CN106530688A - Hadoop-based massive traffic data processing method - Google Patents
Hadoop-based massive traffic data processing method Download PDFInfo
- Publication number
- CN106530688A CN106530688A CN201610899062.XA CN201610899062A CN106530688A CN 106530688 A CN106530688 A CN 106530688A CN 201610899062 A CN201610899062 A CN 201610899062A CN 106530688 A CN106530688 A CN 106530688A
- Authority
- CN
- China
- Prior art keywords
- road
- data
- section
- current
- vehicle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- 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
Landscapes
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
- Navigation (AREA)
Abstract
Provided is a Hadoop-based massive traffic data processing method, which comprises the following steps: 1) distributed road matching, which specifically comprises establishment of a MapReduce framework, trajectory analysis and establishment and expansion of a road network; 2) calculation of road flow and vehicle speed; and 3) road diversion statistics.
Description
Technical field
The present invention relates to a kind of huge traffic data processing method.
Background technology
In the current big data epoch, as the variation of the various and acquisition methods of GPS gathers device causes traffic number
According to day by day huge, traditional method can not meet the analysis of data, in order to obtain the value contained in data, various data point
Analysis is arisen at the historic moment 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
The traffic guidance service of high-quality is analyzed and provides for traveler, always municipal intelligent traffic plans pursued target, will hand over
The process of logical data is concentrated in parallel distributed calculating platform, using the MapReduce distributed computing frameworks pair of Hadoop
Traffic data carries out Stream Processing, ensure that its ageing and high fault tolerance with distributed platform.Hadoop is able in big data
Process in application extensively application have benefited from its own extract in data, deformation and in terms of loading (ETL) on inherent advantage.
Big data is processed engine as far as possible near storage, batch processing as such as ETL is grasped by the distributed structure/architecture of Hadoop
Make relatively suitable, because the batch processing result of of this sort operation can directly move towards storage.The MapReduce functions of Hadoop
Realize and individual task is smashed, and fragment task (Map) is sent on multiple nodes, afterwards again with the shape of individual data collection
Formula loads (Reduce) in data warehouse.Map-Reduce is the core algorithm of Hadoop, also referred to as mapping-reduction algorithm,
Algorithm idea is simple, for giving an example, is exactly that a task is come, and a computer can not do, and MapReduce algorithms can be with
It is mapped to into many subtasks, different computers is given and is gone to complete, every machine does and is over, definitive statute is to a result
User is given, here it is the core concept of MapReduce algorithms.
The content of the invention
The present invention will overcome the data of existing huge traffic data processing method lengthy and jumbled and process slow shortcoming, propose one
The mode using Hadoop platform processing data is planted, a kind of huge traffic data Treatment Analysis side based on Hadoop is designed
Method, preferably solves the lengthy and jumbled process of traffic data slowly, quickly realizes data processing and calculating, to reach effective path adaptation
And the concrete data of related roads flow and various complex calculations, such as link flow, car speed and crossing shunting are calculated.
The present invention devises a kind of MapReduce traffic data processing methods, with the cleaning for facilitating data 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
Calculate, this is entirely different with the work efficiency of a computer multithreading, and even more have under the complicated state for road network
Its solely thick advantage, task hardly can be had influence in the case of fragmentation execution the concrete condition of road network.In order to
Specifically quickly traffic data can be processed and be calculated, a kind of magnanimity traffic based on Hadoop involved in the present invention
Data processing method, including following step：
1) distributed path adaptation：The HDFS data-storage systems of Hadoop platform are transferred data to, is conveniently carried out point
Cloth processing data, the purpose of path adaptation are that effective GPS point is matched place road, carry out accurate traffic statistics.
