CN110400461A - A kind of road network alteration detection method - Google Patents
A kind of road network alteration detection method Download PDFInfo
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- CN110400461A CN110400461A CN201910659719.9A CN201910659719A CN110400461A CN 110400461 A CN110400461 A CN 110400461A CN 201910659719 A CN201910659719 A CN 201910659719A CN 110400461 A CN110400461 A CN 110400461A
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- G—PHYSICS
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- G06F16/29—Geographical information databases
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
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- G—PHYSICS
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
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- G08G1/0125—Traffic data processing
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- G—PHYSICS
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- 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
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Abstract
A kind of road network alteration detection method, belongs to field of intelligent transportation technology.Method includes step S01, acquires vehicle track data point, and according to time sequence；Step S02 calculates the difference of temporally adjacent two track of vehicle data points, determines and retain the track of vehicle data point of crossing turning；Step S03 is clustered for the track of vehicle data point of crossing turning, identifies urban road crossing and crossing type；Step S04 compares the intersection information on intersection information and map that step S03 is obtained, and judgement gives Mapping departments end there are road change signal when new added road, disappearance road, is issued, so that Mapping departments end updates road network map.Detection information of the present invention is comprehensive, can not only find new added road, moreover it is possible to find disappearance road, be truly realized road network change real-time detection.
Description
Technical field
The invention belongs to field of intelligent transportation technology, especially a kind of road network alteration detection method.
Background technique
The update of the electronic map general period is longer, for example GOOGLE map electronic map general 1 year just updates once,
It is primary that Baidu map can accomplish that half a year updates, but this far from meets the needs of users.It is well known that Chinese Urbanization's speed
Accelerate, road update is also frequent, constantly has the road newly repaired to come into operation, while also due to regional planning or real estate are opened
Hair causes some roads to disappear.However traditional map road network update mode, need Surveying And Mapping Institute to send dedicated probe vehicle to city road
Online detection, time-consuming for this mode, and needs to put into a large amount of manpowers.Some scholars propose by satellite remote sensing images
Processing obtains new added road, and this mode is as road may be blocked by building or trees in remote sensing images, and be led
Cause identification difficult, and image procossing calculating cost is also high.
The country has expert to propose to find new added road based on Floating Car track data in recent years, is matched first by bus or train route
Algorithm.As to disclose a kind of K-means for taxi track data initial by application for a patent for invention CN201610458509.X
Cluster centre selection method, by the matched road network of car, then according to whether there is a large amount of tracks of vehicle to be not matched to map
Road network on, to detect to newly increase road.This mode can accomplish that real-time detection is newly-increased using track of vehicle data really
Add road, can accomplish to have discovered whether new added road daily.However this method is matched firstly the need of by complicated bus or train route
Algorithm (i.e. the matching of track to road), and can only accomplish that new added road is found at present, can not accomplish disappearance road and
Shi Faxian.
Summary of the invention
In view of the problems of the existing technology the present invention, proposes a kind of road network alteration detection method, can find in real time new
Increasing or disappearance road for the timely and effective progress road detection in Mapping departments end and complete real-time map update.
The technical scheme is that:
The present invention provides a kind of road network alteration detection method, comprising:
Step S01 acquires vehicle track data point, and according to time sequence；
Step S02 calculates the difference of temporally adjacent two track of vehicle data points, determines and retain the track of vehicle of crossing turning
Data point；
Step S03 is clustered for the track of vehicle data point of crossing turning, identifies urban road crossing and crossing type；
Step S04 compares the intersection information on intersection information and map that step S03 is obtained, judgement there are new added road, disappear
When lost on the way road, issues road change signal and give Mapping departments end, so that Mapping departments end updates road network map.
The present invention can accomplish the vehicle driving trace data according to one day, can not only find the road newly increased, and
It can find the road to disappear, be truly realized road network change real-time detection.And this method does not need progress track of vehicle data and arrives
The matching of road network improves calculating speed to greatly reduce calculating cost.It is newly-increased or disappearance when being detected using this method
Road generates alarm to related geographical Mapping departments, they can send mapping vehicle to leave for according to the testing result of the offer
These new added roads or disappearance road are detected and are verified, and be can be Mapping departments in this way and are accomplished to shoot the arrow at the target, greatly reduce
Manpower and material resources and time.Once measuring change road, it can provide real-time map everyday and update.
