CN111653099A - Bus passenger flow OD obtaining method based on mobile phone signaling data - Google Patents
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Abstract
The invention discloses a method for acquiring a bus passenger flow OD (origin destination) based on mobile phone signaling data, which is based on the mobile phone signaling data, judges a track traveling mode by judging a similarity index of a user track and each bus route and combining track speed characteristics, eliminates the user track of non-bus traveling, determines the bus route taken by a user on the basis of the judgment and combining bus route fare and departure interval characteristics, identifies the bus stop of the user according to the position relationship between the track and the bus stop, and finally converges to obtain the bus passenger flow OD data of branch routes. The bus similarity index is used as a core characteristic of bus trip judgment and track line matching on one hand, and the index is convenient to calculate, easy to realize and applicable to large sample data; on the other hand, the index calculation method not only represents the similarity degree of the track and the line trend in space, but also considers the bus operation time, and reduces the misjudgment rate in the non-line operation time period.
Description
Technical Field
The invention relates to the field of traffic planning, in particular to the field of traffic demand prediction.
Background
The urban traffic continuously advances the urbanization process and accelerates the development of social economy, however, while the urban traffic makes a great contribution to the promotion of social development, many problems are caused by the excessively fast increasing travel demand, and further development of national society and urban economy is restricted, such as increase of traffic accidents, energy consumption, environmental pollution and the like, wherein the urban traffic jam becomes one of the difficulties which plague government departments and traffic researchers. Urban public transport is a green and environment-friendly and convenient trip mode, and can share a large amount of passenger transport demands in cities with less resource consumption, thereby relieving traffic pressure. A lot of cities promote the bus priority to the strategic aspect, but in the actual operation process, because supply and demand imbalance makes waiting for the bus time overlength, the trip reliability is lower, and the line sets up unreasonablely etc. and leads to the bus attraction rate to be in lower level always. On the other hand, unreasonable line capacity allocation also causes low income of bus operation, and causes the government to need continuous subsidies to maintain normal operation of the buses. Therefore, the demand of bus passenger flow is mastered, bus routes are reasonably planned, the bus capacity is configured to be very important, the bus service level can be improved, the traffic pressure is reduced, the financial subsidy expenditure of the government can be reduced, and the win-win management effect is achieved.
In recent years, the development and popularization of communication and internet technologies have led to the research and development of a large amount of potential traffic information stored behind the communication and internet technologies. The mobile phone is rapidly popularized in the nation as a portable communication device along with the development of economy, the possession of the mobile phone of China exceeds 96/hundred people by 2016, basically people in most regions of China have the mobile phone except for part of traffic subordinate groups such as old people and children, a mobile phone terminal can establish contact with an adjacent cellular base station in order to meet the requirements of user communication and internet surfing, meanwhile, the time of accessing the base station by a user and the position information of the base station are recorded, the characteristic that the mobile phone can track individuals in real time and provide the position of the user provides a new idea for the collection of travel information, and the mining of public traffic information is worthy of attention.
The acquisition of the bus passenger flow OD is always a key point of research of researchers at home and abroad, firstly, partial sample data is acquired and sample expansion is carried out according to modes such as follow-up survey, questionnaire survey and the like, and with the starting of intelligent equipment, more and more researches are carried out in recent years on the acquisition of the bus passenger flow OD based on traffic big data mining, wherein the bus passenger flow OD calculation research based on IC data and bus GPS data is the most extensive, relatively technically mature and simple, and the bus passenger flow is acquired by summarizing the getting-on and getting-off stations recorded by passenger IC card information. However, a large number of buses only need to get on and punch cards, and only the number and time of the cards on the buses can be recorded, so that the boarding and disembarking stations of passengers cannot be accurately obtained. With the wide application of the mobile phone signaling data, the method for acquiring the bus OD based on the mobile phone signaling data is more and more researched. Huo xianie et al discloses a method for obtaining an OD of a bus passenger flow based on a mobile phone signaling in 2018. The method is based on the matching of the public traffic line network and the track, the influence of the public traffic line on the judgment of public traffic trips is not considered, misjudgment is easy to occur in a dense area of the public traffic line network, and the auxiliary judgment needs to be carried out by fusing the public traffic GPS data, so that the implementation is complex.
