Precise passenger flow analysis method and system based on mobile phone users

Abstract

The invention relates to the technical field of passenger flow analysis, and discloses a passenger flow accurate analysis method and system based on mobile phone users, and the method comprises the steps: collecting user track data of users in a scenic area, and carrying out the preprocessing; performing path matching on the preprocessed user track data and road network data in the scenic area; identifying tourist track data, and carrying out time-phased statistics on the tourist track data according to the scenic area landscape area; and calculating a seasonal influence factor and a scenic area activity factor of the scenic area landscape area by using the passenger flow optimization model, optimizing and adjusting a tourist number statistical result in combination with a tourist popularity value, and converting the result into a statistical chart. According to the invention, mapping conversion is carried out on mobile phone communication positioning coordinates and scenic spot roads, positioning matching of tourist tracks is carried out based on a limited number of mobile phone communication positioning coordinates, statistics of tourist tracks in different time periods is carried out, and statistical result adjustment in combination with season information and scenic spot activity information is carried out. And summary statistics of the number of tourists in the scenic area is realized.

Description

Technical Field

[0001] The present invention relates to the field of passenger flow analysis, and in particular to a method and system for accurately analyzing passenger flow based on mobile phone users. Background Art

[0002] In recent years, with the continuous economic growth and the improvement of people's living standards, the tourism industry, as an important part of the global economy, has developed rapidly. According to statistics from the World Tourism Organization (UNWTO), the number of international tourists has increased year by year, and tourism consumption has also been rising. As one of the world's largest tourism markets, the development of China's tourism industry has not only promoted the growth of the local economy, but also promoted cultural exchanges and social development. However, the rapid development of the tourism industry has also brought many challenges, especially in passenger flow management and resource allocation. During the peak tourist season, scenic spots often face the problem of passenger flow overload, resulting in a decline in tourist experience, increased environmental pollution and increased safety hazards. Effective passenger flow management can not only improve tourist satisfaction, but also optimize resource allocation and reduce operating costs. Therefore, how to accurately analyze and measure the number of passengers in different areas of the scenic area has become a problem that tourism managers need to solve urgently. Summary of the invention

[0003] In view of this, the present invention provides a method and system for accurately analyzing passenger flow based on mobile phone users. By collecting and analyzing information such as the movement trajectory and residence time of mobile phone users in scenic spots, data support is provided for passenger flow analysis, and real-time monitoring of passenger flow in scenic spots is supported.

[0004] To achieve the above object, the present invention provides a method for accurately analyzing passenger flow based on mobile phone users, comprising the following steps:

[0005] S1: Collecting and preprocessing user trajectory data of users in the scenic area to obtain preprocessed user trajectory data;

[0006] S2: performing path matching on the pre-processed user trajectory data and the road network data in the scenic area to obtain the actual road network trajectory data of the user, wherein the trajectory similarity measurement is an implementation method of the path matching;

[0007] S3: Identify tourist trajectory data from the user's actual road network trajectory data, and count the tourist trajectory data by time period according to the scenic area, so as to obtain the statistical results of the number of tourists in different scenic areas at different time periods;

[0008] S4: mining the tourist trajectory patterns in the scenic area based on the tourist trajectory data to obtain the tourist heat value of the scenic area;

[0009] S5: Construct a passenger flow optimization model, use the passenger flow optimization model to calculate the seasonal influencing factors of the scenic area and the scenic area activity factors, optimize and adjust the tourist number statistics results based on the tourist heat value, and convert the optimized and adjusted tourist number statistics results into statistical charts for display.

[0010] As a further improvement method of the present invention:

[0011] Optionally, collecting user trajectory data of users in the scenic area in step S1 includes:

[0012] Collect user trajectory data of users in the scenic area:

[0013] e= { e n =( e n 1 , e n 2 ,..., e n i ,..., e n num n )|n∈[1,N]} ;

[0014] in:

[0015] represents the user trajectory sequence of the nth user in the scenic area;

[0016] Represents the user trajectory sequence middle Mobile phone signaling data sequence, where Represents the user trajectory sequence The i-th mobile phone signaling data in Represents the user trajectory sequence The total number of mobile phone signaling data, in the embodiment of the present invention, the mobile phone signaling data is provided by the mobile operator;

[0017] e n i = ( dis n i , time n i ),i ∈ [1, num n ] , Indicates mobile phone signaling data The distance information in Indicates mobile phone signaling data The time of generation, including distance information The mobile phone held by the nth user at time The distances between the mobile phone and the three base stations in the scenic area; in the embodiment of the present invention, if the mobile phone communicates with the base stations in the scenic area, mobile phone signaling data is generated;

[0018] The user trajectory data e is preprocessed to obtain preprocessed user trajectory data.

[0019] Optionally, preprocessing the user trajectory data to obtain preprocessed user trajectory data includes:

[0020] Preprocess the user trajectory data e, where the user trajectory sequence in the user trajectory data e The preprocessing process is:

[0021] S11: Get the base station coordinates of three base stations A, B, and C in the scenic area:

[0022] ;

[0023] in:

[0024] The base station coordinates of the three base stations A, B, and C in the scenic area are represented in sequence. are the coordinates of the three base stations A, B, and C in the east-west direction, are the coordinates of the three base stations A, B, and C in the north-south direction, with the center of the scenic area as the origin of the coordinates;

[0025] S12: extract the distance information in the mobile phone signaling data, and construct an equation group for the positioning coordinates corresponding to the distance information in combination with the base station coordinates, and solve the equation group to obtain the positioning coordinates corresponding to any mobile phone signaling data; wherein the mobile phone signaling data The corresponding positioning coordinates are , Respectively represent the coordinates in the east-west direction and the north-south direction; the distance information The corresponding positioning coordinate equations are:

[0026] ;

[0027] ;

[0028] in:

[0029] represents the number of mobile phones held by the nth user at time The distance between the three base stations A, B, and C in the scenic area;

[0030] Indicates distance information The corresponding positioning coordinates, that is, the nth user at time The location coordinates of

