CN114446048A - Rail transit traveler full trip chain analysis method based on mobile phone signaling data - Google Patents
Rail transit traveler full trip chain analysis method based on mobile phone signaling data Download PDFInfo
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
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Abstract
The invention discloses a rail transit traveler full trip chain analysis method based on mobile phone signaling data, which comprises the following steps: extracting mobile phone signaling data information of a rail transit traveler, and identifying traveling track sections, passing sites and starting and ending site data of the traveler; based on a track traffic line network topological structure, identifying transfer stations of each trip of a traveler according to a trip track section and a passing station of each trip of the traveler to obtain a real trip track and a transfer station sequence of the traveler; identifying and verifying the source and destination of each trip of the traveler; and segmenting the travel track of the corresponding trip of the traveler according to the source and destination of each trip of the traveler, and identifying to obtain the connection mode of the traveler, thereby obtaining the analysis result of the full travel chain of the traveler. The invention realizes the analysis of the whole travel chain of the rail traveler and has important significance for researching the current running situation of the rail system so as to plan and adjust the wire net according to the current demand.
Description
Technical Field
The invention belongs to the field of traffic big data application, and particularly relates to a rail transit traveler full trip chain analysis method based on mobile phone signaling data.
Background
The subway system is one of important components of an urban public transport system, realizes the analysis of a whole travel chain of a subway traveler, and has important significance for researching the current running situation of the subway system so as to plan and adjust a line network according to the current demand.
The traditional subway passenger flow related research based on card swiping data is limited by a data source, is limited in a subway system, and cannot obtain the related travel information of travelers outside the subway system. Therefore, the research is often limited to time-varying characteristic analysis of line network passenger flow, site type modeling and the like, and the source and destination of a subway traveler cannot be identified. Meanwhile, the card swiping data only contains the information of the access station of the user, the complete track of the traveler in the subway system cannot be obtained, and further the relevant analysis on the transfer cannot be carried out. The mobile phone signaling data has the characteristic of high space-time coverage rate, and the analysis of aspects such as passenger flow transfer, passenger flow source and destination identification and the like can be performed by utilizing the mobile phone data, which cannot be realized by the traditional card swiping data.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems, the invention provides a rail transit traveler full travel chain analysis method based on mobile phone signaling data, which is characterized in that mobile phone data are used for identifying transfer passenger flow, a passenger flow source place and a destination place, so that rail transit passenger flow data with different scales are obtained through collection, the operation condition of a rail transit system is evaluated, and data support is provided for management and planning of the rail transit system.
The technical scheme is as follows: in order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows: a rail transit traveler full trip chain analysis method based on mobile phone signaling data comprises the following specific steps:
(1) extracting mobile phone signaling data information of a rail transit traveler, identifying travel track sections, passing sites and starting and ending site data of each trip of the traveler based on the time-space characteristics of the mobile phone signaling data information, and performing data processing on the identified data;
(2) based on a track traffic line network topological structure, identifying transfer stations of each trip of a traveler according to a trip track section and a passing station of each trip of the traveler to obtain a real trip track and a transfer station sequence of the traveler;
(3) performing data processing on the mobile phone signaling data information of the rail transit traveler in the step (1), removing table tennis data and drifting data, identifying to obtain a source place and a destination place of each trip of the traveler, and verifying an identification result;
(4) and segmenting the travel track of the corresponding trip of the traveler according to the source and destination of each trip of the traveler, and identifying and obtaining the connection mode of the traveler, thereby obtaining the analysis result of the whole travel chain of the traveler.
Further, the method of the step (1) is specifically as follows:
(1.1) extracting mobile phone signaling data information of all-day rail transit travelers;
(1.2) screening mobile phone signaling data information of a rail transit traveler based on a rail base station matching table to obtain data information of the traveler belonging to a rail base station, wherein the data information comprises an identification number, rail stations and start time and end time corresponding to each station;
the identification number includes but is not limited to a mobile phone number or a device unique identification code of the traveler;
(1.3) sorting the track stations according to the corresponding starting time sequence of each station based on the data information of the travelers to obtain travel track sections, passing stations and starting and ending stations of the travelers in different times, and calculating the travel time of each time of the travelers;
(1.4) based on travel track sections and travel time of travelers in different times, performing data processing on data information of the travelers, and rejecting data information of the travelers with less than 2 passing sites or less than 5 minutes of travel time;
(1.5) calculating travel speeds of travel track sections of different trips of the travelers based on the data information of the travelers, and eliminating the data information of the travelers of which the travel speeds do not belong to a preset first threshold range; the calculation formula of the travel speeds speed of the travel track sections of different times is as follows:
in the formula, n is the total number of passing sites of the travelers in different times; and delta t is the travel time of the travelers in different times.
