Initial low-altitude economic air traffic take-off and landing point layout method based on mobile communication big data

Abstract

The invention discloses an initial low-altitude economic air traffic take-off and landing point layout method based on mobile communication big data. The method comprises the following steps: acquiring mobile phone signaling data within a time period selected by a mobile communication operator in a region; carrying out travel segmentation, selecting travel stop points according to a stop time threshold value of the user track data, obtaining OD point pairs, carrying out track segmentation according to the OD point pairs, obtaining travel travel mode identification, obtaining POI data of traffic in the target area, matching the POI data with a travel track, and identifying a travel mode; pOI data of a key area in the area are obtained, and travel related to the key area is judged; generating a low-altitude air traffic travel OD point library; and identifying air traffic take-off and landing point layout schemes under different volumes. According to the method, travel purpose division is realized, demand identification can be carried out on position points, and the method plays an important role in low-altitude economic air traffic take-off and landing point site selection planning.

Description

Technical Field

[0001] The present invention relates to the technical field of low-altitude economy and air traffic planning, and in particular to an initial layout of low-altitude economic air traffic take-off and landing points based on mobile communication big data. Background Art

[0003] Through the application scenario traction, guide and drive the development of new forms of low-altitude economic development. From the mid- and long-term development perspective, the main scenarios of low-altitude economic urban air traffic mainly include the aerial scene experience within a scenic spot, serving tourists; the fixed-route air bus model within the city, serving local citizens and tourists or those with special transportation needs; and eventually forming an end-to-end air taxi covering the city and between cities, becoming a supplement to ground intercity transportation and an experiential travel method for short-distance intercity travel.

[0004] The current stage is the first year of low-altitude economic development, and the layout planning of various low-altitude infrastructure lacks a standard system. Therefore, it is necessary to adapt to local conditions and rationally plan the location of air traffic take-off and landing points based on the needs of current urban activities. Mobile communication data has the advantages of strong continuity, low collection cost, and wide coverage. Therefore, it has a great advantage in obtaining user activity data. Analysis and mining of mobile communications can provide effective and reliable data guarantees for urban and inter-city transportation activities of different travel modes such as railways, highways, and aviation within the research area. It can realize the traceability analysis of the entire process of the transportation chain, and can obtain end-to-end travel demand hot spots such as business transfers, cultural and sports tourism, and inter-city travel with high efficiency and low cost. Summary of the invention

[0005] The purpose of the present invention is to provide a method for the layout of initial low-altitude economic air traffic take-off and landing points based on mobile communication big data, so as to realize the take-off and landing point selection for application scenarios such as business transfer, cultural and sports tourism, and intercity travel in low-altitude economic urban air traffic within the initial regional scope, and provide a reference for accurately judging the low-altitude air traffic demand within the regional scope.

[0006] To achieve the above object, according to one aspect of the present invention, a method for initial low-altitude economic air traffic take-off and landing point layout based on mobile communication big data is provided, and the method comprises the following steps:

[0007] Obtain mobile phone signaling data within the time period selected by the mobile communication operators in the region;

[0008] Itinerary segmentation: cluster analysis is performed on the stop points. The itinerary stop points are selected according to the stop time threshold of the user trajectory data to obtain OD point pairs. The trajectory is segmented according to the OD point pairs to obtain the travel itinerary;

[0009] Travel mode identification: obtaining POI data of traffic in the target area and matching it with the travel trajectory to identify the travel mode;

[0010] Obtain the POI data of key areas within the region and determine the trips involving key areas;

[0011] Filter medium- and long-distance trips, aggregate the origin and destination points outside the key areas, and generate an OD point library for low-altitude air traffic trips;

[0012] Identify the layout schemes of air traffic takeoff and landing points under different scales.

[0013] Further, the specific steps for obtaining the mobile phone signaling data of a mobile communication operator within a selected time period in the region are as follows:

[0014] Obtain the mobile phone signaling data that has appeared continuously for at least one month in the target intercity area through the operator. The mobile phone signaling data is the trajectory information containing the date, time, stay time, and signal base station provided by the operator.

