CN108320501B - Bus route identification method based on user mobile phone signaling - Google Patents
Bus route identification method based on user mobile phone signaling Download PDFInfo
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- CN108320501B CN108320501B CN201711393085.4A CN201711393085A CN108320501B CN 108320501 B CN108320501 B CN 108320501B CN 201711393085 A CN201711393085 A CN 201711393085A CN 108320501 B CN108320501 B CN 108320501B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/015—Detecting movement of traffic to be counted or controlled with provision for distinguishing between two or more types of vehicles, e.g. between motor-cars and cycles
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Abstract
The invention relates to a public transport line identification method based on user mobile phone signaling, belonging to the technical field of mobile phone signaling collection traffic volume; the central processing unit is connected with the mobile phone signaling data acquisition platform, the real-time traffic processing system, the big data analysis system and the real-time module; the mobile phone signaling data acquisition platform is connected with the real-time traffic processing system; the real-time traffic processing system is connected with the big data analysis system; the real-time module is connected with the real-time traffic processing system and the big data analysis system; the mobile phone signaling data acquisition platform is connected with the traffic mobile platform; the traffic mobile platform is connected with the map API module; by acquiring the data of the map and the public transport and utilizing the geometric relation among the signaling data, the user track and the public transport route, the trip information of the user is judged, and the trip distance and the trip speed of the user can be accurately calculated.
Description
Technical Field
The invention relates to a public transport line identification method based on user mobile phone signaling, and belongs to the technical field of mobile phone signaling traffic collection.
Background
With the rapid development of information technology, numbers come from cities and smart cities, and the digital cities and the smart cities promote the development of technologies such as mobile internet, internet of things and cloud computing, directly promote the formation and development of traffic big data, and provide a new way for a traffic data acquisition method. The traffic information acquisition technology based on mobile phone positioning has the advantages of wide coverage range, large sample size, strong real-time data, less infrastructure re-investment, positioning accuracy meeting the traffic information acquisition accuracy and the like, so that the technology is generally concerned by domestic and foreign traffic research institutions. Under the development situation that the coverage of mobile communication networks is wider and the scale of mobile phone users is larger, the traffic information acquisition technology based on the mobile phone positioning technology has wide application prospect.
The user carries the mobile phone with him, and the movement of the mobile phone accurately reflects the activity rule of the user. Through analyzing the continuous mobile phone signaling, the situation that the word investigation can only acquire the information of resident trips at a specific time point can be avoided, and the time and space characteristics of user activities are comprehensively mastered.
At present, most of analyzed traffic travel modes of users pass through questionnaires, video monitoring portrait recognition and IC card data statistics. However, the statistical methods have certain defects, manual investigation can only obtain resident travel information at specific time, and the number of people is small, so that data cannot be obtained for a long time; the video monitoring has low recognition accuracy and has the possibility of dead angles. The IC card statistical data is used for identification, and the complex transfer or non-outbound round trip conditions cannot be well counted, so that the limitation is large.
Disclosure of Invention
The invention aims to provide a method for identifying a bus route based on user mobile phone signaling, aiming at the defects and shortcomings of the prior art, and the method can effectively identify the travel sections belonging to the bus in daily travel of residents.
In order to achieve the purpose, the invention adopts the technical scheme that: the system comprises a power supply module, a central processing unit, a mobile phone signaling data acquisition platform, a real-time traffic processing system, a big data analysis system, a real-time module, a traffic moving platform, a map API module, a user track and traffic line matching module and a judgment module; the power supply module is connected with the central processing unit; the central processing unit is connected with the mobile phone signaling data acquisition platform, the real-time traffic processing system, the big data analysis system and the real-time module; the mobile phone signaling data acquisition platform is connected with the real-time traffic processing system; the real-time traffic processing system is connected with the big data analysis system; the real-time module is connected with the real-time traffic processing system and the big data analysis system; the mobile phone signaling data acquisition platform is connected with the traffic mobile platform; the traffic mobile platform is connected with the map API module; the user track and traffic line matching module is connected with the traffic mobile platform and the map API module; the judging module is connected with the user track and traffic route matching module.