Preferably, in the step S01 track of vehicle data point include GPS coordinate, travel speed, traveling angle, when
Between.
Preferably, the step S02 includes:
Step S21, the travel speed of the temporally adjacent two track of vehicle data points of calculating is poor, travels differential seat angle, time difference；
Step S22, when travel speed is poor, travels differential seat angle, the time difference respectively meets crossing turning condition, it is determined that vehicle
Track data point is the track of vehicle data point of crossing turning, is otherwise not belonging to the track of vehicle data point of crossing turning；
Step S23, filtering are not belonging to the track of vehicle data point of crossing turning and retain the track of vehicle data point of crossing turning.
Preferably, the crossing turning condition are as follows: travel speed difference is 20-60 seconds；It travels differential seat angle and is greater than 90 degree；When
Between the distance of two neighboring track of vehicle data point be more than 100 meters.
It is obtained preferably, the distance of temporally adjacent two track of vehicle data points is calculated by GPS coordinate.
Preferably, the step S03 includes:
Step S31, for the track of vehicle data point of crossing turning, density-based algorithms are clustered；
Step S32 automatically identifies urban road crossing and crossing type according to clustering algorithm, and crossing type includes crossroad, X
Shape crossing, Y shape crossing, road circuits, T shape crossing.
Preferably, the step S04 includes:
Intersection information on the intersection information and map of step S41, obtaining step S32 identification, intersection information includes urban road crossing
And crossing type；
The intersection information that step S32 is identified match comparing, be increased newly if it exists by step S42 with the intersection information on map
Road, disappearance road then issue road change signal and give Mapping departments end.
Preferably, it is described there are the case where new added road include newly increase crossing, crossing shape occur branch increase
Variation；The case where road there are disappearance includes the variation for reducing crossing, the generation branch reduction of crossing shape.
Preferably, the track of vehicle data point is the track of vehicle data point of taxi.
Preferably, the track of vehicle data point is obtained by being equipped with the vehicle real-time detection of GPS.
The invention has the following advantages:
A kind of road network alteration detection method of the present invention:
1, have real-time, traditional map update -1 year June, this method can with day over update, as long as detect road network change,
Real-time prompting updates.
2 calculating are at low cost, and precision is high, and remote sensing image processing calculating is at high cost, and because building or trees stop,
Precision is low, and the time is long.And the existing new added road based on Floating Car track data just needs to carry out first tracing point to road network
Matching, the method for the present invention do not need carry out bus or train route matching.The present invention passes through the driving direction change value of taxi track data
Urban traffic intersection is detected, to need not move through road network calculating with existing map match, can accurately be changed road network.
3 detection informations are comprehensive, can not only find new added road, moreover it is possible to find disappearance road, it is real to be truly realized road network change
When detect.
4 is practical, can be used for map real-time update, and real-time navigation provides more true road network information for user,
To save the running time of driver, energy save the cost reduces exhaust emissions, can more reduce urban congestion.
Detailed description of the invention
Fig. 1 is a kind of flow diagram of road network alteration detection method of the present invention.
Specific embodiment
Following is a specific embodiment of the present invention in conjunction with the accompanying drawings, technical scheme of the present invention will be further described,
However, the present invention is not limited to these examples.
Such as Fig. 1, a kind of road network alteration detection method of the present invention, comprising:
Step S01 acquires vehicle track data point, and according to time sequence；
Step S02 calculates the difference of temporally adjacent two track of vehicle data points, determines and retain the track of vehicle of crossing turning
Data point；
Step S03 is clustered for the track of vehicle data point of crossing turning, identifies urban road crossing and crossing type；
Step S04 compares the intersection information on intersection information and map that step S03 is obtained, judgement there are new added road, disappear
When lost on the way road, issues road change signal and give Mapping departments end, so that Mapping departments end updates road network map.
The track of vehicle data point of this method is to acquire acquisition in real time using the vehicle for being equipped with GPS gathers terminal.Vehicle
A large amount of GPS track information are collected in the process of moving.Preferably, vehicle selects taxi, can be traveling in each road in city daily
Section, can grasp road information in real time.
