Urban circle resident travel feature identification method and device

Abstract

The invention provides an urban circle resident travel feature identification method and device. The method comprises the following steps: acquiring mobile phone signaling data of each resident in a target urban circle; for each resident, drawing a track point of the resident based on the mobile phone signaling data of the resident, and performing clustering processing on the track point to obtain a travel starting point, a travel stopping point and a travel ending point; determining an urban circle travel result based on the travel starting point, the travel stopping point and the travel ending point; determining urban circle travel time based on the trajectory points and an urban circle travel result; determining a travel path and a travel mode based on the mobile phone signaling data of the resident; determining a travel purpose based on the track point, the travel starting point and the travel ending point; summarizing the results to obtain travel characteristics of the resident; and summarizing the travel characteristics of each resident in the target urban circle to obtain the travel characteristics of the target urban circle. According to the invention, an adjustment scheme can be provided for making traffic facility plans and adjusting traffic resource allocation.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular, relates to a method and device for identifying travel characteristics of urban residents. Background Art

[0002] The metropolitan area is centered on a central city with a clear advantage in primacy, with a commuting range as the spatial area, rather than a single city's administrative area. As an extended form of a city, the metropolitan area usually involves two or more cities, and the travel behavior of metropolitan area residents is usually cross-city and cross-regional travel. There are obvious differences in the travel demand characteristics of metropolitan areas and urban residents. For example, there are relatively few trips and the survey scope is too large, which makes it difficult to obtain comprehensive cross-city travel information through traditional survey methods.

[0003] Existing urban circle traffic surveys are mostly conducted through traditional methods such as manual surveys to collect travel information within the city and aggregate travel flows between major channels and modes. This makes it difficult to comprehensively characterize the travel characteristics of urban circles over the long term, and it is difficult to provide adjustment plans for the reasonable and efficient allocation of traffic resources in urban circles. Summary of the invention

[0004] The embodiments of the present application provide a method and device for identifying the travel characteristics of urban residents, so as to obtain the travel characteristics of urban residents in a more comprehensive and efficient manner and provide a solution for adjusting transportation facility planning and resource allocation.

[0005] This application is implemented through the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a method for identifying travel characteristics of residents in a metropolitan area, including:

[0007] Obtain the mobile phone signaling data of every resident in the target metropolitan area.

[0008] For each resident, based on the mobile phone signaling data of the resident, the trajectory points of the resident are drawn, and the trajectory points are clustered to obtain the travel starting point, travel stop points and travel end point; based on the travel starting point, travel stop points and travel end point, the urban circle travel results are determined; based on the trajectory points and the urban circle travel results, the urban circle travel time is determined; based on the mobile phone signaling data of the resident, the travel path and travel mode are determined; based on the trajectory points, travel starting point and travel end point, the travel purpose is determined; the travel starting point, travel end point, urban circle travel results, travel path, travel mode and travel purpose are summarized to obtain the travel characteristics of the resident.

[0009] Summarize the travel characteristics of each resident in the target metropolitan area to obtain the travel characteristics of the target metropolitan area.

[0010] In combination with the first aspect, in some possible implementations, clustering is performed on the trajectory points to obtain the travel starting point, travel stop point, and travel end point, including:

[0011] The trajectory points of the residents are clustered using a spatiotemporal clustering algorithm, and the clusters obtained by clustering are used as the residents' stay areas.

[0012] The number of trajectory points and the timestamp of each trajectory point in each resident's stay area are obtained, and each resident's stay area is sorted according to the number of trajectory points. The two resident's stay areas with the first and second trajectory points are used as the travel starting area and the travel end area; among them, the area with the earlier timestamp of the trajectory points in the area is the travel starting area.

[0013] Calculate the center point of all trajectory points in the travel starting area, obtain the first center point, and use the first center point as the travel starting point.

[0014] Calculate the center point of all trajectory points in the travel destination area, obtain the second center point, and use the second center point as the travel destination.

[0015] Calculate the time difference between the first and last trajectory points in each resident's stay area sorted by time, excluding the travel start area and the travel end area.

[0016] For each time difference, if the time difference is greater than or equal to the time threshold, the resident stay area corresponding to the time difference is used as the travel stop point area; the center point of all trajectory points in the travel stop point area is calculated to obtain the third center, and the third center point is used as the travel stop point.

[0017] In conjunction with the first aspect, in some possible implementations, determining the metropolitan area travel result based on the travel starting point, the travel stop point, and the travel end point includes:

[0018] The spatial positions of the travel starting point, travel stop point and travel end point are obtained, and spatially matched with the traffic district to obtain the traffic district corresponding to the travel starting point, the traffic district corresponding to the travel stop point and the traffic district corresponding to the travel end point.

[0019] The traffic area corresponding to the travel starting point, the traffic area corresponding to the travel stop point and the traffic area corresponding to the travel end point are connected in time sequence to obtain multiple travel segments.

[0020] For each travel segment, determine whether the travel segment crosses the city boundary based on the traffic areas at both ends of the travel segment. If the travel segment crosses the city boundary, the points at both ends of the travel segment are used as the two endpoints of the metropolitan area travel OD; among which, the metropolitan area travel OD represents travel traffic across the city boundary.

[0021] If there is at least one travel segment whose two endpoints are the two endpoints of the metropolitan area travel OD, the metropolitan area travel result is that there is cross-city travel behavior.

[0022] If there is no travel segment whose two endpoints are the two endpoints of the metropolitan area travel OD, the metropolitan area travel result is that there is no cross-city travel behavior.

[0023] In conjunction with the first aspect, in some possible implementations, determining the metropolitan area travel time based on the trajectory points and the metropolitan area travel results includes:

[0024] When the metropolitan area travel result indicates that there is cross-city travel behavior, the two endpoints of the metropolitan area travel OD are obtained based on the metropolitan area travel result.

[0025] Get the trajectory points at the two endpoints of the metropolitan area travel OD.

[0026] Based on the trajectory points in the two endpoints of the metropolitan area travel OD, a first endpoint and a second endpoint are obtained; wherein the first endpoint is the endpoint with an earlier timing sequence in the two endpoints of the metropolitan area travel OD.

[0027] The trajectory points in the first endpoint are sorted to obtain the trajectory point with the largest timing in the first endpoint.

[0028] The trajectory points in the second endpoint are sorted to obtain the trajectory point with the smallest timing in the second endpoint.

[0029] The difference between the timestamp of the trajectory point with the smallest timing in the second endpoint and the timestamp of the trajectory point with the largest timing in the first endpoint is calculated to obtain the urban circle travel time of the resident.

[0030] When the metropolitan area travel result is that there is no cross-city travel behavior, the metropolitan area travel time is zero.

[0031] In conjunction with the first aspect, in some possible implementations, determining a travel path based on the mobile phone signaling data of the resident includes:

[0032] According to the mobile phone signaling data of the resident, the base stations to which the mobile phone of the resident is connected in sequence are determined, and according to the base stations connected in sequence, a first path buffer sequence is determined; wherein the path buffer includes multiple base stations; and the first path buffer is a collection of path buffers where the base stations connected in sequence are located.

[0033] A second path buffer sequence and a third path buffer sequence are obtained; wherein the second path buffer sequence is a set of path buffers passing along the railway, and the third path buffer sequence is a set of path buffers passing along the highway.

[0034] The similarity between the first path buffer sequence and the second path buffer sequence is calculated to obtain a first similarity.

