CN105894802A - GPS data-based traffic congestion propagation path calculating method - Google Patents
GPS data-based traffic congestion propagation path calculating method Download PDFInfo
- Publication number
- CN105894802A CN105894802A CN201510177699.3A CN201510177699A CN105894802A CN 105894802 A CN105894802 A CN 105894802A CN 201510177699 A CN201510177699 A CN 201510177699A CN 105894802 A CN105894802 A CN 105894802A
- Authority
- CN
- China
- Prior art keywords
- section
- congestion
- road
- propagation path
- period
- 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.)
- Pending
Links
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention relates to a GPS data-based traffic congestion propagation path calculating method. By processing and analyzing a large quantity of data collected by a GPS floating vehicle, an urban road network traffic congestion propagation path can be calculated. First, according to the large quantity of GPS data collected by the floating vehicle, average speed of each road segment in a road network is calculated; according to the average speed of each road segment, a congestion time period of each road segment is calculated; according to the congestion time period of each road segment in the road network, a congestion propagation path and key road segments are calculated. According to the GPS data-based traffic congestion propagation path calculating method, based on a sequence of the congestion time period of each road segment in a regional road network, road congestion propagation condition in the road network can be estimated. The calculated key road segments in the propagation path can be applied to police strength deployment, congestion prediction and road planning.
Description
Technical field
The present invention relates to a kind of traffic congestion propagation path computational methods based on gps data, belong to intelligent transportation system category.
Background technology
Traffic congestion has had a strong impact on daily life, becomes the focus that entire society pays close attention to.Administration means can alleviate traffic congestion to a certain extent.Wherein, traffic police is a kind of important way dredging of specific road section.But, due to reasons such as congested link is many, police strength limited amounts, it is difficult to police strength to be deployed to each congested link.It is, thus, sought for reasonable manner goes to dispose police strength.The reflection of traffic congestion propagation path is situation about propagating between adjacent section of blocking up.By blocking up, propagation path computational methods can automatically find out the key road segment causing region to block up.Accordingly, it is possible not only to promote rationally to dispose police strength, and is conducive to predicting more accurately the section got congestion, more reasonably carries out roading.
A large amount of running datas of Floating Car on GPS (GPS) energy real-time collecting road surface, including the information such as position and speed；Data according to a large amount of Floating Car can estimate the average overall travel speed of road, calculates the time of origin that blocks up；Last binding number word map, the propagation path that blocks up in zoning.This is a kind of method automatically calculating the propagation path that blocks up based on Floating Car gps data, is one new application of intelligent transportation system.
Finding by inquiry, the research work that congestion in road is relevant includes: Japanese plum is refined etc. " traffic congestion based on event simulation and the dissipation strategy study " write, have studied the propagation condition blocking up in single road.The present invention have studied the propagation condition of adjacent road in road network that blocks up, different；" a kind of traffic jam detection method based on bus data acquisition " that Zou rich people etc. write, it is provided that a kind of calculating is blocked up the method for time of origin, and the method for this kind of calculating congestion in road period is a lot.Unlike the present invention, the present invention is to find out the communication process blocking up in road network.
GPS is that one has comprehensive, round-the-clock, all the period of time, high-precision satellite navigation system.Utilize GPS position location satellite, carry out the system positioning, navigating, referred to as GPS the most in real time, be called for short GPS.
Floating Car generally refers to the vehicle being mounted with vehicle-mounted GPS positioning system and travelling on major urban arterial highway.
Numerical map is the digital beings form of map made of paper, is terrain feature and the discrete data of phenomenon in certain coordinate system with coordinate and the attribute determined, in discernible that can summarize on storage medium, the orderly set of computer.
Block up propagation: after a road gets congestion, upstream or the traffic conditions of downstream road can be affected.If this impact runs up to a certain degree, blocking up of downstream road can be caused.Then, this blocking up may proceed to spread in road network, causes blocking up of whole road network.Start to block up during whole road network gets congestion from a road, block up and can propagate between the different roads in road network.The precedence that such a is propagated, referred to as propagation path.
Orthographic projection: projection line is perpendicular to the projection on perspective plane and belongs to rectangular projection, also referred to as parallel projection.In " TOPSIS method orthographic projection " of based on " vertical plane " distance literary composition that Hua little Yi et al. writes, propose the method and can be used for the GPS sampled point matching process to section.
Summary of the invention
It is an object of the invention to overcome the deficiencies in the prior art, by the process of the mass data that GPS Floating Car gathers and analysis, calculating the traffic congestion propagation path of city road network.The technology of the present invention is contemplated that: first, a large amount of gps datas gathered according to Floating Car, calculates the average speed in every section in road network；Then, according to the average speed of every road, blocking up the time period of section is calculated；Finally, the time period got congestion according to every road in road network, block up propagation path and key road segment are calculated.
In order to achieve the above object, the technical step that the present invention provides is as follows:
Step A: calculate Road average-speed
Use the data that GPS Floating Car gathers, calculate the average speed within a time cycle of a certain section in road network.One time cycle can be set to 5,10 or 15 minutes.The gps data collected in this time cycle is the set of series of points, is designated as, and wherein represents the longitude collected a little, represents the latitude collected a little, for gathering the instantaneous velocity of moment point.First, the section longitude and latitude information provided according to numerical map, and the latitude and longitude information of the point collected, by Point matching to corresponding section.This matching process can use orthographic projection etc.；Then, project in calculating this cycle section speed average a little, be designated as.Represent the section all vehicle average overall travel speeds the time cycle.
Step B: calculate section and block up the period
Calculating all sections in road network to get congestion the period, the period of blocking up can search in the range of whole day, it is also possible to searches in the range of half a day, such as 6:00-12:00,13:00-19:00 etc..First, the section average speed of one day is calculated；Then, the state of labelling each time cycle, for section, time cycle, state is.Status indication standard is as follows:
Wherein ,-1 expression is blocked up, and 0 represents general, and 1 represents unobstructed.Then, there is the period blocked up continuously in statistics, reduces the impact on labelling result of blocking up that accidentalia causes.If three time cycles all get congestion, block up the period it is, be considered one；Finally, connect adjacent blocking up the period, then the period of blocking up in section is labeled as, and wherein, represents the time cycle, represents the time cycle.
Step C: calculate the propagation path that blocks up
The scope (alternatively administrative region) that C1 delimit according to user, the section got congestion the earliest in determining this Regional Road Network is gathered.First, according to the time got congestion, the section set got congestion the earliest is selected.Because get congestion at first potentially includes multiple section, so being a set；Then, add up with gather the set of the non-conterminous section got congestion the earliest (be not belonging to and and adjacent section, middle section, and be less than),；Finally, merge two set, for the set got congestion the earliest.
C2 grey iterative generation blocks up propagation path.For ensuing each time cycle, extend set of blocking up, until this period terminates.For any, if, just section and between set up connect.This process, is equivalent to be combined into starting point with collection, forms a directed acyclic graph, and this directed acyclic graph blocks up propagation path exactly.
C3 key road segment selects.Key road segment includes the section set of section set and the out-degree maximum got congestion at first.The section got congestion at first is the section causing whole road network to block up, so the solution for blocking up is most important.The section set that out-degree is maximum, namely can cause the section that most section blocks up.These sections cause blocking up of a large amount of section.Therefore this two classes road can be selected as key road segment.
The invention have the benefit that the computational methods providing a kind of traffic congestion propagation path based on Floating Car GPS.The method can be blocked up according to single road in Regional Road Network the precedence relationship of time, estimates congestion in road propagation condition in road network.The key road segment calculated according to propagation path, can be used for police strength and disposes, blocks up and predict and roading.
Accompanying drawing explanation
Fig. 1 is a kind of based on gps data the traffic congestion propagation path computational methods flow chart of the embodiment of the present invention.
Fig. 2 is preferred embodiment at 6 o'clock in morning on July 21st, 2012 to the congestion in road in region, ten two Xihu Districts to convey feelings the mark figure of condition.
Detailed description of the invention
For making the purpose of the present invention, technical scheme and advantage clearer, develop simultaneously embodiment referring to the drawings, and the present invention is described in detail.
With reference to Fig. 1, the calculation process of the present invention includes:
Step 101: calculate Road average-speed
Use the data that GPS Floating Car gathers, calculate the average speed within a time cycle of the road that road section ID is 0000000001401 in the road network of Hangzhou.The gps data collected in this time cycle is the set of series of points, is designated as.First, the section longitude and latitude information provided according to numerical map, and the latitude and longitude information of the point collected, use orthographic projection by Point matching to corresponding section；Then, project in calculating this cycle section speed average a little, be designated as.
Step 102: calculate section and block up the period
Choose this period in the 6 o'clock to ten two morning on July 21st, 2012, in units of 15 minutes, use averaging method that Floating Car gps data is processed, obtain this section average speed in each time period.Following data be on July 21st, 2012 Hangzhou road ID be the road speeds of 0000000001401:
33.6 32.57 26.83 32 18.5 41.4 28.5 31.63 32.88 23.7 25.45 33.37 34.14 28.5 30.98 27 25.26 25.34 24.38 32.55 34.8 19.67 27.3 19
From 6 o'clock to 12 o'clock every the road speeds of 15 minutes, 24 time points altogether.If having 0, then it it is this time point shortage of data.
Then, the traffic behavior of the corresponding road section of labelling each time cycle, this road section traffic volume status indication result is as follows:
0,0,0,0,0,0 ,-1 ,-1 ,-1 ,-1,0 ,-1 ,-1 ,-1,0,0,1,0,0,0,0 ,-1,0,0
Then, there is the period blocked up continuously in statistics, reduces the impact on labelling result of blocking up that accidentalia causes.It is then one to block up the period；Finally, connect adjacent blocking up the period, then the period of blocking up in section is labeled as.
Step 103: calculate the propagation path that blocks up
1031 users delimit region, Xihu District, the section set got congestion the earliest in determining this Regional Road Network.First, according to the time got congestion, the section set got congestion the earliest is selected；Then, add up with gather the set of the non-conterminous section got congestion the earliest (be not belonging to and and adjacent section, middle section, and be less than),；Finally, merge two set, for the set got congestion the earliest.
1032 grey iterative generations block up propagation path.For each time cycle started from 7:15, extend set of blocking up, until 9:00 terminates.For any section got congestion the earliest, if, just section and between set up connect.This process, is equivalent to be combined into starting point with collection, forms a directed acyclic graph, and this directed acyclic graph blocks up propagation path exactly, and the congestion in road at 6 o'clock in the morning on July 21st, 2012 to region, ten two Xihu Districts conveys feelings condition, as shown in Figure 2.
1033 key road segments select.Key road segment includes the section set of section set and the out-degree maximum got congestion at first.The section got congestion at first is the section causing whole road network to block up, so the solution for blocking up is most important.The section set that out-degree is maximum, namely can cause the section that most section blocks up.These sections cause blocking up of a large amount of section, other section.Therefore in this region, in time period, civilian three-Feng Tanlu-Gu Dun roads, West Road, a civilian-Feng Tanlu-Gu Dun road, West Road, civilian two-Gu Cuilu-Jing Zhou roads, West Road, Xueyuan Road-HUaxing Road-temmoku hill path, Xueyuan Road-civilian two-Gu Cui roads, West Road can be selected as key road segment.
Claims (6)
1. traffic congestion propagation path computational methods based on gps data, it is characterised in that specifically include that
(1) GPS floating car data is used to calculate Road average-speed；
(2) calculate section to block up the period；
(3) propagation path that blocks up is calculated.
2. block up the method for period according to the section that calculates described in claim 1 (2), it is characterised in that calculate section one day
Average speed, the state of labelling each time cycle (can be set to 5,10 or 15 minutes), adding up three times occurs continuously
Period blocked up, and connect adjacent blocking up the period, then the period of blocking up in section is labeled as, and wherein, represents the time cycle,
Represent the time cycle.
3. block up according to the calculating described in claim 1 (3) method of propagation path, it is characterised in that:
(1) the section set got congestion the earliest in determining net；
(2) grey iterative generation blocks up propagation path；
(3) key road segment is selected.
4. according to the section set got congestion the earliest in the determination net described in claim 3 (1), it is characterised in that: according to
The time got congestion, select the section set got congestion the earliest, add up and gather the non-conterminous section got congestion the earliest
Set (be not belonging to and in adjacent section, and be less than), merge two collection and be combined into the set got congestion the earliest, wherein,.
5. block up propagation path according to the grey iterative generation described in claim 3 (2), it is characterised in that: for week each time
Phase, extension blocks up set, until this period terminate, for any, if, just section and between foundation connection.
6. according to the selection key road segment described in claim 3 (3), it is characterised in that: select the section got congestion at first
Set, namely causes the section that whole road network blocks up；Select the section set that out-degree is maximum, namely can cause multichannel
The section that section is blocked up.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510177699.3A CN105894802A (en) | 2015-04-10 | 2015-04-10 | GPS data-based traffic congestion propagation path calculating method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510177699.3A CN105894802A (en) | 2015-04-10 | 2015-04-10 | GPS data-based traffic congestion propagation path calculating method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105894802A true CN105894802A (en) | 2016-08-24 |
Family
ID=57002751
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510177699.3A Pending CN105894802A (en) | 2015-04-10 | 2015-04-10 | GPS data-based traffic congestion propagation path calculating method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105894802A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106530694A (en) * | 2016-11-07 | 2017-03-22 | 深圳大学 | Traffic congestion prediction method and system based on traffic congestion propagation model |
| CN106981194A (en) * | 2017-05-02 | 2017-07-25 | 北京大学 | A kind of recognition methods of highway network key road segment |
| CN107134134A (en) * | 2017-01-23 | 2017-09-05 | 北京博研智通科技有限公司 | Reduce road blocked coefficient to improve the method and system of road passage capability |
| CN108335483A (en) * | 2017-12-25 | 2018-07-27 | 深圳先进技术研究院 | The estimating method and its system of traffic congestion diffusion path |
| CN108806257A (en) * | 2018-07-04 | 2018-11-13 | 河海大学 | A kind of recognition methods in congestion in road region and congested link |
| CN109615851A (en) * | 2018-07-30 | 2019-04-12 | 北京航空航天大学 | A kind of sensing node choosing method in intelligent perception system based on key road segment |
| CN109740411A (en) * | 2018-11-09 | 2019-05-10 | 南京大学 | Intelligent monitor system, monitoring method based on recognition of face and quickly go out alarm method |
| CN110609853A (en) * | 2019-09-18 | 2019-12-24 | 青岛海信网络科技股份有限公司 | Trunk line frequent congestion propagation rule mining method and device |
| CN115294768A (en) * | 2022-08-02 | 2022-11-04 | 阿波罗智联(北京)科技有限公司 | Traffic jam state analysis method, device, equipment and storage medium |
| CN117037501A (en) * | 2023-10-10 | 2023-11-10 | 成都创一博通科技有限公司 | Urban parking management method and management system based on artificial intelligence |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020026277A1 (en) * | 2000-05-10 | 2002-02-28 | Boris Kerner | Method for traffic situation determination on the basis of reporting vehicle data for a traffic network with traffic-controlled network nodes |
| EP2521110A1 (en) * | 2011-05-06 | 2012-11-07 | Alcatel Lucent | A small cell base station, a method of controlling movement of a machine-type-communication (MTC) unit, and a braking system for a vehicle |
| CN103000027A (en) * | 2012-12-19 | 2013-03-27 | 安徽科力信息产业有限责任公司 | Intelligent traffic guidance method based on floating car under congestion condition |
| CN103632541A (en) * | 2012-08-22 | 2014-03-12 | 北京掌城科技有限公司 | Traffic incident road chain detecting and data filing method |
| CN103839411A (en) * | 2014-03-20 | 2014-06-04 | 中国航天系统工程有限公司 | Accident influence range analysis method based on dynamic traffic information |
| CN105006167A (en) * | 2014-04-18 | 2015-10-28 | 杭州远眺科技有限公司 | Research method for traffic jam propagation path |
-
2015
- 2015-04-10 CN CN201510177699.3A patent/CN105894802A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020026277A1 (en) * | 2000-05-10 | 2002-02-28 | Boris Kerner | Method for traffic situation determination on the basis of reporting vehicle data for a traffic network with traffic-controlled network nodes |
| EP2521110A1 (en) * | 2011-05-06 | 2012-11-07 | Alcatel Lucent | A small cell base station, a method of controlling movement of a machine-type-communication (MTC) unit, and a braking system for a vehicle |
| CN103632541A (en) * | 2012-08-22 | 2014-03-12 | 北京掌城科技有限公司 | Traffic incident road chain detecting and data filing method |
| CN103000027A (en) * | 2012-12-19 | 2013-03-27 | 安徽科力信息产业有限责任公司 | Intelligent traffic guidance method based on floating car under congestion condition |
| CN103839411A (en) * | 2014-03-20 | 2014-06-04 | 中国航天系统工程有限公司 | Accident influence range analysis method based on dynamic traffic information |
| CN105006167A (en) * | 2014-04-18 | 2015-10-28 | 杭州远眺科技有限公司 | Research method for traffic jam propagation path |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106530694B (en) * | 2016-11-07 | 2019-01-11 | 深圳大学 | A kind of traffic congestion prediction technique and system based on traffic congestion propagation model |
