CN109348404B - Method for extracting individual travel road track in big data environment - Google Patents
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Abstract
The invention relates to a method for extracting individual travel road tracks in a big data environment. The invention has the advantages that: the method fully depends on the existing big communication data resources between the mobile terminal and the sensor held by the user, utilizes the existing massive continuous encrypted position information of the anonymous mobile terminal in the communication network, can obtain the travel time-space sequence of a large number of individuals in a specified time range in a low-cost, automatic and convenient manner, adopts the methods of space analysis and space operation and plan, excavates the most probable network paths of the individuals between the communication nodes according to the communication records between the individuals and the fixed sensor, and finally arranges to obtain the moving tracks of the individuals between the O-D points.
Description
Technical Field
The invention relates to a method for extracting possible spatial movement tracks of individuals on a hierarchical road network among travel records by calculating the movement speed of the individuals based on the spatial positions and time of the individuals in massive individual travel record data and excavating the action tracks of the individuals on the hierarchical road network by adopting a probability distribution method.
Background
In recent years, with the development of information technology, the data information amount is increased explosively, the data sources are more and more, and the data amount is also more and more huge. Data recorded by information sensors such as mobile phones, WIFI and the Internet of things become the most important data source in big data analysis, and relatively complete individual trip records of the data provide good data support for big data, especially for traffic big data analysis. Taking a mobile phone as an example, in 2015, mobile phone users reach 13.06 hundred million, which accounts for more than 96% of the total population, and signal information continuously generated by mobile phone terminal equipment forms a series of data sets for recording user trips, so that an important data source is provided for traffic trip analysis.
However, the data base of the mobile communication big data represented by the mobile phone big data is the communication record between the handheld mobile terminal and the fixed sensor, so that the base data of the mobile communication big data is discrete rather than continuous, and the travel track of the individual in the space network is difficult to identify and extract from the communication record data of the individual and the fixed sensor. On the other hand, the position of the fixed sensor is not a road generally, so that the data base extracted from the network track of the individual space traveling road is not on the network connection line.
Disclosure of Invention
The purpose of the invention is: a certain algorithm is provided for processing the individual travel record data set, and the travel track of the individual on the spatial road network is excavated on the basis of the processing, so that the individual travel track can be accurately identified, and the load capacity of the road network in different time intervals can be effectively judged.
In order to achieve the above object, the technical solution of the present invention is to provide a method for extracting an individual travel road track in a big data environment, which is characterized by comprising the following steps:
step 1, anonymous encryption mobile terminal sensor data within a certain time range is obtained from a sensor operator, a preliminary individual trip time-space record formed by an individual and a fixed sensor communication record is constructed for each user, the geographic attribute of a fixed sensor is given to each communication node in a preliminary individual trip time-space track, and an individual trip time-space data set is constructed;
step 2, arranging individual travel time-space data sets according to a time sequence, constructing individual travel time-space sequences by taking communication records between individuals and fixed sensors as nodes, identifying travel O-D points in the individual travel time-space sequences, cutting the individual travel time-space sequences according to the O-D points, dividing the individual travel time-space sequences into a plurality of O-D road sections, numbering each road section, and forming individual travel O-D road section data sets;
step 3, calculating the distance, the time consumption and the average speed of the individual between every two nodes in the travel road section according to the individual travel O-D road section data set, and calculating the possible individual travel path of the individual between the two nodes by constructing a network model and taking an actual road traffic network as a geographical base according to the speed between every two nodes in the individual travel road section, wherein the method comprises the following steps:
step 3.1, arranging the road traffic network of the city where the individual is located, grading each road in the road traffic network, and obtaining the average moving speed of each travel mode on each road in each time period according to the existing data;
step 3.2, projecting all communication nodes in each individual O-D road section in the individual trip O-D road section data set into a road traffic network according to the spatial positions of the communication nodes, searching road traffic network intersection nodes closest to the communication nodes, defining the road traffic network intersection nodes as R points, using the R points as S-T points between every two communication nodes, and calculating the shortest distance from the communication nodes to the respective S-T points;
3.3, extracting the shortest distance and the path between every two communication nodes, and calculating the time spent on the shortest path;
step 3.4, cutting the shortest path between every two communication nodes according to the road sections, and calculating the space direction complexity SDF of the shortest path, wherein the space direction complexity SDF is obtained by solving a weighted standard deviation according to the moving direction of the divided road sections;
step 3.5, if the moving time of the individual between every two communication nodes is less than or equal to the time spent on the shortest path, the shortest path is the actual moving road section of the individual between every two communication nodes; otherwise, constructing a space operational model, and solving a movement track of the individual in the space by adopting a method of solving an equation set, taking the movement time as a constraint condition and taking the space direction complexity SDF as an objective function, wherein the movement track is used as an actual movement road section of the individual between every two communication nodes;
step 3.6, adding the shortest distance from the communication node to the S-T point to the two ends of the actual mobile road section obtained by solving to form the most probable individual travel paths between every two communication nodes;
and 4, sorting and spatially fusing the most possible individual travel paths obtained by calculating all pairwise communication nodes, and finally obtaining a specific individual travel track.