[0035] The similarity between the first path buffer sequence and the third path buffer sequence is calculated to obtain a second similarity.

[0036] The first similarity and the second similarity are compared, and the path corresponding to the larger similarity value is used as the travel path.

[0037] In conjunction with the first aspect, in some possible implementations, determining a travel mode based on the mobile phone signaling data of the resident includes:

[0038] Based on the resident's mobile phone signaling data, the time difference between the resident and two adjacent base stations on the travel path is obtained.

[0039] Get the distance between two adjacent base stations.

[0040] The quotient of the distance between two adjacent base stations and the time difference for the resident to connect to the two adjacent base stations on the travel path is calculated to obtain the travel speed of the resident.

[0041] Filter out the bus routes that are the same as the resident's travel route.

[0042] A first statistical value is obtained, wherein the first statistical value represents the sum of the number of travel starting points, travel stop points and travel end points within the bus passenger station in the route of the bus route.

[0043] Based on the travel speed and a preset automobile K-order parabolic distribution membership function, a first membership value is determined.

[0044] Based on the travel speed and a preset K-order parabolic distribution membership function of the bus, a second membership value is determined.

[0045] The product of the first statistical value and the first preset value and the sum of the second membership value are calculated to obtain a third membership value.

[0046] The first membership value and the third membership value are compared, and the means of transportation corresponding to the larger membership value is used as the travel mode.

[0047] The preset car K-order parabola distribution membership function is:

[0048]

[0049] Among them, a1, b1, c1 and d1 all represent preset parameters, x represents the travel speed, k represents the parabola power, and A1(x) represents the first membership value.

[0050] The preset K-order parabola distribution membership function of the bus is:

[0051]

[0052] Among them, a2, b2, c2 and d2 represent preset parameters, and A2(x) represents the second membership value.

[0053] In conjunction with the first aspect, in some possible implementations, determining a travel purpose based on the trajectory point, the travel starting point, and the travel end point includes:

[0054] Based on the trajectory points and the POI data of the activity location, the resident’s place of residence and work is determined according to preset rules.

[0055] The starting point of the trip, the end point of the trip, the residence of the resident and the place of work of the resident are used to obtain the travel purpose of the resident.

[0056] In conjunction with the first aspect, in some possible implementations, the preset rule is:

[0057] When the number of first target days in a month exceeds the preset number, and the resident on the first target day stays the longest at the first location from 22:00 on the day before the first target day to 7:00 on the first target day, and the stay time is longer than the preset time, the first location will be taken as the residence of the resident.

[0058] When the number of second target days in a month exceeds a preset number, and the resident within the second target day stays in the second location the longest from 9:00 on the second target day to 18:00 on the second target day and the stay time is longer than the preset time, the second location will be used as the resident's workplace.

[0059] In combination with the first aspect, in some possible implementations, the method further includes:

[0060] Based on the travel characteristics of the target metropolitan area, formulate transportation facility planning and transportation resource allocation plans.

[0061] Based on the transportation facility planning and transportation resource allocation plan, the transportation system of the target urban area will be optimized and adjusted.

[0062] In a second aspect, an embodiment of the present application provides a device for identifying travel characteristics of residents in a metropolitan area, including:

[0063] The data acquisition module is used to obtain the mobile phone signaling data of each resident in the target metropolitan area.

[0064] The data processing module is used to draw the trajectory points of each resident based on the mobile phone signaling data of the resident, cluster the trajectory points, and obtain the travel starting point, travel stop point and travel end point; determine the urban circle travel results based on the travel starting point, travel stop point and travel end point; determine the urban circle travel time based on the trajectory points and the urban circle travel results; determine the travel path and travel mode based on the mobile phone signaling data of the resident; determine the travel purpose based on the trajectory points, travel starting point and travel end point; summarize the travel starting point, travel end point, urban circle travel results, travel path, travel mode and travel purpose to obtain the travel characteristics of the resident.

[0065] The result output module is used to summarize the travel characteristics of each resident in the target metropolitan area and obtain the travel characteristics of the target metropolitan area.

[0066] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0067] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0068] This application calculates the travel characteristics of each resident through the mobile phone signaling data of each resident in the target urban circle, and summarizes the travel characteristics of each resident to obtain the travel characteristics of the target urban circle, providing a basis for the target urban circle to reasonably and efficiently adjust traffic resources.

[0069] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0071] Figure 1 It is a flowchart of a method for identifying travel characteristics of urban residents provided in an embodiment of the present application;

[0072] Figure 2 It is a schematic diagram of a process for identifying a travel starting point, a travel stop point, and a travel end point provided in an embodiment of the present application;

[0073] Figure 3 This is a schematic diagram of a metropolitan area travel process provided by an embodiment of the present application;

[0074] Figure 4The best matching result of the score matrix, the backtracking path and the sequence provided in an embodiment of the present application;

[0075] Figure 5 This is a travel mode identification process provided by an embodiment of the present application;

[0076] Figure 6 is a schematic diagram of travel speed calculation provided by an embodiment of the present application;

[0077] Figure 7 It is a structural diagram of a device for identifying travel characteristics of urban residents provided in one embodiment of the present application. DETAILED DESCRIPTION

[0078] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0079] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0080] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0081] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0082] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0083] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0084] Existing urban circle traffic surveys are mostly conducted through traditional methods such as manual surveys to collect travel information within the city and aggregate travel flows between major channels and modes. This makes it difficult to comprehensively characterize the travel characteristics of urban circles over the long term, and it is difficult to provide adjustment plans for the reasonable and efficient allocation of traffic resources in urban circles.

[0085] In order to solve the above problems more comprehensively, this solution has made the following considerations:

[0086] First: Research and propose the content of the travel survey for urban residents.

[0087] Combining domestic and foreign experience, relevant economic and social development planning requirements and travel behavior analysis and summary, the urban circle residents' travel survey mainly includes interval flow survey, entry and exit survey and traffic big data survey. It is specifically subdivided into indicators such as inter-city inflow and outflow characteristics, transit traffic characteristics, national and provincial trunk road flow characteristics, job-residence distribution characteristics, foreign employment ratio, population migration, and traffic accessibility (see Table 1 below), and the survey period is usually more than 1 month.

[0088] Table 1

[0089]

[0090] Second: Analysis of the urban circle residents’ travel survey data, and the summary of the multi-source data resource framework for the urban circle residents’ travel survey is shown in Table 2.

[0091] Table 2

[0092]

[0093]

[0094] Third: Build a travel survey process for urban residents based on multi-source data.

[0095] Based on mobile phone signaling data, integrating multi-source big data such as GPS data, video, coil, and toll collection, as well as basic data such as traditional surveys and road networks, a travel survey process system for urban residents based on multi-source traffic big data was constructed, which includes the following steps: ① individual travel feature identification, ② urban circle travel feature analysis, ③ multi-day travel feature analysis, etc., and combined with traffic big data and traditional surveys to identify many characteristics such as inter-city travel and highway travel, and combined with a variety of evaluation indicators for verification, and finally established a verification and calibration analysis mechanism for multi-source data.

[0096] ① Identification of individual travel characteristics

[0097] First, we use signaling data to determine whether the individual travelers are traveling across cities in the metropolitan area. Then, we use basic data such as mobile phone signaling, vehicle GPS, road network, etc. to identify individual travel characteristics such as travel endpoints, OD, paths and methods through clustering, machine learning and other algorithms.