In the existing method for identifying the OD of the bus passenger flow based on the mobile phone signaling data, when the track is matched with the bus line, most of the possible path base stations are listed based on the line station, the calculation is more complex, the bus operation time is not considered, and the OD misjudgment at the non-operation time of the bus is caused, so that the result is higher; on the other hand, the line identification and judgment of repeated sections of lines is lacked, that is, when a plurality of bus lines correspond to the same base station switching sequence, the passenger flow of each line cannot be accurately judged.
Disclosure of Invention
Aiming at the problems in the background technology, the invention is based on mobile phone signaling data, judges the similarity index of a user track and each bus line, combines track speed characteristics to judge the track travel mode, eliminates the user track of non-bus travel, combines bus line fare and departure interval characteristics on the basis to determine the bus line taken by the user, identifies the station where the user gets on or off the bus according to the position relation between the track and the station of the line, and finally converges to obtain the bus passenger flow OD data of the branch line.
The technical scheme is as follows:
a bus passenger flow OD obtaining method based on mobile phone signaling data comprises the following steps:
s1, acquiring mobile phone signaling data and bus route stop information;
s2, extracting user travel track information and a distance matrix between a base station and a line site;
s3, calculating the line similarity of the track and each bus line, and finally determining the bus similarity index of the travel track;
s4, eliminating track data of non-bus trips according to the bus similarity index and the track average speed;
s5, calculating a matching index of the bus travel track possibly matched with the route according to the bus similarity index, the route fare and the route departure interval, and finally determining the bus route matched with the track;
s6, judging the station points of the track by combining the track base station and the station position information of the matched line;
and S7, respectively summarizing station passenger flow OD of each bus line of the whole line network as bus passenger flow OD.
Preferably, the step S1 of obtaining the cell phone signaling data and the bus route stop information includes the following steps:
s11, acquiring user mobile phone signaling data in a research time range of a research area and a base station position information table in a corresponding research range, wherein the base station position information table comprises base station ID, longitude and latitude fields;
and S12, crawling the bus route and stop information table in the research range from the high-grade map, wherein the bus route and stop information table comprises a route name, a route stop name and stop longitude and latitude coordinates.
Preferably, the step S2 of extracting the user travel track information and the base station-to-site distance matrix includes the following steps:
s21, extracting user travel track information, namely determining a time-space threshold value according to the definition of one travel, dividing original mobile phone signaling data into independent travel track data, wherein one travel track data is composed of a string of base station position points, and comprises base station coordinates, and start time and end time for triggering the base station;
s22, calculating the linear distance D between each two base stations and the bus stop according to the base station position information table and the line and stop information table;
s23, for each base station, extracting the station with the minimum distance from the base station from all bus stations contained in the bus line, recording the distance value, traversing all the bus lines and the base stations in sequence, and finally forming a distance matrix between the base station and the bus station;
s24, correcting the distance matrix between the base station and the bus line station: and obtaining a base station-bus line station distance matrix of each time period according to the bus line operation time, namely dividing the time periods according to the bus line operation time, and if a certain line is not in the operation time period, modifying the distances between the line and all base stations in the distance matrix to be positive infinity.
Preferably, in step S3, for each bus route, calculating route similarity between the trajectory and the route, and finally determining the bus similarity index of the trajectory, the method includes the following steps:
s31, for one travel track, according to the base station-bus line station distance matrix in the S2, obtaining a base station-station distance set D corresponding to each line, wherein D is { D ═ D }1,D2,..Di,..,DnIn which D isiRepresents the distance between the ith base station and the nearest station of the line in the track, and the distance is obtained from the distance calculated in S2 and the trackA time interval base station-bus line station distance matrix corresponding to the departure time of the trace;
s32, when DiIf the number of the base stations is less than a certain threshold value, the base station i and the line stop are successfully matched, the threshold value is set according to the base station density and the bus stop density in the research range, and the threshold value is changed into a value which can be adjusted according to the base station density and the bus stop density in the research range and is suggested to be a value of 800m) to calculate D in the setiThe ratio of the number smaller than the threshold to the total number of base stations is defined as the similarity between the track and the linei；
And S33, sequentially traversing all the bus lines for a travel track, and respectively calculating the line similarity of the travel track, wherein the maximum value of the line similarity is the bus-similarity index of the travel track.