[0015] Further, for trip segmentation, perform clustering analysis on the stop points, select the trip stop points according to the stay time threshold of the user trajectory data to obtain OD point pairs, and the specific steps for segmenting the trajectory based on the OD point pairs to obtain the travel itinerary include:

[0016] S21. For the mobile phone signaling trajectory data, use the DBSCAN clustering analysis algorithm. The record of a user's travel trajectory point is l i ={ID, time, stay time , lon, lat}, corresponding to {user ID, time, stay time, longitude coordinate, latitude coordinate} respectively. For the record set L of user j j ={l 1j , l 2j ,…, l nj}(j = 1, 2,…, J), after the spatio-temporal DBSCAN processing of the record set, obtain the clustering cluster set of user j, C j ={c 1j , c 2j ,…, c nj}(j = 1, 2,…, J), c ij ∈C j (i = 1, 2,…m); for the clustering result, regard the outliers as moving points in the user trajectory and regard the multi-point clusters formed by clustering as the stay areas;

[0017] S22. For the stay area c ij ∈C j , given the time threshold T0, calculate the maximum time interval t0 of the trajectory points within c ij . If t0 < T0, then determine that this area is a short-term stay area and retain the first trajectory point of the stay area as the stop point Lost kIf t0>T0, the area is determined to be a long-term stay area, and the median point of the regional trajectory points is taken as the stay point Lost of the stay area k ;

[0018] S23. Obtain the stop points and moving points in chronological order, and the travel trajectory sequence P of the itinerary user j according to the OD point j ={p 1j ,p 2j ,…,p hj}(j=1,2,…,J), h is the total number of trajectories formed by user j.

[0019] Further, the travel mode is identified, the POI data of the traffic in the target area is obtained, and matched with the travel trajectory. The specific steps of identifying the travel mode include:

[0020] S31. Obtain POI data of railway hubs, airports, and major passenger stations through the map developer platform interface, import the POI data into GIS, adjust the coverage range, and draw the surface layer data of the railway line buffer according to the vector map, and finally obtain the GIS surface layer data of the coverage range of each type of base station;

[0021] S32. Lost k and the travel trajectory sequence P j Match and get the airport coverage trajectory sequence P Aj , given the residence time threshold T Astay Distance threshold D from track Atravel , if the residence time t Astay >T Astay , d Atravel >D Atravel Determine the signal appearance and disappearance points Lost k ,k is the encoding number of the trajectory information, and at the same time determines the Lost k Is it in the airport GIS surface data? If so, it is considered Lost. k is a point near the airport, P kj For air travel;

[0022] S33. The travel trajectory sequence P j Match with the bus station to obtain the coverage trajectory sequence P of the bus station Cj , and calculate the passenger station stay time t Cstay , given the passenger station stay time threshold T Cstay , if t Cstay >T Cstay , then determine P Cj For passenger travel;

[0023] S34. The travel trajectory sequence Pj Match with the railway hub and railway line buffer to obtain the coverage trajectory sequence P of the railway hub Rj , the coverage trajectory sequence P of the railway line Lj , and calculate the dwell time t at the railway hub Rstay , according to P Lj Trajectory sequence, get the total travel time set T of the railway line t ={t 1j ,t 2j ,…,t mj}(j=1,2,…,J), total travel distance set D t ={d 1j ,d 2j ,…,d mj}(j=1,2,…,J), m is the total number of trajectories formed by the starting and ending points of user J’s matching routes, and the route matching time set T l ={t 1j ,t 2j ,…,t nj}(j=1,2,…,J), route matching travel distance set D l ={d 1j ,d 2j ,…,d nj}(j=1,2,…,J), through Calculate and finally get the railway line matching degree O j , given the railway hub stay time threshold T Rstay , Railway line matching degree O Ltravel , if t Rstay >T Rstay , and O Ltravel >O j , then determine P Lj For rail travel.

[0024] Furthermore, the specific steps of obtaining POI data of key areas in the region and determining travel involving key areas include:

[0025] S41. Obtain POI data of key areas within the intercity area through the map developer platform interface, import the POI data into GIS, adjust and supplement the coverage of various functional areas, and number various POI areas to obtain key area number library POI set ={poi1,poi2,…,poi n}, and add the office building, business district, industrial park and industrial park layer areas to the business connection layer Area com , tourist attractions are included in the cultural and sports tourism layer Area tour , transportation hubs are included in the intercity travel layer Areainter ;

[0026] S42.P oj , P Aj , P Cj , P Lj Respectively with Area com 、Area tour 、Area inter Layers are matched, if the trajectory {p1,p2,…,p h} only matches the starting and ending points p1 and p h In the layer, the trajectory is {p1,p2,…,p h}, if the trajectory {p1,p2,…,p h} matches point p k In the layer, the trajectory is {p1,p2,…,p k}、{p k ,p k+ ,…,p h}; Based on the matched trajectory, get the travel start and end point set OD of user j j = {O time ,I bs ,D time ,D bs ,W type ,W layer}, where O time is the starting time, O bs is the starting point, D time is the end time, D bs is the end position, W type is the travel mode label, W layer Label the layer.