Wherein step 1) specifically include：
(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 processed to data simultaneously as independent node, including cleaning, the arrow of hiring out car data
Amendment of quantity map etc., main purpose are the GPS point data of comprehensive rejecting logical error, specifically include time entanglement and speed
Degree does not meet the data of convention；The correction in main solving road single-direction and dual-direction and road driving direction in road amendment.Due to
Multiple host greatly improves work efficiency as node processing data simultaneously, carries out quick traffic statistics and speed calculation；
(1.2) build the framework of MapReduce path adaptations：By the MapReduce framework receive datas of Hadoop platform
Road network is carried out according to the Hangzhou road network information with actual road network, the high efficiency of the system is further improved.Use
Road network information in dom4j document handling modus processing data files, by the border of road network, node and road section information in file
Be read out and parse by MapReduce frameworks, multiserver is carried out while the road network work of processing data；
(1.3) track of vehicle parsing：Track of vehicle to having read is parsed.Track is solved by this computational methods first
Analyse into associated a series of GPS point data, the processing format of the data fit MapReduce framework after parsing.By file
It is the text comprising latitude and longitude information, temporal information and vehicle ID that GPS point representated by corresponding data row is arranged, and is
It is easy to the carrying out of MapReduce tasks, the side-play amount of each data row is easy to data directory as the Key values of Map tasks,
Content in row carries out quick Key-Value using MapReduce and reads and place as the corresponding Value of current Key values
Reason, completes the parsing of each track in road network；
(1.4) create extension road network：Road network is extended using the position of each GPS point, with candidate GPS as rectangle
Center, sets up the minimum rectangle that the length of side is M, and M is the revisable rectangle length of side, and the minimum rectangle set up is referred to as minimum to be limited
Rectangle MBR (minimum bounding rectangle), searches the MBR of current point using the find methods in prtree algorithms
It is compared with the MBR (using the rectangle formed as diagonal by the line of a road) in all sections, it is every to have common face
Each long-pending MBR is used as candidate matches section.The distance in GPS candidate points and each candidate matches section is calculated, is obtained
The id information in section corresponding to beeline takes out, then best match section of the section as candidate's GPS point.Thus utilize
Hadoop platform completes the Rapid matching of all GPS points and road network information, calculates as follow-up vehicle flow and streamed data comes
Source；
2) calculating of vehicle flow and car speed：Road section ID is indexed by the road network that the match is successful and obtains correspondence vehicle
Driving trace, regards link flow number of times of the vehicle current time through current road segment as, is currently matched according to section
Tracking quantity determines current vehicle flowrate, and the link flow summation corresponding to a road is used as the vehicle flow.Furthermore with road
Relative position of the current road segment with next section in space in Duan Xulie, the angle of two sections vectors of calculating and apposition
Mould, is considered as straight trip if angle is less than 30 degree, for turning around if more than 150 degree, may determine that steering is a left side according to the mould of apposition
Turn or right-hand rotation, it is to turn left that mould is positive number, and negative is right-hand rotation；For road vehicle running speed v, three kinds of average speed are calculated first
Degree, three kinds of average speeds are respectively last section tail point to the average speed v of current point1, current point is 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) be every kind of speed weighted value, and ∑ ωi(i=1,2,3)=1；
Then the road section information for each GPS point for hiring out wheel paths being matched is (including speed, flow, next road
The information such as ID) derive, as the input file of next shunting engineering；
3) road shunting statistics：Road direction is first determined whether, road is judged 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.Shunting statistics is then comprised the steps of：
(3.1) if nodeID2 is identical with nodeID3, specify that the direction on this two roads is that nodeID1 points to nodeID2,
NodeID2 points to nodeID4；
(3.2) direction of shunting is calculated and then by the locus on two roads, the angle for defining two roads here is big
150 degree of situation for turning around, angle is between 150 degree to 30 degree and section vector difference-product is determining 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 without any one identical ID
Also all adjacent sections of current road Way1 are found first, is then looked up all adjacent comprising other sections between section
The corresponding adjacent segments set in section, whether there is and Way2 identical road section IDs in all sections in judging to gather, if existing
Then represent between Way1 and Way2, there is a connection section, continue successive ignition according to the method described above if not existing, you can
Find all connection sections between current road segment and next section；
(3.4) to judging the concrete condition for shunting per two adjacent segments repeat steps (3.2).
The present invention technology design be：Build MapReduce frameworks and process traffic data, realize multiple stage computers parallel
Execution task, substantially increases work efficiency, and for the lengthy and jumbled property for overcoming traffic data is very helpful；For road network
MBR algorithms in matching somebody with somebody largely improve the matching speed of GPS point and section, and improve accuracy；Road point
In the situation that the calculating of stream will likely be present is considered in, it is ensured that the accuracy of Calculation of the shunted current and effectiveness.