[0098] ② Analysis of travel characteristics in urban areas

[0099] The data is aggregated and expanded to obtain the characteristics of traffic travel distribution, travel mode division and traffic flow of each mode in each area of ​​the metropolitan area. Then, the data of highway toll stations, industry statistics, surveillance videos and coils are used to analyze the actual traffic flow of important sections and cross-regions, and the traditional manual survey data is used to supplement and verify the travel characteristics of the metropolitan area, so as to achieve multi-angle comparison and verification with the recognition results of signaling and other data.

[0100] ③Analysis of multi-day travel characteristics

[0101] The travel characteristics obtained above are compared with relevant indicators of industry published data, special investigation reports, and traffic model results. After the verification method is feasible, the travel characteristics of the urban circle for multiple days are analyzed by accumulating data over time.

[0102] Fourth: Propose an urban residents’ travel survey indicator system that integrates multi-source data.

[0103] According to the survey content of urban circle residents' travel, on the basis of integrating new big data and traditional traffic survey data, a key survey technical indicator system of urban circle residents' travel integrating multi-source data was constructed, including four categories: transportation industry statistical indicators, transportation big data statistical indicators, special survey data indicators and traffic model technical indicators. Transportation industry statistical indicators include: road, rail, water and air data, railway passenger dispatch, civil aviation transportation airport passenger throughput; transportation big data statistical indicators include: urban circle travel based on toll data, bus, subway and intercity line card swiping data, and important sections of multi-source data such as integrated video; traffic model technical indicators include: total urban travel generation, regional passenger flow, mode selection, urban travel mode selection, urban traffic flow distribution of traditional methods, passenger satisfaction, travel time and space distribution, energy consumption; special survey data indicators include: important section flow characteristics and mode proportion, entry and exit road flow characteristics and mode division, travel OD characteristics, mode selection preference, floating population travel distribution characteristics, passenger bus passenger flow characteristics.

[0104] Based on the above problems and considerations, an embodiment of the present application proposes a method for identifying travel characteristics of residents in an urban circle. Through the mobile phone signaling data of each resident in the target urban circle, the travel characteristics of each resident are calculated, and the travel characteristics of each resident are summarized to obtain the travel characteristics of the target urban circle, and then obtain a traffic resource allocation plan, providing an adjustment plan for the urban circle to reasonably and efficiently adjust traffic resources.

[0105] Figure 1 is a schematic diagram of the process of identifying the travel characteristics of urban residents provided by an embodiment of the present application, with reference to Figure 1 , the detailed description of the method for identifying the travel characteristics of residents in the metropolitan area is as follows:

[0106] Step 101, obtaining the mobile phone signaling data of each resident in the target metropolitan area.

[0107] Specifically, mobile phone signaling data is generated by the communication between base stations and mobile terminals in a mobile communication system, and is a type of data passively collected from base stations through a mobile communication network.

[0108] Step 102, for each resident, based on the mobile phone signaling data of the resident, draw the trajectory points of the resident, cluster the trajectory points to obtain the travel starting point, travel stop points and travel end point; determine the urban circle travel results based on the travel starting point, travel stop points and travel end point; determine the urban circle travel time based on the trajectory points and the urban circle travel results; determine the travel path and travel mode based on the mobile phone signaling data of the resident; determine the travel purpose based on the trajectory points, travel starting point and travel end point; summarize the travel starting point, travel end point, urban circle travel results, travel path, travel mode and travel purpose to obtain the travel characteristics of the resident.

[0109] Exemplarily, clustering the trajectory points to obtain the travel starting point, travel stop point and travel end point includes:

[0110] The trajectory points of the residents are clustered using a spatiotemporal clustering algorithm, and the clusters obtained by clustering are used as the residents' stay areas.

[0111] The number of trajectory points and the timestamp of each trajectory point in each resident's stay area are obtained, and each resident's stay area is sorted according to the number of trajectory points. The two resident's stay areas with the first and second trajectory points are used as the travel starting area and the travel end area; among them, the area with the earlier timestamp of the trajectory points in the area is the travel starting area.

[0112] Calculate the center point of all trajectory points in the travel starting area, obtain the first center point, and use the first center point as the travel starting point.

[0113] Calculate the center point of all trajectory points in the travel destination area, obtain the second center point, and use the second center point as the travel destination.

[0114] Calculate the time difference between the first and last trajectory points in each resident's stay area sorted by time, excluding the travel start area and the travel end area.

[0115] For each time difference, if the time difference is greater than or equal to the time threshold, the resident stay area corresponding to the time difference is used as the travel stop point area; the center point of all trajectory points in the travel stop point area is calculated to obtain the third center, and the third center point is used as the travel stop point.

[0116] Specifically, the above method can more accurately determine the travel starting point, travel stop point and travel end point, avoid errors caused by signal drift, make the final result more accurate, and ensure the accuracy of traffic resource adjustment.

[0117] Specifically, the spatiotemporal clustering algorithm may be a density-based spatiotemporal clustering algorithm of ST-DBSCAN; assuming that there is a sample set X = {x1, x2, x3, ..., x n}, where the parameters (Eps, MinPts) are used to describe the compactness of sample distribution, Eps is the clustering radius parameter, and MinPts is the sample number threshold.

[0118] 1) E-neighborhood: For the sample set X = {x1, x2, x3, ..., x n Any sample x in i , whose E-neighborhood contains the sample set X and x i All samples whose distance is less than Eps satisfy the following formula:

[0119] N E (x i )={x l ∈X|dis(x l ,x i )≤Eps}

[0120] In the formula, dis(x i ,x l ) is any sample x i 、x l The spatial distance, Eps is the clustering radius parameter, X is the sample set, N E (x i ) is the sample x i The set of all samples in the E-neighborhood of .

[0121] 2) Core point: For sample x i , if the number of points contained in its E-neighborhood is greater than the sample number threshold, that is, N E (x i )≥MinPts, then x i It is the core point (the first center point and the second center point). In actual calculation, since the core point is related to the starting point and the end point of the trip, the number threshold will actually filter out the E-neighborhood of the two points, and then the core point corresponding to the starting point and the core point corresponding to the end point can be distinguished according to the time sequence. If it does not meet the requirements, it is a boundary object and needs to be judged later whether it is a travel stop point.

[0122] Specifically, in another specific embodiment, Figure 2 As shown, the identification steps and processes of the travel starting point, travel stop point and travel end point specifically include:

[0123] S1: Based on the base station database, the signaling sequence data is matched with the longitude and latitude of the corresponding base station according to the base station cell code CELLID.

[0124] S2: For resident i traveling in the urban area, the signaling sequence set after data preprocessing contains information such as the base station connected when the signaling event occurs and the signaling timestamp, and the signaling sequence set is sorted according to the timestamp.

[0125] S3: Prepare the algorithm input data, namely, the signaling sequence data sample set X of urban circle travel residents, the clustering radius parameter Eps, and the number threshold MinPts.

[0126] S4: Traverse the objects in the data sample set X. For a sample object x ii If the number of trajectory points in its E-neighborhood is less than the number threshold MinPts, this object is marked as a noise point, otherwise a new cluster is established to add it.

[0127] S5: Traverse sample object x i All points in the neighborhood are processed and S4 is executed until all sample objects are processed, that is, they are added to the cluster or marked as noise points;

[0128] S6: Obtain a set of all clusters, select the clusters corresponding to the core points from all clusters as the travel start area point cluster and the travel end area point cluster, and perform a stay time threshold judgment on the remaining clusters. Calculate the time difference between the first point and the last point in the point cluster, and if the time difference is greater than the time threshold, identify this point cluster as a travel stay point cluster.

[0129] S7: Find the centroid point of the resident's travel stop point cluster and use its position as the location coordinate of the travel stop point. The same applies to the travel start point and the travel end point. Sort the trajectory points in chronological order, take the timestamp of the first trajectory point as the stop start time, and take the timestamp of the last trajectory point as the stop end time.

[0130] Specifically, in another specific embodiment, the travel starting point, travel stop point and travel end point can also be identified based on the card payment data.

[0131] The charging data includes bus and subway card swiping data, highway toll data, road, rail, water and air ticket data, etc. Passengers often need to swipe their cards when entering and exiting the station, so the inter-city travel endpoints of the metropolitan area can be obtained through the corresponding station information, crawlers or POI data.

[0132] The boarding end is the boarding card swiping point, and the alighting end point is calculated using the Poisson distribution and line matching method. The main steps are as follows:

[0133] (1) Calibration of Poisson distribution parameters.

[0134] For each bus line, a Poisson distribution is set, and the distribution parameter λ is adjusted through analysis of previous bus surveys and inquiries to ensure that the distribution is the same as the distribution of the number of bus stops actually taken by passengers on this line.

[0135] (2) Extraction of bus stop data.

[0136] After completing the boarding identification, the alighting station information is identified based on the passenger's card swiping data characteristics on that day, combined with the bus connection travel mode and travel distance characteristics.

[0137] 1) There is a single card swiping data, and the number of bus stops taken by the passenger is randomly obtained using Poisson probability distribution, so as to infer the alighting station information.

[0138] 2) There are multiple card swipe data, and inference is made in four cases:

[0139] ① The first and last trips are return lines, that is, the same route has different up and down routes. The boarding station of the first card swipe data i is the alighting station of the last data i+n, and the boarding station of the last card swipe data i+n is the alighting station of the first trip i.

[0140] ② The first and last trips are loops, that is, the stations in the direction of the last data gradually approach the stations of the first card swiping data. Then, in the direction of the last data i+n, the station with the shortest distance from the station of the first card swiping data i and less than 1500 meters is the get-off station, where 1500m is determined by the current urban walking limit distance.

[0141] ③ Same line and different lines, except for the first and last card swipes, if the two adjacent card swipe data have the same travel route, the two trips are on the same line, otherwise they are on different lines. In two trips on the same line, the boarding station of the i+1 data is the alighting station of the i data; in two trips on different lines, in the direction of the bus line where the i data is located, the station with the shortest distance from the i+1 boarding station and less than 1500m is the alighting station.

[0142] ④ For other travel situations, use Poisson probability distribution to randomly obtain the number of bus stops that passengers take and infer the information of the alighting stops.

[0143] Identifying the travel starting point, travel stop point and travel end point based on card swiping and charging data is a supplementary method for identifying the travel starting point, travel stop point and travel end point in this scheme. When the method of obtaining the travel starting point, travel stop point and travel end point by clustering based on trajectory points cannot be implemented specifically, the travel starting point, travel stop point and travel end point can be identified based on card swiping and charging data.

[0144] Specifically, in order to ensure the smooth implementation of the plan and reduce the requirements for the initial data obtained (mobile phone signaling information of each resident in the target urban agglomeration), replaceable parts are introduced into the overall plan to enhance the feasibility and applicability of the plan, so that it can adapt to more complex and changing situations and ensure the accuracy of the results.

[0145] Exemplarily, based on the travel starting point, the travel stop point and the travel end point, determining the metropolitan area travel result includes:

[0146] The spatial positions of the travel starting point, travel stop point and travel end point are obtained, and spatially matched with the traffic district to obtain the traffic district corresponding to the travel starting point, the traffic district corresponding to the travel stop point and the traffic district corresponding to the travel end point.

[0147] The traffic area corresponding to the travel starting point, the traffic area corresponding to the travel stop point and the traffic area corresponding to the travel end point are connected in time sequence to obtain multiple travel segments.

[0148] For each travel segment, determine whether the travel segment crosses the city boundary based on the traffic areas at both ends of the travel segment. If the travel segment crosses the city boundary, the points at both ends of the travel segment are used as the two endpoints of the metropolitan area travel OD; among which, the metropolitan area travel OD represents travel traffic across the city boundary.

[0149] If there is at least one travel segment whose two endpoints are the two endpoints of the metropolitan area travel OD, the metropolitan area travel result is that there is cross-city travel behavior.

[0150] If there is no travel segment whose two endpoints are the two endpoints of the metropolitan area travel OD, the metropolitan area travel result is that there is no cross-city travel behavior.

[0151] Specifically, Figure 3 As shown in the figure, the departure city of residents includes 3 communities, O1, O2 and O3, and the arrival city includes 3 communities, D1, D2 and D3. For residents, 4 endpoints are identified, so the residents have 3 trips in total, among which the starting point of trip segment 2 is O2, which belongs to traffic community 4, and the end point is D2, which belongs to traffic community 5. The starting point and end point of the trip cross the urban boundary, which is an inter-urban travel behavior; for trip segments 1 and 3 with a starting point of O1 and a end point of D1, and a starting point of O3 and a end point of D3, they belong to intra-city travel, not inter-city travel behavior.

[0152] Specifically, in another specific embodiment, the metropolitan area travel results can also be identified based on the card payment data.

[0153] (1) For data on subways, roads, railways, waterways, and airlines that require card swiping at all stations, the identified travel endpoints are arranged in chronological order.

[0154] (2) According to the card swiping attributes of the boarding and alighting stations, each set of boarding and alighting station card swiping records is matched, and the corresponding travel OD information is identified in combination with POI and other data.

[0155] (3) For buses and other vehicles that only use the card swiping data for boarding, the above-mentioned alighting station estimation method is used to obtain the corresponding travel OD information in combination with the actual latitude and longitude of the station.

[0156] Identifying metropolitan area travel results based on card swiping and charging data is a supplementary method for identifying metropolitan area travel results in this solution. When the method of obtaining metropolitan area travel results based on travel starting points, travel stops and travel destinations cannot be implemented, identifying metropolitan area travel results based on card swiping and charging data can be used.

[0157] Exemplarily, determining the metropolitan area travel time based on the trajectory points and the metropolitan area travel results includes:

[0158] When the metropolitan area travel result indicates that there is cross-city travel behavior, the two endpoints of the metropolitan area travel OD are obtained based on the metropolitan area travel result.

[0159] Get the trajectory points at the two endpoints of the metropolitan area travel OD.

[0160] Based on the trajectory points in the two endpoints of the metropolitan area travel OD, a first endpoint and a second endpoint are obtained; wherein the first endpoint is the endpoint with an earlier timing sequence in the two endpoints of the metropolitan area travel OD.

[0161] The trajectory points in the first endpoint are sorted to obtain the trajectory point with the largest timing in the first endpoint.

[0162] The trajectory points in the second endpoint are sorted to obtain the trajectory point with the smallest timing in the second endpoint.

[0163] The difference between the timestamp of the trajectory point with the smallest timing in the second endpoint and the timestamp of the trajectory point with the largest timing in the first endpoint is calculated to obtain the urban circle travel time of the resident.

[0164] When the metropolitan area travel result is that there is no cross-city travel behavior, the metropolitan area travel time is zero.

[0165] Specifically, in another specific embodiment, the travel time can also be identified based on the card payment data.

[0166] Unlike spatiotemporal trajectory data, card swiping data will record the card swiping time. Therefore, for subway card swiping data, highway toll data, and road, rail, water, and air ticket data, the boarding and alighting station times can be directly extracted based on the data records to calculate the travel time. However, bus travel data does not include alighting data, so the travel time cannot be directly obtained through this method. Therefore, it is necessary to integrate bus GPS and station coordinate data to calculate the travel time.

[0167] (1) Calculate the bus alighting point based on the above method.

[0168] (2) The bus number to be taken is determined based on the boarding card swiping time and the boarding station, combined with the GPS data of all buses on the route.

[0169] (3) Based on the GPS data of the bus, the time when the bus arrives at the alighting station is matched and recorded as the alighting time of the bus.

[0170] (4) Calculate the bus travel time based on the boarding card swiping time and the alighting time.

[0171] Identifying travel time based on card payment data is a supplementary method for identifying travel time in this solution. When the method of determining the metropolitan area travel time based on trajectory points and metropolitan area travel results cannot be implemented, identifying travel time based on card payment data can be used.

[0172] Exemplarily, determining a travel route based on the mobile phone signaling data of the resident includes:

[0173] According to the mobile phone signaling data of the resident, the base stations to which the mobile phone of the resident is connected in sequence are determined, and according to the base stations connected in sequence, a first path buffer sequence is determined; wherein the path buffer includes multiple base stations; and the first path buffer is a collection of path buffers where the base stations connected in sequence are located.

[0174] A second path buffer sequence and a third path buffer sequence are obtained; wherein the second path buffer sequence is a set of path buffers passing along the railway, and the third path buffer sequence is a set of path buffers passing along the highway.

[0175] The similarity between the first path buffer sequence and the second path buffer sequence is calculated to obtain a first similarity.

[0176] The similarity between the first path buffer sequence and the third path buffer sequence is calculated to obtain a second similarity.

[0177] The first similarity and the second similarity are compared, and the path corresponding to the larger similarity value is used as the travel path.

[0178] Specifically, in order to avoid the influence of signal drift, multiple base stations are classified into one path buffer. The travel path is judged by the similarity between the actual passed path buffer list and the path buffer list of different paths, thereby increasing the accuracy of travel path judgment.

[0179] Specifically, the road network of the metropolitan area has fewer road nodes, the road network topology is simple, the length of each road section in the road network is much longer than the length of the urban road section, and the interval between parallel or adjacent roads is long, and the road network density is low. It makes it difficult for a certain mobile phone signal base station to cover two roads. Therefore, according to the distribution characteristics of the distance between the mobile phone signaling data and the base station, a path matching sequence is constructed by setting a buffer. The buffer refers to the influence range of a certain width of the road section at the geographical level. According to the confidence coefficient of 95% successful connection of the mobile phone to the base station, a strip buffer with a width of 1,400m and a length of 3-5km is set for highways and national and provincial trunk roads; a strip buffer with a width of 800m and a length of 5-10km is set for high-speed rail tracks. No buffer is set for special areas such as tunnels, and areas within 2 kilometers from intersection nodes to avoid difficulties in base station map matching caused by dense signal base station distribution.

[0180] Each buffer includes multiple mobile phone signal base stations. If a passenger has a series of continuous Cell-ID sequences belonging to the same buffer, the sequence will be replaced with the buffer number. Compared with the Cell-ID sequence for urban circle travel, the buffer coding sequence is shorter, which can effectively improve the efficiency of path identification and support the work of large-scale group travel path identification.

[0181] Specifically, taking the road buffer sequence as an example, the preset sequence S of setting the road buffer and the actual sequence T of actually passing the buffer are shown in the following table.

[0182] Road buffer sequence table:

[0183] sequence Buffer number sequence Preset sequence S {1，2，3，4，5} The actual sequence T {1，3，5，6}

[0184] The scoring rule for sequence matching is set as shown in the following formula. When the elements in the two sequences match, 1 point is scored, and no point is scored when there is a mismatch or a gap is inserted.

[0185]

[0186] Where T is the actual travel buffer sequence; S is the preset travel buffer sequence; S i is any i-th data in sequence S; T j is any j-th data in sequence T; score(S i ,T j ) is the score result of any element matching.

[0187] The iterative formula of the score matrix is ​​as follows:

[0188]

[0189] Where M is the score matrix; M i,j is the score of the i-th row and j-th column in the matrix M; score(S i ,T j ) is the score result of any element matching.

[0190] Fill in the score matrix according to this rule. After all cells are calculated, start backtracking the best path to get the best matching result. Figure 4 shown.

[0191] Due to the geometric relationship of the road network, the length of the passenger travel path or the preset travel path, a longer sequence will more easily match more identical sequence elements with the passenger travel path sequence. Using the number of sequence matching elements as an indicator to measure sequence similarity is difficult to ensure the reliability of the path matching results. In this regard, this project proposes the similarity indicator listed in the following formula, where Len is the number of sequence elements. The sequence similarity of the S sequence and the T sequence solved using the above formula is 66.7%.

[0192]

[0193] Where, Len(S) is the number of elements in the default row sequence S; Len(T) is the number of elements in the identified row sequence T; score(S, T) is the final matching score of sequences S and T; similarity is the similarity between sequences S and T.

[0194] Exemplarily, determining the travel mode based on the mobile phone signaling data of the resident includes:

[0195] Based on the resident's mobile phone signaling data, the time difference between the resident and two adjacent base stations on the travel path is obtained.

[0196] Get the distance between two adjacent base stations.

[0197] The quotient of the distance between two adjacent base stations and the time difference for the resident to connect to the two adjacent base stations on the travel path is calculated to obtain the travel speed of the resident.

[0198] Filter out the bus routes that are the same as the resident's travel route.

[0199] A first statistical value is obtained, wherein the first statistical value represents the sum of the number of travel starting points, travel stop points and travel end points within the bus passenger station in the route of the bus route.

[0200] Based on the travel speed and a preset automobile K-order parabolic distribution membership function, a first membership value is determined.

[0201] Based on the travel speed and a preset K-order parabolic distribution membership function of the bus, a second membership value is determined.

[0202] The product of the first statistical value and the first preset value and the sum of the second membership value are calculated to obtain a third membership value, wherein the first preset value is 0.1.

[0203] The first membership value and the third membership value are compared, and the means of transportation corresponding to the larger membership value is used as the travel mode.

[0204] The preset car K-order parabola distribution membership function is:

[0205]

[0206] Among them, a1, b1, c1 and d1 all represent preset parameters, x represents the travel speed, k represents the parabola power, and A1(x) represents the first membership value.

[0207] The preset K-order parabola distribution membership function of the bus is:

[0208]

[0209] Among them, a2, b2, c2 and d2 represent preset parameters, and A2(x) represents the second membership value.

[0210] Specifically, by distinguishing between car travel and bus travel, we can arrange bus travel schedules according to actual conditions to avoid empty seats in the car and waste of resources. At the same time, the number of car trips will also affect the traffic conditions of traffic routes. When the number of car trips is large, we can arrange staff to clear the sections that may cause congestion and reduce traffic pressure.

[0211] Specific, combined Figure 5 ,After distinguishing highway and high-speed rail travel according to the ,travel path extraction results, this method builds a fuzzy recognition model, ,first screens out individuals with the same travel path as the bus ,line, and then distinguishes car and bus travel according to the ,average travel speed and the endpoints of the metropolitan circle travel.

[0212] Travel speed is an important basis for distinguishing different travel modes (cars and buses). According to the distance between the projection points of mobile communication base stations on the road and the time when the communication event occurs, the moving speed between two consecutive base stations is calculated, such as Figure 6 As shown. There is an error in using mobile phone signal base stations to represent the real location of mobile phone residents. The closer the distance between the front and rear base stations, the shorter the time of the communication event, and the greater the calculation error. Therefore, by increasing the number of connected signal base stations and constructing a vertical foot for speed estimation, the speed estimation error can be greatly reduced. Therefore, the calculation formula for travel speed is:

[0213] V i,i+n =D i,i+n / T i,i+n

[0214] In the formula, C i is the foot point of the projection of base station i; D i,i+n Represents C i and C i+n The distance between the feet of the projections; T i,i+n Indicates the corresponding time difference.

[0215] Specifically, in another specific embodiment, the mode of travel can also be identified based on the card swiping charging data. When traveling by subway, bus, highway, road, rail, water or air, the corresponding metropolitan area inter-city travel mode can be directly queried based on the data source. It should be noted that there may be multiple modes of transfer for inter-city travel in the metropolitan area, so it is necessary to identify the corresponding mode of inter-city travel (mode with a large time and space interval). On the other hand, when the card swiping data is missing due to a system failure, the timetable of all modes of travel in the time period, GPS data, etc. can be combined to assist in determining the travel mode.

[0216] Identifying travel modes based on card swipe charging data is a supplementary method for identifying travel modes in this solution. When the method of determining the travel mode based on the resident’s mobile phone signaling data cannot be implemented, identifying the travel mode based on card swipe charging data can be used.

[0217] Exemplarily, determining the travel purpose based on the trajectory point, the travel starting point, and the travel end point includes:

[0218] Based on the trajectory points and the POI data of the activity location, the resident’s place of residence and work is determined according to preset rules.

[0219] The starting point of the trip, the end point of the trip, the residence of the resident and the place of work of the resident are used to obtain the travel purpose of the resident.

[0220] Specifically, the starting point of the trip, the destination of the trip, the residence of the resident and the place of work of the resident, and the purpose of the resident's trip, including:

[0221] When the starting point of a trip is the resident’s place of residence and the end point of a trip is the resident’s place of work, the purpose of the trip is work.

[0222] When the starting point of the trip is the resident’s workplace and the end point of the trip is the resident’s residence, the purpose of the trip is to go home.

[0223] In addition, when the destination of the trip is not the resident’s workplace and the stay duration is greater than 4 hours, it is considered a non-work trip.

[0224] If you want to more accurately determine the purpose of non-work travel, you can extract the POI type of the trip destination.

[0225] (1) Search for all POI types within 200m of the destination, perform weighted probability analysis based on the number of POIs and their type weights, and extract the top three main POIs and their distribution ratios. Schools, hospitals, and government agencies have greater weights, while other types of POIs have lower weights.

[0226] (2) The model constructed using Monte Carlo, support vector machine and other algorithms can directly predict the purpose of the resident's non-work trip based on the time of arrival at the trip destination, the time of departure from the trip destination, the type of POI near the trip destination, and the distribution ratio.

[0227] Exemplarily, the preset rules are:

[0228] When the number of first target days in a month exceeds the preset number, and the resident on the first target day stays the longest at the first location from 22:00 on the day before the first target day to 7:00 on the first target day, and the stay time is longer than the preset time, the first location will be taken as the residence of the resident.

[0229] When the second target days within a month exceed the preset number, and the resident within the second target day stays in the second location for the longest time from 9:00 on the second target day to 18:00 on the second target day and the stay time is longer than the preset time, the second location will be used as the resident's workplace.

[0230] Step 103, summarizing the travel characteristics of each resident in the target metropolitan area to obtain the travel characteristics of the target metropolitan area.

[0231] Exemplarily, the method further comprises:

[0232] Based on the travel characteristics of the target metropolitan area, formulate transportation facility planning and transportation resource allocation plans.

[0233] Based on the transportation facility planning and transportation resource allocation plan, the transportation system of the target urban area will be optimized and adjusted.

[0234] The above-mentioned method for identifying travel characteristics of urban circle residents calculates the travel characteristics of each resident through the mobile phone signaling data of each resident in the target urban circle, and summarizes the travel characteristics of each resident to obtain the travel characteristics of the target urban circle, providing a basis for the target urban circle to reasonably and efficiently adjust traffic resources.

[0235] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0236] Corresponding to the method for identifying travel characteristics of urban residents described in the above embodiment, Figure 7 A structural block diagram of a device for identifying travel characteristics of urban residents provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0237] See also Figure 7 The device for identifying travel characteristics of urban residents in the embodiment of the present application may include: a data acquisition module 201, a data processing module 202 and a result output module 203.

[0238] Data acquisition module 201, used to acquire mobile phone signaling data of each resident in the target metropolitan area;

[0239] The data processing module 202 is used to draw the trajectory points of each resident based on the mobile phone signaling data of the resident, perform clustering processing on the trajectory points, and obtain the travel starting point, travel stop point and travel end point; determine the urban circle travel result based on the travel starting point, travel stop point and travel end point; determine the urban circle travel time based on the trajectory points and the urban circle travel result; determine the travel path and travel mode based on the mobile phone signaling data of the resident; determine the travel purpose based on the trajectory points, travel starting point and travel end point; summarize the travel starting point, travel end point, urban circle travel result, travel path, travel mode and travel purpose to obtain the travel characteristics of the resident;

[0240] The result output module 203 is used to summarize the travel characteristics of each resident in the target metropolitan area to obtain the travel characteristics of the target metropolitan area.

[0241] Exemplarily, the data processing module 202 is further configured to:

[0242] The trajectory points of the residents are clustered using a spatiotemporal clustering algorithm, and the clusters obtained by clustering are used as the residents' stay areas.

[0243] The number of trajectory points and the timestamp of each trajectory point in each resident's stay area are obtained, and each resident's stay area is sorted according to the number of trajectory points. The two resident's stay areas with the first and second trajectory points are used as the travel starting area and the travel end area; among them, the area with the earlier timestamp of the trajectory points in the area is the travel starting area.

[0244] Calculate the center point of all trajectory points in the travel starting area, obtain the first center point, and use the first center point as the travel starting point.

[0245] Calculate the center point of all trajectory points in the travel destination area, obtain the second center point, and use the second center point as the travel destination.

[0246] Calculate the time difference between the first and last trajectory points in each resident's stay area sorted by time, excluding the travel start area and the travel end area.

[0247] For each time difference, if the time difference is greater than or equal to the time threshold, the resident stay area corresponding to the time difference is used as the travel stop point area; the center point of all trajectory points in the travel stop point area is calculated to obtain the third center, and the third center point is used as the travel stop point.

[0248] Exemplarily, the data processing module 202 is further configured to:

[0249] The spatial positions of the travel starting point, travel stop point and travel end point are obtained, and spatially matched with the traffic district to obtain the traffic district corresponding to the travel starting point, the traffic district corresponding to the travel stop point and the traffic district corresponding to the travel end point.

[0250] The traffic area corresponding to the travel starting point, the traffic area corresponding to the travel stop point and the traffic area corresponding to the travel end point are connected in time sequence to obtain multiple travel segments.

[0251] For each travel segment, determine whether the travel segment crosses the city boundary based on the traffic areas at both ends of the travel segment. If the travel segment crosses the city boundary, the points at both ends of the travel segment are used as the two endpoints of the metropolitan area travel OD; among which, the metropolitan area travel OD represents travel traffic across the city boundary.

[0252] If there is at least one travel segment whose two endpoints are the two endpoints of the metropolitan area travel OD, the metropolitan area travel result is that there is cross-city travel behavior.

[0253] If there is no travel segment whose two endpoints are the two endpoints of the metropolitan area travel OD, the metropolitan area travel result is that there is no cross-city travel behavior.

[0254] Exemplarily, the data processing module 202 is further configured to:

[0255] When the metropolitan area travel result indicates that there is cross-city travel behavior, the two endpoints of the metropolitan area travel OD are obtained based on the metropolitan area travel result.

[0256] Get the trajectory points at the two endpoints of the metropolitan area travel OD.

[0257] Based on the trajectory points in the two endpoints of the metropolitan area travel OD, a first endpoint and a second endpoint are obtained; wherein the first endpoint is the endpoint with an earlier timing sequence in the two endpoints of the metropolitan area travel OD.

[0258] The trajectory points in the first endpoint are sorted to obtain the trajectory point with the largest timing in the first endpoint.

[0259] The trajectory points in the second endpoint are sorted to obtain the trajectory point with the smallest timing in the second endpoint.

[0260] The difference between the timestamp of the trajectory point with the smallest timing in the second endpoint and the timestamp of the trajectory point with the largest timing in the first endpoint is calculated to obtain the urban circle travel time of the resident.

[0261] When the metropolitan area travel result is that there is no cross-city travel behavior, the metropolitan area travel time is zero.

[0262] Exemplarily, the data processing module 202 is further configured to:

[0263] According to the mobile phone signaling data of the resident, the base stations to which the mobile phone of the resident is connected in sequence are determined, and according to the base stations connected in sequence, a first path buffer sequence is determined; wherein the path buffer includes multiple base stations; and the first path buffer is a collection of path buffers where the base stations connected in sequence are located.

[0264] A second path buffer sequence and a third path buffer sequence are obtained; wherein the second path buffer sequence is a set of path buffers passing along the railway, and the third path buffer sequence is a set of path buffers passing along the highway.

[0265] The similarity between the first path buffer sequence and the second path buffer sequence is calculated to obtain a first similarity.

[0266] The similarity between the first path buffer sequence and the third path buffer sequence is calculated to obtain a second similarity.

[0267] The first similarity and the second similarity are compared, and the path corresponding to the larger similarity value is used as the travel path.

[0268] Exemplarily, the data processing module 202 is further configured to:

[0269] Based on the resident's mobile phone signaling data, the time difference between the resident and two adjacent base stations on the travel path is obtained.

[0270] Get the distance between two adjacent base stations.

[0271] The quotient of the distance between two adjacent base stations and the time difference for the resident to connect to the two adjacent base stations on the travel path is calculated to obtain the travel speed of the resident.

[0272] Filter out the bus routes that are the same as the resident's travel route.

[0273] A first statistical value is obtained, wherein the first statistical value represents the sum of the number of travel starting points, travel stop points and travel end points within the bus passenger station in the route of the bus route.

[0274] Based on the travel speed and a preset automobile K-order parabolic distribution membership function, a first membership value is determined.

[0275] Based on the travel speed and a preset K-order parabolic distribution membership function of the bus, a second membership value is determined.

[0276] The product of the first statistical value and the first preset value and the sum of the second membership value are calculated to obtain a third membership value.

[0277] The first membership value and the third membership value are compared, and the means of transportation corresponding to the larger membership value is used as the travel mode.

[0278] The preset car K-order parabola distribution membership function is:

[0279]

[0280] Among them, a1, b1, c1 and d1 all represent preset parameters, x represents the travel speed, k represents the parabola power, and A1(x) represents the first membership value.

[0281] The preset K-order parabola distribution membership function of the bus is:

[0282]

[0283] Among them, a2, b2, c2 and d2 represent preset parameters, and A2(x) represents the second membership value.

[0284] Exemplarily, the data processing module 202 is further configured to:

[0285] Based on the trajectory points and the POI data of the activity location, the resident’s place of residence and work is determined according to preset rules.

[0286] The starting point of the trip, the end point of the trip, the residence of the resident and the place of work of the resident are used to obtain the travel purpose of the resident.

[0287] Exemplarily, the preset rules are:

[0288] When the number of first target days in a month exceeds the preset number, and the resident on the first target day stays the longest at the first location from 22:00 on the day before the first target day to 7:00 on the first target day, and the stay time is longer than the preset time, the first location will be taken as the residence of the resident.

[0289] When the second target days within a month exceed the preset number, and the resident within the second target day stays in the second location for the longest time from 9:00 on the second target day to 18:00 on the second target day and the stay time is longer than the preset time, the second location will be used as the resident's workplace.

[0290] Exemplarily, the result output module 203 is further used for:

[0291] Based on the travel characteristics of the target metropolitan area, formulate transportation facility planning and transportation resource allocation plans.

[0292] Based on the transportation facility planning and transportation resource allocation plan, the transportation system of the target urban area will be optimized and adjusted.

[0293] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0294] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0295] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the steps in each embodiment of the above-mentioned method for identifying travel characteristics of urban residents.

[0296] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in each embodiment of the above-mentioned method for identifying travel characteristics of urban residents when executing the computer program product.

[0297] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0298] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0299] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0300] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0301] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0302] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for identifying travel characteristics of urban residents. It is characterized in that include: Obtain mobile phone signaling data for every resident in the target metropolitan area; For each resident, based on the mobile phone signaling data of the resident, the trajectory points of the resident are drawn, and the trajectory points are clustered to obtain the travel starting point, travel stop point and travel end point; Determine the metropolitan area travel result based on the travel starting point, the travel stop point and the travel end point; determine the metropolitan area travel time based on the trajectory point and the metropolitan area travel result; Determine the travel path and mode based on the mobile phone signaling data of the resident; determine the travel purpose based on the trajectory point, the travel starting point and the travel end point; Summarize the travel starting point, the travel destination, the metropolitan area travel result, the travel route, the travel mode and the travel purpose to obtain the travel characteristics of the resident; The travel characteristics of each resident in the target metropolitan area are summarized to obtain the travel characteristics of the target metropolitan area.

2. The method for identifying travel characteristics of urban residents as claimed in claim 1, It is characterized in that The clustering process of the trajectory points to obtain a travel starting point, a travel stop point and a travel end point includes: The trajectory points of the residents are clustered by spatiotemporal clustering algorithm, and the clusters obtained by clustering are used as the residents' stay areas; Obtain the number of trajectory points and the timestamp of each trajectory point in each resident's stay area, and sort each resident's stay area according to the number of trajectory points, and use the two resident's stay areas with the first and second trajectory points as the travel start area and the travel end area; wherein the area with the earlier timestamp of the trajectory points in the area is the travel start area; Calculate the center point of all trajectory points in the travel starting area to obtain a first center point, and use the first center point as the travel starting point; Calculate the center point of all trajectory points in the travel destination area to obtain a second center point, and use the second center point as the travel destination; Calculate the time difference between the first trajectory point and the last trajectory point in each resident stay area sorted by time after excluding the travel start area and the travel end area; For each time difference, if the time difference is greater than or equal to the time threshold, the resident stay area corresponding to the time difference is used as the travel stop point area; the center point of all trajectory points in the travel stop point area is calculated to obtain the third center, and the third center point is used as the travel stop point.