[0027] Furthermore, the specific steps of screening medium and long distance trips, aggregating the starting and ending points outside the key areas, and generating the OD point database of low-altitude air traffic trips include:

[0028] S51. Set the threshold ε0 of the distance between the start and end points of the trip, according to OD j Medium bs , D bs Calculate the starting and ending distance Dis j , if Dis j >ε0, then determine OD j For medium and long distance travel trajectories;

[0029] S52. Aggregate all medium and long distance OD trajectory sets to generate various low-altitude travel OD point libraries set ={[O bs ,D bs ,W type ,Wlayer ,num],……}, where num is the aggregated travel volume.

[0030] Furthermore, the specific steps for identifying the layout of air traffic take-off and landing points under different volumes include:

[0031] S61. According to OD set Middle W layer The properties of O bs ,D bs Respectively with the corresponding Area com 、Area tour and Area inter The layer is verified. If O bs At POI set In the layer bs =O bs ,des bs =D bs , if D bs In the layer, layer bs =D bs ,des bs =O bs , and match it with the original POI data to get the corresponding POI i Value, integrate to get the travel point distribution layer of various layers set ={[layer bs ,poi i ,des bs ,W type ,W layer ,num],...};

[0032] S62. Divide air traffic into three categories according to volume and set traffic volume thresholds ε in key areas com , ε tour , ε inter , aggregate poi i , W lar Calculate the travel volume com_num of each POI point i 、tour_num i 、inter_num i If the travel volume is greater than the traffic volume threshold, it is determined to be a high travel demand point Com hiigh Otherwise, it is judged as a low travel demand point Com low ; Similarly, we get the cultural and sports tourism spots Tour hiigh 、Tour low , Intercity travel points hiigh 、Inter low ;

[0033] S63. Set the traffic volume threshold δ of each type of low-altitude traffic high demand source point com , δ tour , δ inter , according to Com hiigh 、Tour hiigh 、Inter hiigh , filter layer set , and obtain the travel distribution layer of different types of high-demand points com 、layer tour 、layer inter , further aggregate and calculate to get the travel volume com_num of the source point set ={[des bs ,num],...}, tour_num set ={[des bs ,num],...},inter_num set ={[des bs ,num],……}, respectively, com_num set 、tour_num set 、inter_num set The num value in δ com , δ tour , δ inter Make a judgment, if num>δ, then des bs It is a high demand point for low-altitude air traffic source points, otherwise it is a low demand point for source points, and is integrated into a high travel source point for business travel according to the judgment rules. hiigh ={bs1,bs2,……,bs x}、Low travel source point com_point low ={bs1,bs2,……,bs y}, as well as high and low travel source points for cultural and sports tourism and intercity travel tour_point hiigh 、tour_point low 、inter_point hiigh 、inter_point low .

[0034] S64.Com hiigh 、Tour hiigh 、Inter high High-demand key area points and com_point hiigh 、tour_point hiigh 、inter_point hiighThe high-demand source points are integrated and determined as the initial low-altitude urban air traffic layout points based on the actual space size; com_point low 、tour_point low 、inter_point low Determine the layout points for subsequent low-altitude urban air traffic.

[0035] According to another aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for initial low-altitude economic air traffic take-off and landing point layout based on mobile communication big data of the present invention.

[0036] According to another aspect of the present invention, there is provided a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for initial low-altitude economic air traffic take-off and landing point layout based on mobile communication big data of the present invention when executing the program.

