CN109816982B - Virtual road network non-motor lane attribute correction method based on shared bicycle track - Google Patents
Virtual road network non-motor lane attribute correction method based on shared bicycle track Download PDFInfo
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Abstract
The invention discloses a virtual road network non-motor lane attribute correction method based on shared bicycle track, which comprises a field processing and multi-source data loading method of the shared bicycle track data, an acquisition method of a frequency distribution histogram of the shared bicycle track, an acquisition method of skewness coefficients and kurtosis coefficients of the frequency distribution of the shared bicycle track, a non-motor lane isolation mode and a width judgment method. The method can correct the errors of the isolation mode and the width of the non-motor vehicle lane in the virtual road network according to the shared bicycle track data, so that the urban virtual traffic system platform is more accurate and intelligent, the workload of manual correction is reduced, and the efficiency is improved.
Description
Technical Field
The invention relates to the field of urban road traffic management and simulation, in particular to a virtual road network non-motor lane attribute correction method based on shared bicycle tracks.
Background
The urban slow traffic system comprises a walking system and a non-motor vehicle system, and is an important component in urban comprehensive traffic. The bicycle-based non-motor vehicle traffic system has very important significance for promoting people-oriented and low-carbon environment-friendly urban traffic construction at the present stage. With the further development of the innovative mode of combining the internet plus with the sharing economy, the sharing bicycle becomes an important mode for the residents in the city to go out. The sharing bicycle travel mode promotes residents to select green travel on one hand, and accumulates massive original traffic data on the other hand, so that richer data are provided for planning, construction and management and control of the urban traffic system.
With the rapid development of information technology, the planning, construction and management and control of traffic systems increasingly depend on urban virtual traffic system platforms constructed based on big data. The urban virtual traffic system platform can centralize multi-source data in one system, comprehensively extract effective information of various data and process the effective information, reproduce complex traffic phenomena on the virtual platform, and an analyst can perform various operations according to different services to solve problems in time. However, in the road network construction work of the urban virtual traffic system platform, the width of the non-motor vehicle lane generally adopts the recommended value of an open source electronic map, the error is large, and meanwhile, the information of the isolation mode of part of road sections is wrong. At present, the problem is solved by adopting a manual identification method mostly, related materials of government departments are obtained through early-stage traffic investigation or field measurement is carried out, and the workload is large.
At present, the intelligent lock of a part of sharing bicycle enterprises is continuously optimized, and now supports a three-mode satellite positioning scheme of a Beidou positioning system + a GPS (global positioning system) + a GLONASS (Glonass satellite navigation system), and a DGPS (differential global positioning system) positioning technology is applied to improve the positioning precision of a bicycle to a sub-meter level. The isolation and width of the urban non-motor vehicle lane constrain the microscopic movement of the rider, so that the road attribute of the non-motor vehicle lane can be judged through the track data of the bicycle. The massive and accurate sharing of the single-vehicle track data provides a new way for matching the urban road data through the single-vehicle track point data and further correcting the non-motor lane attribute of the urban road.
Disclosure of Invention
In order to solve the problems, the invention provides a virtual road network non-motor lane attribute correction method based on shared bicycle tracks, which utilizes mass and accurate shared bicycle track data and is assisted with the analysis of mathematical statistics to judge the isolation mode and width of a specified non-motor lane, thereby providing a high-efficiency method for the basic road network correction of an urban virtual traffic system platform. The method can correct the attribute of the non-motor lane in the virtual road network according to the shared bicycle track data, so that the urban virtual traffic platform is more accurate and intelligent, the workload of manual correction is reduced, and the efficiency is improved, therefore, the invention provides the virtual road network non-motor lane attribute correction method based on the shared bicycle track, which comprises the following steps:
(1) a field processing and multi-source data loading method for sharing single-vehicle track data;
(2) a shared bicycle track frequency distribution histogram acquisition method;
(3) a skewness coefficient and kurtosis coefficient acquisition method for sharing the single-vehicle track frequency distribution;
(4) a method for discriminating the isolation mode and width of a non-motor vehicle lane.
According to the further improvement of the invention, in the step (1), the original shared bicycle track data comprises an order id, a user id, a vehicle id, a track point number and time, longitude and latitude corresponding to the track point. Setting a reference time interval as T1-T2, extracting track point longitude and latitude data in the reference time interval to obtain shared bicycle track point longitude and latitude data, and loading the processed shared bicycle track data into a platform; the original urban road network is loaded into an urban virtual traffic system platform, the platform can display the urban road network and road section attributes, a road to be researched is designated, the ascending or descending direction of the road to be researched is selected, in the platform, the urban road is not a straight line but a curve with a certain linear shape, in the platform, the starting point and the ending point of one road are intersections of the roads in a road network, the roads pass through different topological points, and the road sections among the topological points are straight lines, so that the roads have a specific linear shape, the number of the topological points passed by the selected road is recorded as m, and the roads can be recorded as m +1 road sections which pass through the topological points from the starting point to the ending point in sequence.