Preferably, step S4 is to remove trajectory data of non-public transport trips according to the public transport similarity index and the average trajectory speed, and includes the following steps:
s41, calculating the average speed of the track trip, namely the ratio of the track trip distance to the track trip time consumption;
s42, determining upper and lower threshold values of the bus travel speed according to the average bus travel speed distribution in the research range, simultaneously determining a bus similarity index threshold value, marking the track meeting the conditions that the average speed is greater than the bus similarity index threshold value and is at the upper and lower threshold values of the bus travel speed as a bus travel, reserving the record, and eliminating other travel tracks which do not meet the conditions.
Preferably, the step S5 of calculating the matching index of the possible matching route of the bus travel track, and finally determining the bus route matched with the track includes the following steps:
s51, classifying the one-ticket system and multi-ticket system routes in the research range;
s52, dividing the lines into three categories of high frequency, medium frequency and low frequency according to departure intervals of the lines;
s53, for the travel track reserved in S3, extracting the bus routes with the route similarity larger than a certain threshold, and combining the route fare and the departure interval frequency respectivelyCalculating the matching index M of the line iiThe calculation formula is as follows:
Mi＝αTi+βFi+γsimilarityi
wherein α, β and gamma are respectively public transport fare utility, departure interval utility and public transport line similarity value utility, and are calibrated according to user travel selection behaviors in a research range, and the suggested values are respectively 0.3, 0.2 and 0.5, similarityiThe route similarity of the trajectory obtained in S3 and the route; t isiAnd FiThe fare and departure frequency of the line bus are respectively, and the value-taking rule is as follows:
s54, corresponding MiThe line with the largest value is the bus line successfully matched with the track.
Preferably, the step S6, in combination with the station location information of the track base station and the matching line, of determining the upper and lower station points of the track, includes the following steps:
s61, extracting a base station-site distance set calculated in the step S31 corresponding to the matching line determined in the S5 for one travel track;
s62, the station corresponding to the first base station successfully matched with the line station in the set is the getting-on station of the user, the station corresponding to the last base station successfully matched with the line station is the getting-off station of the user, and the definition of successful matching is shown in step S32.
Preferably, the step S7 respectively summarizes the stop passenger flow OD of each bus route and the stop passenger flow OD of the whole line network, including the following steps:
s71, converging the track getting on and off bus stops successfully matched with each bus line within the research time range of each bus line, and acquiring a line passenger flow OD matrix;
and S72, traversing all the bus lines in sequence, converging the passenger flow OD matrix, and finally obtaining the bus passenger flow OD.
The invention has the advantages of
The invention firstly provides a public transport similarity index calculation method suitable for mobile phone signaling track data, which is used as a core characteristic of public transport trip judgment and track line matching on one hand, and the index calculation is convenient to realize and is simple and suitable for large sample data; on the other hand, the index calculation method not only represents the similarity degree of the track and the line trend in space, but also takes the bus operation time into consideration, and reduces the misjudgment rate in the non-line operation time period; secondly, judging the travel mode according to the bus similarity and the average speed of the track, and eliminating the non-bus travel track; finally, when the bus routes are matched, the influence of the bus fare and the departure frequency on the travel selection behavior of residents is considered, the passenger flow identification precision of each route is improved, and data support is provided for refined passenger flow OD analysis.
Drawings
FIG. 1 is a flow chart of the method of the present invention
FIG. 2 is a cross-sectional view showing the OD information of the passenger flow of the site in the network section according to the embodiment
Detailed Description
The invention is further illustrated by the following examples, without limiting the scope of the invention:
the invention takes the bus passenger flow OD of Kunshan city in Jiangsu province as an example for analysis. The following detailed description of the embodiments of the present invention refers to the accompanying drawings 1 and the technical solutions of the present application:
1. data acquisition
(1) Mobile phone signaling data
The method comprises the steps of obtaining mobile operator signaling data according to S1, and triggering signaling events when a user uses a mobile phone, wherein the signaling data are recorded in calling, called, location area switching and short messages. The signaling data must contain a unique ID number, date, time, base station number, latitude and longitude. Taking the mobile operator mobile phone signaling data of mobile operators of 2019, 5 month and 22 days in Jiangsu province as an example, the format of the original mobile phone signaling data acquired by the present example is shown in the following table.