The invention discloses a public transport line identification method based on user mobile phone signaling, which comprises the following operation steps:
step 1, dividing a city by using a GeoHash grid, wherein the city can be divided into small grids with the length of X meters and the width of Y meters, numbering each small grid, and recording each small grid as QUOTE
Wherein QUOTE
For the number of rows, QUOTE, in the coverage area S of the small grid
The number of columns of the small grid in the coverage area S;
step 2, obtaining traffic road information of the bus, and selecting continuous roads of the bus line as characteristic lines of the bus for each bus line through a certain map API and based on a certain region selection rule;
step 3, acquiring signaling switching data reported by the user in one day, and extracting motion track points P (1) and P (2) · P (n) of the user and grids corresponding to the points; if the user has continuous and repeated reported data under the same base station and the total duration exceeds the set threshold time t, clustering by using a circle with the radius of R and the base station as a base point to obtain a user travel stagnation point; according to the stagnation point, the user is given oneDividing the track of the sun trip to obtain the QUOTE of the track section
(ii) a Optimizing a signaling switching track of a user based on a certain rule to obtain a relatively smooth moving track and a corresponding track point;
step 4, mapping the mobile phone signaling position coordinates in the track segment of the user in a GeoHash grid, recording effective travel information by using the time sequence and the grid coordinates, and optimizing the track;
step 5, screening the tracks, and if the tracks are too short and are not in the GeoHash range, not considering the tracks;
step 6, matching the track segments of the users with the bus routes, and recording the track segments and the bus routes as H if the track segments and the bus routes can be successfully matched;
step 7, arranging the bus routes taken by the user, or judging whether the user takes a private car or not;
and 8, classifying a track section according to the route of the bus in the previous step, calculating the average speed in the period of time, and if the average speed is greater than a threshold value, determining that the user necessarily takes the bus.
Preferably, in step 6, the user track segment is matched with the bus route: and optimally slicing the track of the user, matching the bus lines by a geometric method, and adding the bus lines into the alternative set H if the matching is successful.
Preferably, in step 6, the user trajectory is optimized according to the included angle of the user trajectory segment, and whether the partial segment can be deleted can be judged according to the cosine value of the included angle of the continuous segment.
Preferably, in step 6, the segment of the user track is calculated to be matched with the bus route, and whether the user is matched with the bus route and the transfer relation are judged according to the geometric relationship, the speed threshold and the transfer times of the segment of the track and the bus route segment.
Preferably, the handover trajectory of the user base station in step 6 is specifically a base station handover sequence generated by the user of the mobile communication network due to location update in the communication network.
The mobile phone signaling data is subjected to signal acquisition and backup by the central processing unit. The mobile user data is processed by the mobile data platform and is matched with possible bus routes. Through mobile phone signaling of the mobile user and a data platform system, the mobile track of the user, whether the user takes a motor vehicle or not and whether the user takes a certain bus or not can be fully analyzed. The system is convenient for management departments to obtain and accurately obtain the travel information of the user, can improve the traffic condition and know the traffic change condition
After adopting the structure, the invention has the beneficial effects that: according to the method for identifying the bus route based on the mobile phone signaling of the user, the trip information of the user is judged by acquiring the map and the data of the bus and utilizing the geometric relation among the signaling data, the user track and the bus route, and the trip distance and the trip speed of the user can be accurately calculated.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a structural frame diagram of the present invention;
FIG. 2 is a matching graph of a user trajectory and a road trajectory in the present invention;
description of reference numerals:
the system comprises a power supply module 01, a central processing unit 02, a mobile phone signaling data acquisition platform 03, a real-time traffic processing system 04, a big data analysis system 05, a real-time module 06, a traffic moving platform 07, a map API module 08, a user track and traffic line matching module 09 and a judgment module 10.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
Referring to fig. 1 and fig. 2, the embodiment includes a power module 01, a central processing unit 02, a mobile phone signaling data acquisition platform 03, a real-time traffic processing system 04, a big data analysis system 05, a real-time module 06, a traffic moving platform 07, a map API module 08, a user trajectory and traffic route matching module 09, and a determination module 10; the power supply module 01 is connected with the central processing unit 02; the central processing unit 02 is connected with a mobile phone signaling data acquisition platform 03, a real-time traffic processing system 04, a big data analysis system 05 and a real-time module 06; the mobile phone signaling data acquisition platform 03 is connected with the real-time traffic processing system 04; the real-time traffic processing system 04 is connected with the big data analysis system 05; the real-time module 06 is connected with the real-time traffic processing system 04 and the big data analysis system 05; the mobile phone signaling data acquisition platform 03 is connected with the traffic mobile platform 07; the traffic mobile platform 07 is connected with a map API module 08; the user track and traffic route matching module 09 is connected with the traffic mobile platform 07 and the map API module 08; the judging module 10 is connected with the user track and traffic line matching module 09.