The present invention can be specifically applied to Mobile Telephone Gps system, can help user to show the real-time road of current region,
Including the flow and the diffluent information at speed conditions and crossing in current certain section, user can be analyzed by these information
Congestion in road situation, and select suitable route to avoid through the section of congestion, improves the accuracy and effectively of navigation system
Property.
It is an advantage of the invention that：Deployment is simple, and easy to maintenance, data processing is rapid.
Description of the drawings
Fig. 1 is the road real time processing system block diagram for implementing the inventive method.
Fig. 2 is that the direction in space in non-conterminous two sections of the present invention calculates schematic diagram.
Fig. 3 is the road network process schematic of the present invention.
Fig. 4 is that the road speeds of the present invention calculate schematic diagram
Specific embodiment
Huge traffic data processing method based on Hadoop involved in the present invention, including following step：
1) distributed path adaptation：The HDFS data-storage systems of Hadoop platform are transferred data to, is conveniently carried out point
Cloth processing data, the purpose of path adaptation are that effective GPS point is matched place road, carry out accurate traffic statistics.
Wherein step 1) specifically include：
(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 processed to data simultaneously as independent node, including cleaning, the arrow of hiring out car data
Amendment of quantity map etc., main purpose are the GPS point data of comprehensive rejecting logical error, specifically include time entanglement and speed
Degree does not meet the data of convention；The correction in main solving road single-direction and dual-direction and road driving direction in road amendment.Due to
Multiple host greatly improves work efficiency as node processing data simultaneously, carries out quick traffic statistics and speed calculation；
(1.2) build the framework of MapReduce path adaptations：By the MapReduce framework receive datas of Hadoop platform
Road network is carried out according to the Hangzhou road network information with actual road network, the high efficiency of the system is further improved.Use
Road network information in dom4j document handling modus processing data files, by the border of road network, node and road section information in file
Be read out and parse by MapReduce frameworks, multiserver is carried out while the road network work of processing data；
(1.3) track of vehicle parsing：Track of vehicle to having read is parsed.Track is solved by this computational methods first
Analyse into associated a series of GPS point data, the processing format of the data fit MapReduce framework after parsing.By file
It is the text comprising latitude and longitude information, temporal information and vehicle ID that GPS point representated by corresponding data row is arranged, and is
It is easy to the carrying out of MapReduce tasks, the side-play amount of each data row is easy to data directory as the Key values of Map tasks,
Content in row carries out quick Key-Value using MapReduce and reads and place as the corresponding Value of current Key values
Reason, completes the parsing of each track in road network；
(1.4) create extension road network：Road network is extended using the position of each GPS point, with candidate GPS as rectangle
Center, sets up the minimum rectangle that the length of side is M, and M is the revisable rectangle length of side, and the minimum rectangle set up is referred to as minimum to be limited
Rectangle MBR (minimum bounding rectangle), searches the MBR of current point using the find methods in prtree algorithms
It is compared with the MBR (using the rectangle formed as diagonal by the line of a road) in all sections, it is every to have common face
Each long-pending MBR is used as candidate matches section.The distance in GPS candidate points and each candidate matches section is calculated, is obtained
The id information in section corresponding to beeline takes out, then best match section of the section as candidate's GPS point.Thus utilize
Hadoop platform completes the Rapid matching of all GPS points and road network information, calculates as follow-up vehicle flow and streamed data comes
Source；
2) calculating of vehicle flow and car speed：Road section ID is indexed by the road network that the match is successful and obtains correspondence vehicle
Driving trace, regards link flow number of times of the vehicle current time through current road segment as, is currently matched according to section
Tracking quantity determines current vehicle flowrate, and the link flow summation corresponding to a road is used as the vehicle flow.Furthermore with road
Relative position of the current road segment with next section in space in Duan Xulie, the angle of two sections vectors of calculating and apposition
Mould, is considered as straight trip if angle is less than 30 degree, for turning around if more than 150 degree, may determine that steering is a left side according to the mould of apposition
Turn or right-hand rotation, it is to turn left that mould is positive number, and negative is right-hand rotation；For road vehicle running speed v, three kinds of average speed are calculated first
Degree, three kinds of average speeds are respectively last section tail point to the average speed v of current point1, current point is 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) be every kind of speed weighted value, and ∑ ωi(i=1,2,3)=1；
Then the road section information for each GPS point for hiring out wheel paths being matched is (including speed, flow, next road
The information such as ID) derive, as the input file of next shunting engineering；
3) road shunting statistics：Road direction is first determined whether, road is judged 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.Shunting statistics is then comprised the steps of：
(3.1) if nodeID2 is identical with nodeID3, specify that the direction on this two roads is that nodeID1 points to nodeID2,
NodeID2 points to nodeID4；
(3.2) direction of shunting is calculated and then by the locus on two roads, the angle for defining two roads here is big
150 degree of situation for turning around, angle is between 150 degree to 30 degree and section vector difference-product is determining 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 without any one identical ID
Also all adjacent sections of current road Way1 are found first, is then looked up all adjacent comprising other sections between section
The corresponding adjacent segments set in section, whether there is and Way2 identical road section IDs in all sections in judging to gather, if existing
Then represent between Way1 and Way2, there is a connection section, continue successive ignition according to the method described above if not existing, you can
Find all connection sections between current road segment and next section；
(3.4) to judging the concrete condition for shunting per two adjacent segments repeat steps (3.2).