(2) Base station information
In step S2, the base station information is obtained from the mobile operator, and includes the base station number and the base station longitude and latitude. And matching with the bus route stop information to obtain a base station-route stop distance matrix as a basic data information table for calculating the bus route similarity.
(3) Bus route stop information
In step S1, the bus route stop information is directly crawled through an API interface provided by germany, and the main purpose is to perform route similarity matching on the trajectory and identify the stop of the bus getting on or off the bus. The basic format of the bus route station (taking Kunshan 130 routes as an example) crawled by the present embodiment is shown as the following table:
the bus route station information should include a route name, a station longitude, and a station latitude.
2. And (6) data processing.
(1) Mobile phone signaling data preprocessing
Because 4G mobile phone signaling trigger events are various, repeated redundant positioning data can exist in original mobile phone signaling data, the original data is firstly cleaned according to a data cleaning rule, the data comprises ping-pong data, drift data and repeatedly positioned base station data, and effective data with higher quality is obtained after cleaning.
(2) Travel OD information extraction
In step S2, a base station service radius of 800m is respectively selected, a parking time threshold is set to 40min, spatial-temporal clustering is performed on the travel trajectory of each user, and a set of parking points S ═ S { S ═ S on the current day is obtained1,…,SnArranging according to a time sequence, wherein adjacent parking points respectively form a starting point O point and an end point D point of a trip, and form OD data of the trip together with track data between two points in an original track, as shown in the figure, wherein order is 1 and 8 and respectively forms two parking points, and 1 to 8 form a complete track sequence:
(3) base station-bus line station distance matrix calculation
According to the step S2, calculating the linear distance D between each two base stations and the bus stop based on the previously acquired base station position information table and the line stop information table; for each base station, extracting the station with the minimum distance from the base station from all bus stations contained in the bus line, recording the distance value, traversing all the bus lines and the base stations in sequence, and finally obtaining a base file with a distance matrix between the base station and the bus line station as similarity calculation.
3. Trajectory bus similarity calculation
According to the step S3, for each bus route, firstly calculating the route similarity of the route and the track, finally determining the bus similarity index of the route, sequentially calculating the route similarity of each base station track and each bus route, and according to the bus stop density and the base station coverage density in Kunzan city, taking the distance threshold D as 800m when calculating the bus similarity, namely when the nearest distance between the base station positioning point and the bus stop of the route is less than 800m, considering that the matching is successful. Wherein, the maximum value of the line similarity is the bus similarity index of the track.
The obtained part of track similarity is shown in the table, and the last column of bus _ similarity is the bus similarity value of the track:
4. screening bus travel track
According to the step S4, the track data of non-bus trips are eliminated based on the bus similarity index and the track average speed of the track, the track data with the similarity index larger than 80% and the track average trip speed between 15km/h and 25km/h are reserved according to the running characteristics of the Kun mountain bus, and finally the track information sets of all bus trips are obtained.
5. Trajectory matching line determination
Firstly, the operation time, departure shift and ticket price information of each bus line in Kun mountain are obtained as shown in the following table:
according to the step S51, the fares of the bus routes are classified, the route with the highest fare of 1 Yuan is a one-ticket route, and the route with the highest fare of more than 1 Yuan is marked as a multi-ticket route.
According to the step S52, on the basis of the normal interval time of the bus lines, lines which are less than 10 minutes are marked as high frequency, lines which are 10-20 minutes are marked as medium frequency, and lines which are more than 20 minutes are marked as high frequency.
According to the step S53, for the trajectory determined as the bus trip in the step S4, the bus route with the route similarity greater than 0.8 in the step S32 is extracted, and the route matching index M is calculated, wherein the calculation formula is as follows:
Mi＝αTi+βFi+γsimilarityi
wherein α, β and gamma are respectively 0.3, 0.2 and 0.5, and T isiAnd FiThe fare and departure frequency of the line bus are respectively, and the value-taking rule is as follows:
corresponding MiThe line with the largest value is a bus line successfully matched with the track, and finally the matched lines of all bus travel tracks are obtained as shown in the following table:
6. trajectory entry and exit station determination
According to the step S6, extracting the base station-site distance set calculated in the step S31 corresponding to the matching line determined in the step S5; and the station corresponding to the first base station successfully matched with the line station in the set is the getting-on station of the user, the station corresponding to the last base station successfully matched with the line station is the getting-off station of the user, and the information of the getting-on and getting-off stations of each track is sequentially acquired.