Preferably, in the step 3.4, the moving direction of the individual on the kth road segment between the ith communication node and the jth communication node is set as
And there are N road segments between the ith communication node and the jth communication node, i.e. k is 1,2
To be provided with
At 0 degrees, the direction of movement of the individual over the N road segments is adjusted to [ -180,180,]within the interval, the moving direction of the kth road section is adjusted to
The spatial complexity between the ith and jth communication nodes can be expressed as:
Preferably, if there are L edges in the road traffic network, there are M intersection nodes, the starting point is node B, and the end point is node D, the equation set in step 3 is expressed as:
s.t.
in the formula (I), the compound is shown in the specification,
an SDF value representing the shortest path;
SDF is the SDF value of the solution path;
lm,nis a (0-1) Boolean variable, which indicates that the road section from the mth intersection node to the nth intersection node is used for solving the path if lm,nIf 1, the road section from the mth intersection node to the nth intersection node is used for solving the obtained path, otherwise, lm,n＝0；
INmRepresenting the number of times that the individual starts from the mth intersection node in the solved path; OUTmRepresenting the times of the individuals reaching the mth intersection node in the solved path; according to the network flow theorem, IN is determined if the mth intersection node is the starting point of an individualm-OUTmIf the mth intersection node is the end point of the individual, INm-OUTm1, the rest of the intermediate nodes INm-OUTm＝0；
TIMES,TRepresenting the time difference between the communication nodes;
vm,n,t,pthe average moving speed of the p travel modes in the section from the mth intersection node to the nth intersection node in the time range t is represented;
Preferably, the step 4 comprises:
step 4.1, splicing the most probable paths between every two communication nodes to form a primary complete O-D path;
step 4.2, except the O point and the D point, removing the distance from the communication node to the nearest traffic intersection in the O-D path, and completely mapping the O-D path to the road traffic network;
4.3, sending out the information from each R point and traversing forwards and backwards at the same time, if a repeated path exists near the R point, deleting repeated road sections until no continuous repeated road sections exist, namely, an individual reaches the R point through the road sections k to i to j, then passes through the R point which is away from the road sections j to i to l, deleting the road sections i to j and j to i, the individual directly goes from k to i to l, and recording a merging point i;
4.4, traversing each merging point i, traversing from the merging point forward and backward again, and if the merging point i subtracts 180 degrees from the direction from the backward nth road section a to the direction from the b, the difference between the included angle in the direction from the forward nth road section x to the direction from the y is smaller than a threshold value C, and the two road sections are communicated, deleting all the road sections between the two road sections, and directly deleting the individual from a to y;
and 4.5, after the redundant road sections are deleted, rearranging the travel paths of the individuals among the O-D points, and completing the track extraction of the spatial road traffic network for the O-D travel of the individuals.
The method is based on the big data of the mobile terminal, processing and screening are carried out, and a time-space data set of the individual trip is constructed through the communication record between the mobile terminal and the fixed sensor which are held by the individual; through individual trip O-D point identification, splitting an individual trip time-space sequence into single O-D trip records; mining the most probable path between communication nodes on the spatial road traffic network by calculating the time and the speed of the individual between the communication nodes on the O-D path; and further sorting the paths on the basis of obtaining the most probable paths between every two communication nodes, and finally obtaining the movement track of the individual on the space road traffic network between the O-D points.