Further, the method of the step (2) is specifically as follows:
(2.1) extracting mobile phone signaling data information of a station hall base station and a station rail base station of a transfer station to form a set { T };
and (2.2) screening the mobile phone signaling data information belonging to the set { T } from the mobile phone signaling data information of the track traffic traveler in the step (1), matching the mobile phone signaling data information with the data processed traveler to obtain travel track segments, approach stations and starting and ending station data of corresponding trips with transfer behaviors, and further deducing to obtain a real travel track and a transfer station sequence of the traveler.
Further, in the step (3), the mobile phone signaling data information of the rail transit traveler in the step (1) is processed to remove ping-pong data, and the method specifically comprises the following steps:
calculating the stay time of a traveler in the current base station aiming at each piece of mobile phone signaling data information, and if the stay time in the current base station is less than a preset second threshold and the sum of the stay time in the current base station and the previous base station of the current base station is greater than a preset third threshold;
or the base stations recorded in the mobile phone signaling data of the adjacent starting moments are the same;
marking the current base station as ping-pong data, deleting the ping-pong data, and replacing the starting time of the next base station of the current base station with the starting time corresponding to the current base station;
and repeating the steps until the mobile phone signaling data information does not contain ping-pong data.
Further, in the step (3), the mobile phone signaling data information of the rail transit traveler in the step (1) is processed to remove drift data, and the method specifically includes:
calculating and judging whether the distance between the current base station and a previous base station of the current base station and the distance between the current base station and a next base station of the current base station are both larger than a preset fourth threshold value or not aiming at each piece of mobile phone signaling data information;
meanwhile, calculating and judging whether the transfer speeds of the current base station and a base station before the current base station and the transfer speeds of the current base station and a base station after the current base station are both larger than a preset fifth threshold value;
if the above judgments are all true, marking the current base station as drift data, and deleting;
and repeating the steps until no drift data exists in the mobile phone signaling data information.
Further, the identification in step (3) obtains the source and destination of each trip of the traveler, and verifies the identification result, the method is as follows:
acquiring departure time and arrival time of each trip of the traveler based on the trip track segment and the starting and ending station data of each trip of the traveler;
extracting all the stay points of the traveler within 2 hours before the departure time based on the mobile phone signaling data information of the traveler, and screening to obtain one stay point which has the stay time greater than a preset sixth threshold and is closest to the departure time as a source place;
extracting all stay points of the traveler within 2 hours after the departure time based on the mobile phone signaling data information of the traveler, and screening to obtain one stay point which has the stay time greater than a preset sixth threshold and is closest to the arrival time and is used as a destination;
the source and destination identification results of each trip of the traveler are verified using the following two evaluation indexes, which are expressed as follows:
wherein Δ s is Bi,j,tTo
And SiThe absolute value of the difference in distance; b isi,j,tA jth source base station of an ith station at time t;
the nearest site of the jth source base station; siIs the ith site; n isi,j,tThe number of people at the jth source base station of the ith station at the time t; criterion1,i,tFor time t, [ delta ] s is applied to ni,j,tThe weighted average distance of (d); when q is>At 0, Si+qFor the qth site upstream of the ith site, when q is<At 0, Si+qA q site downstream of the ith site;
at time t, site SiAnd site Si+qThe distance of (d);
when evaluating index result value criterion1,i,tAnd
when the identification data belong to the preset seventh threshold range, the identification result is correct; otherwise, the recognition result is incorrect, and recognition needs to be carried out again.