[0037] Compared with the prior art, the present invention has at least the following beneficial effects:

[0038] In view of the fact that there is no clear standard system for the current planning of low-altitude economic air traffic take-off and landing points, the present invention utilizes the advantages of mobile phone signaling data in mobile communication data, such as good quality, high sample volume, good time continuity, and wide spatial coverage, to realize the division of travel purposes such as business transfer, cultural and sports tourism, and intercity travel, and to identify the demand for the location points of key areas and main sources, which plays an important role in the site selection planning of low-altitude economic air traffic take-off and landing points. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments will be briefly introduced below. Obviously, the drawings in the following description only relate to some embodiments of the present invention, but are not intended to limit the present invention.

[0040] Figure 1 It is a flow chart of a preferred embodiment of the present invention;

[0041] Figure 2 It is a flow chart of itinerary segmentation in a preferred embodiment of the present invention;

[0042] Figure 3 is a flow chart of travel mode identification in a preferred embodiment of the present invention;

[0043] Figure 4 It is a flow chart of obtaining POI data of key areas in a region and determining travel involving the key areas in a preferred embodiment of the present invention;

[0044] Figure 5 It is a flow chart of generating a low-altitude air traffic travel OD point library in a preferred embodiment of the present invention;

[0045] Figure 6 It is a flow chart of a preferred embodiment of the present invention for identifying the layout plan of air traffic take-off and landing points under different volumes. DETAILED DESCRIPTION

[0046] To make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them.

[0047] Unless otherwise defined, technical or scientific terms used herein shall have the same meaning as commonly understood by one of ordinary skill in the art to which the invention belongs.

[0048] The following is combined with Figure 1-6 The specific embodiments of the present invention are described in detail with examples.

[0049] like Figure 1 As shown, the present invention provides a method for initial low-altitude economic air traffic take-off and landing point layout based on mobile communication big data, comprising the following steps:

[0050] Step S1: Acquire mobile phone signaling data of mobile communication operators in the region for a period of time;

[0051] Step S2: Split the itinerary, perform cluster analysis on the stay points, select the itinerary stay points according to the stay time threshold of the user trajectory data, obtain OD point pairs, and split the trajectory according to the OD point pairs to obtain the travel itinerary;

[0052] Step S3: mode division, obtaining POI data of railway lines, railway hubs, airports, and major passenger stations in the target area, matching them with travel trajectories, and identifying travel modes;

[0053] Step S4: Acquire POI data of key areas such as tourist attractions, office buildings, business districts, industrial parks, and industrial parks in the region, and determine travel involving key areas;

[0054] Step S5: Filter medium and long distance trips, aggregate the start and end points outside the key areas, and generate a low-altitude air traffic trip OD point database;

[0055] Step S6: Identify layout plans for air traffic take-off and landing points under different volumes.

[0056] Step S1 includes:

[0057] Obtain mobile signaling data that has appeared in the target intercity area for at least one consecutive month through the operator. The mobile signaling data is the trajectory information including date, time, stay time, and signal base station provided by the operator.

[0058] As Figure 2 shown, step S2 includes:

[0059] Step S21: For the mobile signaling trajectory data, use the DBSCAN clustering analysis algorithm. The record of a user's travel trajectory point is l i = {ID, time, stay time , lon, lat}, corresponding to {user ID, time, stay time, longitude coordinate, latitude coordinate} respectively. For the record set L j of user j 1j = {l 2j , l nj}, …, l j}(j = 1, 2, …, J), after the spatio-temporal DBSCAN processing of the record set, obtain the clustering cluster set of user j, C 1j = {c 2j , c nj}, …, c ij ∈ C j (i = 1, 2, … m) may be a multi-point cluster or may be a single outlier. For the clustering result, regard the outlier as a moving point in the user's trajectory and regard the multi-point cluster formed by clustering as a staying area.

[0060] Step S22: For the staying area c ij ∈ C j , given the time threshold T0, calculate the maximum time interval t0 of the trajectory points within c ij . If t0 < T0, then determine that this area is a short-term staying area and retain the first trajectory point of the staying area as the staying point Lost k ; if t0 > T0, then determine that this area is a long-term staying area and use the median point of the area trajectory points as the staying point Lost k of this staying area.

[0061] Step S23: Obtain the staying points and moving points in chronological order, and according to the OD points, form the travel trajectory sequence P j of user j 1j = {p 2j , p hj}, …, p

[0062] As Figure 3 shown, step 3 includes:

[0063] Step S31, obtain the POI data of railway hubs, airports, and major passenger stations through the Amap developer platform interface, import the POI data into GIS, frame and adjust the coverage range, and draw the railway line buffer surface data based on the vector map, and finally obtain the GIS surface data of the coverage range of each type of base station.

