CN106530688B - Huge traffic data processing method based on Hadoop - Google Patents
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- G08G1/00—Traffic control systems for road vehicles
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Abstract
Huge traffic data processing method based on Hadoop, including the following steps: (1) distributed path adaptation specifically includes and builds MapReduce frame, track parsing, creation extension road network；(2) calculating of vehicle flow and car speed；(3) road shunts statistics.The present invention can be specifically applied to Mobile Telephone Gps system, user can be helped to show the real-time road of current region, the diffluent information of flow and speed conditions and crossing including current certain section, user can analyze road jam situation by these information, and suitable route is selected to avoid the section by congestion, improve the accuracy and validity of navigation system.
Description
Technical field
The present invention relates to a kind of huge traffic data processing methods.
Background technique
In current big data era, since the diversification of a variety of and acquisition methods of GPS gathers device makes traffic number
According to increasingly huge, conventional method can not meet the analysis of data, in order to obtain the value contained in data, various data point
Analysis is come into being with method for digging.
It is difficult directly to extract valuable information from mass data, accurately and timely current situation of traffic can be carried out
It analyzes and provides good traffic guidance service for traveler, be always that municipal intelligent traffic plans pursued target, will hand over
The processing of logical data concentrates in parallel distributed computing platform, utilizes the MapReduce distributed computing framework pair of Hadoop
Traffic data carries out Stream Processing, ensure that its timeliness and high fault tolerance with distributed platform.Hadoop is able in big data
The inherent advantage for having benefited from its own in data extraction, deformation and load (ETL) aspect is widely applied in processing application.
The distributed structure/architecture of Hadoop grasps batch processing as such as ETL by big data processing engine as far as possible close to storage
It is relatively suitable to make, because the batch processing result of of this sort operation can directly be moved towards to store.The MapReduce function of Hadoop
It realizes and smashes individual task, and send fragment task (Map) on multiple nodes, later again with the shape of individual data collection
Formula loads in (Reduce) to data warehouse.Map-Reduce is the core algorithm of Hadoop, also referred to as mapping-reduction algorithm,
Algorithm idea is simple, for example for, be exactly that a task is come, a computer can not do, and MapReduce algorithm can be with
It is mapped to many subtasks, different computers is given and goes to complete, every machine, which is done, to be over, definitive statute a to result
User is given, here it is the core concepts of MapReduce algorithm.
Summary of the invention
The disadvantage that the present invention will overcome the data of existing huge traffic data processing method lengthy and jumbled and processing is slow proposes one
Kind designs a kind of huge traffic data processing analysis side based on Hadoop in the way of Hadoop platform processing data
Method better solves the lengthy and jumbled processing of traffic data slowly, data processing and calculating is fast implemented, to reach effective path adaptation
And related roads flow and various complex calculations, such as the specific data that link flow, car speed and crossing shunt calculate.
The present invention devises a kind of MapReduce traffic data processing method, with facilitate data cleaning and quick road
Net matching, different from traditional multi-thread data processing mode, MapReduce is to assign the task to multiple stage computers to transport parallel
It calculates, this is entirely different with the working efficiency of a computer multithreading, and is even more to have under the complicated state for road network
Task can hardly be influenced the concrete condition of road network by its only thick advantage in the case where fragmentation executes.In order to
Can specifically quickly traffic data be handled and be calculated, a kind of magnanimity traffic based on Hadoop according to the present invention
Data processing method, including the following steps:
1) distributed path adaptation: transferring data to the HDFS data-storage system of Hadoop platform, facilitates point
Cloth handles data, and the purpose of path adaptation is road where being matched to effective GPS point, carries out accurate traffic statistics.
Wherein step 1) specifically includes:
(1.1) multinode data processing and calculating: by the data of the transmitting data file comprising taxi information to HDFS
Storage system, order multiple computers are simultaneously handled data as independent node, cleaning, arrow including hiring out car data
The amendment etc. of quantity map, main purpose are the GPS point data of comprehensive rejecting logic error, specifically include time entanglement and speed
Degree does not meet the data of convention；The correction in upper main solving road single-direction and dual-direction and road driving direction is corrected in road.Due to
Multiple host is used as node processing data to greatly improve working efficiency simultaneously, carries out quick traffic statistics and speed calculates；
(1.2) it builds the frame of MapReduce path adaptation: number is read by the MapReduce frame of Hadoop platform
Road network is carried out according to the Hangzhou road network information of practical road network, further increases the high efficiency of the system.It uses
Dom4j document handling modus handles the road network information in data file, by the boundary of road network, node and road section information in file
It is read out and parses by MapReduce frame, carry out the road network work that multiserver handles data simultaneously；
(1.3) track of vehicle parses: parsing to the track of vehicle read.This calculation method first solves track
Associated a series of GPS point data are analysed into, the processing format of the data fit MapReduce frame after parsing.By file
It is the text file comprising latitude and longitude information, temporal information and vehicle ID that GPS point representated by corresponding data line, which arranges, is
Convenient for the progress of MapReduce task, the offset of each data line is convenient for data directory as the Key value of Map task,
Content in row carries out quick Key-Value using MapReduce and reads and place as the corresponding Value of current Key value
Reason completes the parsing of each track in road network；
(1.4) creation extension road network: road network is extended using the position of each GPS point, using candidate GPS as rectangle
The minimum rectangle that side length is M is established at center, and M is revisable rectangle side length, and the minimum rectangle established is known as minimum limit
Rectangle MBR (minimum bounding rectangle) searches the MBR of current point using the find method in prtree algorithm
It is compared with the MBR (being formed by rectangle as diagonal line using the line of a road) in all sections, it is all to have common face
Each long-pending MBR is as candidate matches section.The distance for calculating GPS candidate point and each candidate matches section, obtains
The id information in section corresponding to the shortest distance takes out, then best match section of the section as candidate GPS point.Thus it utilizes
Hadoop platform completes the Rapid matching of all GPS points and road network information, calculates as subsequent vehicle flow and streamed data comes
Source；
2) calculating of vehicle flow and car speed: road section ID is indexed by the road network of successful match and obtains corresponding vehicle
Driving trace is regarded link flow as number that vehicle current time passes through current road segment, is currently matched to according to section
Tracking quantity determines current vehicle flow, and link flow summation corresponding to a road is as the vehicle flow.Furthermore with road
Current road segment and the relative position of lower a road section in space in Duan Xulie calculate the angle and apposition of two section vectors
Mould is considered as straight trip if angle is less than 30 degree, if more than 150 degree to turn around, may determine that steering for a left side according to the mould of apposition
Turn or turn right, mould is that positive number is left-hand rotation, and negative is to turn right；For road vehicle running speed v, three kinds of average speed are calculated first
Degree, three kinds of average speeds are respectively average speed v of the last section tail point to current point1, current point to next section starting point
Average speed v2With the instantaneous velocity v of present road3:
V=ω1v1+ω2v2+ω3v3
Wherein ωi(i=1,2,3) is the weighted value of every kind of speed, and ∑ ωi(i=1,2,3)=1；
Then road section information (including speed, flow, the next road each GPS point of taxi wheel paths being matched to
The information such as ID) export, as next input file for shunting engineering；
3) road shunts statistics: first determining whether road direction, judges road using two node nodeID on every road
Flow direction, it is assumed that two the nodes nodeID1 and nodeID2 of current road segment Way1, two nodes of next section Way2
NodeID3 and nodeID4.Statistics is shunted then to comprise the steps of:
(3.1) if nodeID2 is identical with nodeID3, provide that the direction on this two roads is that nodeID1 is directed toward nodeID2,
NodeID2 is directed toward nodeID4；
(3.2) direction of shunting is then calculated by the spatial position on two roads, the angle for defining two roads here is big
150 degree of the case where are to turn around, angle between 150 degree to 30 degree and section vector difference-product determines left-hand rotation or the right side
Turn, angle is considered as straight trip less than 30 degree；
(3.3) if nodeID1-nodeID4 defines what two continuous GPS points were matched to without any one identical ID
Also all adjacent sections current road Way1 are found first, are then found all adjacent comprising other sections between section
The corresponding adjacent segments set in section judges to whether there is road section ID identical with Way2 in set in all sections, if it exists
It then indicates then to continue successive ignition according to the method described above if it does not exist there are a connection section between Way1 and Way2
Find all connection sections between current road segment and next section；
(3.4) concrete condition that step (3.2) judgement shunts is repeated to every two adjacent segments.
Technical concept of the invention is: building MapReduce frame processing traffic data, it is parallel to realize multiple stage computers
Execution task, greatly improves work efficiency, and for overcoming the lengthy and jumbled property of traffic data to be very helpful；For road network
MBR algorithm in matching largely improves the matching speed in GPS point and section, and improves accuracy；Road point
The case where calculating of stream will likely exist ensure that the accuracy and validity of Calculation of the shunted current in being considered in.
The present invention can be specifically applied to Mobile Telephone Gps system, can help user to show the real-time road of current region,
The diffluent information of flow and speed conditions and crossing including current certain section, user can be analyzed by these information
Congestion in road situation, and select suitable route to avoid the section by congestion improves the accuracy and effectively of navigation system
Property.
The invention has the advantages that deployment is simple, easy to maintain, data processing is rapid.
Detailed description of the invention
Fig. 1 is the road real time processing system block diagram for implementing the method for the present invention.
Fig. 2 is that the direction in space in non-conterminous two sections of the invention calculates schematic diagram.
Fig. 3 is road network process schematic of the invention.
Fig. 4 is that road speeds of the invention calculate schematic diagram
Specific embodiment
Huge traffic data processing method according to the present invention based on Hadoop, including the following steps:
1) distributed path adaptation: transferring data to the HDFS data-storage system of Hadoop platform, facilitates point
Cloth handles data, and the purpose of path adaptation is road where being matched to effective GPS point, carries out accurate traffic statistics.
Wherein step 1) specifically includes:
(1.1) multinode data processing and calculating: by the data of the transmitting data file comprising taxi information to HDFS
Storage system, order multiple computers are simultaneously handled data as independent node, cleaning, arrow including hiring out car data
The amendment etc. of quantity map, main purpose are the GPS point data of comprehensive rejecting logic error, specifically include time entanglement and speed
Degree does not meet the data of convention；The correction in upper main solving road single-direction and dual-direction and road driving direction is corrected in road.Due to
Multiple host is used as node processing data to greatly improve working efficiency simultaneously, carries out quick traffic statistics and speed calculates；
(1.2) it builds the frame of MapReduce path adaptation: number is read by the MapReduce frame of Hadoop platform
Road network is carried out according to the Hangzhou road network information of practical road network, further increases the high efficiency of the system.It uses
Dom4j document handling modus handles the road network information in data file, by the boundary of road network, node and road section information in file
It is read out and parses by MapReduce frame, carry out the road network work that multiserver handles data simultaneously；
(1.3) track of vehicle parses: parsing to the track of vehicle read.This calculation method first solves track
Associated a series of GPS point data are analysed into, the processing format of the data fit MapReduce frame after parsing.By file
It is the text file comprising latitude and longitude information, temporal information and vehicle ID that GPS point representated by corresponding data line, which arranges, is
Convenient for the progress of MapReduce task, the offset of each data line is convenient for data directory as the Key value of Map task,
Content in row carries out quick Key-Value using MapReduce and reads and place as the corresponding Value of current Key value
Reason completes the parsing of each track in road network；
(1.4) creation extension road network: road network is extended using the position of each GPS point, using candidate GPS as rectangle
The minimum rectangle that side length is M is established at center, and M is revisable rectangle side length, and the minimum rectangle established is known as minimum limit
Rectangle MBR (minimum bounding rectangle) searches the MBR of current point using the find method in prtree algorithm
It is compared with the MBR (being formed by rectangle as diagonal line using the line of a road) in all sections, it is all to have common face
Each long-pending MBR is as candidate matches section.The distance for calculating GPS candidate point and each candidate matches section, obtains
The id information in section corresponding to the shortest distance takes out, then best match section of the section as candidate GPS point.Thus it utilizes
Hadoop platform completes the Rapid matching of all GPS points and road network information, calculates as subsequent vehicle flow and streamed data comes
Source；
2) calculating of vehicle flow and car speed: road section ID is indexed by the road network of successful match and obtains corresponding vehicle
Driving trace is regarded link flow as number that vehicle current time passes through current road segment, is currently matched to according to section
Tracking quantity determines current vehicle flow, and link flow summation corresponding to a road is as the vehicle flow.Furthermore with road
Current road segment and the relative position of lower a road section in space in Duan Xulie calculate the angle and apposition of two section vectors
Mould is considered as straight trip if angle is less than 30 degree, if more than 150 degree to turn around, may determine that steering for a left side according to the mould of apposition
Turn or turn right, mould is that positive number is left-hand rotation, and negative is to turn right；For road vehicle running speed v, three kinds of average speed are calculated first
Degree, three kinds of average speeds are respectively average speed v of the last section tail point to current point1, current point to next section starting point
Average speed v2With the instantaneous velocity v of present road3:
V=ω1v1+ω2v2+ω3v3
Wherein ωi(i=1,2,3) is the weighted value of every kind of speed, and ∑ ωi(i=1,2,3)=1；
Then road section information (including speed, flow, the next road each GPS point of taxi wheel paths being matched to
The information such as ID) export, as next input file for shunting engineering；
3) road shunts statistics: first determining whether road direction, judges road using two node nodeID on every road
Flow direction, it is assumed that two the nodes nodeID1 and nodeID2 of current road segment Way1, two nodes of next section Way2
NodeID3 and nodeID4.Statistics is shunted then to comprise the steps of:
(3.1) if nodeID2 is identical with nodeID3, provide that the direction on this two roads is that nodeID1 is directed toward nodeID2,
NodeID2 is directed toward nodeID4；
(3.2) direction of shunting is then calculated by the spatial position on two roads, the angle for defining two roads here is big
150 degree of the case where are to turn around, angle between 150 degree to 30 degree and section vector difference-product determines left-hand rotation or the right side
Turn, angle is considered as straight trip less than 30 degree；
(3.3) if nodeID1-nodeID4 defines what two continuous GPS points were matched to without any one identical ID
Also all adjacent sections current road Way1 are found first, are then found all adjacent comprising other sections between section
The corresponding adjacent segments set in section judges to whether there is road section ID identical with Way2 in set in all sections, if it exists
It then indicates then to continue successive ignition according to the method described above if it does not exist there are a connection section between Way1 and Way2
Find all connection sections between current road segment and next section；
(3.4) concrete condition that step (3.2) judgement shunts is repeated to every two adjacent segments.
Described above is that the form that the case study on implementation that the present invention provides illustrates transport data processing is enumerated, it is clear that
The present invention is not only limited to above-mentioned case study on implementation, without departing from essence spirit of the present invention and without departing from involved by substantive content of the present invention
And various deformations can be made under the premise of range to it and be implemented.The data processing method proposed is for solving the lengthy and jumbled of data
And processing is slowly very effective.
Claims (1)
1. the huge traffic data processing method based on Hadoop, comprising the following steps:
1) distributed path adaptation: transferring data to the HDFS data-storage system of Hadoop platform, facilitates progress distributed
Data are handled, the purpose of path adaptation is road where being matched to effective GPS point, carries out accurate traffic statistics；Specifically
Include:
(1.1) multinode data processing and calculating: the data of the transmitting data file comprising taxi information to HDFS are stored
System, order multiple computers are simultaneously handled data as independent node, cleaning, vector including taxi car data
The amendment of figure, the GPS point data of comprehensive rejecting logic error, specifically include time entanglement and speed does not meet the number of convention
According to；The correction in upper solving road single-direction and dual-direction and road driving direction is corrected in road；Multiple host is used as node processing simultaneously
Data greatly improve working efficiency, carry out quick traffic statistics and speed calculates；
(1.2) build the frame of Mapreduce path adaptation: by the MapReduce frame of Hadoop platform read data with
The Hangzhou road network information of practical road network carries out road network, further increases the high efficiency of the system；Use dom4j text
Part processing mode handles the road network information in data file, and the boundary of road network, node and road section information in file are passed through
Mapreduce frame is read out and parses, and carries out the road network work that multiserver handles data simultaneously；
(1.3) track of vehicle parses: parsing to the track of vehicle read；Track is parsed into associated one first
The GPS point data of series, the processing format of the data fit Mapreduce frame after parsing；By the corresponding data line institute of file
The GPS point arrangement represented is the text file comprising latitude and longitude information, temporal information and vehicle ID, for the ease of Mapreduce
The progress of task, the offset of each data line are convenient for data directory as the Key value of Map task, the content conduct in row
The currently corresponding Value of Key value carries out quick Key-Value using Mapreduce and reads and handle, every in completion road network
The parsing of one track；
(1.4) creation extension road network: road network is extended using the position of each GPS point, using candidate GPS as in rectangle
The heart establishes the minimum rectangle that side length is M, and M is revisable rectangle side length, and the minimum rectangle established is known as minimum restriction square
Shape MBR is compared using the MBR that the find method in prtree algorithm searches current point with the MBR in all sections, all to have
Each MBR of common area is as candidate matches section；The distance of GPS candidate point and each candidate matches section is calculated,
The id information in section corresponding to the obtained shortest distance takes out, then best match section of the section as candidate GPS point；By
This completes the Rapid matching of all GPS points and road network information using Hadoop platform, calculates and shunts as subsequent vehicle flow
Data source；
2) calculating of vehicle flow and car speed: road section ID is indexed by the road network of successful match and obtains the traveling of corresponding vehicle
As the number that vehicle current time passes through current road segment, the track being currently matched to according to section are regarded link flow in track
Quantity determines current vehicle flow, and link flow summation corresponding to a road is as the vehicle flow；Furthermore with section sequence
Current road segment and the relative position of lower a road section in space, calculate the angle of two section vectors and the mould of apposition in column, if
Angle is then considered as straight trip less than 30 degree, if more than 150 degree then to turn around, may determine that steering according to the mould of apposition to turn left still
It turns right, mould is that positive number is left-hand rotation, and negative is to turn right；For road vehicle running speed v, three kinds of average speeds are calculated first, three
Kind average speed is respectively average speed v of the last section tail point to current point1, current point is averaged to next section starting point
Speed v2With the instantaneous velocity v of present road3:
V=ω1v1+ω2v2+ω3v3
Wherein ωi(i=1,2,3) is the weighted value of every kind of speed, and ∑ ωi(i=1,2,3)=1；
Then the road section information each GPS point of taxi wheel paths being matched to exports, and shunts the defeated of engineering as next
Enter file, the road section information includes speed, flow, the ID on next road；
3) road shunts statistics: it first determines whether road direction, the flow direction of road is judged using two node nodeID on every road,
Assuming that two node nodeID1 and nodeID2 of current road segment Way1, two node nodeID3 of next section Way2 and
nodeID4；Statistics is shunted then to comprise the steps of:
(3.1) if nodeID2 is identical with nodeID3, provide that the direction on this two roads is that nodeID1 is directed toward nodeID2,
NodeID2 is directed toward nodeID4；
(3.2) direction of shunting is then calculated by the spatial position on two roads, the angle for defining two roads here is 150 degree big
The case where to turn around, the case where angle is between 150 degree to 30 degree, determines left-hand rotation or right-hand rotation, angle with section vector difference-product
It is considered as straight trip less than 30 degree；
(3.3) if nodeID1-nodeID4 defines the section that two continuous GPS points are matched to without any one identical ID
Between also comprising other sections, find all adjacent sections current road Way1 first, then find all adjacent segments
Corresponding adjacent segments set judges in set in all sections with the presence or absence of road section ID identical with Way2, if it exists then table
Show between Way1 and Way2 there are a connection section, then continues successive ignition according to the method described above if it does not exist, can find
All connection sections between current road segment and next section；
(3.4) concrete condition that step (3.2) judgement shunts is repeated to every two adjacent segments.
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| CN111105627B (en) * | 2018-10-25 | 2021-12-07 | 腾讯科技（深圳）有限公司 | Method, device and system for determining average speed of road section |
| CN109785614A (en) * | 2018-12-17 | 2019-05-21 | 北京掌行通信息技术有限公司 | A kind of monitoring method and device of magnanimity mobile position data |
| CN111613046B (en) * | 2019-02-26 | 2022-09-23 | 阿里巴巴集团控股有限公司 | Information processing method, device and system |
| CN109903561B (en) * | 2019-03-14 | 2021-05-07 | 智慧足迹数据科技有限公司 | Method and device for calculating pedestrian flow between road sections and electronic equipment |
| CN111815942B (en) * | 2019-04-10 | 2022-12-23 | 阿里巴巴集团控股有限公司 | Track processing method and device and electronic equipment |
| CN110706481A (en) * | 2019-08-28 | 2020-01-17 | 安徽科力信息产业有限责任公司 | Control method and device for improving speed of discovering and processing traffic jam |
| CN113132907B (en) * | 2019-12-27 | 2022-11-08 | 中国移动通信集团浙江有限公司 | Pedestrian flow distribution identification method and device, computing device and storage device |
| CN112396837A (en) * | 2020-11-13 | 2021-02-23 | 重庆中信科信息技术有限公司 | Congestion area vehicle source path analysis method and system based on traffic big data |
| CN113781771B (en) * | 2021-08-17 | 2022-10-28 | 四川省生态环境科学研究院 | Online operation method of IVE model |
| CN114613167A (en) * | 2022-04-15 | 2022-06-10 | 山东金宇信息科技集团有限公司 | Intelligent traffic management method based on vehicle flow regulation |
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