7. Bus passenger flow OD acquisition
And (4) summarizing the information of the upper and lower stop points of the track according to the step S7 to finally obtain the net bus passenger flow OD, wherein a screenshot of the information of the net bus passenger flow OD of the net partial stop points of 5-month-22-year-2019 is shown in FIG. 2.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (8)
1. A bus passenger flow OD obtaining method based on mobile phone signaling data is characterized by comprising the following steps:
s1, acquiring mobile phone signaling data and bus route stop information;
s2, extracting user travel track information and a distance matrix between a base station and a line site;
s3, calculating the line similarity of the track and each bus line, and finally determining the bus similarity index of the travel track;
s4, eliminating track data of non-bus trips according to the bus similarity index and the track average speed;
s5, calculating a matching index of the bus travel track possibly matched with the route according to the bus similarity index, the route fare and the route departure interval, and finally determining the bus route matched with the track;
s6, judging the station points of the track by combining the track base station and the station position information of the matched line;
and S7, respectively summarizing station passenger flow OD of each bus line of the whole line network as bus passenger flow OD.
2. The method for obtaining the bus passenger flow OD based on the mobile phone signaling data as claimed in claim 1, wherein the step S1 of obtaining the mobile phone signaling data and the bus route stop information comprises the following steps:
s11, acquiring user mobile phone signaling data in a research time range of a research area and a base station position information table in a corresponding research range, wherein the base station position information table comprises base station ID, longitude and latitude fields;
and S12, crawling the bus route and stop information table in the research range from the high-grade map, wherein the bus route and stop information table comprises a route name, a route stop name and stop longitude and latitude coordinates.
3. The method for obtaining the OD of the public transport passenger flow based on the signaling data of the mobile phone as claimed in claim 1, wherein the step S2 is to extract the travel track information of the user and the distance matrix between the base station and the station, comprising the following steps:
s21, extracting user travel track information, namely determining a time-space threshold value according to the definition of one travel, dividing original mobile phone signaling data into independent travel track data, wherein one travel track data is composed of a string of base station position points, and comprises base station coordinates, and start time and end time for triggering the base station;
s22, calculating the linear distance D between each two base stations and the bus stop according to the base station position information table and the line and stop information table;
s23, for each base station, extracting the station with the minimum distance from the base station from all bus stations contained in the bus line, recording the distance value, traversing all the bus lines and the base stations in sequence, and finally forming a distance matrix between the base station and the bus station;
s24, correcting the distance matrix between the base station and the bus line station: and obtaining a base station-bus line station distance matrix of each time period according to the bus line operation time, namely dividing the time periods according to the bus line operation time, and if a certain line is not in the operation time period, modifying the distances between the line and all base stations in the distance matrix to be positive infinity.
4. The method as claimed in claim 1, wherein step S3 is a step of calculating the line similarity between the trajectory and each bus route, and finally determining the bus similarity index of the trajectory, and comprises the steps of:
s31, for one travel track, according to the base station-bus line station distance matrix in the S2, obtaining a base station-station distance set D corresponding to each line, wherein D is { D ═ D }1,D2,..Di,..,DnIn which D isiThe distance between the ith base station in the track and the nearest station of the line is represented, and the distance is derived from the time interval base station-bus line station distance matrix corresponding to the departure time of the track calculated in the step S2;
s32, when DiIf the number of the base station i is less than a certain threshold value, the base station i is considered to be successfully matched with the line site, and D in the set is calculatediThe ratio of the number smaller than the threshold to the total number of base stations is defined as the similarity between the track and the linei；
And S33, sequentially traversing all the bus lines for a travel track, and respectively calculating the line similarity of the travel track, wherein the maximum value of the line similarity is the bus-similarity index of the travel track.
5. The method for obtaining the bus passenger flow OD based on the mobile phone signaling data as claimed in claim 1, wherein the step S4 is to remove the trajectory data of the non-bus trip according to the bus similarity index and the average trajectory speed, and the method comprises the following steps:
s41, calculating the average speed of the track trip, namely the ratio of the track trip distance to the track trip time consumption;
s42, determining upper and lower threshold values of the bus travel speed according to the average bus travel speed distribution in the research range, simultaneously determining a bus similarity index threshold value, marking the track meeting the conditions that the average speed is greater than the bus similarity index threshold value and is at the upper and lower threshold values of the bus travel speed as a bus travel, reserving the record, and eliminating other travel tracks which do not meet the conditions.
6. The method for obtaining the bus passenger flow OD based on the mobile phone signaling data as claimed in claim 1, wherein the step S5 of calculating the matching index of the possible matching route of the bus travel track, and finally determining the bus route matched with the track, comprises the following steps:
s51, classifying the one-ticket system and multi-ticket system routes in the research range;
s52, dividing the lines into three categories of high frequency, medium frequency and low frequency according to departure intervals of the lines;
s53, for the travel track reserved in S3, extracting the bus routes with the route similarity larger than a certain threshold, and respectively calculating the matching index M of the route i by combining the route fare and the departure interval frequencyiThe calculation formula is as follows:
Mi＝αTi+βFi+γsimilarityi
wherein α, β and gamma are respectively public transport fare utility, departure interval utility and public transport line similarity value utility, and are calibrated according to user travel selection behaviors in a research range, and the suggested values are respectively 0.3, 0.2 and 0.5, similarityiThe route similarity of the trajectory obtained in S3 and the route; t isiAnd FiThe fare and departure frequency of the line bus are respectively, and the value-taking rule is as follows:
s54, corresponding MiThe line with the largest value is the bus line successfully matched with the track.
7. The method as claimed in claim 4, wherein the step S6 of determining the station points of the track according to the station position information of the track base station and the matching line comprises the following steps:
s61, extracting a base station-site distance set calculated in the step S31 corresponding to the matching line determined in the S5 for one travel track;
s62, the station corresponding to the first base station successfully matched with the line station in the set is the getting-on station of the user, the station corresponding to the last base station successfully matched with the line station is the getting-off station of the user, and the definition of successful matching is shown in step S32.
8. The method as claimed in claim 1, wherein step S7 summarizes the station passenger flow OD of each bus route and the station passenger flow OD of the global network, respectively, comprising the steps of:
s71, converging the track getting on and off bus stops successfully matched with each bus line within the research time range of each bus line, and acquiring a line passenger flow OD matrix;
and S72, traversing all the bus lines in sequence, converging the passenger flow OD matrix, and finally obtaining the bus passenger flow OD.
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| CN202010525192.3A CN111653099B (en) | 2020-06-10 | 2020-06-10 | Bus passenger flow OD obtaining method based on mobile phone signaling data |
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| CN112288131A (en) * | 2020-09-24 | 2021-01-29 | 和智信(山东)大数据科技有限公司 | Bus stop optimization method, electronic device and computer-readable storage medium |
| CN112530166A (en) * | 2020-12-01 | 2021-03-19 | 江苏欣网视讯软件技术有限公司 | Method and system for analyzing and identifying bus station for getting on or off bus during traveling based on signaling data and big data |
| CN112530166B (en) * | 2020-12-01 | 2021-11-05 | 江苏欣网视讯软件技术有限公司 | Method and system for analyzing and identifying bus station for getting on or off bus during traveling based on signaling data and big data |
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| CN112601187A (en) * | 2020-12-10 | 2021-04-02 | 江苏欣网视讯软件技术有限公司 | Bus frequent passenger prediction method and system based on mobile phone signaling |
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| CN113766430A (en) * | 2021-09-14 | 2021-12-07 | 广州瀚信通信科技股份有限公司 | Urban rail congestion analysis method and device based on 5G network |
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| CN117275274A (en) * | 2023-11-20 | 2023-12-22 | 河北省交通规划设计研究院有限公司 | Conventional bus trip information identification method, device and medium |
| CN117275274B (en) * | 2023-11-20 | 2024-02-02 | 河北省交通规划设计研究院有限公司 | Conventional bus trip information identification method, device and medium |
| CN118708825A (en) * | 2024-08-28 | 2024-09-27 | 山东科技大学 | Subway transfer passenger flow calculation method based on electronic map application program interface |
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