[0064] Step S32: Lost k and the travel trajectory sequence P j Match and get the airport coverage trajectory sequence P Aj , given the residence time threshold T Astay Distance threshold D from track Atravel , if the residence time t Astay >T Astay , d Atravel >D Atravel Determine the signal appearance and disappearance points Lost k ,k is the encoding number of the trajectory information, and at the same time determines the Lost k Is it in the airport GIS surface data? If so, it is considered Lost. k is a point near the airport, P Aj For air travel;

[0065] Step S33: The travel trajectory sequence P j Match with the bus station to obtain the coverage trajectory sequence P of the bus station Cj , and calculate the passenger station stay time t Cstay , given the passenger station stay time threshold T Cstay , if t Cstay >T Cstay , then determine P Cj For passenger travel;

[0066] Step S34: convert the travel trajectory sequence P j Match with the railway hub and railway line buffer to obtain the coverage trajectory sequence P of the railway hub Rj , the coverage trajectory sequence P of the railway line Lj , and calculate the dwell time t at the railway hub Rstay , according to P Lj Trajectory sequence, get the total travel time set T of the railway line t ={t 1j ,t 2j ,…,t mj}(j=1,2,…,J), total travel distance set D t ={d 1j ,d 2j ,…,d mj}(j=1,2,…,J), m is the total number of trajectories formed by the starting and ending points of user J’s matching routes, and the route matching time set T l ={t 1j ,t 2j ,…,t nj}(j=1,2,…,J), route matching travel distance set D l ={d 1j ,d 2j ,…,d nj}(j=1,2,…,J), through Calculate and finally get the railway line matching degree O j , given the railway hub stay time threshold T Rstay , Railway line matching degree O Ltravel , if t Rstay >T Rstay , and O Ltravel >O j , then determine P Lj For rail travel.

[0067] like Figure 4 As shown, step 4 includes:

[0068] Step S41, through the Amap developer platform interface, obtain preliminary POI data of key areas such as tourist attractions, office buildings, business districts, industrial parks, and industrial parks within the inter-city area, import the POI data into GIS, adjust and supplement the coverage of various functional areas, and number various POI areas to obtain the key area number library POI set ={poi1,poi2,…,poi n}, and include office buildings, business districts, industrial parks, industrial parks and other layer areas into the business connection layer Area com , tourist attractions are the cultural and sports tourism layer Area tour , airports, railway hubs, passenger stations, etc. are intercity travel layers Area inter ;

[0069] Step S42: P Oj , P Aj , P Cj , P Lj Respectively with Area com 、Area tour 、Area inter Equal layers are matched. If the trajectory {p1,p2,…,p h} only matches the starting and ending points p1 and p h In the layer, the trajectory is {p1,p2,…,p h}, if the trajectory {p1,p2,…,p h} matches point p k In the layer, the trajectory is {p1,p2,…,p k}、{p k ,p k+ ,…,p h}; Based on the matched trajectory, get the travel start and end point set OD of user j j = {O time ,O bs ,D time ,D bs ,W type ,W layer}, where O time is the starting time, O bs is the starting point, D time is the end time, D bs is the end position, W type is the travel mode label, W layer Label the layer.

[0070] like Figure 5 As shown, step 5 includes:

[0071] Step S51, set the starting and ending distance threshold ε0 according to OD j Medium bs , D bs Calculate the starting and ending distance Dis j , if Dis j >ε0, then determine OD j For medium and long distance travel trajectories;

[0072] Step S52: Aggregate all medium and long distance OD trajectory sets to generate various low-altitude travel OD point libraries set ={[O bs ,D bs ,W type ,W layer ,num],……}, where num is the aggregated travel volume.

[0073] like Figure 6 As shown, step 6 includes:

[0074] Step S61, according to OD set Middle W layer The properties of O bs ,D bs Respectively with the corresponding Area com 、Area tour 、Area inter Wait for the layer to verify, if O bs At POI set In the layer bs =Obs ,des bs =D bs , if D bs In the layer, layer bs =D bs ,des bs =O bs , and match it with the original POI data to get the corresponding POI i Value, integrate to get the travel point distribution layer of various layers set ={[layer bs ,poi i ,des bs ,W type ,W layer ,num],...};

[0075] Step S62: Divide air traffic into three categories according to volume, and set the traffic volume threshold ε in key areas com , ε tour , ε inter , aggregate poi i , W 1ayer Calculate the travel volume com_num of each POI point i 、tour_num i 、inter_num i , taking business connection as an example, if com_num i >ε com_max , then it is determined to be a high travel demand point Com hiigh Otherwise, it is judged as a low travel demand point Com low According to the above rules, we can get the cultural and sports tourism spots Tour hiigh 、Tour low , Intercity travel points hiigh 、Inter low .