Further, the method of the step (4) specifically comprises the following steps:
based on the starting and ending site, the source place and the destination place of each trip of the traveler, obtaining all triangulation data of the traveler from the source place to the starting site and from the ending site to the destination place;
performing data processing on the triangulation data, and deleting ping-pong data, drift data and long-residence data;
generating a triangulation location track section by using the processed triangulation location data;
calculating the speed and the acceleration of each section in the triangular positioning track section, further obtaining the average travelling speed and the average acceleration of the triangular positioning track section, and obtaining a connection mode according to the average travelling speed and the average acceleration of the triangular positioning track section;
wherein the distance d of the kth section in the track segment is triangulatedkThe calculation formula of (a) is as follows:
in the formula (x)k,yk) And (x)k+1,yk+1) Respectively locating the coordinate values of the starting track point and the ending track point of the kth section in the track section by triangulation;
travel speed v of kth section in triangular positioning track sectionkAcceleration akThe calculation formulas of (A) are respectively as follows:
in the formula, tkLocating a trace point (x) in a trace segment for triangulationk,yk) Time stamp of tk+1Locating a trace point (x) in a trace segment for triangulationk+1,yk+1) A timestamp of (d); m is m track points in the triangular positioning track segment;
triangle positioning track segment planeAverage trip speed
Average acceleration
The calculation formulas of (A) are respectively as follows:
if the average travel speed of the triangular positioning track segment
When the speed is less than 4m/s, the connection mode is slow moving;
if the average travel speed of the triangular positioning track segment
Not less than 4m/s and average acceleration
Greater than 0.15m/s2When the vehicle is in a vehicle connection mode;
if the average travel speed of the triangular positioning track segment
Not less than 4m/s and average acceleration
Not more than 0.15m/s2Meanwhile, the connection mode is a bus.
Has the advantages that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
the invention provides a rail transit traveler full-trip chain analysis method based on mobile phone signaling data, which solves the problem that the traditional rail passenger flow related research based on card swiping data is limited by a data source and cannot obtain the related trip information of a traveler outside a rail system; meanwhile, the method increases the correlation analysis of transfer, realizes the analysis of the whole travel chain of the track traveler, and has important significance for researching the current running situation of the track system and planning and adjusting the wire net according to the current demand.
Drawings
Fig. 1 is a schematic flow chart of a method for analyzing a whole travel chain of a rail transit traveler based on mobile phone signaling data according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating ping-pong data and drift data in accordance with an embodiment;
FIG. 3 is a distribution thermodynamic diagram for a client source according to an embodiment;
FIG. 4 is an embodiment of a passenger flow to distribution thermodynamic diagram.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
The invention relates to a method for analyzing a whole trip chain of a rail transit traveler based on mobile phone signaling data, which specifically comprises the following steps with reference to fig. 1:
(1) extracting starting and ending stations of each track section based on the space-time characteristics of mobile phone signaling data of a rail transit traveler, and correcting possible base station data drifting or missing nearby the starting and ending stations by using ground base stations around the rail stations;
(2) for the people who have the transfer, the real transfer track of the traveler is obtained by extracting the transfer station sequence passing through the people and combining the track line network topological relation;
(3) based on a traveler stop point identification method, according to time constraints of passengers for getting on and off the train, extracting a source destination of the passengers outside a track system, and establishing a reliability evaluation index of a source destination identification result;
(4) and segmenting the traveler track according to the source destination of the traveler, and identifying the connection mode of the traveler by adopting the vehicle speed, the acceleration threshold value and the like.
Further, in the step (1), based on the space-time characteristics of the mobile phone signaling data of the rail transit traveler, the starting and ending stations of the traveler are identified, and the data of the ground base station is used for correcting the result, specifically:
the mobile phone signaling data includes information of base stations through which travelers pass. The track base station information specially serving the track system can obtain the riding station of the track system according to the position information. And extracting the mobile phone signaling data belonging to the rail transit traveler by matching the rail base station matching table with the base station information in the mobile phone signaling data. And segmenting travel track sections of different trips of the traveler according to a time threshold value, and acquiring a bus station sequence of the traveler in the rail system every day. Meanwhile, since each traveler may perform a plurality of orbital trips every day, the tracks of different trips need to be divided. Specifically, based on the mobile phone signaling data of the track traffic traveler, a sequence of track stations passed by the traveler in different trips can be obtained preliminarily, and the first station and the last station of the sequence are regarded as the starting station and the destination station of the track trip.
However, due to the possible offset of the mobile phone terminal in the communication process with the base station, there may be a deviation in the first and last records of the station sequence. On the other hand, due to communication drift and the like, a traveler may communicate with an underground base station between two track stations even in a station hall of a track station, and therefore, when the head and the tail of the traveler are recorded as the underground base station between two track stations, it is impossible to accurately determine which track station the traveler is at. Therefore, the invention corrects the starting and ending points of the travelers based on the ground base stations around the station.