Track of vehicle data point includes GPS coordinate, travel speed, traveling angle, time in the step S01.Each go out
Hiring a car has respective license plate, and license plate number is the ID of corresponding vehicle.According to time-sequencing, it can be seen that the vehicle under corresponding vehicle ID
Track data point arranges to form driving trace with time sequencing.Driving trace is since road limits, and there are on-rectilinear movement rails
Mark, if crossing turn, as caused by lane change turn offset, as road it is not straight caused by traveling turning offset, such as travel mesh
Ground position caused by traveling turning offset situations such as.
Since there are the offsets of above-mentioned various situations, if only detection travels differential seat angle to judge the track of vehicle of crossing turning
If data point, it is possible that detection error, as by the track of vehicle data point of lane change in normally travel or into parking lot
Track of vehicle data point or the track of vehicle data point that other are not belonging to crossing turning into track of vehicle data point of gas station etc.
Consider then count error at that, can not effectively identify that road is newly-increased or disappears.For this purpose, not only to consider to travel differential seat angle, also
Consider other factors.
Specifically, the step S02 includes:
Step S21, the travel speed of the temporally adjacent two track of vehicle data points of calculating is poor, travels differential seat angle, time difference；
Step S22, when travel speed is poor, travels differential seat angle, the time difference respectively meets crossing turning condition, it is determined that vehicle
Track data point is the track of vehicle data point of crossing turning, is otherwise not belonging to the track of vehicle data point of crossing turning；
Step S23, filtering are not belonging to the track of vehicle data point of crossing turning and retain the track of vehicle data point of crossing turning.
The crossing turning condition are as follows: travel speed difference is 20-60 seconds；It travels differential seat angle and is greater than 90 degree；Temporally adjacent two
The distance of a track of vehicle data point is more than 100 meters.Only the case where three's condition is all satisfied, above-mentioned special case could be excluded,
The track of vehicle data point for ensuring compliance with condition is the track of vehicle data point of crossing turning.
The operating range difference is calculated by Pythagorean theorem and is obtained using the GPS coordinate of two track of vehicle data points,
Also the product that can use travel speed difference and running time difference calculates.
Specifically, the step S03 includes:
Step S31, for the track of vehicle data point of crossing turning, density-based algorithms are clustered；
Step S32 automatically identifies urban road crossing and crossing type according to clustering algorithm, and crossing type includes crossroad, X
Shape crossing, Y shape crossing, road circuits, T shape crossing.
DBSCAN clustering algorithm can be used in density-based algorithms.First using each data point as the center of circle, with
Eps describes a circle for radius.This circle is referred to as the eps neighborhood of xi.Secondly, being carried out to the point for including in this circle
It counts.If the number of the point inside a circle has been more than density threshold MinPts, the center of circle of the circle is denoted as core
Heart point, also known as kernel object.If the number put in the eps neighborhood of some point is less than density threshold but falls in core point
In neighborhood, then the point is referred to as boundary point.It is exactly noise spot neither core point is also not the point of boundary point.Third, core point
All points in the eps neighborhood of xi are all that the direct density of xi is through.Finally, if making xi and xj for xk
Can be reachable by xk density, then, just xi with xj density is claimed to be connected.The point that density is connected is linked together, is just formed
Our clustering cluster.All traffic intersections and crossing type of city road network are formed after cluster.Traffic intersection, crossing class
Type constitutes intersection information.
Specifically, the step S04 includes:
Intersection information on the intersection information and map of step S41, obtaining step S32 identification, intersection information includes urban road crossing
And crossing type；
The intersection information that step S32 is identified match comparing, be increased newly if it exists by step S42 with the intersection information on map
Road, disappearance road then issue road change signal and give Mapping departments end.
It is described there are the case where new added road include newly increase crossing, the variation that increases of branch occurs for crossing shape.It is described
It include the variation for reducing crossing, the generation branch reduction of crossing shape there are the case where disappearance road.For example, when straight line crossing becomes
When crossroad, that is, newly increase crossing, from scratch, and crossing shape becomes a plurality of from one at crossing.Then think new added road,
Road change occurs.For example, that is, crossing shape reduces one from a plurality of when cross building mouth becomes at T-shaped crossing.Then think to disappear
Road change occurs for road.After judging road change, signal (comprising information after intersection information and change) is changed by road
It is sent to Mapping departments end, by Mapping departments end according to intersection information to carrying out detection confirmation on the spot.Once confirmation is strictly according to the facts, measurement
It obtains real road shape and draws road, and issue map rejuvenation prompt.
It should be understood by those skilled in the art that foregoing description and the embodiment of the present invention shown in the drawings are only used as illustrating
And it is not intended to limit the present invention.The purpose of the present invention completely effectively realizes.Function and structural principle of the invention is in reality
It applies and shows and illustrate in example, under without departing from the principle, embodiments of the present invention can have any deformation or modification.
Claims (10)
1. a kind of road network alteration detection method characterized by comprising
Step S01 acquires vehicle track data point, and according to time sequence；
Step S02 calculates the difference of temporally adjacent two track of vehicle data points, determines and retain the track of vehicle of crossing turning
Data point；
Step S03 is clustered for the track of vehicle data point of crossing turning, identifies urban road crossing and crossing type；
Step S04 compares the intersection information on intersection information and map that step S03 is obtained, judgement there are new added road, disappear
When lost on the way road, issues road change signal and give Mapping departments end, so that Mapping departments end updates road network map.
2. a kind of road network alteration detection method according to claim 1, which is characterized in that vehicle rail in the step S01
Mark data point includes GPS coordinate, travel speed, traveling angle, time.
3. a kind of road network alteration detection method according to claim 2, which is characterized in that the step S02 includes:
Step S21, the travel speed of the temporally adjacent two track of vehicle data points of calculating is poor, travels differential seat angle, time difference；
Step S22, when travel speed is poor, travels differential seat angle, the time difference respectively meets crossing turning condition, it is determined that vehicle
Track data point is the track of vehicle data point of crossing turning, is otherwise not belonging to the track of vehicle data point of crossing turning；
Step S23, filtering are not belonging to the track of vehicle data point of crossing turning and retain the track of vehicle data point of crossing turning.
4. a kind of road network alteration detection method according to claim 3, which is characterized in that the crossing turning condition are as follows:
Travel speed difference is 20-60 seconds；It travels differential seat angle and is greater than 90 degree；The distance of temporally adjacent two track of vehicle data points is more than
100 meters.
5. a kind of road network alteration detection method according to claim 4, which is characterized in that temporally adjacent two vehicles
The distance of track data point is calculated by GPS coordinate and is obtained.
6. a kind of road network alteration detection method according to claim 1, which is characterized in that the step S03 includes:
Step S31, for the track of vehicle data point of crossing turning, density-based algorithms are clustered；
Step S32 automatically identifies urban road crossing and crossing type according to clustering algorithm, and crossing type includes crossroad, X
Shape crossing, Y shape crossing, road circuits, T shape crossing.
7. a kind of road network alteration detection method according to claim 1, which is characterized in that the step S04 includes:
Intersection information on the intersection information and map of step S41, obtaining step S32 identification, intersection information includes urban road crossing
And crossing type；
The intersection information that step S32 is identified match comparing, be increased newly if it exists by step S42 with the intersection information on map
Road, disappearance road then issue road change signal and give Mapping departments end.
8. a kind of road network alteration detection method as claimed in claim 7, which is characterized in that the case where there are new added roads packet
It includes and newly increases crossing, the variation that branch increases occurs for crossing shape；The case where road there are disappearance includes reducing crossing, road
The variation of mouth-shaped generation branch reduction.
9. a kind of road network alteration detection method according to claim 1, which is characterized in that the track of vehicle data point is
The track of vehicle data point of taxi.
10. a kind of road network alteration detection method according to claim 1, which is characterized in that the track of vehicle data point
It is to be obtained by being equipped with the vehicle real-time detection of GPS.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111127891A (en) * | 2019-12-27 | 2020-05-08 | 中国交通通信信息中心 | Road network checking method based on floating car track big data |
| CN113012454A (en) * | 2019-12-20 | 2021-06-22 | 中移(上海)信息通信科技有限公司 | Map message, automatic driving method, device, equipment and medium |
| CN113269964A (en) * | 2021-05-24 | 2021-08-17 | 哈尔滨翼成科技有限公司 | Lane dynamic demarcation method and equipment |
| CN116052453A (en) * | 2022-12-30 | 2023-05-02 | 广州小鹏自动驾驶科技有限公司 | Road junction determining method, device, electronic equipment and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101957208A (en) * | 2010-09-09 | 2011-01-26 | 福建工程学院 | Method for discovering new road based on probe vehicle technology |
| CN102561121A (en) * | 2011-01-01 | 2012-07-11 | 谢志浩 | Now urban traffic network |
| CN104504897A (en) * | 2014-09-28 | 2015-04-08 | 北京工业大学 | Intersection traffic flow characteristic analysis and vehicle moving prediction method based on trajectory data |
| CN105679027A (en) * | 2016-02-23 | 2016-06-15 | 衡阳师范学院 | Traffic road change condition statistic method |
| CN105788273A (en) * | 2016-05-18 | 2016-07-20 | 武汉大学 | Urban intersection automatic identification method based on low precision space-time trajectory data |
| CN106778605A (en) * | 2016-12-14 | 2017-05-31 | 武汉大学 | Remote sensing image road net extraction method under navigation data auxiliary |
| CN106840176A (en) * | 2016-12-28 | 2017-06-13 | 济宁中科先进技术研究院有限公司 | GPS space-time datas increment road network real-time update and path matching system |
| CN107170236A (en) * | 2017-06-14 | 2017-09-15 | 中山大学 | A kind of important intersection extracting method of road network based on floating car data |
| CN109215372A (en) * | 2018-10-15 | 2019-01-15 | 百度在线网络技术（北京）有限公司 | Road network information update method, device and equipment |
-
2019
- 2019-07-22 CN CN201910659719.9A patent/CN110400461B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101957208A (en) * | 2010-09-09 | 2011-01-26 | 福建工程学院 | Method for discovering new road based on probe vehicle technology |
| CN102561121A (en) * | 2011-01-01 | 2012-07-11 | 谢志浩 | Now urban traffic network |
| CN104504897A (en) * | 2014-09-28 | 2015-04-08 | 北京工业大学 | Intersection traffic flow characteristic analysis and vehicle moving prediction method based on trajectory data |
| CN105679027A (en) * | 2016-02-23 | 2016-06-15 | 衡阳师范学院 | Traffic road change condition statistic method |
| CN105788273A (en) * | 2016-05-18 | 2016-07-20 | 武汉大学 | Urban intersection automatic identification method based on low precision space-time trajectory data |
| CN106778605A (en) * | 2016-12-14 | 2017-05-31 | 武汉大学 | Remote sensing image road net extraction method under navigation data auxiliary |
| CN106840176A (en) * | 2016-12-28 | 2017-06-13 | 济宁中科先进技术研究院有限公司 | GPS space-time datas increment road network real-time update and path matching system |
| CN107170236A (en) * | 2017-06-14 | 2017-09-15 | 中山大学 | A kind of important intersection extracting method of road network based on floating car data |
| CN109215372A (en) * | 2018-10-15 | 2019-01-15 | 百度在线网络技术（北京）有限公司 | Road network information update method, device and equipment |
Non-Patent Citations (2)
| Title |
|---|
| 唐炉亮 等: "利用轨迹大数据进行城市道路交叉口识别及结构提取", 《测绘学报》 * |
| 陆锋 等: "一种支持城市路网交通关系自动化的智能模型", 《测绘学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113012454A (en) * | 2019-12-20 | 2021-06-22 | 中移(上海)信息通信科技有限公司 | Map message, automatic driving method, device, equipment and medium |
| CN111127891A (en) * | 2019-12-27 | 2020-05-08 | 中国交通通信信息中心 | Road network checking method based on floating car track big data |
| CN113269964A (en) * | 2021-05-24 | 2021-08-17 | 哈尔滨翼成科技有限公司 | Lane dynamic demarcation method and equipment |
| CN116052453A (en) * | 2022-12-30 | 2023-05-02 | 广州小鹏自动驾驶科技有限公司 | Road junction determining method, device, electronic equipment and storage medium |
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