The operation steps of the embodiment are as follows:
step 1, dividing a city by using a GeoHash grid, wherein the city can be divided into small grids with the length of X meters and the width of Y meters, numbering each small grid, and recording each small grid as QUOTE
Wherein QUOTE
For the number of rows, QUOTE, in the coverage area S of the small grid
The number of columns in the coverage area S for the small grid.
And 2, acquiring traffic road information of the bus. And selecting continuous roads of the bus routes as the characteristic routes of the buses for each bus route based on a certain region selection rule through a certain map API.
The selection of the characteristic road section has the following rules:
(1) cutting the bus characteristic segment into a plurality of road segments by taking the road intersection as a cutting point, and cutting a long and straight road according to the condition;
(2) the road segments which are taken as the characteristics of a single bus need to be kept continuous as much as possible, and certain road segments are too short to be taken as a subset of a characteristic road segment set;
(3) different bus lines can share a certain road segment, but the characteristic segment set needs to uniquely identify a specific bus line.
And 3, mapping the mobile phone signaling position coordinates in the track segment of the user in a GeoHash grid, and recording effective travel information by using the time sequence and the grid coordinates. And recording the small grids passed by the bus according to the bus running sequence. If the distance that the bus travels in a certain grid is too short, the grid is deleted.
And 4, acquiring signaling switching data reported by the user in one day, and extracting motion track points P (1) and P (2) ·. If the user has continuous and repeated reported data under the same base station and the total time length exceeds the set threshold time t, clustering is carried out by using a circle with the radius of R and the base station as a base point to obtain a standing point of the user in trip. According to the stagnation point, the track of the user going out one day is divided to obtain a set QUOTE of track sections
. And then screening the whole section of track, and if the track is not in Nanjing urban area, too short or the average speed in the section does not meet the corresponding index requirement, not analyzing the section of track. And optimizing the signaling switching track of the user based on a certain optimization mode to obtain a relatively smooth moving track and corresponding track points.
The optimization method comprises the following steps:
for each track segment m (i), three consecutive reporting positions are selected, which contain two switching messages, so that two switching vectors can be generated.
QUOTE 
， QUOTE 
Recalculating included angle information of handover vectors
Optimizing the switching track by using the included angle as confidence coefficient, and obtaining an ordered included angle set QUOTE by calculating the included angle between continuous track points
If there is a round trip of the user's signaling data on this track segment, some consecutive quantes can be derived
Is close to 1 or-1, the piece of data may be deleted except for the last point. After multilayer optimization, a relatively smooth moving track can be obtained.
Step 5, further using QUOTE
Is a threshold, if the corner of the track is greater than the threshold, then the track segment is segmented at this point. This step cuts the trajectory into a set of relatively straight segments for matching with road segments.
Step 6, for each fragment, deriving a mesh QUOTE containing this fragment
Since the signal has error, S needs to be extended for each QUOTE
Adding QUOTE to S
All grids adjacent up, down, left and right extend S. At publicAnd finding out the bus routes passing through the grids in the S in the bus routes, and only taking the bus route part contained in the S. And calculating the matching degree of the track sections and the characteristic road sections one by one according to the sections and the bus lines. And if the included angle between the segment and the bus line segment is less than pi/6 and the average distance is less than a preset threshold (some points can be taken out from the bus line, the segment is perpendicular to the segment, and the average distance is calculated), the two segments are considered to have a matching relation. Recording the bus lines with matching relation with the segments as H, QUOTE
Each of which is QUOTE
All represent a bus line, QUOTE
And representing the bus lines matched with the roads in a road section, and recording as (0) if no matched bus line exists.