[0031] S13: All positioning coordinates are combined into a user positioning coordinate sequence :

[0032] ;

[0033] S14: Calculate the user positioning coordinate sequence The abnormal index ; Among them, the abnormal index The calculation formula is:

[0034] ;

[0035] ;

[0036] in:

[0037] Indicates the preset distance threshold between adjacent positioning coordinates;

[0038] Indicates positioning coordinates The distance between

[0039] S15: If the abnormal index If the value is higher than the preset abnormal threshold, it means that the user positioning coordinate sequence There is an obvious positioning error, which affects the user's positioning coordinate sequence. And user trajectory sequence Carry out cleaning treatment;

[0040] Otherwise, it constitutes a user trajectory sequence The preprocessing results :

[0041] ;

[0042] ;

[0043] in:

[0044] Indicates mobile phone signaling data The processing result;

[0045] The preprocessing results as the g-th group of preprocessed user trajectory sequences in the preprocessed user trajectory data E, where g represents the number of the current preprocessed user trajectory sequences, and the initial value of g is 1;

[0046] All preprocessed user trajectory sequences constitute preprocessed user trajectory data :

[0047] E={ E m =( E m 1 , E m 2 ,..., E m i ,..., E m num m )|m∈[1,M]} ;

[0048] ;

[0049] in:

[0050] M represents the total number of user trajectory sequences after preprocessing, represents the mth group of preprocessed user trajectory sequences, Represents the user trajectory sequence The processing result of the i-th mobile phone signaling data, Represents the user trajectory sequence The total number of processing results of mobile phone signaling data.

[0051] Optionally, in step S2, the preprocessed user trajectory data is path matched with the road network data in the scenic area to obtain the user's actual road network trajectory data, including:

[0052] The pre-processed user trajectory data is matched with the road network data in the scenic area to obtain the user's actual road network trajectory data, where the mth group of pre-processed user trajectory sequences The path matching process is:

[0053] S21: Construct road network data within the scenic area ,in represents the vertex coordinate set of the turning points or intersections of roads in the scenic area, G represents the road set in the scenic area, and the road includes the road starting point, the road end point and the road middle point, among which the coordinates of the road starting point, the road end point and the road middle point all belong to the vertex coordinate set;

[0054] S22: From the road network data within the scenic area Extract user trajectory sequences from the roads in The set of candidate roads with any positioning coordinates in The candidate road extraction method is as follows:

[0055] To locate the coordinates A circular area is constructed with R as the center and R as the radius, and the roads contained or intersected in the circular area are used as positioning coordinates. The candidate roads; the positioning coordinates The candidate road set is , Q m i = { Q m i (j)|j∈[1,J]} , Represents the candidate road set The jth candidate road in the set, J represents the set of candidate roads The total number of candidate roads;

[0056] S23: Calculate the road direction of any candidate road in the candidate road set; The road directions are:

[0057] ;

[0058] in:

[0059] Indicates candidate roads road direction;

[0060] Indicates candidate roads The coordinates of the road end point;

[0061] Indicates candidate roads The starting point coordinates of the road;

[0062] S24: Combine the road directions of the candidate roads and calculate the probability of any candidate road being the actual road network trajectory; As positioning coordinates The trajectory probability of the associated actual road network trajectory is:

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] in:

[0069] Indicates candidate roads As positioning coordinates The trajectory probability of the associated actual road network trajectory;

[0070] represents an exponential function with a natural constant as base;

[0071] Indicates positioning coordinates The distance between

[0072] Indicates positioning coordinates The coordinate values ​​in ;

[0073] Indicates that the coordinates will be located Projection to candidate roads Projection distance;

[0074] represents the probability control parameter;

[0075] Indicates candidate roads Similarity of road trajectory directions; Indicates candidate roads Similarity of road projection distance; Indicates candidate roads The actual road network trajectory transfer probability, that is, the positioning coordinates The roads between them are transferred to candidate roads The probability of

[0076] S25: Select the candidate road with the highest trajectory probability as The actual road network trajectory corresponding to the positioning coordinates in the The corresponding user's actual road network trajectory:

[0077] ;

[0078] in:

[0079] Represents the user trajectory sequence Central positioning coordinates The actual road network trajectory corresponding to As the actual road network trajectory The trajectory time.

[0080] Optionally, the step S3 of identifying tourist trajectory data from the user's actual road network trajectory data includes:

[0081] The tourist trajectory data is identified from the user's actual road network trajectory data, and the identification process of the tourist trajectory data is as follows:

[0082] S31: Calculate the length of the actual road network trajectory of the user; where the actual road network trajectory of the user The trajectory length is:

[0083] ;

[0084] in:

[0085] Indicates the user's actual road network trajectory The length of the trajectory;

[0086] Represents the actual road network trajectory Length of road;

[0087] S32: Calculate the trajectory time of the user's actual road network trajectory; where the user's actual road network trajectory The trajectory time is:

[0088] ;

[0089] in:

[0090] Indicates the user's actual road network trajectory The trajectory time;

[0091] S33: Calculate the area of ​​the smallest circumscribed rectangle that contains the actual road network trajectory of the user as the scenic area coverage of the actual road network trajectory of the user; wherein the actual road network trajectory of the user The coverage of scenic spots is ;

[0092] S34: Calculate the probability coefficient of the user's actual road network trajectory being the tourist's trajectory, where the user's actual road network trajectory The probability coefficient of the tourist trajectory is :

[0093] ;

[0094] in:

[0095] represents an exponential function with a natural constant as base;

[0096] If the probability coefficient is greater than the preset probability threshold, the user's actual road network trajectory is marked as a tourist trajectory, and all tourist trajectories constitute tourist trajectory data;

[0097] The tourist trajectory data is counted by region and time period to obtain the statistical results of the number of tourists in different regions at different time periods.