This correction method is based on the following assumptions: before entering a track station, a traveler can communicate with a base station in a range of 500 meters around the station by using a mobile phone terminal held by the traveler. Therefore, according to the time information recorded from the beginning to the end of the mobile phone signaling data of the traveler each time, the ground base station where the traveler is located before is found, the site information of the traveler is further obtained, and the corrected starting and ending point information is obtained. The method comprises the following specific steps:
1) and screening tracks appearing in the track base station through a track base station matching table and sequencing the tracks:
and associating the track base station matching table in the mobile phone signaling data day by day, and screening out the identification numbers (mobile phone numbers, equipment unique identification codes and the like) of passengers appearing in the track base stations and the base station sequences contained in the identification numbers.
2) Removing error data:
and (4) eliminating travelers with less than 2 orbit base stations (including station halls and station orbits) appearing all day.
3) Extracting track segment information:
and extracting the track segment time, the station, the next mobile phone signaling record information and the like of the traveler. The time interval between the recordings and before and after the recording is calculated. And obtaining a track section formed by adjacent stations in the traveler time sequencing signaling data.
4) Rejecting error data;
and (4) eliminating travelers with track base stations (including station halls and station rails) less than 2 or trip time less than 5min all day.
5) And eliminating the non-orbit data of the ground base station to obtain the passenger OD result.
And (4) eliminating data with higher speed of a plurality of ground base stations (considered as car travelers). Selecting the track trip data (threshold value) with the speed less than 5 as the following formula:
where speed is the estimated traveler speed, n is the number of stations separated, Δ t is the time interval recorded from beginning to end, and 1000 represents the assumed inter-station distance is 1000 m.
6) And adding ground base stations around the station, and re-identifying the OD.
On the basis of the former OD, the OD obtained by the non-station hall base station is arranged at the starting and ending point, the base station within the range of 500m around the added station is associated, and the base station where the first/last data of the data before/after 10min of the OD is located is taken as the starting/ending point of the traveler.
Further, in the step (2), the path site track and the net topology of the traveler are comprehensively considered, and the transfer site of the traveler is identified, specifically:
for the problem of identification of transfer traffic, there are several preconditions that need to be unambiguous. Firstly, the transfer of passengers must take place at the transfer station; secondly, passengers present at a transfer station include not only 1) the flow of passengers transferred at the station, but also 2) the flow of passengers passing through the station without getting off the vehicle, and 3) the flow of passengers whose origin or destination is the transfer station. The three types of passenger flows have certain characteristics in space and time, and the research also designs an identification method according to the characteristics.
For the passenger flow type 1), the outstanding characteristics are that the stations through which passengers pass before and after are in different lines by taking the transfer station as a demarcation point; for the passenger flow type 2), the characteristic is that the passengers are in the same route with the transfer station as a demarcation point; for the passenger flow type 3), the characteristics are obvious, and only the first station and the last station of the station sequence need to be judged whether to be transfer stations.
Based on the above analysis, the invention provides a method for identifying transfer points of transfer passengers. Firstly, screening out all signaling records appearing at a transfer site; then, according to the result in the step (1), obtaining the starting and ending point of each traveler, and matching according to the mobile phone number; finally, a station sequence including start and end point information and transfer information can be obtained, such as: starting station → transfer station 1 → transfer station 2 → transfer station → terminal station. Since the transfer routes that can be realized by the transfer stations are limited, the actual transfer trajectory of the traveler can be deduced from the sequence described above. The method comprises the following specific steps:
1) and extracting information of the station hall base station and the station rail base station of the transfer station, wherein the set of the base stations is marked as { T }.
2) And screening the mobile phone signaling records of the base stations in the set { T } from the original signaling table, and sequencing according to the unique identification number and time of the traveler to obtain a base station sequence of the mobile phone signaling data records of the transfer site passed by each traveler (the traveler may or may not transfer at the site).
3) And (3) matching each traveler base station sequence information table obtained in the step (2) with the traveler starting and ending sites obtained in the step (1) so as to obtain the sequence of the starting and ending sites and the passing transfer sites of each traveler.
4) And deducing to obtain the real passing line of the traveler and the transfer station sequence of the transfer actually generated based on the line switching sequence which can be realized by each transfer station.
Further, in the step (3), based on the traveler stop point identification method, according to the time constraint of the passengers getting on and off the train, the source destination of the passengers outside the track system is extracted, and the reliability evaluation index of the source destination identification result is established.