The invention further improves, in the step (2), the length of the road section i is recorded as L, the initial point of the road section i is recorded as the origin of coordinates, the advancing direction is recorded as the positive direction of the Y axis, the positive direction of the Y axis rotates rightwards for 90 degrees and is recorded as the positive direction of the X axis, the shared single-vehicle track point in a rectangular frame enclosed by (0, 0), (0, L), (50m, L), (50m, 0) is projected to the X axis to obtain a point set D, the sample size of the point set D is judged, when the sample size N is not more than the minimum allowable sample size N' as 1000, the judgment error is probably caused by accidental errors, so a manual identification method is adopted; when the sample size is larger than the minimum allowable sample size, the operation can be continued, the frequency of the point falling in each interval of the X axis is counted by taking 0.1m as the interval length, the group with the frequency smaller than 0.01 is discarded, and the frequency distribution histogram of the shared bicycle track of the road section i is obtained.
The invention is further improved, the skewness coefficient s and the kurtosis coefficient k of the shared bicycle track probability distribution of the road section i are calculated in the step (3), and the calculation method of the skewness coefficient s and the kurtosis coefficient k refers to the formula:
wherein:
n: sharing the sample capacity of the abscissa of the bicycle track;
xj: the abscissa of the jth shared bicycle trajectory;
In a further improvement of the present invention, in the step (4), the skewness coefficient threshold α is set to 1 and the kurtosis coefficient β is set to 1, the absolute value of the skewness coefficient s is recorded as | s |, and when | s | is smaller than α, the form of the non-motor vehicle lane is determined to be physically isolated; when | s | is not less than alpha and k is not less than beta, judging that the form of the non-motor lane is the lineation isolation; when | s | is not less than alpha and k is less than beta, judging that no isolation facility exists between the motorway and the non-motorway on the road section, and defaulting the width of the non-motorway to be W-0 when no isolation facility exists between the motorway and the non-motorway; when the form of the non-motor vehicle lane is physical isolation, respectively counting and accumulating 47.5% of points leftwards and rightwards by taking an average value in a frequency distribution histogram as a center, and recording the corresponding accumulated length of the 95% of points on an X axis as the width of the non-motor vehicle lane; when the shape of the non-motor vehicle lane is the scribe isolation, taking the average value in the frequency distribution histogram as the center, counting and accumulating 27.5% of points leftwards, counting and accumulating 47.5% of points rightwards, recording the corresponding accumulated length of the 75% of points on the X axis as the width of the non-motor vehicle lane, and circularly performing the steps (2) - (4) until i > m + 1.
Compared with the prior art, the virtual road network non-motor lane attribute correction method based on the shared bicycle track can judge the isolation mode and width of the designated non-motor lane based on mass and accurate shared bicycle track data and by means of mathematical statistics, and provides a high-efficiency method for basic road network correction of an urban virtual traffic system platform. Compared with a manual observation method, the method can reduce the workload, improve the efficiency and effectively utilize large data resources.
Drawings
FIG. 1 is a general flow diagram of the process of the present invention;
FIG. 2 illustrates an exemplary regional road network structure and starting and ending points and topology points of a selected road;
FIG. 3 is a histogram of the shared bicycle trajectory frequency distribution for a selected road segment.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
the invention provides a virtual road network non-motor lane attribute correction method based on a shared bicycle track.
The present example performs attribute identification of a non-motor lane for the road shown in fig. 2, including the steps of:
(1) method for field processing and multi-source data loading of shared bicycle track data
The original shared bicycle track data comprises an order id, a user id, a vehicle id, track point numbers and time, longitude and latitude corresponding to the track point, and the data format is shown in table 1. Set the reference period to 2018/01/01.00: 00:00-2018/12/3123:59:59, extracting the longitude and latitude data of the track points in the reference time interval, and processing to obtain a data format shown in table 2. Loading the original urban road network and the processed shared single-vehicle track data into an urban virtual traffic system platform, designating the road to be researched as a double-dragon road in Nanjing City, selecting the downlink direction of the road to be researched, wherein the starting point number is 18, and the end point number is 47. The topological points that the road passes through are 146, 147 and 148 in sequence, and the road sections among the topological points are straight lines, so that the road has a specific linear shape. The number of the passing topological points is 3, and the road can be recorded as 4 road segments which sequentially pass through the topological points from the starting point to the end point. The road network structure of the area and the starting and ending points and topological points of the selected roads are shown in fig. 2.