| CN106530694A (en) * | 2016-11-07 | 2017-03-22 | 深圳大学 | Traffic congestion prediction method and system based on traffic congestion propagation model |
| CN107134134A (en) * | 2017-01-23 | 2017-09-05 | 北京博研智通科技有限公司 | Reduce road blocked coefficient to improve the method and system of road passage capability |
| CN106981194B (en) * | 2017-05-02 | 2019-06-07 | 北京大学 | A kind of recognition methods of highway network key road segment |
| CN106981194A (en) * | 2017-05-02 | 2017-07-25 | 北京大学 | A kind of recognition methods of highway network key road segment |
| CN108335483A (en) * | 2017-12-25 | 2018-07-27 | 深圳先进技术研究院 | The estimating method and its system of traffic congestion diffusion path |
| CN108806257A (en) * | 2018-07-04 | 2018-11-13 | 河海大学 | A kind of recognition methods in congestion in road region and congested link |
| CN108806257B (en) * | 2018-07-04 | 2020-11-10 | 河海大学 | Method for identifying congested areas and congested road sections |
| CN109615851A (en) * | 2018-07-30 | 2019-04-12 | 北京航空航天大学 | A kind of sensing node choosing method in intelligent perception system based on key road segment |
| CN109740411A (en) * | 2018-11-09 | 2019-05-10 | 南京大学 | Intelligent monitor system, monitoring method based on recognition of face and quickly go out alarm method |
| CN110609853A (en) * | 2019-09-18 | 2019-12-24 | 青岛海信网络科技股份有限公司 | Trunk line frequent congestion propagation rule mining method and device |
| CN115294768A (en) * | 2022-08-02 | 2022-11-04 | 阿波罗智联(北京)科技有限公司 | Traffic jam state analysis method, device, equipment and storage medium |
| CN117037501A (en) * | 2023-10-10 | 2023-11-10 | 成都创一博通科技有限公司 | Urban parking management method and management system based on artificial intelligence |
| CN117037501B (en) * | 2023-10-10 | 2023-12-12 | 成都创一博通科技有限公司 | Urban parking management method and management system based on artificial intelligence |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105894802A (en) | GPS data-based traffic congestion propagation path calculating method | |
| Ericsson et al. | Optimizing route choice for lowest fuel consumption–potential effects of a new driver support tool | |
| Yuan et al. | T-drive: driving directions based on taxi trajectories | |
| Strauss et al. | Speed, travel time and delay for intersections and road segments in the Montreal network using cyclist Smartphone GPS data | |
| CN102110364B (en) | Traffic information processing method and traffic information processing device based on intersections and sections | |
| CN103646187B (en) | Method for obtaining vehicle travel path and OD (Origin-Destination) matrix in statistic period | |
| CN102855760B (en) | On-line queuing length detection method based on floating vehicle data | |
| Kan et al. | Fine-grained analysis on fuel-consumption and emission from vehicles trace | |
| CN108734955A (en) | The method and device of predicting road conditions state | |
| CN102622880A (en) | Traffic information data recovery method and device | |
| CN103761430A (en) | Method for identifying peak periods of road networks on basis of floating cars | |
| CN108663064A (en) | Quickest route determines method and apparatus | |
| CN107949873A (en) | Hourage data point reuse device, hourage data adjustment method and program | |
| CN107403550B (en) | Public transport road network data acquisition method and device and terminal equipment | |
| CN111931317B (en) | Regional congestion road network boundary control method based on vehicle-mounted GPS data | |
| CN103268707A (en) | Signal regulating method for pedestrian crossing road section of bus prior passage | |
| CN107085620A (en) | A kind of taxi and subway are plugged into the querying method and system of travel route | |
| CN103679286A (en) | Path optimizing method and path optimizing device | |
| CN105427592B (en) | Map of navigation electronic based on Floating Car turns to cost computational methods | |
| Jian Daniel et al. | Research and analysis on causality and spatial-temporal evolution of urban traffic congestions—a case study on Shenzhen of China | |
| CN111508262A (en) | Intelligent management method for urban parking space resources | |
| CN102354452B (en) | Method for monitoring road conditions in real time based on simplified road network model | |
| CN110827537B (en) | Method, device and equipment for setting tidal lane | |
| CN105547316A (en) | Path searching method and system of vehicle terminal of floating vehicle | |
| KR102302486B1 (en) | Urban road speed processing method, urban road speed processing device, device and non-volatile computer storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20160824 |
|
| RJ01 | Rejection of invention patent application after publication |