The invention has the advantages that: the method fully depends on the existing big communication data resources between the mobile terminal and the sensor held by the user, utilizes the existing massive continuous encrypted position information of the anonymous mobile terminal in the communication network, can obtain the travel time-space sequence of a large number of individuals in a specified time range in a low-cost, automatic and convenient manner, adopts the methods of space analysis and space operation and plan, excavates the most probable network paths of the individuals between the communication nodes according to the communication records between the individuals and the fixed sensor, and finally arranges to obtain the moving tracks of the individuals between the O-D points.
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FIG. 1 is a general flow diagram of the present invention.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
With reference to fig. 1, the method for extracting individual travel road tracks in a big data environment provided by the invention comprises the following steps:
step 1, anonymous encryption mobile terminal sensor data within a certain time range is obtained from a sensor operator, a preliminary individual trip time-space record formed by an individual and a fixed sensor communication record is established for each user, the geographic attribute of a fixed sensor is given to each communication node in an individual trip time-space track, and an individual trip time-space data set is established;
the anonymous encryption mobile terminal sensor data is encrypted position information of an anonymous mobile phone user time sequence obtained by an operator from a mobile communication network, a fixed broadband network, wireless WIFI, a position service related APP and the like in real time and subjected to desensitization encryption, and the content comprises the following steps: EPID, TYPE, TIME, REGIONCODE, SENSORID, see the Chinese patent with application number 201610273693.0. The specific introduction is as follows:
the EPID (anonymous one-way EncryPtion globally unique mobile terminal identification code) is used for carrying out one-way irreversible EncryPtion on each mobile terminal user, so that each mobile terminal user is uniquely identified, the user number privacy information is not exposed, and the encrypted EPID of each mobile terminal user is required to keep uniqueness, namely the EPID of each mobile phone user is kept unchanged at any moment and is not repeated with other mobile phone users.
TYPE, which is the TYPE of communication action related to the current record, such as internet access, call, calling and called, short message receiving and sending, GPS positioning, sensor cell switching, sensor switching, power on and power off, etc.
TIME is the TIME at which the communication operation related to the current record occurs, and is expressed in milliseconds.
The REGIONCODE and the sensor are sensor encryption position information in which the communication operation related to the current recording occurs. The number of the REGIONCODE, SENSORID sensor, wherein REGIONCODE represents the area where the sensor is located, and SENSORID is the number of the particular sensor.
Step 1.1, the system reads sensor data of an anonymous encryption mobile terminal obtained from a sensor operator, theoretically, the sensor data of the anonymous encryption mobile terminal should be continuous in time and space, and the method comprises the following steps: the unique number EPID of the user, the TYPE TYPE of the communication action, the TIME of the occurrence of the communication action, the REGIONCODE of the area where the sensor is located and the specific number SENSORID of the sensor; wherein, the sensor number is formed by the large area REGION CODE where the sensor is located and the sensor concrete number SENSORID, and the detailed data format and decryption mode are shown in the patent (201610386914.5);
step 1.3, inquiring all communication records of the user in a specified time period according to the user number EPID, and constructing user travel data;