Described above is that the form that the case study on implementation that the present invention is given 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 flesh and blood of the present invention
And which can be made on the premise of scope it is a variety of deformation be carried out.The data processing method for being proposed is lengthy and jumbled for solution data
And process is slowly very effective.
Claims (1)
1. the huge traffic data processing method based on Hadoop, comprises the following steps：
1) distributed path adaptation：The HDFS data-storage systems of Hadoop platform are transferred data to, is conveniently carried out distributed
Processing data, the purpose of path adaptation are that effective GPS point is matched place road, carry out accurate traffic statistics.Specifically
Including：
(1.1) multinode data processing and calculating：By the data storage of the transmitting data file comprising taxi information to HDFS
System, order multiple computers are processed to data simultaneously as independent node, including cleaning, the vector ground of hiring out car data
The amendment of figure, the GPS point data of comprehensive rejecting logical error, specifically includes the number that time entanglement and speed do not meet convention
According to；The correction in main solving road single-direction and dual-direction and road driving direction in road amendment.Due to multiple host conduct simultaneously
Node processing data greatly improve work efficiency, carry out quick traffic statistics and speed calculation；
(1.2) build the framework of Mapreduce path adaptations：By the MapReduce frameworks of Hadoop platform read data with
The Hangzhou road network information of actual road network carries out road network, further improves the high efficiency of the system.It is literary using dom4j
Road network information in part processing mode processing data file, the border of road network, node and road section information in file are passed through
Mapreduce frameworks are read out and parse, and carry out multiserver while the road network work of processing data；
(1.3) track of vehicle parsing：Track of vehicle to having read is parsed.Track is parsed into into associated one first
The GPS point data of series, the processing format of the data fit Mapreduce framework after parsing.By file corresponding data row institute
The GPS point arrangement for representing is the text comprising latitude and longitude information, temporal information and vehicle ID, for the ease of Mapreduce
The carrying out of task, the side-play amount of each data row are easy to data directory, the content conduct in row as the Key values of Map tasks
The corresponding Value of current Key values, carries out quick Key-Value using Mapreduce and reads and process, and completes in road network often
The parsing of one track；
(1.4) create extension road network：Road network is extended using the position of each GPS point, with candidate GPS as rectangle in
The heart, sets up the minimum rectangle that the length of side is M, and M is the revisable rectangle length of side, and the minimum rectangle set up is referred to as minimum to limit square
Shape MBR, the MBR and the MBR in all sections for searching current point using the find methods in prtree algorithms are compared, every to have
Each MBR of common area is used as candidate matches section.The distance in GPS candidate points and each candidate matches section is calculated,
Corresponding to the beeline for obtaining, the id information in section takes out, then best match section of the section as candidate's GPS point.By
This completes the Rapid matching of all GPS points and road network information using Hadoop platform, calculates as follow-up vehicle flow and shunts
Data Source；
2) calculating of vehicle flow and car speed：The traveling that road section ID obtains correspondence vehicle is indexed by the road network that the match is successful
Track, regards link flow number of times of the vehicle current time through current road segment as, according to the track currently matched by section
Quantity determines current vehicle flowrate, and the link flow summation corresponding to a road is used as the vehicle flow.Furthermore with section sequence
Relative position of the current road segment with next section in space in row, calculates the angle of two section vectors and the mould of apposition, if