[0098] Optionally, the tourist trajectory data is counted by time period of the scenic area to obtain the statistical results of the number of tourists in different scenic areas at different time periods, including:

[0099] The scenic area is divided into K scenic area landscape areas. According to several tourist trajectories in the tourist trajectory data, if the trajectory time of the actual road network trajectory in the tourist trajectory belongs to the tth time period, and the actual road network trajectory overlaps with the kth scenic area landscape area, then the tourist trajectory makes the number of tourists in the kth scenic area landscape area in the tth time period increase by one; thus, the number of tourists in each scenic area landscape area in different time periods is calculated, among which the number of tourists in the kth scenic area landscape area in the tth time period is , t ∈[1,24] , each period is 1 hour long. k ∈[1,K] .

[0100] Optionally, in step S4, mining tourist trajectory patterns in a scenic area on the tourist trajectory data includes:

[0101] The tourist trajectory data is used to mine the tourist trajectory patterns in the scenic area to obtain the tourist heat values ​​of different scenic areas. The process of mining the tourist trajectory patterns in the scenic area is as follows:

[0102] S41: extracting a vertex coordinate sequence of a tourist trajectory from the tourist trajectory data, extracting a trajectory time corresponding to an actual road network trajectory with the vertex coordinate as the road starting point in the tourist trajectory, and forming a trajectory time sequence;

[0103] S42: According to the trajectory time associated with the vertex coordinate, if the trajectory time belongs to the t-th time period, the vertex coordinate is divided into the t-th time period to obtain a vertex coordinate set of 24 time periods;

[0104] S43: Calculate the density information of K scenic spots and landscape areas in any time period; the density information of the k-th scenic spot and landscape area in the t-th time period is:

[0105] ;

[0106] in:

[0107] Represents the density information of the k-th scenic area in the t-th time period;

[0108] Represents the density information of the kth scenic area;

[0109] It represents the coordinate set composed of the Y vertex coordinates closest to the k-th scenic area in the vertex coordinate set of the t-th time period, , c represents the coordinate set Coordinates of any vertex in ;

[0110] represents the density control parameter; in the embodiment of the present invention, Set to 2;

[0111] S44: Convert the density information into the tourist heat value of the scenic area in any period of time, where the tourist heat value of the k-th scenic area in the t-th period is:

[0112] ;

[0113] in:

[0114] It represents the tourist heat value of the k-th scenic area in the t-th time period.

[0115] Optionally, in step S5, a passenger flow optimization model is constructed, and the seasonal influencing factors of the scenic area and the scenic area activity factors are calculated using the passenger flow optimization model, and the tourist number statistics are optimized and adjusted in combination with the tourist heat value, including:

[0116] A passenger flow optimization model is constructed, in which the passenger flow optimization model calculates the seasonal influencing factors and scenic spot activity factors, and takes the tourist heat value of the scenic area, seasonal influencing factors, scenic spot activity factors and tourist number statistics as input, and takes the optimized and adjusted tourist number statistics as output. The optimization adjustment process of the tourist number statistics is as follows:

[0117] S51: The passenger flow optimization model calculates the seasonal impact factors of different scenic areas; the seasonal impact factor of the kth scenic area is:

[0118] ;

[0119] in:

[0120] represents the seasonal impact factor of the k-th scenic area;

[0121] represents the average number of tourists in the kth scenic area in the current season; represents the average number of tourists in the kth scenic area in all seasons; in the embodiment of the present invention, the seasonal influencing factor is calculated using the statistical results of the number of tourists without optimization adjustment;

[0122] It indicates the average number of tourists in the entire scenic area in the current season;

[0123] S52: The passenger flow optimization model calculates the scenic area activity factors of different scenic area landscape areas; the scenic area activity factor of the kth scenic area landscape area is:

[0124] ;

[0125] in:

[0126] represents the scenic area activity factor of the kth scenic area in the tth period, A binary variable indicating whether the k-th scenic area holds a special event in the t-th period, , It means that no special activities were held in the kth scenic area in the tth period. It means that the k-th scenic area holds a special event in the t-th period;

[0127] S53: Taking the tourist heat value, seasonal influencing factor, scenic spot activity factor and tourist number statistics of the scenic spot landscape area as input, the optimized and adjusted tourist number statistics are calculated, where the optimized and adjusted tourist number of the kth scenic spot landscape area in the tth period is :

[0128] S k t = s k t [ (1+ γ k 2 (t) ) H k t h 2 ] γ k 1 h 1 ;

[0129] in:

[0130] They represent the factor control parameters of seasonal impact factors and scenic spot activity factors respectively;

[0131] The optimized and adjusted tourist number statistics results are converted into statistical charts for display, and the display results are submitted to scenic area managers and other relevant personnel; the statistical charts include bar charts of the number of tourists in different scenic areas in 24 time periods and the year-on-year and month-on-month change rates, and pie charts of the total number of tourists in different scenic areas and the year-on-year and month-on-month change rates.

[0132] In order to solve the above problems, the present invention provides a passenger flow accurate analysis system based on mobile phone users, the system comprising:

[0133] A data collection and processing module is used to collect and preprocess the user trajectory data of users in the scenic area to obtain the preprocessed user trajectory data;

[0134] The tourist statistics module is used to match the pre-processed user trajectory data with the road network data in the scenic area to obtain the actual road network trajectory data of the user, identify the tourist trajectory data from the actual road network trajectory data of the user, and count the tourist trajectory data by time period according to the scenic area to obtain the statistical results of the number of tourists in different scenic areas at different time periods;

[0135] The result display device is used to mine the tourist trajectory pattern in the scenic area based on the tourist trajectory data, obtain the tourist heat value of the scenic area, calculate the seasonal influencing factor and the scenic area activity factor of the scenic area using the passenger flow optimization model, optimize and adjust the tourist number statistics results in combination with the tourist heat value, and convert the optimized and adjusted tourist number statistics results into statistical charts for display.

[0136] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0137] A memory storing at least one instruction;

[0138] Communication interface, enabling electronic equipment to communicate; and

[0139] The processor executes the instructions stored in the memory to implement the above-mentioned method for accurately analyzing passenger flow based on mobile phone users.