in this example, the extracted real-time signaling record data of the user and the sensor is shown in table 1:
table 1: decrypted newly received real-time signaling record data
And 2, arranging individual trip records according to a time sequence, constructing an individual trip time-space sequence by taking the communication records between the individuals and the fixed sensors as nodes, identifying trip O-D points in the individual trip time-space sequence, and cutting the individual trip time-space sequence according to the O-D points to form an individual trip road section data set. The step 2 comprises the following steps:
step 2.1, sequencing a time-space data set formed by communication with a fixed sensor in the individual trip process according to a time sequence, constructing individual trip time-space sequence data, and calculating Euclidean distances between nodes according to time-space information recorded by nodes of the time-space data set so as to calculate the average moving speed of the individual between the nodes;
in this example, the time differences and distances between the communication nodes are shown in table 2:
table 2: time difference and distance between communication nodes
2.2, mining the long-time staying place of the individual in the trip process according to the average moving speed of the individual among the nodes by adopting a space interpolation and space clustering method, taking the long-time staying place as an O-D point of the individual trip, and judging the O-D road section of the individual trip, wherein the detailed method is shown in a patent (201710843841.2);
in this example, a sample of the O-D road segments in the individual travel spatiotemporal sequence is shown in Table 3:
TABLE 3O-D road segment samples in individual trip spatio-temporal sequences
| RECORDID | EPID | TYPE | TIMESTAMP | REGIONCODE | SENSORID | X | Y |
| R1074 | E1 | T1 | 2017-11-22 07:35:06 | 9622 | 3415 | 4774.443 | 5863.045 |
| R1075 | E1 | T1 | 2017-11-22 08:04:45 | 9622 | 6543 | 5568.195 | 6048.254 |
| R1076 | E1 | T1 | 2017-11-22 08:34:22 | 9622 | 3212 | 6176.738 | 6286.379 |
| R1077 | E1 | T2 | 2017-11-22 08:44:36 | 9622 | 4632 | 6944.031 | 6603.88 |
| R1078 | E1 | T2 | 2017-11-22 09:01:24 | 9622 | 6343 | 7790.699 | 6550.963 |
| R1079 | E1 | T3 | 2017-11-22 09:13:41 | 9622 | 1242 | 8478.617 | 6259.921 |
| R1080 | E1 | T3 | 2017-11-22 09:26:59 | 9622 | 1253 | 8769.66 | 5704.295 |
| R1081 | E1 | T3 | 2017-11-22 09:51:41 | 9622 | 3223 | 9166.535 | 5280.96 |
| R1082 | E1 | T2 | 2017-11-22 10:12:38 | 9622 | 3421 | 9669.245 | 4989.918 |
| R1083 | E1 | T1 | 2017-11-22 10:33:27 | 9622 | 7645 | 9023.341 | 4704.424 |
Step 2.3, carrying out and judgment on the individual travel vehicles according to the moving speed of the individuals among the communication nodes, and judging whether the travel modes are walking, driving or cycling;
in the example, the average travel speed of the individual among the O-D nodes is 700 m/min, and the travel mode is calculated to be motor vehicle travel;
and 2.4, cutting the individual trip space-time sequence data according to O-D points, dividing the individual trip space-time sequence data into a plurality of O-D road sections, and numbering each road section (in the example, the O-D road section number shown in the table 3 is R1) to form an individual O-D road section data set.
And 3, calculating the distance, the time consumption and the average speed of the individual between every two nodes in the trip road section, and calculating the possible path of the individual between the two nodes by constructing a network model and taking the actual road traffic network as a geographical base according to the speed between every two nodes in the individual trip road section. The step 3 comprises the following steps:
step 3.1, arranging road traffic networks of cities where individuals are located, grading each road, and obtaining the average moving speed of each travel mode on each road in each time period according to the existing data;
table 4 example of average moving speed transportation modes of different travel modes on roads of different grades
| Traffic mode | Road grade | Average velocity |
| … | … | … |
| Walking device | General road | 85 m/min |