Table 1 original shared bicycle trajectory data example
TABLE 2 example of processed shared bicycle track point dataset
(2) Shared bicycle track frequency distribution histogram acquisition method
For the road segment 1, the length of the road segment 1 is displayed by the platform to be 124.7m, the starting point of the road segment 1 is recorded as the coordinate origin, the advancing direction is recorded as the positive Y-axis direction, and the positive Y-axis direction rotates by 90 degrees to the right and is recorded as the positive X-axis direction. And (3) projecting the shared bicycle track points in the rectangular frame enclosed by (0, 0), (0, 124.7m), (50m, 124.7m) and (50m, 0) to the X axis to obtain a point set D. And counting the point set D to obtain the sample size N of 1938. The sample size N is greater than the minimum allowable sample size and operation can continue. With 0.1m as the interval length, the frequencies of the points falling in each interval of the X axis are counted, and the group with the frequency less than 0.01 is discarded, so as to obtain the frequency distribution histogram of the shared bicycle track of the road segment 1, as shown in fig. 3.
(3) Skewness coefficient and kurtosis coefficient acquisition method for sharing single-vehicle track frequency distribution
And calculating a skewness coefficient s and a kurtosis coefficient k of the probability distribution of the shared single-vehicle track of the road section 1. The calculation method of the two coefficients refers to the formula:
wherein:
n: sample capacity sharing the abscissa of a bicycle track
xi: ith shared bicycle railAbscissa of trace
The skewness coefficient s and the kurtosis coefficient k of the shared bicycle track probability distribution of the road section 1 are calculated to be-0.024 and-0.5706 respectively.
(4) Method for judging isolation mode and width of non-motor vehicle lane
In this example, the skewness s is-0.0221 and the kurtosis k is-0.5706. s < alpha, the non-motor lane and the motor lane of the road segment 1 are judged to be physically separated. When the shape of the non-motor vehicle lane is physical isolation, 47.5% of points are statistically accumulated leftwards and rightwards by taking the average value in the frequency distribution histogram as the center, and the corresponding length of the 95% of points on the X axis is recorded as the width of the non-motor vehicle lane. According to the result of the frequency distribution histogram, the width of the non-motor vehicle lane is judged to be 2.5 m.
And (5) circularly performing the steps (2) to (4) until i is greater than 4.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.
Claims (1)
1. The virtual road network non-motor lane attribute correction method based on the shared bicycle track is characterized by comprising the following steps of:
(1) a field processing and multi-source data loading method for sharing single-vehicle track data;
the original shared bicycle track data in the step (1) comprises the following steps: order formidUser ofidVehicleidTrack point number, and time, longitude and latitude corresponding to the track point; setting the reference time period asT1-T2Extracting the longitude and latitude data of the track points in the reference time interval to obtain the longitude and latitude data of the track points of the shared bicycle, and loading the processed track data of the shared bicycle to a virtual traffic system platform of the city; loading original city road network into cityThe method comprises the steps that a virtual traffic system platform is arranged in the city, the city virtual traffic system platform displays an urban road network and road section attributes, a target research road is designated in the city virtual traffic system platform, the ascending or descending direction of the target research road is selected, the target research road is a curve with a certain linearity, the starting point and the ending point of the target research road are intersections in a road network, the target research road passes through different topological points, the road sections among the topological points are straight lines, and the number of the topological points passed by the target research road is recorded asmThe target research road is recorded as the target research road sequentially passes through each topological point from the starting point to the end pointm+1 road segments;
(2) a shared bicycle track frequency distribution histogram acquisition method;
recording road sections in step (2)iLength L, will be the road sectioniRecording the starting point of the point D as a coordinate origin, recording the advancing direction as a positive Y-axis direction, recording the positive Y-axis direction by rotating 90 degrees rightwards as a positive X-axis direction, projecting the shared bicycle track points in a rectangular frame surrounded by (0, 0), (0, L), (50m, L) and (50m, 0) to the X-axis to obtain a point set D, judging the sample size of the point set D, and acquiring a shared bicycle track frequency distribution histogram by adopting a manual identification method when the sample size N is not more than the minimum allowable sample size N' = 1000; when the sample size is larger than the minimum allowable sample size N' =1000, taking 0.1m as the interval length, counting the frequency of track points in each interval of the X axis, and discarding the track point group with the frequency smaller than 0.01 to obtain a frequency distribution histogram of the shared single-vehicle track of the road section i;
