SPATIO-TEMPORAL DP METHOD BASED ON SHIP TRAJECTORY CHARACTERISTIC POINT EXTRACTION

Abstract

Disclosed is a spatio-temporal DP method based on ship trajectory characteristic point extraction, which belongs to the technical field of ship trajectory compression and includes: Step 1: performing clustering analysis on AIS raw data using a clustering algorithm to identify outliers in the AIS data and then eliminate noise points; Step 2: identifying and retaining the characteristic trajectory points of the ship course change, ship speed change, and the ship entering and exiting from a certain area and the like; Step 3: compressing the AIS data by taking the start and end points of the ship trajectory and the characteristic trajectory points retained in step 2 as the initial points, and considering the spatio-temporal characteristics of the AIS data at the same time. The present disclosure can effectively compress redundant AIS data.

Claims

1. A spatio-temporal DP (Douglas—Peucker) method based on a ship trajectory characteristic point extraction, comprising: (1) performing a clustering analysis on a AIS (Automatic Identification system) raw data to identify outliers in a AIS data and then eliminate noise points to construct a single-ship AIS time series data record; (2) converting latitude and longitude coordinates of each of a plurality of AIS data points in the single-ship AIS time series data record into Mercator projection coordinates; (3) obtaining a speed change rate and a course change rate of each of the AIS data points, as well as an average speed change rate and an average course change rate during an entire navigation process; (4) identifying and retaining a ship course and speed change points in the single-ship AIS time series data record; (5) identifying and retaining trajectory points of a ship entering and exiting from a certain area in the single-ship AIS time series data record; and (6) compressing the AIS data by taking a start point and an end point of a ship trajectory and the retained the ship course and the speed change points, and the trajectory points of the ship entering and exiting from the certain area as initial points, and considering spatio-temporal characteristics of the AIS data.

2. The method according to claim 1, wherein the speed change rate S.sub.cri of an ith of the AIS data point is obtained from $S_{c r i} = \frac{.Math. S_{i}^{o u t} - S_{i}^{i n} .Math.}{Δ t},$ the course change rate C.sub.cri of the ith of the AIS data point is obtained from $C_{c r i} = \frac{.Math. C_{i}^{o u t} - C_{i}^{i n} .Math.}{Δ t},$ the average speed change rate S.sub.cr during the entire navigation process is obtained from $\overline{S_{c r}} = \frac{1}{n - 2} {.Math.}_{i = 2}^{n - 1} S_{c r i},$ the average course change rate C.sub.cr during the entire navigation process is obtained from $\overline{C_{c r}} = \frac{1}{n - 2} {.Math.}_{i = 2}^{n - 1} C_{c r i},$ S.sub.i.sup.out represents a speed of an i+1th of the AIS data point, S.sub.i.sup.in represents a speed of an i−1th of the AIS data points, C.sub.i.sup.out represents a course of the i+1th of the AIS data points, C.sub.i.sup.in represents a course of the i−1th of the AIS data points, Δtrepresents a time interval between the i+1th of the AIS data points and the i−1th of the AIS data points, and n represents a number of the AIS data points.

3. The method according to claim 2, wherein the step (4) comprises: setting that a ship speed change threshold S.sub.tre=M×S.sub.cr, judging whether the speed change rate S.sub.cri of each of the AIS data points B.sub.i is greater than S.sub.tre in turn, and determining that a speed change point set S=S∪B.sub.i under a condition that S.sub.cri≥S.sub.tre; and setting that a ship course change threshold C.sub.tre=N×C.sub.cr, judging whether the course change rate C.sub.cri of each of the AIS data point P.sub.i is greater than C.sub.tre in turn, and determining that a course change point set C=C∪P.sub.i under a condition that C.sub.cri≥C.sub.tre, wherein M and N represent coefficients.

4. The method according to claim 3, wherein the step (5) comprises: judging whether a product of values after two adjacent AIS data points are respectively substituted into an area boundary line equation is less than 0, and marking and retaining the two adjacent AIS data points as the trajectory points of the ship entering and exiting from a certain area, which constitute a certain-area entry and exit point set E under a condition that the product is less than 0.

5. The method according to claim 4, wherein the step (6) comprises: (6.1) setting a distance threshold d.sub.T, and marking a trajectory in segments with the start point and the end points of the ship trajectory and retained characteristic trajectory points in S, E and C as the initial points, wherein the trajectory between two adjacent trajectory characteristic points is a sub-trajectory segment; (6.2) connecting start points and end points of each of a plurality of segmented trajectories, and establishing a virtual straight line spatio-temporal trajectory according to coordinates of a Mercator coordinate system converted from a longitude and a latitude of the start point and the end point, and a time; calculating the coordinates of the Mercator coordinate system of the AIS data points on each of the sub-trajectory segment on the virtual straight line spatio-temporal trajectory at the same moment, taking a distance between the coordinates of the Mercator coordinate system of the AIS data points on the sub-trajectory segment and the coordinates of the Mercator coordinate system of the AIS data points on the virtual straight line spatio-temporal trajectory at the same moment as a spatio-temporal distance d from the AIS data points to the virtual straight line spatio-temporal trajectory, finding a maximum distance d.sub.max among all of the spatio-temporal distance, and comparing the maximum distance with a preset distance threshold d.sub.T; (6.3) discarding all intermediate data points on the sub-trajectory segment under a condition that d.sub.max<d.sub.T, and taking a straight line connecting the start point and the end point of the sub-trajectory segment as an approximation of the sub-trajectory segment after all intermediate points are discarded, thus completing a processing of the sub-trajectory segment; (6.4) retaining the AIS data points corresponding to the maximum distance as a data point on a result trajectory under a condition that d.sub.max>d.sub.T, dividing the sub-trajectory segment into two parts by the AIS data points corresponding to the maximum distance, and processing the two parts of curves using step (6.2) and step (6.3) until all d.sub.max<d.sub.T; and (6.5) forming the trajectory by sequentially connecting segmentation points after all the sub-trajectory segment are processed, wherein the trajectory is an approximate trajectory after an original trajectory is compressed.

6. The method according to claim 1, wherein the latitude and longitude coordinates of each of the AIS data points in the single-ship AIS time series data record are converted into the Mercator projection coordinates by $r_{0} = \frac{a \times \cos (φ_{0})}{\sqrt{1 - (e^{2} \times \sin^{2} (φ_{0}))}}, q = \ln (\tan (\frac{π}{4} + \frac{φ}{2}) \times {(\frac{1 - e \times \sin φ}{1 + e \times \sin φ})}^{e / 2}),$ x=r.sub.o×λ and y=r.sub.o×q, wherein (λ, φ) represents the latitude and longitude coordinates of the AIS data points, r.sub.o represents radius of a parallel circle of a standard latitude, and q represents a equidistant latitude, φ.sub.o represents the standard latitude of a Mercator projection, a represents long radius of a earth ellipsoid, e represents a first eccentricity of the earth ellipsoid, and (x, y) represents coordinates of a Mercator coordinate system converted from the latitude and longitude coordinates.

7. The method according to claim 3, wherein M∈[9,11], N∈[3,5].

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0028] FIG. 1 is a flowchart of ship trajectory compression provided by an embodiment of the present disclosure;

[0029] FIG. 2 is a schematic diagram of calculating a speed change rate and a course change rate provided by an embodiment of the present disclosure;

[0030] FIG. 3 is a principle diagram of a spatio-temporal DP method based on ship trajectory characteristic point extraction provided by an embodiment of the present disclosure;

[0031] FIG. 4 is a diagram of the overall ship trajectory compression result provided by an embodiment of the present disclosure, wherein (a) represents the original trajectory point diagram, and (b) represents the compressed trajectory point diagram;

[0032] FIG. 5 is a diagram of a single-ship trajectory compression result provided by an embodiment of the present disclosure;

[0033] FIG. 6 is characteristic points of entering and exiting from a bridge area provided by an embodiment of the present disclosure.

DESCRIPTION OF THE EMBODIMENTS

[0034] In order to make the objectives, technical solutions, and advantages of the present disclosure clearer, the present disclosure will be further described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present disclosure, but not to limit the present disclosure. In addition, the technical characteristics involved in the various embodiments of the present disclosure described below can be combined with each other as long as they do not conflict with each other.

[0035] In this embodiment, AIS data collected from the Wuhan section of the Yangtze River on Aug. 9, 2016 is used as the original data for compression. As shown in FIG. 1, the technical solution adopted by the present disclosure is:

[0036] S1: eliminating noise points: performing clustering analysis on AIS raw data using a clustering algorithm to identify outliers in the AIS data and then eliminate noise points to construct a single-ship AIS time series data record;

[0037] in this example, the noise points of the ship's position are mainly eliminated. After the data points with large position deviation are eliminated, a single-ship AIS time series data record is constructed.

[0038] S2: converting the latitude and longitude coordinates of each AIS data point in the single-ship AIS time series data record into Mercator projection coordinates to facilitate distance calculation;

[00006] $r_{0} = \frac{a \times \cos (φ_{0})}{\sqrt{1 - (e^{2} \times \sin^{2} (φ_{0}))}}$ $q = \ln (\tan (\frac{π}{4} + \frac{φ}{2}) \times {(\frac{1 - e \times \sin φ}{1 + e \times \sin φ})}^{e / 2})$ $x = r_{0} \times λ$ $y = r_{0} \times q$

wherein (λ, φ) represents the latitude and longitude coordinates of the AIS data point, r.sub.0 represents the radius of the parallel circle of the standard latitude, q represents the equidistant latitude, φ.sub.o represents the standard latitude of the Mercator projection, a represents the long radius of the earth ellipsoid, e represents the first eccentricity of the earth ellipsoid, and (x, y) represents the coordinates of the Mercator coordinate system converted from the latitude and longitude coordinates.

[0039] In this example, in order to facilitate calculation and improve calculation accuracy, the longitude and latitude coordinates of each AIS data point are uniformly converted into coordinates of the Mercator coordinate system, and the time is uniformly converted to be in seconds.

[0040] S3: obtaining the speed change rate and course change rate of each AIS data point, and the average speed change rate and average course change rate during the entire navigation process;

[00007] $S_{c r i} = \frac{.Math. S_{i}^{o u t} - S_{i}^{i n} .Math.}{Δ t}$ $C_{c r i} = \frac{.Math. C_{i}^{o u t} - C_{i}^{i n} .Math.}{Δ t}$ $\overline{S_{c r}} = \frac{1}{n - 2} {.Math.}_{i = 2}^{n - 1} S_{c r i}$ $\overline{C_{c r}} = \frac{1}{n - 2} {.Math.}_{i = 2}^{n - 1} C_{c r i}$

[0041] Wherein, S.sub.cri and C.sub.cri respectively represent the speed change rate and course change rate of the ith AIS data point, S.sub.cr and C.sub.cr respectively represent the average speed change rate and the average course change rate during the entire navigation process, S.sub.i.sup.out represents the speed of the i+1thAIS data point, S.sub.i.sup.in represents the speed of the i−1thAIS data point, C.sub.i.sup.out represents the course of the i+1th AIS data point, and C.sub.i.sup.in represents the course of the i−1th AIS data point, Δt represents the time interval between the i+1th AIS data point and the i−1th AIS data point, and n represents the number of AIS data points, as shown in FIG. 2.

[0042] S4: identifying and retaining the ship course and speed change points in the single-ship AIS time series data record;

[0043] limited by the accuracy of the sensor, speed and course data of the ship may have small fluctuations. If the average change rate is directly used as the threshold, these fluctuation points may be retained as the trajectory characteristic points of the speed change and course change, resulting in that the data volume after compression is still huge, thus the expansion coefficients M and N are introduced in the present disclosure to expand the average speed change rate and the average course change rate.

[0044] Suppose that an initial speed change point set S={}, the ship speed change threshold S.sub.tre=MΔS.sub.cr is set, and whether the speed change rate S.sub.cri of each AIS data point B.sub.1 is greater than S.sub.tre is judged in turn. If S.sub.cri≥S.sub.tre, then it is determined that a speed change point set S=S∪ B.sub.i;

[0045] Suppose that an initial course change point set C={}, the ship course change threshold C.sub.tre=N×C.sub.cr, is set, and whether the course change rate C.sub.cri of each AIS data point P.sub.i is greater than C.sub.tre is judged in turn. If C.sub.cri≥C.sub.tre, then it is determined that a course change point set C=C∪P.sub.i.

[0046] Wherein, M∈[9,11], N∈[3,5].

[0047] In this example, the speed characteristic points and the course characteristic points are retained by taking M=10 and N=4 for constructing thresholds of the speed change rate and course change rate of the ship.

[0048] S5: identifying and retaining the trajectory points of the ship entering and exiting from a certain area in the single-ship AIS time series data record;

[0049] most ship trajectory compression algorithms do not retain these points as trajectory characteristic points, but these data points often contain the human factors of a deck officer, the usual practice of the deck officer and the potential information of the accustomed route and have a certain value in use.

[0050] Ship entry/exit behaviors include entering/exiting docks, anchorages, bridge area waters, fishing area waters, roundabouts and other closed areas, as well as trajectory points in non-closed areas such as navigation channels, danger lines, and boundary lines, and it is judged whether the product of values after two adjacent AIS data points are respectively substituted into a boundary line equation is less than 0, if the product is less than 0, the two adjacent AIS data points are marked and retained as the trajectory points of the ship entering and exiting a certain area, which constitute a certain-area entry and exit point set E.