| Bicycle riding | General road | 260 m/min |
| Self-driving | Elevated road | 1200 m/min |
| Motor vehicle | General road | 730 m/min |
| Subway | Subway | 400 m/min |
| Motor vehicle | Express way | 900 m/min |
| … | … | … |
Step 3.2, projecting all communication nodes in each O-D section of an individual into a road traffic network according to the spatial positions of the communication nodes, searching a road traffic network port node which is closest to each communication node and is called as an R point, taking the node as an S-T point between every two communication nodes, and calculating the shortest distance from each communication node to the respective S-T point; in this example, the R points of the various nodes in the O-D route segment R1 are shown in Table 5:
TABLE 5O-D road segment R1 distance from node to R point location for each node
| RECORDID | EPID | TYPE | TIMESTAMP | X | Y | RX | RY | Distance |
| R1074 | E1 | T1 | 2017-11-22 07:35:06 | 4774.443 | 5863.045 | 4772.443 | 5846.045 | 17.117 |
| R1075 | E1 | T1 | 2017-11-22 08:04:45 | 5568.195 | 6048.254 | 5560.195 | 6043.254 | 9.434 |
| R1076 | E1 | T1 | 2017-11-22 08:34:22 | 6176.738 | 6286.379 | 6192.738 | 6282.379 | 16.492 |
| R1077 | E1 | T2 | 2017-11-22 08:44:36 | 6944.031 | 6603.88 | 6925.031 | 6593.880 | 21.471 |
| R1078 | E1 | T2 | 2017-11-22 09:01:24 | 7790.699 | 6550.963 | 7795.699 | 6537.963 | 13.928 |
| R1079 | E1 | T3 | 2017-11-22 09:13:41 | 8478.617 | 6259.921 | 8489.617 | 6271.921 | 16.279 |
| R1080 | E1 | T3 | 2017-11-22 09:26:59 | 8769.66 | 5704.295 | 8780.660 | 5700.295 | 11.705 |
| R1081 | E1 | T3 | 2017-11-22 09:51:41 | 9166.535 | 5280.96 | 9179.535 | 5268.960 | 17.692 |
| R1082 | E1 | T2 | 2017-11-22 10:12:38 | 9669.245 | 4989.918 | 9673.245 | 5001.918 | 12.649 |
| R1083 | E1 | T1 | 2017-11-22 10:33:27 | 9023.341 | 4704.424 | 9017.341 | 4695.424 | 10.817 |
3.3, extracting the shortest distance and the path between every two communication nodes by adopting a space analysis method and using a Dijkstra algorithm, and calculating the time spent on the shortest path; in this example, the shortest paths and distances between the nodes of the O-D segment R1 are shown in Table 6:
TABLE 6 shortest paths and distances between nodes in O-D road segment R1
| RECORDID | RECORDID | Distance | Rout |
| R1074 | R1075 | 1002.54 | L12-L14-L10 |
| R1075 | R1076 | 725.35 | L10-L11-L18-L19 |
| R1076 | R1077 | 963.25 | L21-L26-L31 |
| R1077 | R1078 | 1077.37 | L31-L54-L42 |
| R1078 | R1079 | 911.28 | L34-L35 |
| R1079 | R1080 | 765.23 | L36-L37 |
| R1080 | R1081 | 707.94 | L44-L45-L47-L56-L64 |
| R1081 | R1082 | 638.97 | L64-L56-L43 |
| R1082 | R1083 | 735.95 | L41-L40 |
| R1074 | R1075 | 1002.54 | L12-L14-L10 |
Step 3.4, cutting the shortest path among the nodes according to the road sections, and calculating the space direction complexity SDF of the shortest path; the space direction complexity SDF is obtained by solving a weighted standard deviation by adopting the moving direction of a bisection section: let the moving direction of the unit on the kth road section between the ith communication node and the jth communication node be
And there are N road segments between the ith communication node and the jth communication node, i.e. k is 1,2
To be provided with
At 0 degrees, the direction of movement of the individual over the N road segments is adjusted to [ -180,180,]within the interval, the moving direction of the kth road section is adjusted to
The spatial complexity between the ith and jth communication nodes can be expressed as:
In this example, the directional complexity of the shortest path between the nodes of the O-D segment R1 is shown in Table 7:
spatial direction complexity of shortest paths between nodes in Table 7O-D road segment R1
Step 3.5, if the moving time of the individual between the nodes is less than or equal to the time spent on the shortest path, the shortest path is the actual moving road section of the individual between the nodes; otherwise, a space operation model is constructed, an equation solving method is adopted, the moving time is used as a constraint condition, the SDF is used as an objective function, and the moving track of the individual in the space is solved.
Assuming that there are L edges in the road traffic network and M intersection nodes, the starting point is node B and the end point is node D, the above equation set can be expressed as:
s.t.
in the formula (I), the compound is shown in the specification,
an SDF value representing the shortest path;
SDF is the SDF value of the solution path;
lm,nis a (0-1) Boolean variable, which indicates that the road section from the mth intersection node to the nth intersection node is used for solving the path if lm,nIf 1, the road section from the mth intersection node to the nth intersection node is used for solving the obtained path, otherwise, lm,n＝0；
INmRepresenting the number of times that the individual starts from the mth intersection node in the solved path; OUTmRepresenting the times of the individuals reaching the mth intersection node in the solved path; according to the network flow theorem, IN is determined if the mth intersection node is the starting point of an individualm-OUTmIf the mth intersection node is the end point of the individual, INm-OUTm1, the rest of the intermediate nodes INm-OUTm＝0；
TIMES,TRepresenting the time difference between the communication nodes;
vm,n,t,pthe average moving speed of the p travel modes in the section from the mth intersection node to the nth intersection node in the time range t is represented;
the calculation of the SDF also only includes the angles of the road segments included in the solved path
Step 3.6, adding the shortest distance from the communication node to the S-T point to the two ends of the road section obtained by solving, and solving to form the most probable path between every two communication nodes;
in this example, the most probable path between two nodes of the O-D route segment R1 is shown in table 8:
TABLE 8
| RECORDID | RECORDID | Rout |
| R1074 | R1075 | L12-L13-L11-L14-L10 |
| R1075 | R1076 | L10-L6-L8-L11-L18-L17-L19 |
| R1076 | R1077 | L21-L20-L26-L29-L31 |
| R1077 | R1078 | L31-L29-L48-L54-L42 |
| R1078 | R1079 | L34-L35 |
| R1079 | R1080 | L36-L37 |
| R1080 | R1081 | L44-L45-L47-L56-L57-L58-L64 |
| R1081 | R1082 | L64-L-58-L57-L56-L43 |
| R1082 | R1083 | L41-L40-L72-L43 |
| R1074 | R1075 | L12-L14-L02-L10 |
Step 4, sorting and spatially fusing the individual travel paths obtained by calculating every two nodes to finally obtain a specific individual travel track, and the method comprises the following steps:
step 4.1, splicing the most probable paths between every two communication nodes to form a primary complete O-D path;
step 4.2, except the O point and the D point, removing the distance from the communication node to the nearest traffic intersection in the O-D path, and completely mapping the O-D path to the road traffic network;
in this example, the trajectory of an individual moving between O-D on a road traffic network is:
L12→L13→L11→L14→L10→L10→L6→L8→L11→L18→L17→L19→L21→L20→L26→L29→L31→L31→L29→L48→L54→L42→L34→L35→L36→L37→L44→L45→L47→L56→L57→L58→L64→L64→L→58→L57→L56→L43→L41→L40→L72→L43→L12→L14→L02→L10
4.3, sending out the information from each R point and traversing forwards and backwards at the same time, if a repeated path exists near the R point, deleting repeated road sections until no continuous repeated road sections exist, namely, an individual reaches the R point through the road sections k to i to j, then passes through the R point which is away from the road sections j to i to l, deleting the road sections i to j and j to i, the individual directly goes from k to i to l, and recording a merging point i;
4.4, traversing each merging point i, traversing forwards and backwards again from the merging point, and if the difference between the direction from the point i to the nth road section a to b after the point i is subtracted by 180 degrees and the included angle between the direction from the nth road section x to y before is smaller than a threshold value C, and the two road sections are communicated, deleting all the road sections between the two road sections, and directly deleting the individual from a to y;
and 4.5, after the redundant road sections are deleted, rearranging the travel paths of the individuals among the O-D points, and completing the track extraction of the spatial road traffic network for the O-D travel of the individuals.
In this example, the O-D travel path obtained by rearrangement after the redundancy is deleted is:
L12→L13→L11→L14→L6→L8→L11→L18→L17→L19→L21→L20→L26→L48→L54→L42→L34→L35→L36→L37→L44→L45→L47→L43→L41→L40→L72→L43→L12→L14→L02→L10
the invention aims to extract time-space information in communication data between individual handheld terminal equipment and a fixed sensor and construct an individual trip time-space data set; extracting a long-time staying place of the individual in the space from the individual trip data set by adopting a space interpolation and clustering method so as to divide O-D points of the individual in the trip in a time sequence; aiming at O-D points of individual trip, a space analysis and calculation method is adopted, on the basis of calculating the shortest path by using Dijkstra algorithm, a space direction complexity index of a moving path is constructed, the moving time between communication nodes is taken as a constraint condition, and an equation set is constructed by a space operational research algorithm to solve the most probable path of the individual moving between the communication nodes; and on the basis of obtaining the most probable path among the communication nodes, carrying out redundancy processing and sorting on the most probable path among the communication nodes, and finally obtaining the most probable path among the O-Ds of the individual. According to the method, the massive anonymous mobile terminal continuous encrypted position information existing in the communication network is utilized, so that a large amount of individual trip time-space sequence data in a specified time range can be obtained in a low-cost, automatic and convenient mode, O-D points of individual spatial trips are judged and identified on the basis, and the moving tracks of individuals are mined by adopting a space operation and analysis technology, so that the moving processes and paths of the individuals on a road traffic network are obtained quickly and efficiently, and a data basis is provided for time-sharing road load condition statistics.
Claims (4)
1. A method for extracting individual travel road tracks in a big data environment is characterized by comprising the following steps:
step 1, anonymous encryption mobile terminal sensor data within a certain time range is obtained from a sensor operator, a preliminary individual trip time-space record formed by an individual and a fixed sensor communication record is constructed for each user, the geographic attribute of a fixed sensor is given to each communication node in a preliminary individual trip time-space track, and an individual trip time-space data set is constructed;
step 2, arranging individual travel time-space data sets according to a time sequence, constructing individual travel time-space sequences by taking communication records between individuals and fixed sensors as nodes, identifying travel O-D points in the individual travel time-space sequences, cutting the individual travel time-space sequences according to the O-D points, dividing the individual travel time-space sequences into a plurality of O-D road sections, numbering each road section, and forming individual travel O-D road section data sets;
step 3, calculating the distance, the time consumption and the average speed of the individual between every two nodes in the travel road section according to the individual travel O-D road section data set, and calculating the possible individual travel path of the individual between the two nodes by constructing a network model and taking an actual road traffic network as a geographical base according to the speed between every two nodes in the individual travel road section, wherein the method comprises the following steps:
step 3.1, arranging the road traffic network of the city where the individual is located, grading each road in the road traffic network, and obtaining the average moving speed of each travel mode on each road in each time period according to the existing data;
step 3.2, projecting all communication nodes in each individual O-D road section in the individual trip O-D road section data set into a road traffic network according to the spatial positions of the communication nodes, searching road traffic network intersection nodes closest to the communication nodes, defining the road traffic network intersection nodes as R points, using the R points as S-T points between every two communication nodes, and calculating the shortest distance from the communication nodes to the respective S-T points;
3.3, extracting the shortest distance and the path between every two communication nodes, and calculating the time spent on the shortest path;
step 3.4, cutting the shortest path between every two communication nodes according to the road sections, and calculating the space direction complexity SDF of the shortest path, wherein the space direction complexity SDF is obtained by solving a weighted standard deviation according to the moving direction of the divided road sections;
step 3.5, if the moving time of the individual between every two communication nodes is less than or equal to the time spent on the shortest path, the shortest path is the actual moving road section of the individual between every two communication nodes; otherwise, constructing a space operational model, and solving a movement track of the individual in the space by adopting a method of solving an equation set, taking the movement time as a constraint condition and taking the space direction complexity SDF as an objective function, wherein the movement track is used as an actual movement road section of the individual between every two communication nodes;
step 3.6, adding the shortest distance from the communication node to the S-T point to the two ends of the actual mobile road section obtained by solving to form the most probable individual travel paths between every two communication nodes;
and 4, sorting and spatially fusing the most possible individual travel paths obtained by calculating all pairwise communication nodes, and finally obtaining a specific individual travel track.
2. The method for extracting the trajectory of the individual traveling road under the big data environment as claimed in claim 1, wherein in the step 3.4, the moving direction of the individual on the kth road section between the ith communication node and the jth communication node is defined as
And there are N road segments between the ith communication node and the jth communication node, i.e. k is 1,2
To be provided with
At 0 degrees, the direction of movement of the individual over the N road segments is adjusted to [ -180,180,]within the interval, the moving direction of the kth road section is adjusted to
The spatial direction complexity between the ith communication node and the jth communication node can be expressed as:
3. The method for extracting individual travel road track in big data environment according to claim 2, wherein if there are L edges in the road traffic network, there are M intersection nodes, the starting point is node B, and the end point is node D, the equation set in step 3 is expressed as:
s.t.
in the formula (I), the compound is shown in the specification,
an SDF value representing the shortest path;
SDF is the SDF value of the solution path;
lm,nis a (0-1) Boolean variable, which indicates that the road section from the mth intersection node to the nth intersection node is used for solving the path if lm,nIf 1, the road section from the mth intersection node to the nth intersection node is used for solving the obtained path, otherwise, lm,n＝0；
INmRepresenting the number of times that the individual starts from the mth intersection node in the solved path; OUTmRepresenting the times of the individuals reaching the mth intersection node in the solved path; according to the network flow theorem, IN is determined if the mth intersection node is the starting point of an individualm-OUTmIf the mth intersection node is the end point of the individual, INm-OUTm1, the rest of the intermediate nodes INm-OUTm＝0；
TIMES,TRepresenting the time difference between the communication nodes;
vm,n,t,pthe average moving speed of the p travel modes in the section from the mth intersection node to the nth intersection node in the time range t is represented;
4. The method for extracting individual travel road track in big data environment according to claim 1, wherein the step 4 comprises:
step 4.1, splicing the most probable paths between every two communication nodes to form a primary complete O-D path;
step 4.2, except the O point and the D point, removing the distance from the communication node to the nearest traffic intersection in the O-D path, and completely mapping the O-D path to the road traffic network;
4.3, sending out the information from each R point and traversing forwards and backwards at the same time, if a repeated path exists near the R point, deleting repeated road sections until no continuous repeated road sections exist, namely, an individual reaches the R point through the road sections k to i to j, then passes through the R point which is away from the road sections j to i to l, deleting the road sections i to j and j to i, the individual directly goes from k to i to l, and recording a merging point i;
4.4, traversing each merging point i, traversing from the merging point forward and backward again, and if the merging point i subtracts 180 degrees from the direction from the backward nth road section a to the direction from the b, the difference between the included angle in the direction from the forward nth road section x to the direction from the y is smaller than a threshold value C, and the two road sections are communicated, deleting all the road sections between the two road sections, and directly deleting the individual from a to y;
and 4.5, after the redundant road sections are deleted, rearranging the travel paths of the individuals among the O-D points, and completing the track extraction of the spatial road traffic network for the O-D travel of the individuals.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014003321A1 (en) * | 2012-06-27 | 2014-01-03 | 명지대학교 산학협력단 | Start point-based traffic allocation method using shortest path |
| CN103853901A (en) * | 2012-11-29 | 2014-06-11 | 深圳先进技术研究院 | Traffic track data preprocessing method and system |
| CN103853725A (en) * | 2012-11-29 | 2014-06-11 | 深圳先进技术研究院 | Traffic track data noise reduction method and system |
| CN107770744A (en) * | 2017-09-18 | 2018-03-06 | 上海世脉信息科技有限公司 | The identification of travelling OD node and hop extracting method under big data environment |
Family Cites Families (1)
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-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014003321A1 (en) * | 2012-06-27 | 2014-01-03 | 명지대학교 산학협력단 | Start point-based traffic allocation method using shortest path |
| CN103853901A (en) * | 2012-11-29 | 2014-06-11 | 深圳先进技术研究院 | Traffic track data preprocessing method and system |
| CN103853725A (en) * | 2012-11-29 | 2014-06-11 | 深圳先进技术研究院 | Traffic track data noise reduction method and system |
| CN107770744A (en) * | 2017-09-18 | 2018-03-06 | 上海世脉信息科技有限公司 | The identification of travelling OD node and hop extracting method under big data environment |
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