And 7, obtaining the segments of the buses which the user may take after the operation of the step 6. We identify which buses the user specifically takes according to the following principle.
a. The two bus routes may have overlapped parts, and the judgment can be performed by using the longest principle, namely, if the track of the user is matched with the n bus routes, the bus route with the longest matching length is selected. In H, QUOTE is calculated first
When each element in the H is continuously present in other sets for the maximum times, and the k-th item is set to be finished, deleting the previous k items in the H to obtain a new H. This is repeated for H until H is an empty set.
b. If a longer characteristic road section appears on a certain matched bus route, the starting point and the end point of the appearance of the characteristic route are calculated according to time, and the user can be considered to take the bus route or walk in the time. And if the proportion of the user without the bus line is calculated to be more or the times are changed to be more, the user can be considered to take a private car or walk.
And 8, classifying a track section according to the route of the bus in the previous step, calculating the average speed in the period of time, and if the average speed is greater than a threshold value, determining that the user necessarily takes the bus.
The above description is only for the purpose of illustrating the technical solutions of the present invention and not for the purpose of limiting the same, and other modifications or equivalent substitutions made by those skilled in the art to the technical solutions of the present invention should be covered within the scope of the claims of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (3)
1. A public transport line identification method based on user mobile phone signaling is characterized in that: the operation steps are as follows:
step 1, dividing a city by using a GeoHash grid, wherein the city can be divided into small grids with the length of X meters and the width of Y meters, numbering each small grid, and recording each small grid as G (X)iYj) Wherein X isiFor the number of rows, Y, of the small grid in the coverage area SjThe number of columns of the small grid in the coverage area S;
step 2, obtaining traffic road information of the bus, and selecting continuous roads of the bus line as the characteristic line of the bus for each bus line through a certain map API and based on a certain selection rule;
step 3, acquiring signaling switching data reported by a user in one day, mapping the position coordinates of the mobile phone signaling in the track segment of the user in a GeoHash grid, and recording effective trip information by using a time sequence and the coordinates of the grid;
step 4, extracting motion track points P (1) and P (2).. P (n) of the user and grids corresponding to the points; if the user has continuous and repeated reported data under the same base station and the total duration exceeds the set threshold time t, clustering by using a circle with the radius of R and the base station as a base point to obtain a user travel stagnation point; dividing a track of a user going out one day according to a stagnation point to obtain M = { M (1), M (2), M (3).. M (k) } of a track section; optimizing a signaling switching track of a user based on a certain rule to obtain a relatively smooth moving track and a corresponding track point;
firstly, optimally slicing the track of a user, and then adding the track into an alternative set H if matching is successfully carried out on the bus lines by a geometric method;
optimizing the user track according to the included angle of the user track segment, and judging whether partial segments can be deleted according to the cosine value of the included angle of the continuous segment;
the optimization method comprises the following steps:
for each track segment m (i), three consecutive reporting positions are selected, which contain two switching messages, so that two switching vectors can be generated:
recalculating included angle information of handover vectors
Optimizing the switching track by using the included angle as confidence coefficient, and obtaining an ordered included angle set by calculating the included angle between continuous track points
If there is a round trip of the user's signaling data on this track segment, some continuity can be obtained
Is close to 1 or-1, the piece of data can delete the data except the last point; after multilayer optimization, a relatively smooth moving track can be obtained;
step 5, screening the tracks, and if the tracks are too short and are not in the GeoHash range, not considering the tracks;
step 6, arranging the bus routes taken by the user, or judging whether the user takes a private car or walks;
and 7, classifying a track section according to the route of the bus according to the steps, calculating the average speed in the period of time, and if the average speed is greater than a threshold value, determining that the user necessarily takes the bus.
2. The method for identifying the bus route based on the mobile phone signaling of the user according to claim 1, characterized in that: in the step 4, whether the user is matched with the bus route and the transfer relation are judged by means of the geometric relation, the speed threshold and the transfer times of the track segments and the bus route segments when the segments of the user track are matched with the bus route.
3. The method for identifying the bus route based on the mobile phone signaling of the user according to claim 1, characterized in that: the handover trajectory of the user base station in step 4 is specifically a base station handover sequence generated by the mobile communication network user due to location update in the communication network.
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| CN111435570B (en) * | 2019-01-11 | 2021-12-28 | 阿里巴巴集团控股有限公司 | Bus route matching method and device |
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| CN112511971B (en) * | 2020-11-26 | 2022-08-16 | 西安建筑科技大学 | Travel mode identification method based on mobile phone signaling 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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