Angle is then considered as straight trip less than 30 degree, for turning around if more than 150 degree, may determine that steering for turning left still according to the mould of apposition
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 speeds are calculated first, three
Plant average speed last section tail point is respectively to the average speed v of current point1, current point it is average to next section starting point
Speed v2With the instantaneous velocity v of present road3：
V=ω1v1+ω2v2+ω3v3
Wherein ωi(i=1,2,3) be every kind of speed weighted value, and ∑ ωi(i=1,2,3)=1；
Then the road section information for each GPS point for hiring out wheel paths being matched is derived, used as the defeated of next shunting engineering
Enter file, described road section information includes speed, flow, the ID on next road；
3) road shunting statistics：Road direction is first determined whether, the flow direction of road is judged using two node nodeID on every road,
Assume current road segment Way1 two nodes nodeID1 and nodeID2, two node nodeID3 of next section Way2 and
nodeID4.Shunting statistics is then comprised the steps of：
(3.1) if nodeID2 is identical with nodeID3, specify that the direction on this two roads is that nodeID1 points to nodeID2,
NodeID2 points to nodeID4；
(3.2) direction of shunting is calculated and then by the locus on two roads, the angle for defining two roads here is big 150 degree
Situation for turning around, angle is between 150 degree to 30 degree and section vector difference-product is determining left-hand rotation or right-hand rotation, angle
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 without any one identical ID
Between also comprising other sections, find all adjacent sections of current road Way1 first, then look up all adjacent segments
Corresponding adjacent segments set, whether there is and Way2 identical road section IDs in all sections in judging to gather, the table if existing
Show, continue successive ignition according to the method described above if not existing, you can find
All connection sections between current road segment and next section；
(3.4) to judging the concrete condition for shunting per two adjacent segments repeat steps (3.2).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610899062.XA CN106530688B (en) | 2016-10-14 | 2016-10-14 | Huge traffic data processing method based on Hadoop |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610899062.XA CN106530688B (en) | 2016-10-14 | 2016-10-14 | Huge traffic data processing method based on Hadoop |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106530688A true CN106530688A (en) | 2017-03-22 |
| CN106530688B CN106530688B (en) | 2019-06-14 |
Family
ID=58331844
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610899062.XA Active CN106530688B (en) | 2016-10-14 | 2016-10-14 | Huge traffic data processing method based on Hadoop |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106530688B (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN109903561A (en) * | 2019-03-14 | 2019-06-18 | 智慧足迹数据科技有限公司 | Flow of the people calculation method, device and electronic equipment between section |
| 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 |
| CN111105627A (en) * | 2018-10-25 | 2020-05-05 | 腾讯科技（深圳）有限公司 | Method, device and system for determining average speed of road section |
| CN111613046A (en) * | 2019-02-26 | 2020-09-01 | 阿里巴巴集团控股有限公司 | Information processing method, device and system |
| WO2020207291A1 (en) * | 2019-04-10 | 2020-10-15 | 阿里巴巴集团控股有限公司 | Trace processing method and apparatus, and electronic 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 |
| CN113132907A (en) * | 2019-12-27 | 2021-07-16 | 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 |
| CN113781771A (en) * | 2021-08-17 | 2021-12-10 | 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 |
| CN115480275A (en) * | 2022-09-15 | 2022-12-16 | 中华人民共和国广东海事局 | Motion state acquisition method and device, computer equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020198694A1 (en) * | 2001-06-22 | 2002-12-26 | Qi Yang | Traffic data management and simulation system |
| CN101608922A (en) * | 2008-06-18 | 2009-12-23 | 北京东方泰坦科技有限公司 | Method for quickest routing planning based on Real-time Traffic Information |
| CN103235974A (en) * | 2013-04-25 | 2013-08-07 | 中国科学院地理科学与资源研究所 | Method for improving processing efficiency of massive spatial data |
-
2016
- 2016-10-14 CN CN201610899062.XA patent/CN106530688B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020198694A1 (en) * | 2001-06-22 | 2002-12-26 | Qi Yang | Traffic data management and simulation system |