[0076] Step S63, setting the traffic volume threshold δ of each type of low-altitude traffic high demand source point com , δ tour , δ inter , according to Com hiigh 、Tour high 、Inter hiigh , filter layer set , and obtain the travel distribution layer of different types of high-demand points com 、layer tour 、layer inter , further aggregate and calculate to get the travel volume com_num of the source point set={[des bs ,num],...}, tour_num set ={[des bs ,num],...},inter_num set ={[des bs ,num],……}, respectively, com_num set 、tour_num set 、inter_num set The num value in δ com , δ your , δ inter Make a judgment, if num>δ, then des bs It is a high demand point for low-altitude air traffic source points, otherwise it is a low demand point for source points, and is integrated into a high travel source point com_point for business travel according to the judgment rules hiigh ={bs1,bs2,……,bs x}、Low travel source point com_point low ={bs1,bs2,……,bs y ], as well as high and low travel source points for cultural and sports tourism and intercity travel tour_point hiigh 、tour_point low 、inter_point hiigh 、inter_point low .

[0077] Step S64, Com hiigh 、Tour hiigh 、Inter hiigh High-demand key area points and com_point hiigh 、tour_point hiigh 、inter_point hiigh The high-demand source points are integrated and determined as the initial low-altitude urban air traffic layout points based on the actual space size; com_point low 、tour_point low 、inter_point low Determine the layout points for subsequent low-altitude urban air traffic.

[0078] Embodiment 2:

[0079] The computer-readable storage medium of this embodiment stores a computer program thereon, which, when executed by a processor, implements the steps in the method for initial low-altitude economic air traffic take-off and landing point layout based on mobile communication big data of Example 1.

[0080] The computer-readable storage medium of this embodiment may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal; the computer-readable storage medium of this embodiment may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, etc. equipped on the terminal; further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device.

[0081] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or is to be output.

[0082] Embodiment 3:

[0083] The computer device of this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for initial low-altitude economic air traffic take-off and landing point layout based on mobile communication big data based on random sample consistency of Example 1 are implemented.

[0084] In this embodiment, the processor may be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, readily available programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0085] Those skilled in the art will appreciate that the disclosed content of the embodiments may be provided as methods, systems, or computer program products. Therefore, the present solution may adopt the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Moreover, the present solution may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program codes.

[0086] The present solution is described with reference to the method according to the embodiment of the present solution and the flowchart and / or block diagram of the computer program product. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions; these computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 one or more processes and / or methods Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 one or more processes and / or methods Figure 1 A function specified in one or more boxes.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 one or more processes and / or methods Figure 1 The steps for the functions specified in one or more boxes.

[0089] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0090] The examples described in the present invention are merely descriptions of the preferred implementation modes of the present invention, and are not intended to limit the concept and scope of the present invention. Without departing from the design concept of the present invention, various modifications and improvements made to the technical solutions of the present invention by engineers and technicians in this field should all fall within the protection scope of the present invention.

Claims

1. A method for initial low-altitude economic air traffic take-off and landing point layout based on mobile communication big data, characterized in that: The steps include: Obtain mobile phone signaling data within the time period selected by the mobile communication operators in the region; Itinerary segmentation: cluster analysis is performed on the stop points. The itinerary stop points are selected according to the stop time threshold of the user trajectory data to obtain OD point pairs. The trajectory is segmented according to the OD point pairs to obtain the travel itinerary; Travel mode identification: obtaining POI data of traffic in the target area and matching it with the travel trajectory to identify the travel mode; Obtain POI data for key areas within the region and determine travel involving key areas; Filter medium and long distance trips, aggregate the starting and ending points outside the key areas, and generate an OD point database for low-altitude air traffic trips; Identify air traffic landing and take-off point layout options at different volumes.