The step comprises four processes, wherein the process 1 is used for removing ping-pong data in original data, the process 2 is used for removing drift data, the process 3 is used for identifying a source and a destination of a traveler, and the process 4 is used for verifying an identification result. The table tennis data and the drift data are shown in fig. 2.
Process 1: ping-pong data processing
1) If the base stations recorded by the upper and lower adjacent mobile phone signaling data are the same, combining the two pieces of recording information, namely subtracting the starting time of the previous record from the ending time of the next record to obtain the staying time of the traveler in each base station;
2) judging the time characteristic and the space characteristic of each mobile phone signaling data record
Time characteristics: the staying time at the current base station is less than a certain threshold value, and the staying time of the previous base station and the later base station is greater than the threshold value;
spatial characteristics: the base station of the previous record is the same as the base station of the next record;
3) in the step 2, if the two characteristics are both in accordance with each other, marking the data as ping-pong data and deleting the data;
4) after the ping-pong data is deleted, a new identical neighbor record will appear, return to (1), and iterate multiple times.
And (2) a process: drift data processing
1) Calculating the distance between each stay record and the previous base station and the distance between each stay record and the next base station;
2) if the distances between the current staying base station and the front and rear base stations are both larger than a certain threshold value, and the transfer speed is larger than the threshold value, judging that the record is drift data, and deleting the record;
3) and combining the information of the same adjacent base stations to obtain the staying time of the traveler in each base station.
And 3, process: the source and destination are identified, referring to fig. 3 and 4, which are respectively the passenger flow source and destination distribution thermodynamic diagrams, and the method is as follows:
1) according to the information of the starting and ending sites of the traveler, the departure and arrival time of the traveler is obtained
2) Sequencing travelers in time sequence within 2 hours before the departure time of the travelers from the starting station, and taking a stop point with the latest stop time of more than or equal to 15 minutes as a source point.
3) And taking the stopping point with the first stopping time of more than or equal to 15 minutes within 2 hours after the traveler arrives at the terminal as the destination.
And 4, process: source to source identification result verification
Since all sites have a certain service range, intuitively, the source-destination identification result should be theoretically concentrated in a certain area around the site, and according to this basic assumption, the process provides two indexes for judging the reliability of the identification result.
The following symbols are first defined, as in the table below.
Index 1: possibly wrong base station
Theoretically, the passenger will choose the nearest track station to get on the bus, so if the base station j is the source base station of the station i, the station i should theoretically be the nearest station to the base station j; however, since the coverage area of the base station is relatively large, the representative position is not accurate, and assuming that the station closest to the base station j is actually q, the distance difference between the base station j and the stations i and q should be as small as possible. Therefore, the accuracy of identifying whether a passenger is riding the nearest track site is verified by considering as an index a weighted average of the distance difference to the number of base stations.
Wherein Δ s is Bi,j,tTo
And SiThe absolute value of the difference in distance.
Index 2: number of source base station repetitions of k sites in a neighborhood
Theoretically, the source base station sets of passengers at different sites should be disjoint (without regard to the base stations in the middle area between the two sites).
Thus, the distance of two sites that are q sites apart can be used to measure the rationality of the recognition result.
Wherein q may be a positive number or a negative number, and represents the q-th site downstream and the q-th site upstream of the current site respectively.
Further, in the step (4), the traveler track is segmented according to the source destination of the traveler, and the connection mode of the traveler is identified by adopting the vehicle speed, the acceleration threshold value and the like. The method comprises the following specific steps:
1) decoding the triangulation location data of the traveler;
2) extracting triangular positioning data before and after the passengers enter the station based on the passenger departure and termination station information obtained in the step (1);
3) obtaining the time points of departure of the travelers from the source place and arrival at the destination place according to the source place and destination place information of the travelers obtained in the step (3);
4) taking the time point extracted in the last step as a starting point of a trip before the trip or an end point of a trip after the trip, and extracting all triangular positioning tracks of each traveler before the trip and after the trip;
5) extracting data that the identified transfer stations have a dwell time (transfer time) of 3 minutes to 1.5 hours;
6) calculating the driving speed and the acceleration of each section of each triangular positioning track;
calculating the linear distance between each track point and the previous track point, and obtaining the running speed according to the time difference; then calculating to obtain acceleration according to the vehicle speeds of the two adjacent sections; and finally, obtaining the average speed and acceleration of the last trip before arrival and the first trip after departure.
