[0028] Constraint 4: Inbound connection matching constraint:

[0029] ;

[0030] Constraint 5: Outbound connection matching constraint:

[0031] ;

[0032] Constraint 6: Connection Integrity Constraint:

[0033] ;

[0034] Constraint 7: Variable Value Constraints

[0035] ;

[0036] ;

[0037] in, Candidate points for vertical take-off and landing fields; This refers to the planned total number of takeoff and landing fields; It is a set of candidate points for vertical take-off and landing fields; It is a combination of air and ground transportation, consisting of all combinations of passenger air transportation routes and ground transportation connection modes; As decision variables, if the candidate points of the vertical take-off and landing field If selected as the departure or destination vertical takeoff and landing field, then The value is 1 if it is 1, otherwise it is 0.

[0038] Furthermore, step 3 specifically includes the following steps:

[0039] Step 3.1: In the itinerary set With the set of candidate points for vertical take-off and landing fields Based on this, self-loop routes with the same vertical takeoff and landing field as the origin and destination are eliminated to obtain a set of candidate air routes, where each element corresponds to a binary decision variable for the air route. ;

[0040] Step 3.2: For each trip and takeoff / landing combination If no ground-based shuttle service is available Make the station connection a binary decision variable It has valid spatiotemporal or cost data records, or there is no ground-based exit connection mode. Make the exit connection a binary decision variable If valid spatiotemporal or cost data records are available, the corresponding binary decision variables for the aerial path are discarded. ;

[0041] Step 3.3: Define the average net revenue per person for the trip as:

[0042] ;

[0043] in, Net income per capita For the integration of ground transportation modes for entering the station, This is a collection of ground transportation modes for exiting the station. ;

[0044] Step 3.4: For each trip The remaining candidate flight itineraries, according to Sort the values ​​in descending order and keep only the top values. indivual ;

[0045] Step 3.5: Apply association constraints only to combinations of vertical takeoff and landing field candidate points that actually appear in the candidate paths and the trips; for each trip Candidate sites for vertical take-off and landing If the candidate points for vertical take-off and landing fields On the trip If a candidate path is selected as either a departure vertical takeoff and landing field or a destination vertical takeoff and landing field, then constraint condition 3 is added.

[0046] Furthermore, step 4 specifically includes the following steps:

[0047] Step 4.1: Adjust the parameters of the CPF-VNLM vertical take-off and landing field network location model coupled with population flow. The adjustment includes setting the number of take-off and landing fields with different configuration sizes from 10 to 100.

[0048] Step 4.2: Solve the CPF-VNLM site selection model of low-altitude aircraft vertical take-off and landing field network coupled with population flow using an integer programming solver to obtain the optimal layout scheme of the vertical take-off and landing field network. Perform spatial visualization analysis and efficiency comparison on the solution results to evaluate the effectiveness of the site selection method.

[0049] Compared with the prior art, the beneficial effects of this application are as follows:

[0050] 1. In terms of demand representation: In response to the problem that existing models deviate from the characteristics of real residents’ travel flow, resulting in distorted demand representation, this application innovatively integrates multi-source geospatial big data and uses OD flow extracted from mobile phone signaling to accurately depict dynamic travel demand. On this basis, a complete multimodal travel chain optimization framework of ground connection-air flight-ground connection is designed, which highly overlaps with the high-demand commuter axis revealed in the flow pattern, effectively solving the problem of mismatch between vertical take-off and landing field network layout and actual demand.

[0051] 2. This application designs a reduction and pruning strategy, which greatly improves the computational efficiency of mixed integer programming models under massive OD flow and effectively solves the computational bottleneck of massive population flow.

[0052] 3. This application optimizes the entire journey chain of ground connection-air flight-ground connection through integrated optimization, revealing the dominant role of ground connection efficiency and providing a clear path to optimize the overall system performance by improving connection convenience. Attached Figure Description

[0053] Figure 1 A flowchart illustrating a method for selecting a city vertical take-off and landing field network that couples population flow, provided in an embodiment of this application;

[0054] Figure 2 This application provides a flowchart for constructing a candidate network of vertical take-off and landing airports based on multi-source geospatial big data (where (a) represents the candidate take-off and landing site screening method under the environmental constraints of urban built-up areas; (b) represents the multimodal transport service process that couples air and ground; and (c) represents a schematic diagram of a vertical take-off and landing site).