[0140] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned method for accurate passenger flow analysis based on mobile phone users.

[0141] Compared with the prior art, the present invention proposes a method and system for accurately analyzing passenger flow based on mobile phone users, which has the following advantages:

[0142] First, this scheme proposes a tourist trajectory positioning and matching method based on base stations and mobile phone communications. According to the distance between the mobile phone and the base station, the mobile phone is located to obtain a user trajectory sequence containing the mobile phone location and positioning time, and the abnormal index of the user trajectory sequence is calculated according to the change amplitude of the mobile phone location and the change amplitude of the positioning time. The higher the abnormal index, the greater the position change of the mobile phone in a short period of time. The user trajectory sequence with too high anomaly index is cleaned, and the road mapping method is used to extract the adjacent scenic area roads of any positioning coordinates in the user trajectory sequence as candidate roads. According to the distance and road direction between the candidate road and the positioning coordinates, the road trajectory direction similarity, road projection distance similarity, and actual road network trajectory transfer probability of the candidate road are calculated, and then the trajectory probability of the candidate road as the actual road network trajectory associated with the positioning coordinate is constructed, so as to realize the mapping conversion between the positioning coordinates and the scenic area road, and realize the tourist trajectory positioning matching based on a limited number of mobile phone communication positioning coordinates.

[0143] At the same time, this scheme proposes a scenic spot passenger flow statistics method. According to the trajectory length, trajectory time and scenic spot coverage of the user's actual road network trajectory, the trajectory with a shorter length, a longer trajectory time and a smaller scenic spot coverage is selected as the trajectory of the scenic spot staff, so as to realize the screening and processing of the tourist trajectory in the scenic spot, and according to the time information of the tourist trajectory, the number of tourists in different scenic spots at different time periods is obtained, and according to the distance from the positioning coordinates in the tourist trajectory to the different scenic spots, the density information of each scenic spot is calculated and converted into a heat value, and the seasonal influencing factor and the scenic spot activity factor are calculated through the passenger flow optimization model, and the tourist heat value, seasonal influencing factor, scenic spot activity factor and the tourist number statistical results of the scenic spot landscape area are used as input to optimize and adjust the tourist number statistical results, and the calculation formula of the seasonal influencing factor and the scenic spot activity factor is continuously updated with new data to improve the accuracy and reliability of the statistics. BRIEF DESCRIPTION OF THE DRAWINGS

[0144] Figure 1 A flow chart of a method for accurately analyzing passenger flow based on mobile phone users provided by one embodiment of the present invention;

[0145] Figure 2 A functional module diagram of a passenger flow precision analysis system based on mobile phone users provided by an embodiment of the present invention;

[0146] Figure 2 In: 100 a passenger flow precision analysis system based on mobile phone users, 101 a data collection and processing module, 102 a tourist statistics module, 103 a result display device;

[0147] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0148] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0149] The embodiment of the present application provides a method for accurate analysis of passenger flow based on mobile phone users. The execution subject of the method for accurate analysis of passenger flow based on mobile phone users includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for accurate analysis of passenger flow based on mobile phone users can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0150] Embodiment 1:

[0151] S1: Collect user trajectory data of users in the scenic area and preprocess them to obtain preprocessed user trajectory data.

[0152] The step S1 collects user trajectory data of users in the scenic area, including:

[0153] Collect user trajectory data of users in the scenic area:

[0154] e= { e n =( e n 1 , e n 2 ,..., e n i ,..., e n num n )|n∈[1,N]} ;

[0155] in:

[0156] represents the user trajectory sequence of the nth user in the scenic area;

[0157] Represents the user trajectory sequence middle Mobile phone signaling data sequence, where Represents the user trajectory sequence The i-th mobile phone signaling data in Represents the user trajectory sequence The total number of mobile phone signaling data, in the embodiment of the present invention, the mobile phone signaling data is provided by the mobile operator;

[0158] e n i = ( dis n i , time n i ),i ∈ [1, num n ] , Indicates mobile phone signaling data The distance information in Indicates mobile phone signaling data The time of generation, including distance information The mobile phone held by the nth user at time The distances between the mobile phone and the three base stations in the scenic area; in the embodiment of the present invention, if the mobile phone communicates with the base stations in the scenic area, mobile phone signaling data is generated;

[0159] The user trajectory data e is preprocessed to obtain preprocessed user trajectory data.

[0160] The user trajectory data is preprocessed to obtain the preprocessed user trajectory data, including:

[0161] Preprocess the user trajectory data e, where the user trajectory sequence in the user trajectory data e The preprocessing process is:

[0162] S11: Get the base station coordinates of three base stations A, B, and C in the scenic area:

[0163] ;

[0164] in:

[0165] The base station coordinates of the three base stations A, B, and C in the scenic area are represented in sequence. are the coordinates of the three base stations A, B, and C in the east-west direction, are the coordinates of the three base stations A, B, and C in the north-south direction, with the center of the scenic area as the origin of the coordinates;

[0166] S12: extract the distance information in the mobile phone signaling data, and construct an equation group for the positioning coordinates corresponding to the distance information in combination with the base station coordinates, and solve the equation group to obtain the positioning coordinates corresponding to any mobile phone signaling data; wherein the mobile phone signaling data The corresponding positioning coordinates are , Respectively represent the coordinates in the east-west direction and the north-south direction; the distance information The corresponding positioning coordinate equations are:

[0167] ;

[0168] ;

[0169] in:

[0170] represents the number of mobile phones held by the nth user at time The distance between the three base stations A, B, and C in the scenic area;

[0171] Indicates distance information The corresponding positioning coordinates, that is, the nth user at time The location coordinates of

[0172] S13: All positioning coordinates are combined into a user positioning coordinate sequence :

[0173]

[0174] S14: Calculate the user positioning coordinate sequence The abnormal index ; Among them, the abnormal index The calculation formula is:

[0175] ;

[0176] ;

[0177] in:

[0178] Indicates the preset distance threshold between adjacent positioning coordinates;