Suppose a certain track contains track points { (x)1,y1),(x2,y2),…,(xm,ym) With a corresponding timestamp of { t }1,t2,…,tmThen the distance of each section is, since the error of the triangulation data is within 150m, calculating the length of each section using Euclidean distance would make the calculation result larger than the actual distance, thus multiplying the calculated distance by 0.5:
the speed of each segment is:
the corresponding acceleration is:
the corresponding average speed is:
the corresponding average acceleration is:
7) removing ping-pong and long-standing data;
8) calculating the average speed and acceleration of each travel track;
9) judging a trip mode;
Claims (7)
1. The rail transit traveler full trip chain analysis method based on the mobile phone signaling data is characterized by comprising the following specific steps:
(1) extracting mobile phone signaling data information of a rail transit traveler, identifying travel track sections, passing sites and starting and ending site data of each trip of the traveler based on the time-space characteristics of the mobile phone signaling data information, and performing data processing on the identified data;
(2) based on a track traffic line network topological structure, identifying transfer stations of each trip of a traveler according to a trip track section and a passing station of each trip of the traveler to obtain a real trip track and a transfer station sequence of the traveler;
(3) performing data processing on the mobile phone signaling data information of the rail transit traveler in the step (1), removing ping-pong data and drifting data, identifying to obtain a source place and a destination place of each trip of the traveler, and verifying an identification result;
(4) and segmenting the travel track of the corresponding trip of the traveler according to the source and destination of each trip of the traveler, and identifying and obtaining the connection mode of the traveler, thereby obtaining the analysis result of the full travel chain of the traveler.
2. The method for analyzing the all-trip chain of the rail transit traveler based on the mobile phone signaling data according to claim 1, wherein the method in the step (1) is specifically as follows:
(1.1) extracting mobile phone signaling data information of all-day rail transit travelers;
(1.2) screening mobile phone signaling data information of a rail transit traveler based on a rail base station matching table to obtain data information of the traveler belonging to the rail base station, wherein the data information comprises an identification number, rail stations and start time and end time corresponding to each station;
the identification number includes but is not limited to a mobile phone number or a device unique identification code of the traveler;
(1.3) sorting the track sites according to the corresponding starting time sequence of each site based on the data information of the travelers to obtain travel track sections, passing sites and starting and ending sites of the travelers in different times, and calculating the travel time of each time of the travelers;
(1.4) carrying out data processing on the data information of the travelers based on travel track sections and travel time of different trips of the travelers, and rejecting the data information of the travelers with less than 2 passing sites or less than 5 minutes of travel time;
(1.5) calculating travel speeds of travel track sections of different times of the travelers based on the data information of the travelers, and eliminating the data information of the travelers of which the travel speeds do not belong to a preset first threshold range; the calculation formula of the travel speeds speed of the travel track sections of different times is as follows:
in the formula, n is the total number of passing sites of the travelers in different times; and delta t is the travel time of the travelers in different times.
3. The method for analyzing the all-trip chain of the rail transit traveler based on the mobile phone signaling data according to claim 1, wherein the method in the step (2) is specifically as follows:
(2.1) extracting mobile phone signaling data information of a station hall base station and a station rail base station of a transfer station to form a set { T };
and (2.2) screening the mobile phone signaling data information belonging to the set { T } from the mobile phone signaling data information of the track traffic traveler in the step (1), matching the mobile phone signaling data information with the data processed traveler to obtain travel track segments, approach sites and starting and ending site data of corresponding trips with transfer behaviors, and further deducing to obtain the real travel track and transfer site sequence of the traveler.
4. The method for analyzing the all travel chains of the rail transit traveler based on the mobile phone signaling data according to claim 1, wherein in the step (3), the mobile phone signaling data information of the rail transit traveler in the step (1) is processed to remove ping-pong data, and the method specifically comprises the following steps:
calculating the stay time of a traveler in the current base station aiming at each piece of mobile phone signaling data information, and if the stay time in the current base station is less than a preset second threshold and the sum of the stay time in the current base station and the previous base station of the current base station is more than a preset third threshold;
or the base stations recorded in the mobile phone signaling data of the adjacent starting moments are the same;
marking the current base station as ping-pong data, deleting the ping-pong data, and replacing the starting time of the next base station of the current base station with the starting time corresponding to the current base station;
and repeating the steps until no ping-pong data exists in the mobile phone signaling data information.