3. The method for identifying travel characteristics of urban residents as claimed in claim 2, It is characterized in that The determining of the metropolitan area travel result based on the travel starting point, the travel stop point and the travel end point includes: The spatial positions of the travel starting point, the travel stop point and the travel end point are obtained, and spatially matched with the traffic sub-areas to obtain the traffic sub-areas corresponding to the travel starting point, the traffic sub-areas corresponding to the travel stop point and the traffic sub-areas corresponding to the travel end point; Connecting the traffic area corresponding to the travel starting point, the traffic area corresponding to the travel stop point, and the traffic area corresponding to the travel end point in time sequence to obtain multiple travel segments; For each travel segment, determine whether the travel segment crosses the city boundary according to the traffic zones at both ends of the travel segment. If the travel segment crosses the city boundary, the points at both ends of the travel segment are used as the two endpoints of the metropolitan area travel OD; wherein the metropolitan area travel OD represents the travel traffic that crosses the city boundary; If there is at least one travel segment whose two endpoints are the two endpoints of the metropolitan area travel OD, then the metropolitan area travel result is that there is cross-city travel behavior; If there is no travel segment whose two endpoints are the two endpoints of the metropolitan area travel OD, the metropolitan area travel result is that there is no cross-city travel behavior.

4. The method for identifying travel characteristics of urban residents as claimed in claim 3, It is characterized in that The determining the metropolitan area travel time based on the trajectory point and the metropolitan area travel result includes: When the metropolitan area travel result indicates that there is cross-city travel behavior, two endpoints of the metropolitan area travel OD are obtained based on the metropolitan area travel result; Get the trajectory points at the two endpoints of the metropolitan area travel OD; Based on the trajectory points of the two endpoints of the metropolitan area travel OD, a first endpoint and a second endpoint are obtained; wherein the first endpoint is the endpoint with a forward timing sequence among the two endpoints of the metropolitan area travel OD; Sort the trajectory points in the first endpoint to obtain the trajectory point with the largest time sequence in the first endpoint; Sort the trajectory points in the second endpoint to obtain the trajectory point with the smallest timing in the second endpoint; Calculate the difference between the timestamp of the trajectory point with the smallest time sequence in the second endpoint and the timestamp of the trajectory point with the largest time sequence in the first endpoint to obtain the urban circle travel time of the resident; When the metropolitan area travel result is that there is no cross-city travel behavior, the metropolitan area travel time is zero.

5. The method for identifying travel characteristics of urban residents as claimed in claim 1, It is characterized in that The determining of the travel route based on the mobile phone signaling data of the resident includes: According to the mobile phone signaling data of the resident, the base stations to which the mobile phone of the resident is sequentially connected are determined, and according to the sequentially connected base stations, a first path buffer sequence is determined; wherein the path buffer includes multiple base stations; and the first path buffer is a set of path buffers where the sequentially connected base stations are located; Acquire a second path buffer sequence and a third path buffer sequence; wherein the second path buffer sequence is a set of path buffers passing along a railway, and the third path buffer sequence is a set of path buffers passing along a highway; Calculating the similarity between the first path buffer sequence and the second path buffer sequence to obtain a first similarity; Calculating the similarity between the first path buffer sequence and the third path buffer sequence to obtain a second similarity; The first similarity and the second similarity are compared, and the path corresponding to the larger similarity value is used as the travel path.

6. The method for identifying travel characteristics of urban residents as claimed in claim 5, It is characterized in that The determining of the travel mode based on the mobile phone signaling data of the resident includes: Based on the mobile phone signaling data of the resident, the time difference of the resident connecting to two adjacent base stations on the travel path is obtained; Obtaining the distance between the two adjacent base stations; Calculate the quotient of the distance between the two adjacent base stations and the time difference for the resident to connect to the two adjacent base stations on the travel path to obtain the travel speed of the resident; Filter out the bus routes that are the same as the travel route of the resident; Obtaining a first statistical value; wherein the first statistical value represents the sum of the number of travel starting points, travel stop points, and travel end points within the bus passenger station in the route of the bus line; Determining a first membership value based on the travel speed and a preset automobile K-order parabola distribution membership function; Determining a second membership value based on the travel speed and a preset K-order parabola distribution membership function of a bus; Calculate the sum of the product of the first statistical value and the first preset value and the second membership value to obtain a third membership value; comparing the first membership value and the third membership value, and taking the means of transport corresponding to the larger membership value as the travel mode; The preset automobile K-order parabola distribution membership function is: Among them, a 1 、b 1 、c 1 and d 1 All represent preset parameters, x represents the travel speed, k represents the parabola power, A 1 (x) represents the first membership value; The preset K-order parabola distribution membership function of the bus is: Among them, a 2 、b 2 、c 2 and d 2 Indicates preset parameters, A 2 (x) represents the second membership value.

7. The method for identifying travel characteristics of urban residents as claimed in claim 1, It is characterized in that The determining of the travel purpose based on the trajectory point, the travel starting point and the travel end point includes: Based on the trajectory points, combined with the activity location POI data, the residence and workplace of the resident are determined according to preset rules; The trip starting point, the trip end point, the residence of the resident and the workplace of the resident are used to obtain the travel purpose of the resident.

8. The method for identifying travel characteristics of urban residents as claimed in claim 7, It is characterized in that The preset rules are: When the number of first target days in a month exceeds the preset number, and the resident on the first target day has the longest stay time at the first location from 22:00 on the day before the first target day to 7:00 on the first target day and the stay time is longer than the preset time, the first location is regarded as the residence of the resident; When the number of second target days in a month exceeds the preset number, and the resident within the second target day stays at the second location the longest from 9:00 on the second target day to 18:00 on the second target day and the stay time is longer than the preset time, the second location will be used as the resident's workplace.

9. The method for identifying travel characteristics of urban residents as claimed in claim 1, It is characterized in that The method further comprises: Formulate transportation facility planning and transportation resource allocation plans based on the travel characteristics of the target metropolitan area; According to the transportation facility planning and transportation resource allocation plan, the transportation system of the target metropolitan area is optimized and adjusted.

10. A device for identifying travel characteristics of urban residents, It is characterized in that include: A data acquisition module is used to obtain the mobile phone signaling data of each resident in the target metropolitan area; A data processing module is used to draw the trajectory points of each resident based on the mobile phone signaling data of the resident, and cluster the trajectory points to obtain the travel starting point, travel stop point and travel end point; Determine the metropolitan area travel result based on the travel starting point, the travel stop point and the travel end point; determine the metropolitan area travel time based on the trajectory point and the metropolitan area travel result; Determine the travel path and mode based on the mobile phone signaling data of the resident; determine the travel purpose based on the trajectory point, the travel starting point and the travel end point; Summarize the travel starting point, the travel destination, the metropolitan area travel result, the travel route, the travel mode and the travel purpose to obtain the travel characteristics of the resident; The result output module is used to summarize the travel characteristics of each resident in the target metropolitan area to obtain the travel characteristics of the target metropolitan area.

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