[0179] Indicates positioning coordinates The distance between

[0180] S15: If the abnormal index If the value is higher than the preset abnormal threshold, it means that the user positioning coordinate sequence There is an obvious positioning error, which affects the user's positioning coordinate sequence. And user trajectory sequence Carry out cleaning treatment;

[0181] Otherwise, it constitutes a user trajectory sequence The preprocessing results :

[0182] ;

[0183] ;

[0184] in:

[0185] Indicates mobile phone signaling data The processing result;

[0186] The preprocessing results as the g-th group of preprocessed user trajectory sequences in the preprocessed user trajectory data E, where g represents the number of the current preprocessed user trajectory sequences, and the initial value of g is 1;

[0187] All preprocessed user trajectory sequences constitute preprocessed user trajectory data :

[0188] E={ E m =( E m 1 , E m 2 ,..., E m i ,..., E m num m )|m∈[1,M]} ;

[0189] ;

[0190] in:

[0191] M represents the total number of user trajectory sequences after preprocessing, represents the mth group of preprocessed user trajectory sequences, Represents the user trajectory sequence The processing result of the i-th mobile phone signaling data, Represents the user trajectory sequence The total number of processing results of mobile phone signaling data.

[0192] S2: Perform path matching on the preprocessed user trajectory data and the road network data in the scenic area to obtain the user's actual road network trajectory data.

[0193] In the step S2, the pre-processed user trajectory data is matched with the road network data in the scenic area to obtain the user's actual road network trajectory data, including:

[0194] The pre-processed user trajectory data is matched with the road network data in the scenic area to obtain the user's actual road network trajectory data, where the mth group of pre-processed user trajectory sequences The path matching process is:

[0195] S21: Construct road network data within the scenic area ,in represents the vertex coordinate set of the turning points or intersections of roads in the scenic area, G represents the road set in the scenic area, and the road includes the road starting point, the road end point and the road middle point, among which the coordinates of the road starting point, the road end point and the road middle point all belong to the vertex coordinate set;

[0196] S22: From the road network data within the scenic area Extract user trajectory sequences from the roads in The set of candidate roads with any positioning coordinates in The candidate road extraction method is as follows:

[0197] To locate the coordinates A circular area is constructed with R as the center and R as the radius, and the roads contained or intersected in the circular area are used as positioning coordinates. The candidate roads; the positioning coordinates The candidate road set is , Q m i = { Q m i (j)|j∈[1,J]} , Represents the candidate road set The jth candidate road in the set, J represents the set of candidate roads The total number of candidate roads;

[0198] S23: Calculate the road direction of any candidate road in the candidate road set; The road directions are:

[0199] ;

[0200] in:

[0201] Indicates candidate roads road direction;

[0202] Indicates candidate roads The coordinates of the road end point;

[0203] Indicates candidate roads The starting point coordinates of the road;

[0204] S24: Combine the road directions of the candidate roads and calculate the probability of any candidate road being the actual road network trajectory; As positioning coordinates The trajectory probability of the associated actual road network trajectory is:

[0205] ;

[0206] ;

[0207] ;

[0208] ;

[0209] ;

[0210] in:

[0211] Indicates candidate roads As positioning coordinates The trajectory probability of the associated actual road network trajectory;

[0212] represents an exponential function with a natural constant as base;

[0213] Indicates positioning coordinates The distance between

[0214] Indicates positioning coordinates The coordinate values ​​in ;

[0215] Indicates that the coordinates will be located Projection to candidate roads Projection distance;

[0216] represents the probability control parameter;

[0217] Indicates candidate roads Similarity of road trajectory directions; Indicates candidate roads Similarity of road projection distance; Indicates candidate roads The actual road network trajectory transfer probability, that is, the positioning coordinates The roads between them are transferred to candidate roads probability;

[0218] S25: Select the candidate road with the highest trajectory probability as The actual road network trajectory corresponding to the positioning coordinates in the The corresponding user's actual road network trajectory:

[0219] ;

[0220] in:

[0221] Represents the user trajectory sequence Central positioning coordinates The actual road network trajectory corresponding to As the actual road network trajectory The trajectory time.

[0222] S3: Identify tourist trajectory data from the user's actual road network trajectory data, and count the tourist trajectory data by time period according to the scenic area, so as to obtain the statistical results of the number of tourists in different scenic areas at different time periods.

[0223] The step S3 identifies the tourist trajectory data from the user's actual road network trajectory data, including:

[0224] The tourist trajectory data is identified from the user's actual road network trajectory data, and the identification process of the tourist trajectory data is as follows:

[0225] S31: Calculate the length of the actual road network trajectory of the user; where the actual road network trajectory of the user The trajectory length is:

[0226] ;

[0227] in:

[0228] Indicates the user's actual road network trajectory The length of the trajectory;

[0229] Represents the actual road network trajectory Length of road;

[0230] S32: Calculate the trajectory time of the user's actual road network trajectory; where the user's actual road network trajectory The trajectory time is:

[0231] ;

[0232] in:

[0233] Indicates the user's actual road network trajectory The trajectory time;

[0234] S33: Calculate the area of ​​the smallest circumscribed rectangle that contains the actual road network trajectory of the user as the scenic area coverage of the actual road network trajectory of the user; wherein the actual road network trajectory of the user The coverage of scenic spots is ;

[0235] S34: Calculate the probability coefficient of the user's actual road network trajectory being the tourist's trajectory, where the user's actual road network trajectory The probability coefficient of the tourist trajectory is :

[0236] ;

[0237] in:

[0238] represents an exponential function with a natural constant as base;

[0239] If the probability coefficient is greater than the preset probability threshold, the user's actual road network trajectory is marked as a tourist trajectory, and all tourist trajectories constitute tourist trajectory data;

[0240] The tourist trajectory data is counted by region and time period to obtain the statistical results of the number of tourists in different regions at different time periods.

[0241] The tourist trajectory data is counted by time period according to the scenic area, and the statistical results of the number of tourists in different scenic areas at different time periods are obtained, including:

[0242] The scenic area is divided into K scenic area landscape areas. According to several tourist trajectories in the tourist trajectory data, if the trajectory time of the actual road network trajectory in the tourist trajectory belongs to the tth time period, and the actual road network trajectory overlaps with the kth scenic area landscape area, then the tourist trajectory makes the number of tourists in the kth scenic area landscape area in the tth time period increase by one; thus, the number of tourists in each scenic area landscape area in different time periods is calculated, among which the number of tourists in the kth scenic area landscape area in the tth time period is , t ∈[1,24] , each period is 1 hour long. k ∈[1,K] .

[0243] S4: Mining the tourist trajectory patterns in the scenic area based on the tourist trajectory data to obtain the tourist heat value of the scenic area.

[0244] The step S4 includes mining the tourist trajectory patterns in the scenic area based on the tourist trajectory data, including:

[0245] The tourist trajectory data is used to mine the tourist trajectory patterns in the scenic area to obtain the tourist heat values ​​of different scenic areas. The process of mining the tourist trajectory patterns in the scenic area is as follows:

[0246] S41: extracting a vertex coordinate sequence of a tourist trajectory from the tourist trajectory data, extracting a trajectory time corresponding to an actual road network trajectory with the vertex coordinate as the road starting point in the tourist trajectory, and forming a trajectory time sequence;

[0247] S42: According to the trajectory time associated with the vertex coordinate, if the trajectory time belongs to the t-th time period, the vertex coordinate is divided into the t-th time period to obtain a vertex coordinate set of 24 time periods;

[0248] S43: Calculate the density information of K scenic spots and landscape areas in any time period; the density information of the k-th scenic spot and landscape area in the t-th time period is:

[0249] ;

[0250] in:

[0251] Represents the density information of the k-th scenic area in the t-th time period;

[0252] Represents the density information of the kth scenic area;

[0253] It represents the coordinate set composed of the Y vertex coordinates closest to the k-th scenic area in the vertex coordinate set of the t-th time period, , c represents the coordinate set Coordinates of any vertex in ;

[0254] represents the density control parameter; in the embodiment of the present invention, Set to 2;

[0255] S44: Convert the density information into the tourist heat value of the scenic area in any period of time, where the tourist heat value of the k-th scenic area in the t-th period is:

[0256] ;

[0257] in:

[0258] It represents the tourist heat value of the k-th scenic area in the t-th time period.

[0259] S5: Construct a passenger flow optimization model, use the passenger flow optimization model to calculate the seasonal influencing factors of the scenic area and the scenic area activity factors, optimize and adjust the tourist number statistics results based on the tourist heat value, and convert the optimized and adjusted tourist number statistics results into statistical charts for display.

[0260] In the step S5, a passenger flow optimization model is constructed, and the seasonal influencing factors of the scenic area and the activity factors of the scenic area are calculated using the passenger flow optimization model, and the tourist number statistics are optimized and adjusted in combination with the tourist heat value, including:

[0261] A passenger flow optimization model is constructed, in which the passenger flow optimization model calculates the seasonal influencing factors and scenic spot activity factors, and takes the tourist heat value of the scenic area, seasonal influencing factors, scenic spot activity factors and tourist number statistics as input, and takes the optimized and adjusted tourist number statistics as output. The optimization adjustment process of the tourist number statistics is as follows:

[0262] S51: The passenger flow optimization model calculates the seasonal impact factors of different scenic areas; the seasonal impact factor of the kth scenic area is:

[0263] ;

[0264] in:

[0265] represents the seasonal impact factor of the k-th scenic area;

[0266] represents the average number of tourists in the kth scenic area in the current season; represents the average number of tourists in the kth scenic area in all seasons; in the embodiment of the present invention, the seasonal influencing factor is calculated using the statistical results of the number of tourists without optimization adjustment;

[0267] It indicates the average number of tourists in the entire scenic area in the current season;

[0268] S52: The passenger flow optimization model calculates the scenic area activity factors of different scenic area landscape areas; the scenic area activity factor of the kth scenic area landscape area is:

[0269] ;

[0270] in:

[0271] represents the scenic area activity factor of the kth scenic area in the tth period, A binary variable indicating whether the k-th scenic area holds a special event in the t-th period, , It means that no special activities were held in the kth scenic area in the tth period. It means that the k-th scenic area holds a special event in the t-th period;

[0272] S53: Taking the tourist heat value, seasonal influencing factor, scenic spot activity factor and tourist number statistics of the scenic spot landscape area as input, the optimized and adjusted tourist number statistics are calculated, where the optimized and adjusted tourist number of the kth scenic spot landscape area in the tth period is :

[0273] S k t = s k t [ (1+ γ k 2 (t) ) H k t h 2 ] γ k 1 h 1 ;

[0274] in:

[0275] They represent the factor control parameters of seasonal impact factors and scenic spot activity factors respectively;

[0276] The optimized and adjusted tourist number statistics results are converted into statistical charts for display, and the display results are submitted to scenic area managers and other relevant personnel; the statistical charts include bar charts of the number of tourists in different scenic areas in 24 time periods and the year-on-year and month-on-month change rates, and pie charts of the total number of tourists in different scenic areas and the year-on-year and month-on-month change rates.

[0277] Embodiment 2:

[0278] like Figure 2 , is a functional module diagram of a precise passenger flow analysis system 100 based on mobile phone users provided in one embodiment of the present invention, which can implement the precise passenger flow analysis method based on mobile phone users in Example 1.

[0279] According to the functions implemented, the passenger flow precision analysis system based on mobile phone users may include a data collection and processing module 101, a tourist statistics module 102 and a result display device 103. The module of the present invention may also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions.

[0280] The data collection and processing module 101 is used to collect and pre-process the user trajectory data of users in the scenic area to obtain the pre-processed user trajectory data;

[0281] The tourist statistics module 102 is used to match the pre-processed user trajectory data with the road network data in the scenic area to obtain the actual road network trajectory data of the user, identify the tourist trajectory data from the actual road network trajectory data of the user, and count the tourist trajectory data by time period according to the scenic area to obtain the statistical results of the number of tourists in different scenic areas at different time periods;

[0282] The result display device 103 is used to mine the tourist trajectory pattern in the scenic area based on the tourist trajectory data, obtain the tourist heat value of the scenic area, calculate the seasonal influencing factor and the scenic area activity factor of the scenic area using the passenger flow optimization model, and optimize and adjust the tourist number statistics results in combination with the tourist heat value.

[0283] In detail, the modules in the passenger flow accurate analysis system 100 based on mobile phone users in the embodiment of the present invention are used in the same manner as above. Figure 1 The same technical means are used as the accurate passenger flow analysis method based on mobile phone users described in , and can produce the same technical effects, so I will not go into details here.

[0284] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0285] It should be noted that the serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. And the terms "including", "comprising" or any other variants thereof in this article are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "including a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0286] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0287] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for accurate passenger flow analysis based on mobile phone users, characterized in that: The method comprises: S1: Collecting and preprocessing user trajectory data of users in the scenic area to obtain preprocessed user trajectory data; S2: Path matching is performed between the pre-processed user trajectory data and the road network data in the scenic area to obtain the user's actual road network trajectory data; S3: Identify tourist trajectory data from the user's actual road network trajectory data, and count the tourist trajectory data by time period according to the scenic area, so as to obtain the statistical results of the number of tourists in different scenic areas at different time periods; S4: mining the tourist trajectory patterns in the scenic area based on the tourist trajectory data to obtain the tourist heat value of the scenic area; S5: Construct a passenger flow optimization model, use the passenger flow optimization model to calculate the seasonal influencing factors of the scenic area and the scenic area activity factors, optimize and adjust the tourist number statistics results based on the tourist heat value, and convert the optimized and adjusted tourist number statistics results into statistical charts for display.

2. The method for accurate passenger flow analysis based on mobile phone users as claimed in claim 1, characterized in that: The step S1 collects user trajectory data of users in the scenic area, including: Collect user trajectory data of users in the scenic area: ； in: represents the user trajectory sequence of the nth user in the scenic area; Represents the user trajectory sequence middle Mobile phone signaling data sequence, where Represents the user trajectory sequence The i-th mobile phone signaling data in, Represents the user trajectory sequence Total number of mobile phone signaling data; , Indicates mobile phone signaling data The distance information in Indicates mobile phone signaling data The time of generation, including distance information The mobile phone held by the nth user at time The distance between the three base stations in the scenic area; The user trajectory data e is preprocessed to obtain preprocessed user trajectory data.

3. The method for accurate passenger flow analysis based on mobile phone users as claimed in claim 1, characterized in that: The user trajectory data is preprocessed to obtain the preprocessed user trajectory data, including: Preprocess the user trajectory data e, where the user trajectory sequence in the user trajectory data e The preprocessing process is: S11: Get the base station coordinates of three base stations A, B, and C in the scenic area ; S12: extract the distance information in the mobile phone signaling data, and construct an equation group for the positioning coordinates corresponding to the distance information in combination with the base station coordinates, and solve the equation group to obtain the positioning coordinates corresponding to any mobile phone signaling data; wherein the mobile phone signaling data The corresponding positioning coordinates are ; S13: All positioning coordinates are combined into a user positioning coordinate sequence : ； S14: Calculate the user positioning coordinate sequence The abnormal index ; S15: If the abnormal index If the value is higher than the preset abnormal threshold, it means that the user positioning coordinate sequence There is an obvious positioning error, which affects the user's positioning coordinate sequence. And user trajectory sequence Carry out cleaning treatment; Otherwise, it constitutes a user trajectory sequence The preprocessing results : ； ； in: Indicates mobile phone signaling data The processing result; The preprocessing results as the g-th group of preprocessed user trajectory sequences in the preprocessed user trajectory data E, where g represents the number of the current preprocessed user trajectory sequences, and the initial value of g is 1; All preprocessed user trajectory sequences constitute preprocessed user trajectory data : ； ； in: M represents the total number of user trajectory sequences after preprocessing, represents the mth group of preprocessed user trajectory sequences, Represents the user trajectory sequence The processing result of the i-th mobile phone signaling data, Represents the user trajectory sequence The total number of processing results of mobile phone signaling data.

4. The method for accurate passenger flow analysis based on mobile phone users as claimed in claim 3, characterized in that: In the step S2, the pre-processed user trajectory data is matched with the road network data in the scenic area to obtain the user's actual road network trajectory data, including: The pre-processed user trajectory data is matched with the road network data in the scenic area to obtain the user's actual road network trajectory data, where the mth group of pre-processed user trajectory sequences The path matching process is: S21: Construct road network data within the scenic area ,in represents the vertex coordinate set of the turning points or intersections of roads in the scenic area, G represents the road set in the scenic area, and the road includes the road starting point, the road end point and the road middle point, among which the coordinates of the road starting point, the road end point and the road middle point all belong to the vertex coordinate set; S22: From the road network data within the scenic area Extract user trajectory sequences from the roads in A set of candidate roads with arbitrary positioning coordinates in; S24: combining the road directions of the candidate roads, calculating the trajectory probability of any candidate road in the candidate road set being the actual road network trajectory; S25: Select the candidate road with the highest trajectory probability as The actual road network trajectory corresponding to the positioning coordinates in the The corresponding user's actual road network trajectory: ； in: Represents the user trajectory sequence Central positioning coordinates The actual road network trajectory corresponding to As the actual road network trajectory The trajectory time.

5. The method for accurate passenger flow analysis based on mobile phone users as claimed in claim 4, characterized in that: The step S3 identifies the tourist trajectory data from the user's actual road network trajectory data, including: The tourist trajectory data is identified from the user's actual road network trajectory data, and the identification process of the tourist trajectory data is as follows: S31: Calculate the length of the actual road network trajectory of the user; where the actual road network trajectory of the user The trajectory length is ; S32: Calculate the trajectory time of the user's actual road network trajectory; where the user's actual road network trajectory The trajectory time is ; S33: Calculate the area of ​​the smallest circumscribed rectangle that contains the actual road network trajectory of the user as the scenic area coverage of the actual road network trajectory of the user; wherein the actual road network trajectory of the user The coverage of scenic spots is ; S34: Calculate the probability coefficient of the user's actual road network trajectory being the tourist's trajectory, where the user's actual road network trajectory The probability coefficient of the tourist trajectory is : ； in: represents an exponential function with a natural constant as base; If the probability coefficient is greater than the preset probability threshold, the user's actual road network trajectory is marked as a tourist trajectory, and all tourist trajectories constitute tourist trajectory data; The tourist trajectory data is counted by region and time period to obtain the statistical results of the number of tourists in different regions at different time periods.

6. The method for accurate passenger flow analysis based on mobile phone users as claimed in claim 5, characterized in that: The tourist trajectory data is counted by time period according to the scenic area, and the statistical results of the number of tourists in different scenic areas at different time periods are obtained, including: The scenic area is divided into K scenic landscape areas. According to several tourist trajectories in the tourist trajectory data, if the trajectory time of the actual road network trajectory in the tourist trajectory belongs to the tth time period, and the actual road network trajectory overlaps with the kth scenic landscape area, then the tourist trajectory makes the number of tourists in the kth scenic landscape area in the tth time period increase by one; thus, the number of tourists in each scenic landscape area in different time periods is calculated, among which the number of tourists in the kth scenic landscape area in the tth time period is , , each period is 1 hour long. .

7. The method for accurate passenger flow analysis based on mobile phone users as claimed in claim 6, characterized in that: The step S4 includes mining the tourist trajectory patterns in the scenic area based on the tourist trajectory data, including: The tourist trajectory data is used to mine the tourist trajectory patterns in the scenic area to obtain the tourist heat values ​​of different scenic areas. The process of mining the tourist trajectory patterns in the scenic area is as follows: S41: extracting a vertex coordinate sequence of a tourist trajectory from the tourist trajectory data, extracting a trajectory time corresponding to an actual road network trajectory with the vertex coordinate as the road starting point in the tourist trajectory, and forming a trajectory time sequence; S42: According to the trajectory time associated with the vertex coordinate, if the trajectory time belongs to the t-th time period, the vertex coordinate is divided into the t-th time period to obtain a vertex coordinate set of 24 time periods; S43: Calculate the density information of K scenic spots and landscape areas in any time period; the density information of the k-th scenic spot and landscape area in the t-th time period is ; S44: Convert the density information into the tourist heat value of the scenic area in any period of time, where the tourist heat value of the k-th scenic area in the t-th period is: ； in: It represents the tourist heat value of the k-th scenic area in the t-th time period.

8. The method for accurate passenger flow analysis based on mobile phone users as claimed in claim 1, characterized in that: In the step S5, a passenger flow optimization model is constructed, and the seasonal influencing factors of the scenic area and the activity factors of the scenic area are calculated using the passenger flow optimization model, and the tourist number statistics are optimized and adjusted in combination with the tourist heat value, including: A passenger flow optimization model is constructed, in which the passenger flow optimization model calculates the seasonal influencing factors and scenic spot activity factors, and takes the tourist heat value of the scenic area, seasonal influencing factors, scenic spot activity factors and tourist number statistics as input, and takes the optimized and adjusted tourist number statistics as output. The optimization adjustment process of the tourist number statistics is as follows: S51: The passenger flow optimization model calculates the seasonal impact factors of different scenic areas; the seasonal impact factor of the kth scenic area is: ； in: represents the seasonal impact factor of the k-th scenic area; represents the average number of tourists in the kth scenic area in the current season; represents the average number of tourists in the kth scenic area in all seasons; It indicates the average number of tourists in the entire scenic area in the current season; S52: The passenger flow optimization model calculates the scenic area activity factors of different scenic area landscape areas; the scenic area activity factor of the kth scenic area landscape area is: ； in: represents the scenic area activity factor of the kth scenic area in the tth period, A binary variable indicating whether the k-th scenic area holds a special event in the t-th period, , It means that no special activities were held in the kth scenic area in the tth period. It means that the k-th scenic area holds a special event in the t-th period; S53: Taking the tourist heat value, seasonal influencing factor, scenic spot activity factor and tourist number statistics of the scenic spot landscape area as input, the optimized and adjusted tourist number statistics are calculated, where the optimized and adjusted tourist number of the kth scenic spot landscape area in the tth period is : ； in: They represent the factor control parameters of seasonal impact factors and scenic spot activity factors respectively; The optimized and adjusted tourist number statistics results are converted into statistical charts for display; the statistical charts include bar charts of the number of tourists in different scenic areas in 24 time periods and the year-on-year and month-on-month change rates, and pie charts of the total number of tourists in different scenic areas and the year-on-year and month-on-month change rates.

9. A precise passenger flow analysis system based on mobile phone users, characterized in that: The system comprises: A data collection and processing module is used to collect and preprocess the user trajectory data of users in the scenic area to obtain the preprocessed user trajectory data; The tourist statistics module is used to match the pre-processed user trajectory data with the road network data in the scenic area to obtain the actual road network trajectory data of the user, identify the tourist trajectory data from the actual road network trajectory data of the user, and count the tourist trajectory data by time period according to the scenic area to obtain the statistical results of the number of tourists in different scenic areas at different time periods; A result display device is used to mine tourist trajectory patterns in a scenic area based on tourist trajectory data, obtain tourist heat values ​​in the scenic area's landscape area, calculate seasonal influencing factors and scenic area activity factors in the scenic area using a passenger flow optimization model, optimize and adjust tourist headcount statistics results in combination with the tourist heat values, and convert the optimized and adjusted tourist headcount statistics results into statistical charts for display, so as to realize a precise passenger flow analysis method based on mobile phone users as described in any one of claims 1 to 8.

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