5. The method for analyzing the all travel chains of the rail transit traveler based on the mobile phone signaling data according to claim 1, wherein in the step (3), the mobile phone signaling data information of the rail transit traveler in the step (1) is processed to remove drift data, and the method specifically comprises the following steps:
calculating and judging whether the distance between the current base station and a previous base station of the current base station and the distance between the current base station and a next base station of the current base station are both larger than a preset fourth threshold value or not aiming at each piece of mobile phone signaling data information;
meanwhile, calculating and judging whether the transfer speeds of the current base station and a base station before the current base station and the transfer speeds of the current base station and a base station after the current base station are both larger than a preset fifth threshold value;
if the above judgments are all true, marking the current base station as drift data, and deleting;
and repeating the steps until no drift data exists in the mobile phone signaling data information.
6. The method for analyzing the whole travel chain of the rail transit traveler based on the mobile phone signaling data as claimed in claim 1, wherein the identification in the step (3) obtains a source place and a destination place of each trip of the traveler, and verifies an identification result, and the method specifically comprises the following steps:
acquiring departure time and arrival time of each trip of the traveler based on the trip track segment and the starting and ending station data of each trip of the traveler;
extracting all the stay points of the traveler within 2 hours before the departure time based on the mobile phone signaling data information of the traveler, and screening to obtain one stay point which has the stay time greater than a preset sixth threshold and is closest to the departure time and is used as a source;
extracting all stay points of the traveler within 2 hours after the departure time based on the mobile phone signaling data information of the traveler, and screening to obtain one stay point which has the stay time greater than a preset sixth threshold and is closest to the arrival time and is used as a destination;
the source and destination identification results of each trip of the traveler are verified using the following two evaluation indexes, which are expressed as follows:
wherein Δ s is Bi,j,tTo
And SiThe absolute value of the difference in distance; bi,j,tA jth source base station of an ith station at time t;
is the nearest site of the jth source base station; siIs the ith site; n isi,j,tThe number of people at the jth source base station of the ith station at the time t; criterion1,i,tFor time t, [ delta ] s is applied to ni,j,tThe weighted average distance of (d); when q is>At 0, Si+qFor the qth site upstream of the ith site, when q is<At 0, Si+qA q-th site downstream from the ith site;
at time t, site SiAnd site Si+qThe distance of (d);
when evaluating index result value criterion1,i,tAnd
when the identification data belong to the preset seventh threshold range, the identification result is correct; otherwise, the recognition result is incorrect, and recognition needs to be carried out again.
7. The method for analyzing the all-trip chain of the rail transit traveler based on the mobile phone signaling data according to claim 1, wherein the method in the step (4) specifically comprises the following steps:
based on the starting and ending site, the source place and the destination place of each trip of the traveler, obtaining all triangulation data of the traveler from the source place to the starting site and from the ending site to the destination place;
performing data processing on the triangulation data, and deleting ping-pong data, drift data and long-residence data;
generating a triangulation location track section by using the processed triangulation location data;
calculating the traveling speed and the acceleration of each section in the triangular positioning track section, further obtaining the average traveling speed and the average acceleration of the triangular positioning track section, and obtaining a connection mode according to the average traveling speed and the average acceleration of the triangular positioning track section;
wherein the distance d of the kth section in the track segment is triangulatedkThe calculation formula of (a) is as follows:
in the formula (x)k,yk) And (x)k+1,yk+1) Respectively locating coordinate values of starting and ending track points of the kth section in the track section by the triangle;
travel speed v of kth section in triangulation positioning track sectionkAcceleration akThe calculation formulas of (A) are respectively as follows:
in the formula, tkLocating a trace point (x) in a trace segment for triangulationk,yk) Time stamp of tk+1Locating a trace point (x) in a trace segment for triangulationk+1,yk+1) A timestamp of (d); m is m track points in the triangular positioning track segment;
average travel speed of triangular positioning track segment
Average acceleration
The calculation formulas of (A) are respectively as follows:
if the average travel speed of the triangular positioning track segment
When the speed is less than 4m/s, the connection mode is slow moving;
if the average travel speed of the triangular positioning track segment
Not less than 4m/s and average acceleration
Greater than 0.15m/s2When the vehicle is in a vehicle connection mode;
if the average travel speed of the triangular positioning track segment
Not less than4m/s and average acceleration
Not more than 0.15m/s2Meanwhile, the connection mode is a bus.
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