2. The method according to claim 1, characterized in that The specific steps to obtain mobile phone signaling data within the time period selected by the mobile communication operator in the region are: The mobile phone signaling data that has appeared in the target inter-city area for at least one month is obtained through the operator. The mobile phone signaling data is the trajectory information provided by the operator and includes date, time, stay time, and signal base station.

3. The method according to claim 2, characterized in that Itinerary segmentation: cluster analysis is performed on the stay points. The itinerary stay points are selected according to the stay time threshold of the user trajectory data to obtain OD point pairs. The specific steps of segmenting the trajectory according to the OD point pairs to obtain the travel itinerary include: S21. For mobile phone signaling trajectory data, the DBSCAN clustering analysis algorithm is used. The record of a user travel trajectory point is l i ={ID,time,stay time ,lon,lat}, respectively correspond to {user ID, time, stay time, longitude coordinate, latitude coordinate} for user j’s record set L j = {l 1j ,l 2j ,…,l nj }(j=1,2,…,J), after the spatiotemporal DBSCAN processing, the clustering set of user j is obtained, C j ={c 1j ,c 2j ,…,c nj }(j=1,2,…,J),c ij ∈C j (i=1,2,…m); for the clustering results, outliers are regarded as moving points in the user trajectory, and the multi-point clusters formed by clustering are regarded as stay areas; S22. For the stay area c ij ∈C j , given the time threshold T0, calculate the maximum time interval t0 of the trajectory points within c ij . If t0 < T0, then determine that this area is a short-term stay area, and retain the first trajectory point of the stay area as the stay point Lost k ; if t0 > T0, then determine that this area is a long-term stay area, and use the median point of the area trajectory points as the stay point Lost of this stay area k ; S23. Obtain the stop points and moving points in chronological order, and the travel trajectory sequence P of the itinerary user j according to the OD point j ={p 1j ,p 2j ,…,p hl }(j=1,2,…,J), h is the total number of trajectories formed by user j.

4. The method according to claim 3, characterized in that Travel mode identification, obtaining POI data of traffic in the target area and matching it with the travel trajectory. The specific steps of identifying travel mode include: S31. Obtain POI data of railway hubs, airports, and major passenger stations through the map developer platform interface, import the POI data into GIS, adjust the coverage range, and draw the surface layer data of the railway line buffer according to the vector map, and finally obtain the GIS surface layer data of the coverage range of each type of base station; S32. Lost k and the travel trajectory sequence P j Match and get the airport coverage trajectory sequence P Aj , given the residence time threshold T Astay Distance threshold D from track Atravel , if the residence time t Astay >T Astay , d Atravel >D Atravel Determine the signal appearance and disappearance points Lost k ,k is the encoding number of the trajectory information, and at the same time determines the Lost k Is it in the airport GIS surface data? If so, it is considered Lost. k is a point near the airport, P Aj For air travel; S33. The travel trajectory sequence P j Match with the bus station to obtain the coverage trajectory sequence P of the bus station Cj , and calculate the passenger station stay time t Cstay , given the passenger station stay time threshold T Cstay , if t Cstay >T Cstay , then determine P Cj For passenger travel; S34. The travel trajectory sequence P j Match with the railway hub and railway line buffer to obtain the coverage trajectory sequence P of the railway hub Rj , the coverage trajectory sequence P of the railway line Lj , and calculate the dwell time t at the railway hub Rstay , according to P Lj Trajectory sequence, get the total travel time set T of the railway line t ={t 1j ,t 2j ,…,t mj }(j=1,2,…,J), total travel distance set D t ={d 1j ,d 2j ,…,d mj }(j=1,2,…,J), m is the total number of trajectories formed by the starting and ending points of user J’s matching routes, and the route matching time set T l ={t 1j ,t 2j ,…,t nj }(j=1,2,…,J), route matching travel distance set D l ={d 1j ,d 2j ,…,d nj }(j=1,2,…,J), through Calculate and finally get the railway line matching degree O j , given the railway hub stay time threshold T Rstay , Railway line matching degree O Ltravel , if t Rstay >T Rstay , and O Ltravel >O j , then determine P Lj For rail travel.