[0055] Figure 3 A comparison diagram of the efficiency before and after reduction of the CPF-VNLM vertical take-off and landing field network location model coupled with population flow is provided for the embodiments of this application. Detailed Implementation

[0056] The present application will be further explained below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are for illustrative purposes only and are not intended to limit the scope of the present application. It should be noted that the terms "front", "rear", "left", "right", "up" and "down" used in the following description refer to the directions in the accompanying drawings, and the terms "inner" and "outer" refer to the directions toward or away from the geometric center of a specific component, respectively.

[0057] like Figure 1 As shown in this embodiment, a method for selecting a city vertical take-off and landing field network that couples population flow specifically includes the following steps:

[0058] Step 1: Integrate multi-source geospatial big data to construct a set of candidate vertical take-off and landing sites that meet the environmental constraints of urban built-up areas; such as... Figure 2 As shown in (a), it specifically includes:

[0059] Step 1.1: Based on urban basic geographic data, land use planning and airspace constraints, screen candidate areas for configurable vertical take-off and landing fields;

[0060] Step 1.2: Based on the construction standards for vertical take-off and landing sites, extract building areas that meet the area and shape requirements from the candidate areas;

[0061] Step 1.3: Integrate mobile phone signal population spatiotemporal distribution data, extract high-density population flow areas, and perform spatial overlay analysis with the areas selected in Steps 1.1 and 1.2 to select candidate locations that simultaneously meet physical conditions and population demand potential;

[0062] Step 1.4: Analyze the selected candidate locations and construct a set of candidate points for vertical take-off and landing fields.

[0063] like Figure 2 As shown, Step 2: Construct the CPF-VNLM vertical take-off and landing field network location model coupled with population flow. The set of candidate vertical take-off and landing fields obtained in Step 1 is used as the departure and destination vertical take-off and landing fields of the model. The schematic diagram of the vertical take-off and landing fields is shown below. Figure 2 As shown in (c); determine the objective function, multiple constraints, and binary decision variables with the core objective of maximizing cost savings in air-ground intermodal travel; such as Figure 2 As shown in (b), the specific steps include: Step 2.1: Taking the maximization of urban population flow air-ground intermodal travel cost savings as the objective, the objective function of the coupled population flow vertical take-off and landing field network location model CPF-VNLM is as follows:

[0064] ;

[0065] Among them, the itinerary As a group The flow of travel consists of ground-based inbound shuttle services, air shuttle services, and ground-based outbound shuttle services; the set of services... It is the collection of all OD flow processes;

[0066] For the itinerary The number of people traveling; For the destination's vertical take-off and landing field; This is the departure vertical takeoff and landing field; For the itinerary The original travel time; For the itinerary The value of time; For the itinerary The original cost of transportation; Passengers pass through the departure vertical take-off and landing field Vertical take-off and landing field to destination Flight time in the air; For passengers waiting for the aircraft; For the time of takeoff and landing of the aircraft; For the itinerary From the departure vertical takeoff and landing field to the destination vertical take-off and landing field The cost of air travel; This is a ground-based shuttle service to the station. For the itinerary Using the ground shuttle bus mode at the station Arrival at the departure vertical take-off and landing field Travel time; For the itinerary Using the ground shuttle bus mode at the station Arrival at the departure vertical take-off and landing field The cost of transportation; This is a ground-based shuttle service from the station. For the itinerary Use the exit ground shuttle mode Arrival at the destination vertical take-off and landing field Travel time; For the itinerary Using ground shuttle mode Arrival at the destination vertical take-off and landing field The cost of transportation; For the binary decision variables of the air path, if the journey Select the vertical takeoff and landing field from the departure point. to the destination vertical take-off and landing field ,but The value is 1 if it is 1, otherwise it is 0. For the binary decision variable of connecting to the station, if the journey Using the ground shuttle bus mode at the station Arrival at the departure vertical take-off and landing field ,but The value is 1 if it is 1, otherwise it is 0. For the binary decision variable of exiting the station for connecting to the destination; if the itinerary Use the exit ground shuttle mode Departure from the destination vertical takeoff and landing field ,but The value is 1 if it is 1, otherwise it is 0.

[0067] Step 2.2: Define the constraints of the model, including: number of take-off and landing fields, route uniqueness constraint, station activation constraint, inbound connection matching constraint, outbound connection matching constraint, connection integrity constraint, and variable value constraint.

[0068] The constraints specifically include the following:

[0069] Constraint 1: Number of takeoff and landing fields:

[0070] ;

[0071] Constraint 2: Route Uniqueness Constraint:

[0072] ;

[0073] Constraint 3: Site Activation Constraint:

[0074] ;

[0075] Constraint 4: Inbound connection matching constraint:

[0076] ;

[0077] Constraint 5: Outbound connection matching constraint:

[0078] ;

[0079] Constraint 6: Connection Integrity Constraint:

[0080] ;

[0081] Constraint 7: Variable Value Constraints

[0082] ;

[0083] ;

[0084] in, Candidate points for vertical take-off and landing fields; This refers to the planned total number of takeoff and landing fields; It is a set of candidate points for vertical take-off and landing fields; It is a combination of air and ground transportation, consisting of all combinations of passenger air transportation routes and ground transportation connection modes; As decision variables, if the candidate points of the vertical take-off and landing field If selected as the departure or destination vertical takeoff and landing field, then The value is 1 if it is 1, otherwise it is 0.

[0085] Step 3: Based on the CPF-VNLM low-altitude aircraft vertical takeoff and landing field network location model coupled with population flow from Step 2, design a model reduction and pruning strategy; specifically including the following steps:

[0086] Step 3.1: In the itinerary set With the set of candidate points for vertical take-off and landing fields Based on this, self-loop routes with the same vertical takeoff and landing field as the origin and destination are eliminated to obtain a set of candidate air routes, where each element corresponds to a binary decision variable for the air route. ;

[0087] Step 3.2: For each trip and takeoff / landing combination If no ground-based shuttle service is available Make the station connection a binary decision variable It has valid spatiotemporal or cost data records, or there is no ground-based exit connection mode. Make the exit connection a binary decision variable If valid spatiotemporal or cost data records are available, the corresponding binary decision variables for the aerial path are discarded. ;

[0088] Step 3.3: Define the average net revenue per person for the trip as:

[0089] ;

[0090] in, Net income per capita For the integration of ground transportation modes for entering the station, This is a collection of ground transportation modes for exiting the station. ;

[0091] Step 3.4: For each trip The remaining candidate flight itineraries, according to Sort the values ​​in descending order and keep only the top values. indivual ;

[0092] Step 3.5: Apply association constraints only to combinations of vertical takeoff and landing field candidate points that actually appear in the candidate paths and the trips; for each trip Candidate sites for vertical take-off and landing If the candidate points for vertical take-off and landing fields On the trip If a candidate path is selected as either a departure vertical takeoff and landing field or a destination vertical takeoff and landing field, then constraint condition 3 is added.

[0093] Step 4: Based on the model reduction and pruning strategy in Step 3, and combined with an integer programming solver, solve the CPF-VNLM vertical take-off and landing field network location model coupled with population flow to obtain the optimal layout scheme of the vertical take-off and landing field network.

[0094] Step 4 specifically includes the following steps:

[0095] Step 4.1: Adjust the parameters of the CPF-VNLM vertical take-off and landing field network location model coupled with population flow. The adjustment includes setting the number of take-off and landing fields with different configuration sizes from 10 to 100.

[0096] Step 4.2: Solve the CPF-VNLM site selection model of low-altitude aircraft vertical take-off and landing field network coupled with population flow using an integer programming solver to obtain the optimal layout scheme of the vertical take-off and landing field network. Perform spatial visualization analysis and efficiency comparison on the solution results to evaluate the effectiveness of the site selection method.

[0097] The experimental data are as follows:

[0098] Experimental hardware and software environment description: (1) Intel Core i9 32 GB memory and NVIDIA RTX 3090Ti graphics card; (2) Operating system is Windows 10 64-bit; (3) ArcGIS 10.8 is used for geospatial data processing, and the integer programming software Gurobi 10.1, the programming language Python 3.9 and its related spatial packages are used to solve the CPF-VNLM problem. This paper selects Nanjing as the research target. Nanjing has a dense urban population and strong traffic demand. According to Baidu Map data in 2023, the average commuting speed in Nanjing during peak hours is only 27.64 km / h, ranking first in the list of congestion among China's megacities. There is an urgent need to improve commuting efficiency. Therefore, Nanjing is used as the research area for the vertical take-off and landing field network site selection, and more than 840 communities / administrative villages in Nanjing are used as the basic research units.

[0099] Experiment 1: This study set up four different configurations of station numbers (including 10, 40, 70, and 100) and used Gurobi 10.1 to solve the Nanjing take-off and landing field network location problem. The spatial distribution of the results is as follows.

[0100] When the number of stations is 10, the cost of inbound connection is 23,673 yuan, the cost of outbound connection is 23,673 yuan, the total net savings of UAM is 25,968 yuan, the service trips account for 9.8%, and the number of users served accounts for 11.6%.

[0101] When the number of stations is configured to be 40, the cost of inbound connection is 41,878 yuan, the cost of outbound connection is 38,028 yuan, the total net savings of UAM is 45,075 yuan, the service trips account for 19.8%, and the number of users served accounts for 21.0%.

[0102] When the number of stations is configured to be 70, the cost of inbound connection is 45,897 yuan, the cost of outbound connection is 44,038 yuan, the total net savings of UAM is 48,930 yuan, the service trips account for 23.1%, and the number of users served accounts for 24.0%.

[0103] When the number of configured stations is 100, the cost of inbound connection is 48,795 yuan, and the cost of outbound connection is 47,361 yuan, resulting in a net saving of 49,945 yuan for UAM. The service trips account for 24.6% of the total, and the number of users served accounts for 25.5%. Specifically, when the number of configured stations is 10, the stations are highly concentrated in the five main urban districts and the Jiangning-Pukou-Liuhe cross-river corridor, highly overlapping with the high-demand commuting axes revealed in the flow pattern. When the number of take-off and landing airports reaches more than 40, airports begin to be deployed in peripheral areas such as Lishui and Gaochun. This distribution pattern indicates that increasing the number of take-off and landing airports helps expand the coverage of UAM services. New stations mainly expand along the cross-river corridors identified by population flow and in peripheral high-demand areas, such as Pukou and Liuhe north of the Yangtze River. These areas are geographically isolated from the central urban area, and UAM services can compensate for the shortcomings of ground transportation, improving the efficiency of long-distance travel, thus potentially attracting more potential users.

[0104] Experiment 2: Based on the results of the previous section, this study aims to evaluate the solution performance of the improved CPF-VNLM (Vertical Take-off and Landing Field Network) model for coupled population flow in massive OD (Occupational Discharge) flow data. The differences in computational efficiency and solution quality with and without reduction and pruning strategies were compared, within the original data scale. , , 5. Before preprocessing, the variable size is: path variables common One, a binary decision variable for station connection Binary decision variables for outbound connection each One decision variable There are 200 variables, totaling 25,163,800. This is achieved by introducing accessibility filtering and net profit thresholds. With Top-K After pruning, only 8 candidate paths are retained per process. The number of variables is significantly reduced to 53,176 after preprocessing, including the number of paths. Binary decision variables for station access Binary decision variables for exit connection each Decision variables Maintain 200. Overall, approximately... The reduction of variables significantly improves the computability and branch-and-bound efficiency of mixed integer programming.

[0105] The performance comparison results are shown in Table 1, which compares the parameters before and after reduction and pruning:

[0106] Table 1

[0107] The average target value decreased from 72,553 yuan to 71,190 yuan, indicating that the problem structure after reduction and pruning is more compact. The solution time was sharply reduced from 526.57 seconds to 0.22 seconds, with the pruning strategy improving the solution efficiency by 93.2% while only losing 1.2% of the optimal solution quality. This demonstrates the excellent balance between efficiency and quality, verifying the significant effect of the pruning strategy in reducing problem complexity. The number of branch nodes decreased significantly from 221,974 to 3,031, proving that the pruning strategy effectively compressed the search space of the branch and bound algorithm. The MIP Gap index of the Gurobi solution was 0, confirming that both sets of experiments obtained the global optimum. In terms of the overall coverage of the takeoff and landing field, the pruning strategy achieved 19.8% (1817 / 9192), which is only 0.32 percentage points lower than the 20.12% (1849 / 9192) without pruning, indicating that the pruning strategy has a good effect in maintaining service coverage.