(3) a skewness coefficient and kurtosis coefficient acquisition method for sharing the single-vehicle track frequency distribution;
calculating road section in step (3)iShared bicycle trajectory probability distribution skewness coefficientsAnd kurtosis coefficientkThe calculation method of the two coefficients refers to the formula:
wherein:
n: sharing the sample capacity of the abscissa of the bicycle track;
(4) a non-motor vehicle lane isolation mode and width discrimination method;
setting skewness coefficient threshold in step (4)α=1 and kurtosis factor thresholdβ=1, degree of skewing coefficientsHas an absolute value of
When is coming into contact with
Is less thanαJudging the form of the non-motor vehicle lane to be physical isolation; when in use
Not less thanαAnd iskNot less thanβJudging the form of the non-motor vehicle lane as the marking isolation; when in use
Not less thanαAnd iskIs less thanβJudging that no isolation facility exists between the motor vehicle lane and the non-motor vehicle lane on the road section; when the non-motor vehicle lane and the motor vehicle lane have no isolation facility, the width of the non-motor vehicle lane defaults to W = 0; when the shape of the non-motor vehicle lane is physical isolation, the average value in the frequency distribution histogram is taken as the center, and the left and the right are takenAccumulating 47.5% of points to the right by statistics, and recording the corresponding accumulated length of the 95% of points on the X axis as the width of the non-motor vehicle lane; when the form of the non-motor vehicle lane is the lineation isolation, taking the average value in the frequency distribution histogram as the center, counting points which accumulate 27.5% leftwards, counting points which accumulate 47.5% rightwards, and recording the corresponding accumulated length of the 75% points on the X axis as the width of the non-motor vehicle lane; circularly performing the steps (2) to (4) untili>m+1。
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| CN111288999B (en) * | 2020-02-19 | 2021-08-31 | 深圳大学 | Pedestrian road network attribute detection method, device and equipment based on mobile terminal |
| CN111537990B (en) * | 2020-04-27 | 2022-02-22 | 西安象德信息技术有限公司 | Lane calibration method and device and electronic equipment |
| CN112163166B (en) * | 2020-10-27 | 2022-10-14 | 腾讯科技（深圳）有限公司 | Method and device for detecting road attribute, computer readable medium and electronic equipment |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203440771U (en) * | 2013-08-05 | 2014-02-19 | 无锡市政设计研究院有限公司 | Moving vehicle induction guard bar |
| CN104331422A (en) * | 2014-10-14 | 2015-02-04 | 广州市香港科大霍英东研究院 | Road section type presumption method |
| CN104361648A (en) * | 2014-10-10 | 2015-02-18 | 江苏科技大学 | Event data recorder provided with signal lamps for reminding other vehicles and control method of event data recorder |
| CN104361649A (en) * | 2014-10-14 | 2015-02-18 | 江苏科技大学 | Automobile data recorder capable of prompting other vehicles and control method thereof |
| CN104700617A (en) * | 2015-04-02 | 2015-06-10 | 武汉大学 | High-precision lane information extracting method based on low-precision GPS track data |
| CN105138779A (en) * | 2015-08-31 | 2015-12-09 | 武汉大学 | Vehicle-mounted GPS space-time trajectory big data optimization method and system |
| CN106289036A (en) * | 2016-08-03 | 2017-01-04 | 福建工程学院 | A kind of road width measuring method analyzed based on floating car data |
| CN108871363A (en) * | 2018-06-15 | 2018-11-23 | 杭州后博科技有限公司 | It is a kind of to be ridden the non-motorized lane road conditions judgment method and system of data based on history |
-
2019
- 2019-02-25 CN CN201910135932.XA patent/CN109816982B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203440771U (en) * | 2013-08-05 | 2014-02-19 | 无锡市政设计研究院有限公司 | Moving vehicle induction guard bar |
| CN104361648A (en) * | 2014-10-10 | 2015-02-18 | 江苏科技大学 | Event data recorder provided with signal lamps for reminding other vehicles and control method of event data recorder |
| CN104331422A (en) * | 2014-10-14 | 2015-02-04 | 广州市香港科大霍英东研究院 | Road section type presumption method |
| CN104361649A (en) * | 2014-10-14 | 2015-02-18 | 江苏科技大学 | Automobile data recorder capable of prompting other vehicles and control method thereof |
| CN104700617A (en) * | 2015-04-02 | 2015-06-10 | 武汉大学 | High-precision lane information extracting method based on low-precision GPS track data |
| CN105138779A (en) * | 2015-08-31 | 2015-12-09 | 武汉大学 | Vehicle-mounted GPS space-time trajectory big data optimization method and system |
| CN106289036A (en) * | 2016-08-03 | 2017-01-04 | 福建工程学院 | A kind of road width measuring method analyzed based on floating car data |
| CN108871363A (en) * | 2018-06-15 | 2018-11-23 | 杭州后博科技有限公司 | It is a kind of to be ridden the non-motorized lane road conditions judgment method and system of data based on history |
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