5. The method according to claim 4, characterized in that The specific steps of obtaining POI data of key areas in the region and determining travel involving key areas include: S41. Obtain POI data of key areas within the intercity area through the map developer platform interface, import the POI data into GIS, adjust and supplement the coverage of various functional areas, and number various POI areas to obtain key area number library POI set ={poi1,poi2,…,poi n }, and add the office building, business district, industrial park and industrial park layer areas to the business connection layer Area com , tourist attractions are included in the cultural and sports tourism layer Area tour , transportation hubs are included in the intercity travel layer Area inter ; S42.P Oj , P Aj , P Cj , P Lj Respectively with Area com 、Area tour 、Area inter Layers are matched, if the trajectory {p1,p2,…,p h } only matches the starting and ending points p1 and p h In the layer, the trajectory is {p1,p2,…,p h }, if the trajectory {p1,p2,…,p h } matches point p k In the layer, the trajectory is {p1,p2,…,p k }、{p k ,p k+ ,…,p h }; Based on the matched trajectory, get the travel start and end point set OD of user j j = {O time ,O bs ,D time ,D bs ,W type ,W layer }, where O time is the starting time, O bs is the starting point, D time is the end time, D bs is the end position, W type is the travel mode label, W layer Label the layer.

6. The method according to claim 5, characterized in that The specific steps of screening medium and long distance trips, aggregating the starting and ending points outside the key areas, and generating the OD point database of low-altitude air traffic trips include: S51. Set the threshold ε0 of the distance between the start and end points of the trip, according to OD j Medium bs , D bs Calculate the starting and ending distance Dis j , if Dis j >ε0, then determine OD j For medium and long distance travel trajectories; S52. Aggregate all medium and long distance OD trajectory sets to generate various low-altitude travel OD point libraries set ={[O bs ,D bs ,W type ,W layer ,num],……}, where num is the aggregated travel volume.

7. The method according to claim 6, characterized in that The specific steps to identify the layout of air traffic take-off and landing points under different volumes include: S61. According to OD set Middle W layer The properties of O bs ,D bs Respectively with the corresponding Area com 、Area tour and Area inter The layer is verified. If O bs At POI set In the layer bs =O bs ,des bs =D bs , if D bs In the layer, layer bs =D bs ,des bs =O bs , and match it with the original POI data to get the corresponding POI i Value, integrate to get the travel point distribution layer of various layers set ={[layer bs ,poi i ,des bs ,W type ,W layer ,num],...}; S62. Divide air traffic into three categories according to volume and set traffic volume thresholds ε in key areas com , ε tour , ε inter , aggregate poi i , W layer Calculate the travel volume com_num of each POI point i 、tour_num i 、inter_num i If the travel volume is greater than the traffic volume threshold, it is determined to be a high travel demand point Com high Otherwise, it is judged as a low travel demand point Com low ; Similarly, we get the cultural and sports tourism spots Tour high 、Tour low , Intercity travel points high 、Inter low ; S63. Set the traffic volume threshold δ of each type of low-altitude traffic high demand source point com , δ tour , δ inter , according to Com high 、Tour high 、Inter high , filter layer set , and obtain the travel distribution layer of different types of high-demand points com 、layer tour 、layer inter , further aggregate and calculate to get the travel volume com_num of the source point set ={[des bs ,num],...}, tour_num set ={[des bs ,num],...},inter_num set ={[des bs ,num],……}, respectively, com_num set 、tour_num set 、inter_num set The num value in δ com , δ tour , δ inter Make a judgment, if num>δ, then des bs It is a high demand point for low-altitude air traffic source points, otherwise it is a low demand point for source points, and is integrated into a high travel source point com_point for business travel according to the judgment rules high ={bs1,bs2,……,bs x }、Low travel source point com_point low ={bs1,bs2,……,bs y }, as well as high and low travel source points for cultural and sports tourism and intercity travel your_point high 、tour_point low 、inter_point high 、inter_point low . S64.Com high 、Tour high 、Inter high High-demand key area points and com_point high 、tour_point high 、inter_point high The high-demand source points are integrated and determined as the initial low-altitude urban air traffic layout points based on the actual space size; com_point low 、tour_point low 、inter_point low Determine the layout points for subsequent low-altitude urban air traffic.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method for initial low-altitude economic air traffic take-off and landing point layout based on mobile communication big data are implemented as described in any one of claims 1 to 7.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the method for initial low-altitude economic air traffic take-off and landing point layout based on mobile communication big data are implemented as described in any one of claims 1 to 7.

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