[0108] Figure 3 This application provides a comparison of the efficiency of the CPF-VNLM site selection model for coupled population flow before and after reduction. To verify the effectiveness of the pruning strategy, the solution time is selected as the evaluation index, and four site configuration scales (10, 40, 70, and 100) are further compared. Under different problem scales, the pruning strategy can significantly improve the solution efficiency, proving that the strategy has good scalability.

[0109] In summary, this application discloses a method for urban vertical take-off and landing field network site selection coupled with population flow. Addressing the actual travel characteristics of urban air-ground intermodal transportation, it integrates multi-source geospatial big data and population flow OD data, transforming site selection planning into a practical application of real-world population movement needs. To address the computational bottleneck of massive data, the method improves the CPF-VNLM vertical take-off and landing field network site selection model by performing reduction and pruning preprocessing. This reduces the feasible region of integer programming by approximately 99.79% while maintaining solution quality, improving the model's solution efficiency in real-world urban scenarios by 93%. Furthermore, to address the insufficient system synergy caused by unclear collaborative relationships among air-ground-person flow elements, the method performs integrated optimization by coupling the entire journey chain of ground connection-air flight-ground connection, clarifying the key impact of ground connection on user travel choices and providing guidance for improving the efficiency of air-ground intermodal transportation services. This application is applicable to the solution research of large-scale low-altitude aircraft vertical take-off and landing field network site selection optimization problems.

[0110] The technical means disclosed in this application are not limited to those disclosed in the above embodiments, but also include technical solutions composed of any combination of the above technical features.

Claims

1. A method for selecting urban vertical take-off and landing field networks coupled with population flow, characterized in that, Specifically, the steps include the following: Step 1: Integrate multi-source geospatial big data to construct a set of candidate vertical take-off and landing sites that meet the environmental constraints of urban built-up areas; Step 2: Construct a vertical take-off and landing field network location model CPF-VNLM that couples population flow. Use the set of candidate vertical take-off and landing fields obtained in Step 1 as the departure and destination vertical take-off and landing fields of the model. Determine the objective function, multiple constraints and binary decision variables with maximizing the cost savings of air-ground intermodal travel as the core. Step 3: Based on the CPF-VNLM low-altitude aircraft vertical take-off and landing field network location model coupled with population flow from Step 2, design a model reduction and pruning strategy; Step 4: Based on the model reduction and pruning strategy in Step 3, and combined with an integer programming solver, solve the CPF-VNLM vertical take-off and landing field network location model coupled with population flow to obtain the optimal layout scheme of the vertical take-off and landing field network.

2. The method for selecting a city vertical take-off and landing field network coupled with population flow according to claim 1, characterized in that: Step 1 specifically includes the following steps: Step 1.1: Based on urban basic geographic data, land use planning and airspace constraints, screen candidate areas for configurable vertical take-off and landing fields; Step 1.2: Based on the construction standards for vertical take-off and landing sites, extract building areas that meet the area and shape requirements from the candidate areas; Step 1.3: Integrate mobile phone signal population spatiotemporal distribution data, extract high-density population flow areas, and perform spatial overlay analysis with the areas selected in Steps 1.1 and 1.2 to select candidate locations that simultaneously meet physical conditions and population demand potential; Step 1.4: Analyze the selected candidate locations and construct a set of candidate points for vertical take-off and landing fields.

3. The method for selecting a location for an urban vertical take-off and landing field network coupled with population flow as described in claim 2, characterized in that, Step 2 specifically includes the following steps: Step 2.1: Taking maximizing the savings in travel costs through air-ground intermodal transportation of urban population flow as the objective, the objective function of the CPF-VNLM vertical take-off and landing field network location model coupled with population flow is as follows: ； Among them, the itinerary As a group The flow of travel consists of ground-based inbound shuttle services, air shuttle services, and ground-based outbound shuttle services; the set of services... It is the collection of all OD flow processes; For the itinerary The number of people traveling; For the destination's vertical take-off and landing field; For the departure vertical takeoff and landing field; For the itinerary The original travel time; For the itinerary The time value; For the itinerary The original cost of transportation; Passengers pass through the departure vertical take-off and landing field Vertical take-off and landing field to destination Flight time in the air; For passengers waiting for the aircraft; For the time of takeoff and landing of the aircraft; For the itinerary From the departure vertical takeoff and landing field to the destination vertical take-off and landing field The cost of air travel; This is a ground-based shuttle service to the station. For the itinerary Using the ground shuttle bus mode at the station Arrival at the departure vertical take-off and landing field Travel time; For the itinerary Using the ground shuttle bus mode at the station Arrival at the departure vertical take-off and landing field The cost of transportation; This is a ground-based shuttle service from the station. For the itinerary Use the exit ground shuttle mode Arrival at the destination vertical take-off and landing field Travel time; For the itinerary Using ground shuttle mode Arrival at the destination vertical take-off and landing field The cost of transportation; For the binary decision variables of the air path, if the journey Select the vertical takeoff and landing field from the departure point. to the destination vertical take-off and landing field ,but The value is 1 if it is 1, otherwise it is 0. For the binary decision variable of connecting to the station, if the journey Using the ground shuttle bus mode at the station Arrival at the departure vertical take-off and landing field ,but The value is 1 if it is 1, otherwise it is 0. For the binary decision variable of exiting the station for connecting to the destination; if the itinerary Use the exit ground shuttle mode Departure from the destination vertical takeoff and landing field ,but The value is 1 if it is 1, otherwise it is 0. Step 2.2: Define the constraints of the model, including: number of take-off and landing fields, route uniqueness constraint, station activation constraint, inbound connection matching constraint, outbound connection matching constraint, connection integrity constraint, and variable value constraint.

4. The method for selecting a city vertical take-off and landing field network coupled with population flow according to claim 3, characterized in that, The constraints specifically include the following: Constraint 1: Number of takeoff and landing sites: ； Constraint 2: Route Uniqueness Constraint: ； Constraint 3: Site Activation Constraint: ； Constraint 4: Inbound connection matching constraint: ； Constraint 5: Outbound connection matching constraint: ； Constraint 6: Connection Integrity Constraint: ； Constraint 7: Variable Value Constraints ； ； in, Candidate points for vertical take-off and landing fields; The total planned number of takeoff and landing fields; It is a set of candidate points for vertical take-off and landing fields; It is a combination of air and ground transportation, consisting of all combinations of passenger air transportation routes and ground transportation connection modes; As decision variables, if the candidate points of the vertical take-off and landing field If selected as the departure or destination vertical takeoff and landing field, then The value is 1 if it is 1, otherwise it is 0.

5. The method for selecting a location for an urban vertical take-off and landing field network coupled with population flow according to claim 4, characterized in that, Step 3 specifically includes the following steps: Step 3.1: In the itinerary set With the set of candidate points for vertical take-off and landing fields Based on this, self-loop routes with the same vertical takeoff and landing field as the origin and destination are eliminated to obtain a set of candidate air routes, where each element corresponds to a binary decision variable for the air route. ; Step 3.2: For each trip and takeoff / landing combination If no ground-based shuttle service is available Make the station connection a binary decision variable It has valid spatiotemporal or cost data records, or there is no ground-based exit connection mode. Make the exit connection a binary decision variable If valid spatiotemporal or cost data records are available, the corresponding binary decision variables for the aerial path are discarded. ; Step 3.3: Define the average net revenue per person for the trip as: ； in, Net income per capita For the integration of ground transportation modes for entering the station, This is a collection of ground transportation modes for exiting the station. ; Step 3.4: For each trip The remaining candidate flight itineraries, according to Sort the values ​​in descending order and keep only the top values. indivual ; Step 3.5: Apply association constraints only to combinations of vertical takeoff and landing field candidate points that actually appear in the candidate paths and the trips; for each trip Candidate sites for vertical take-off and landing If the candidate points for vertical take-off and landing fields On the trip If a candidate path is selected as either a departure vertical takeoff and landing field or a destination vertical takeoff and landing field, then constraint 3 is added.

6. The method for selecting a location for an urban vertical take-off and landing field network coupled with population flow as described in claim 5, characterized in that, Step 4 specifically includes the following steps: Step 4.1: Adjust the parameters of the CPF-VNLM vertical take-off and landing field network location model coupled with population flow. The adjustment includes setting the number of take-off and landing fields with different configuration sizes from 10 to 100. Step 4.2: Solve the CPF-VNLM site selection model of low-altitude aircraft vertical take-off and landing field network coupled with population flow using an integer programming solver to obtain the optimal layout scheme of the vertical take-off and landing field network. Perform spatial visualization analysis and efficiency comparison on the solution results to evaluate the effectiveness of the site selection method.

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