| CN101608922A (en) * | 2008-06-18 | 2009-12-23 | 北京东方泰坦科技有限公司 | Method for quickest routing planning based on Real-time Traffic Information |
| CN103235974A (en) * | 2013-04-25 | 2013-08-07 | 中国科学院地理科学与资源研究所 | Method for improving processing efficiency of massive spatial data |
Non-Patent Citations (2)
| Title |
|---|
| JIAN HUANG 等: "Parallel Map Matching on Massive Vehicle GPS Data Using MapReduce", 《2013 IEEE INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS》 * |
| 费贤举 等: "一种海量大数据云存储系统框架设计", 《常州工学院学报》 * |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111105627A (en) * | 2018-10-25 | 2020-05-05 | 腾讯科技（深圳）有限公司 | Method, device and system for determining average speed of road section |
| 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 |
| CN111613046A (en) * | 2019-02-26 | 2020-09-01 | 阿里巴巴集团控股有限公司 | Information processing method, device and system |
| CN109903561A (en) * | 2019-03-14 | 2019-06-18 | 智慧足迹数据科技有限公司 | Flow of the people calculation method, device and electronic equipment between section |
| WO2020207291A1 (en) * | 2019-04-10 | 2020-10-15 | 阿里巴巴集团控股有限公司 | Trace processing method and apparatus, and electronic device |
| 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 |
| CN113132907A (en) * | 2019-12-27 | 2021-07-16 | 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 |
| CN113781771A (en) * | 2021-08-17 | 2021-12-10 | 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 |
| CN115480275A (en) * | 2022-09-15 | 2022-12-16 | 中华人民共和国广东海事局 | Motion state acquisition method and device, computer equipment and storage medium |
| CN115480275B (en) * | 2022-09-15 | 2023-08-08 | 中华人民共和国广东海事局 | Motion state acquisition method and device, computer equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106530688B (en) | 2019-06-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106530688A (en) | Hadoop-based massive traffic data processing method | |
| CN110634291B (en) | High-precision map topology automatic construction method and system based on crowdsourcing data | |
| AU2020101023A4 (en) | A Novel Deep Learning Approach for Distributed Traffic Flow Forecasting | |
| EP3323689A1 (en) | Peripheral environment recognition device and computer program product | |
| EP3115983B1 (en) | Probe information collection method, collection apparatus, program, storage medium, and travel time calculating apparatus | |
| Ding et al. | Network-matched trajectory-based moving-object database: Models and applications | |
| CN107705586B (en) | Traffic flow control method and device for road intersection | |
| CN107944375A (en) | Automatic Pilot processing method and processing device based on scene cut, computing device | |
| CN103149576A (en) | Map matching method of floating car data | |
| CN111680377A (en) | Traffic situation simulation method and system and electronic equipment | |
| CN115002679B (en) | Trajectory deviation rectifying processing method and device | |
| CN112163446A (en) | Obstacle detection method and device, electronic equipment and storage medium | |
| Huang et al. | Cloud computing-based map-matching for transportation data center | |
| CN107697071B (en) | A kind of driving safety grade based on field theory determines method and device | |
| CN111462486B (en) | Intersection similarity measurement method based on traffic signal control | |
| CN104680775B (en) | The parallel extraction method of the real-time optimal path of road network based on huge traffic data | |
| Liu et al. | Learning to route via theory-guided residual network | |
| CN112631338A (en) | Air route planning method and device, computer equipment and storage medium | |
| Xia et al. | Parallelized extraction of traffic state estimation rules based on bootstrapping rough set | |
| Chatterjee et al. | Dynamic congestion analysis for better traffic management using social media | |
| CN110335478A (en) | Across sub-district inter-linked controlling method based on deep learning | |
| Zheng et al. | ES-band: a novel approach to coordinate green wave system with adaptation evolutionary strategies | |
| CN114822049B (en) | Vehicle flow direction monitoring and analyzing method and system | |
| Sbai et al. | Big Data Analytics architecture for intelligent transportation systems: A tunisian case study | |
| CN113793516B (en) | Main path-based signalized intersection control method, terminal and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |