CN109243175A - High speed algorithm based on mobile big data - Google Patents
High speed algorithm based on mobile big data Download PDFInfo
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- CN109243175A CN109243175A CN201811068770.4A CN201811068770A CN109243175A CN 109243175 A CN109243175 A CN 109243175A CN 201811068770 A CN201811068770 A CN 201811068770A CN 109243175 A CN109243175 A CN 109243175A
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- user
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- high speed
- big data
- road
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Classifications
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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
-
- 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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
-
- 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
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
-
- 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
- G08G1/056—Detecting movement of traffic to be counted or controlled with provision for distinguishing direction of travel
Abstract
The present invention proposes that key step includes: S1 based on the high speed algorithm of mobile big data: according to pre-set " move road and lead to high speed signaling system cell resource table ", the CDR data belonged on highway are filtered out from CDR data source；S2: setting high-speed road travels user according to table；S3: judge whether it is expressway traveling user；S4: direction initialization table judges user's driving direction；S5: it calculates single user and is averaged movement speed；S6: average movement speed of multiple users on single section is calculated；S7: the network downstream rate and speech perception percent of call completed on each section are counted.It is existing to the longer disadvantage of the feedback delay of traffic information that the present invention solves highway monitoring system in the prior art, it can more accurately judge the congestion of road, real-time traffic information is provided for user, make the more suitable unobstructed road of its selection, or change existing route in time, it avoids and congestion in road is further aggravated, improve the utilization rate of road.
Description
Technical field
The present invention relates to field of intelligent transportation technology, more particularly, to a kind of high speed algorithm based on mobile big data.
Background technique
With the continuous development of freeway traffic grid, highway has become the first choice of people's trip, and with vapour
Vehicle is gradually popularized, and the vehicle flowrate of highway is also increasing, be gradually introduced highway monitoring system to traffic information into
Row prompt, facilitates user to select convenient and fast traffic path, improves out line efficiency.But current highway monitoring system is to road conditions
The feedback delay of information is longer, cannot timely feedback to real-time road condition information, cannot meet the needs of users.
Summary of the invention
The present invention in order to overcome the deficiencies of the prior art, provides a kind of high speed algorithm based on mobile big data, can be more
Accurately judge the congestion of road, and real-time road can timely be fed back to user.
In order to solve the above technical problems, technical scheme is as follows:
High speed algorithm based on mobile big data, which is characterized in that it the following steps are included:
S1: according to preset " move road and lead to high speed signaling system cell resource table ", from CDR (Call Detail
Record, call detail record) the CDR data belonged on highway are filtered out in data source；
S2: setting high-speed road travels user according to table；
S3: judge whether it is expressway traveling user；
S4: direction initialization table judges user's driving direction；
S5: it calculates single user and is averaged movement speed；
S6: average movement speed of multiple users on single section is calculated；
S7: the network downstream rate and speech perception percent of call completed on each section are counted.
Further, the traveling user of setting high-speed road described in step S2 is according to table, the method is as follows: takes the institute of expressway
There is cell, calculates the distance of neighboring community, and be divided into a section with 10~20 km distances, cell is corresponding with section to close
It is table is expressway traveling user according to table.
Further, expressway traveling user is judged whether it is described in step S3, the method is as follows: rejecting expressway
In the case where upper resident user, found by the data after 6 CDR table data and high speed cell dimension table, source purpose cell association
User before system time M minutes to there is two records or more in N minutes first, be judged as that expressway travels user, wherein M > N
> 0.
Further, 6 CDR tables are respectively: AIU_MM, AIU_MOC, AIU_MTC, AIU_MOSMS, AIU_MTSMS,
S1-mme；The value of M is that the value of 40, N is 20.
Further, the definition of the resident user is: daily 3~4 points of the period of morning, residing in high speed always
User on some coverage cell of highway.
Further, the high speed cell dimension table is highway coverage cell table；The source purpose cell is to use
The coverage cell that family uses；Two records refer to that there are two the above cell CGI (Common in the purpose cell of source
Gateway Interface, CGI(Common gateway interface)) it is identical as the CGI of cell in high speed cell dimension table；The CGI includes CI
(Cell Identity, cell identification) and LAC (LocationArea Code, position area identification code).
Further, step S4 is comprised the concrete steps that: " being moved road by what is pre-set and is led to high speed signaling system cell money
Source table " and CDR data, user in chronological order using to coverage cell compare, if nearest coverage cell sequence
A coverage cell serial number positive number number is subtracted, determines that the direction of user's traveling is forward direction, is otherwise determined as reversed.
Further, step S5 specifically includes the following steps:
S51: the CDR of expressway traveling user is extracted, CDR is associated with to obtain source purpose cell with high speed cell dimension table；
S52: the CDR of user on each section is extracted from the purpose cell of source；
S53: calculating the average speed of expressway traveling user, and calculation formula is as follows:
Wherein, T is the maximum time point of adjacent two records and the difference of minimum time point, when L is maximum time point, is minimum
Between point with CGI be associated with after obtain at a distance from point-to-point transmission, user velocity is more than that 140km/h is set as 120km/h.
Further, step S6 specifically includes the following steps:
S61: the user number N on single section is counted；
S62: the speed V of user i on single section is countedi, i=1,2 ..., N；
S63: average movement speed on single section is soughtFormula is as follows:
Further, user described in step S5 and step S6 does not include the user that speed is 0 or 200km/h.
Further, the method for step S7 is: the coverage cell in each section is associated with KPI (Key Performance
Indicator, KPI Key Performance Indicator) in database, it can directly extract the downstream rate of coverage cell, speech perception is connected
Rate.
Compared with prior art, the beneficial effect of technical solution of the present invention is: the feelings of resident user on rejecting expressway
Under condition, user is found in system time by the data after 6 CDR table data and high speed cell dimension table and source purpose cell association
There are two records or more in first 40 minutes to first 20 minutes, be judged as that expressway travels user, be effectively reduced and calculate the time,
It can more accurately judge the congestion of road, and real-time road can timely be fed back to user.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow charts of the high speed algorithm embodiment of mobile big data.
Specific embodiment
The attached figures are only used for illustrative purposes and cannot be understood as limitating the patent；
In order to better illustrate this embodiment, the certain components of attached drawing have omission, zoom in or out, and do not represent actual product
Size；
To those skilled in the art, it is to be understood that certain known features and its explanation, which may be omitted, in attached drawing
's.
The following further describes the technical solution of the present invention with reference to the accompanying drawings and examples.
As shown in Figure 1, the present invention is based on the high speed algorithms of mobile big data, comprising the following steps:
S1: according to preset " move road and lead to high speed signaling system cell resource table ", from CDR (Call Detail
Record, call detail record) the CDR data belonged on highway are filtered out in data source；
S2: setting high-speed road travels user according to table；
S3: judge whether it is expressway traveling user；
S4: direction initialization table judges user's driving direction；
S5: it calculates single user and is averaged movement speed；
S6: average movement speed of multiple users on single section is calculated；
S7: the network downstream rate and speech perception percent of call completed on each section are counted.
Specifically, the traveling user of setting high-speed road described in step S2 is according to table, the method is as follows: takes all of expressway
Cell calculates the distance of neighboring community, and is divided into a section, the corresponding relationship of cell and section with 10~20 km distances
Table is expressway traveling user according to table.
Specifically, expressway traveling user is judged whether it is described in step S3, the method is as follows: on rejecting expressway
In the case where resident user, use is found by the data after 6 CDR table data and high speed cell dimension table, source purpose cell association
Family before system time M minutes to there is two records or more in N minutes first, be judged as that expressway travels user, wherein M > N >
0。
Specifically, 6 CDR tables are respectively: AIU_MM, AIU_MOC, AIU_MTC, AIU_MOSMS, AIU_MTSMS, S1-
mme；The value of M is that the value of 40, N is 20.
Specifically, the definition of the resident user is: daily 3~4 points of the period of morning, it is public to reside in high speed always
User on some coverage cell of road.
Specifically, the high speed cell dimension table is highway coverage cell table；The source purpose cell is user
Use the coverage cell arrived；Two records refer to that there are two the above cell CGI (Common in the purpose cell of source
Gateway Interface, CGI(Common gateway interface)) it is identical as the CGI of cell in high speed cell dimension table；The CGI includes CI
(Cell Identity, cell identification) and LAC (LocationArea Code, position area identification code).
Specifically, step S4 is comprised the concrete steps that: " being moved road by what is pre-set and is led to high speed signaling system cell resource
Table " and CDR data, user in chronological order using to coverage cell compare, if nearest coverage cell serial number
A coverage cell serial number positive number is subtracted, determines that the direction of user's traveling is forward direction, is otherwise determined as reversed.
Specifically, step S5 specifically includes the following steps:
S51: the CDR of expressway traveling user is extracted, CDR is associated with to obtain source purpose cell with high speed cell dimension table；
S52: the CDR of user on each section is extracted from the purpose cell of source；
S53: calculating the average speed of expressway traveling user, and calculation formula is as follows:
Wherein, T is the maximum time point of adjacent two records and the difference of minimum time point, when L is maximum time point, is minimum
Between point with CGI be associated with after obtain at a distance from point-to-point transmission, user velocity is more than that 140km/h is set as 120km/h.
Specifically, step S6 specifically includes the following steps:
S61: the user number N on single section is counted；
S62: the speed V of user i on single section is countedi, i=1,2 ..., N；
S63: average movement speed on single section is soughtFormula is as follows:
Specifically, user described in step S5 and step S6 does not include the user that speed is 0 or 200km/h.
Specifically, the method for step S7 is: the coverage cell in each section is associated with KPI (Key Performance
Indicator, KPI Key Performance Indicator) in database, it can directly extract the downstream rate of coverage cell, speech perception is connected
Rate.
Specifically, one section of expressway that Guangzhou is had chosen in the present embodiment determines that user is chosen when user's driving direction to be entered
Guangzhou is 1, and Guangzhou is 0 out, and the content of system output includes the time of user's traveling, place section, driving direction, number, speed
Degree, and with the congestion of map view presentation high-speed line, red indicates that speed is less than or equal to 30,000 ms/hour, blue expression
Speed is greater than 30,000 ms/hour and is less than or equal to 80,000 ms/hour, and green indicates that speed is greater than 80,000 ms/hour.
The same or similar label correspond to the same or similar components；
The terms describing the positional relationship in the drawings are only for illustration, should not be understood as the limitation to this patent；
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Made any modifications, equivalent replacements, and improvements etc., should be included in the claims in the present invention within the spirit and principle of invention
Protection scope within.
Claims (10)
1. the high speed algorithm based on mobile big data, which is characterized in that it the following steps are included:
S1: according to preset " move road and lead to high speed signaling system cell resource table ", the mistake from call detailed recorded data source
Filter out the call detailed recorded data belonged on highway；
S2: setting high-speed road travels user according to table；
S3: judge whether it is expressway traveling user；
S4: direction initialization table judges user's driving direction；
S5: it calculates single user and is averaged movement speed；
S6: average movement speed of multiple users on single section is calculated；
S7: the network downstream rate and speech perception percent of call completed on each section are counted.
2. the high speed algorithm according to claim 1 based on mobile big data, which is characterized in that set described in step S2
Expressway traveling user is determined according to table, the method is as follows: all cells for taking expressway calculate the distance of neighboring community, and with 10
~20 km distances are divided into a section, and the mapping table in cell and section is expressway traveling user according to table.
3. the high speed algorithm according to claim 1 based on mobile big data, which is characterized in that sentence described in step S3
Whether disconnected be expressway traveling user, the method is as follows: detailed by 6 callings in the case where resident user on rejecting expressway
Data after thin record sheet data and high speed cell dimension table, source purpose cell association are found user and are arrived within M minutes before system time
There are two records or more in N minutes first, is judged as that expressway travels user, wherein M > N > 0.
4. the high speed algorithm according to claim 3 based on mobile big data, which is characterized in that the resident user
Definition is: daily 3~4 points of the period of morning, residing in the user on some coverage cell of highway always.
5. the high speed algorithm according to claim 3 based on mobile big data, which is characterized in that the high speed cell dimension
Table is highway coverage cell table；The source purpose cell is the coverage cell that user uses；Two records
Refer to that there are two the universal gateways of the CGI(Common gateway interface) of the above cell and cell in high speed cell dimension table to connect in the purpose cell of source
Mouth is identical；The CGI(Common gateway interface) includes cell identification and position area identification code.
6. the high speed algorithm according to claim 1 based on mobile big data, which is characterized in that the specific steps of step S4
It is: by " move road and lead to high speed signaling system cell resource table " that pre-sets and call detailed recorded data, user is pressed
The coverage cell that time sequencing uses compares, if nearest coverage cell serial number subtracts a coverage cell serial number
For positive number, determines that the direction of user's traveling is forward direction, be otherwise determined as reversed.
7. the high speed algorithm according to claim 1 based on mobile big data, which is characterized in that step S5 specifically include with
Lower step:
S51: the call detail record of expressway traveling user is extracted, call detail record is associated with to obtain with high speed cell dimension table
Source purpose cell；
S52: the call detail record of user on each section is extracted from the purpose cell of source；
S53: calculating the average speed of user, and calculation formula is as follows:
Wherein, T is the maximum time point of adjacent two records and the difference of minimum time point, and L is maximum time point, minimum time point
With the distance between corresponding two cells obtained after CGI(Common gateway interface) association.
8. the high speed algorithm according to claim 1 based on mobile big data, which is characterized in that step S6 specifically include with
Lower step:
S61: the user number N on single section is counted；
S62: the speed V of user i on single section is countedi, i=1,2 ..., N；
S63: average movement speed on single section is soughtFormula is as follows:
9. the high speed algorithm based on mobile big data according to claim 8 or claim 9, which is characterized in that the user is not
The user for being 0 or 200km/h including speed.
10. the high speed algorithm according to claim 1 based on mobile big data, which is characterized in that the method for step S7 is:
The coverage cell in each section is associated in KPI Key Performance Indicator database, can directly extract coverage cell downstream rate,
Speech perception percent of call completed.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104361746A (en) * | 2014-11-05 | 2015-02-18 | 上海新炬网络信息技术有限公司 | Intelligent highway traffic monitoring method |
| CN106332052A (en) * | 2016-08-30 | 2017-01-11 | 上海新炬网络技术有限公司 | Micro-regional public security early-warning method based on mobile communication terminal |
| US20170150307A1 (en) * | 2015-11-23 | 2017-05-25 | Sap Portals Israel Ltd. | Monitoring movement transitions via cellular network data |
| CN106781501A (en) * | 2017-01-13 | 2017-05-31 | 山东浪潮商用***有限公司 | A kind of method that utilization communication network data realizes the monitoring of highway vehicle flowrate |
| CN106781479A (en) * | 2016-12-23 | 2017-05-31 | 重庆邮电大学 | A kind of method for obtaining highway running status in real time based on mobile phone signaling data |
| CN107818332A (en) * | 2017-09-26 | 2018-03-20 | 清华大学 | Interchange Expressway service range analysis method and device |
| CN108171992A (en) * | 2017-12-28 | 2018-06-15 | 重庆邮电大学 | A kind of parallel road vehicle speed calculation method based on mobile phone signaling big data |
-
2018
- 2018-09-13 CN CN201811068770.4A patent/CN109243175A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104361746A (en) * | 2014-11-05 | 2015-02-18 | 上海新炬网络信息技术有限公司 | Intelligent highway traffic monitoring method |
| US20170150307A1 (en) * | 2015-11-23 | 2017-05-25 | Sap Portals Israel Ltd. | Monitoring movement transitions via cellular network data |
| CN106332052A (en) * | 2016-08-30 | 2017-01-11 | 上海新炬网络技术有限公司 | Micro-regional public security early-warning method based on mobile communication terminal |
| CN106781479A (en) * | 2016-12-23 | 2017-05-31 | 重庆邮电大学 | A kind of method for obtaining highway running status in real time based on mobile phone signaling data |
| CN106781501A (en) * | 2017-01-13 | 2017-05-31 | 山东浪潮商用***有限公司 | A kind of method that utilization communication network data realizes the monitoring of highway vehicle flowrate |
| CN107818332A (en) * | 2017-09-26 | 2018-03-20 | 清华大学 | Interchange Expressway service range analysis method and device |
| CN108171992A (en) * | 2017-12-28 | 2018-06-15 | 重庆邮电大学 | A kind of parallel road vehicle speed calculation method based on mobile phone signaling big data |
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Application publication date: 20190118 |