[0051] In this example, the characteristic points of ships entering/exiting from the Second Yangtze River Bridge in Wuhan are retained.

[0052] S6: compressing a ship trajectory by considering the spatio-temporal characteristics of AIS, specifically, as shown in FIG. 3:

[0053] S6.1: setting a distance threshold d.sub.T, and marking the trajectory in segments with the start and end points of the ship trajectory and the trajectory characteristic points in S, E and C retained in the above steps as the initial points, wherein the trajectory between two adjacent trajectory characteristic points is one sub-trajectory segment;

[0054] S6.2: connecting the start and end points of each segmented trajectory, and establishing a virtual straight line spatio-temporal trajectory according to the coordinates (x, y) of the Mercator coordinate system converted from the longitude and latitude of the start and end points, and time; calculating the coordinates B′.sub.j(x′.sub.i, y′.sub.i) of the Mercator coordinate system of an AIS data point B.sub.i(x.sub.i,y.sub.i) of each sub-trajectory segment on the virtual straight line spatio-temporal trajectory at the same moment, calculating a spatio-temporal distance d from the AIS data point to the virtual straight line spatio-temporal trajectory, i.e., the distance between B.sub.i and B′.sub.i, finding the maximum distance d.sub.max among all distances, and comparing the maximum distance with the preset distance threshold d.sub.T;

d=|B.sub.iB′.sub.i|=√{square root over ((x.sub.i−x′.sub.i).sup.2+(y.sub.i−y′.sub.i).sup.2)}

[0055] Wherein, B.sub.i(x.sub.i, y.sub.i) is the coordinates in the Mercator coordinate system of the ith AIS data point of the sub-trajectory segment, and B′.sub.i(x′.sub.i, y′.sub.i) is the coordinates in the Mercator coordinate system of B.sub.i on the virtual straight line spatio-temporal trajectory at the same moment.

[0056] S6.3: discarding all intermediate data points on the sub-trajectory segment if d.sub.max<d.sub.T, and taking a straight line connecting the start and end points of the sub-trajectory segment as the approximation of the sub-trajectory segment after all the intermediate points are discarded, thus completing the processing of the sub-trajectory segment;

[0057] S6.4: retaining the AIS data point corresponding to the maximum distance as the data point on the result trajectory if d.sub.max>d.sub.T, dividing the sub-trajectory segment into two parts by the AIS data point corresponding to the maximum distance, and processing the two parts of curves using step (6.2) and step (6.3) until all d.sub.max<d.sub.T;

[0058] S6.5: forming a trajectory by sequentially connecting the segmentation points after all the sub-trajectory segments are processed, wherein the trajectory is an approximate trajectory after the original trajectory is compressed.

[0059] The overall compression result is shown in FIG. 4. Wherein, (a) in FIG. 4 is the original trajectory point diagram, and (b) in FIG. 4 is the compressed trajectory point diagram. The overall shapes are very similar, which can prove that the method of the present disclosure can retain the shape characteristics of the trajectory while performing high-efficient compression. The results of single-ship trajectory compression are shown in FIG. 5 and FIG. 6, from which it can be seen that the number of trajectory points is small, and the characteristic points are retained relatively complete.

[0060] The distance threshold d.sub.T in this example is 80.

[0061] It should be pointed out that, steps/components described in this application can be split into more steps/components according to the needs of implementation, or two or more steps/components or partial operations of steps/components can be combined into new steps/components to achieve the purpose of the present disclosure.

[0062] Those skilled in the art can easily understand that the above embodiments are only preferred embodiments of the present disclosure and are not intended to limit the present disclosure. Any modification, equivalent replacement and improvement, etc., made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

SPATIO-TEMPORAL DP METHOD BASED ON SHIP TRAJECTORY CHARACTERISTIC POINT EXTRACTION

Assignee

Inventors

Cpc classification

Classification Explorer

B63B79/40

PERFORMING OPERATIONS; TRANSPORTING

Classification Explorer

G08G3/00

PHYSICS

Classification Explorer

G08G3/02

PHYSICS

Classification Explorer

G05D1/0206

PHYSICS

International classification

Classification Explorer

B63B79/40

PERFORMING OPERATIONS; TRANSPORTING

Classification Explorer

G05D1/02

PHYSICS

Abstract

Claims

Description