CN111597210A

CN111597210A - Road network track bidirectional query method based on dynamic pre-storage

Info

Publication number: CN111597210A
Application number: CN202010379782.XA
Authority: CN
Inventors: 孙杰; 沈祥红; 许建秋
Original assignee: Jiangsu Sea Level Data Technology Co ltd
Current assignee: Jiangsu Sea Level Data Technology Co ltd
Priority date: 2020-05-07
Filing date: 2020-05-07
Publication date: 2020-08-28

Abstract

The invention provides a road network track bidirectional query method based on dynamic pre-storage, which is applied to the field of track query optimization. The method can optimize the traditional solution based on the pre-storage according to the dynamic time warping, the point-to-cluster grouping and the bidirectional search based on the dynamic pre-storage, reduce a large amount of repeated calculation during the shortest path query among the tracks and obviously improve the query efficiency. The technology mainly comprises three parts, wherein the first part firstly carries out dynamic time warping on the historical track and is divided into sub-track pairs. And the second part is to determine which road sections the sub-track pairs fall on respectively, the end points of the road sections respectively form SD (Source-Destination) point pair sets, and the point pair sets are grouped by using a clustering method after duplication removal. And the third part executes bidirectional search based on dynamic prestorage on each group of grouped point pair set, and utilizes the result to assist the dynamic shortest path query among the tracks to obtain a final result.

Description

Road network track bidirectional query method based on dynamic pre-storage

One, the technical field

The invention belongs to the field of track query optimization, and is mainly used for reducing a large amount of repeated calculation during shortest path query among tracks and improving query efficiency.

Second, background Art

With the development of the internet of things, sensor equipment is optimized continuously, and the collection of vehicle track data is more and more convenient. The intelligent traffic as one of the important applications of the internet of things cannot inquire and analyze a large amount of track data. The dynamic change of the shortest path between the tracks is analyzed, and the distance peak is found out, so that the method has important significance on the bus route and even the route planning of unmanned automatic driving vehicles. The difficulty is that the time of a given query track may not be completely overlapped, a section of historical track may cross a plurality of road sections of a road network, and the speeds of the mobile objects on the road sections are different, which is not beneficial to the calculation of the dynamic shortest path; and because the urban road network is large in scale, the calculation cost of the shortest path between the tracks is extremely high, and a large amount of repeated calculation exists between the sub-track pairs, the query efficiency is greatly reduced.

Therefore, how to reduce the repeated calculation in the shortest path query among the tracks is an important problem to be solved. The traditional solution adopts the shortest path between the top points of the road network of the pre-stored part to accelerate the query, however, when the mobile object does not fall on the pre-stored path, the repeated calculation of the common path between the sub-track pairs can not be reduced, so the invention designs a bidirectional search algorithm based on dynamic pre-storage, and the algorithm reduces the calculation of the common path between a large number of sub-track pairs, optimizes the traditional method and accelerates the query.

Third, the invention

[ OBJECTS OF THE INVENTION ]

In order to reduce repeated calculation of a large number of common paths between sub-track pairs during dynamic shortest path query between tracks, query efficiency is improved.

[ technical solution ] A

The invention provides an optimization solution for the dynamic shortest path query among tracks, and the solution is designed for the problem that a large amount of repeated calculation exists in the shortest path query among pairs of sub-tracks after the dynamic time of the tracks is regular. The method mainly comprises the following steps:

firstly, dynamically time warping two tracks to be inquired, dividing the tracks into sub track pair sets corresponding to time units, then grouping point pair combinations possibly related to the sub tracks between end points of a road section (default duplication removal), wherein the grouping method adopts a clustering method, and bidirectional searching based on dynamic pre-storage is respectively carried out on each group of point pair inquiry sets after grouping to obtain the shortest path. After the result is obtained, the query result between the sub-track pairs only needs to be added with a small part of movement on the road section. And finally, collecting the query results among all the sub-track pairs to obtain the dynamic shortest path among the tracks to be queried.

[ PROBLEMS ] the present invention

The road network track bidirectional query algorithm based on dynamic prestoring optimizes the traditional solution based on prestoring, can reduce a large amount of repeated calculation during shortest path query among tracks, and obviously improves the query efficiency.

Description of the drawings

FIG. 1 dynamic time warping

FIG. 2 sub-track corresponds to SD point pair situation

FIG. 3 bidirectional search based on dynamic prestoring

Fifth, detailed description of the invention

The invention will be further explained with reference to the drawings.

The method is mainly divided into three parts, wherein the first part firstly carries out dynamic time warping on the historical track and is divided into sub-track pairs. And the second part is to determine which sections the sub-track pairs respectively fall on, and the end points of the sections respectively form an SD (Source-Destination) point pair set. And de-duplicating the point pair set, and then grouping by using a clustering method. And the third part executes bidirectional search based on dynamic prestorage on each group of grouped point pair set, and utilizes the result to assist the dynamic shortest path query among the tracks to obtain a final result.

(1) Dynamic time warping

Because the time of the sampling devices is not synchronous, the time inflection points between the track segments are different, which causes certain trouble to the dynamic shortest path query of the road network, because if two moving objects are not in the same time unit, the calculation of the shortest path between the two moving objects has no meaning, and unnecessary calculation is also generated. The line segment between the inflection points can be regarded as a linear equation with respect to time, and the determined position of each time can be determined by calculation, so that the two trajectories need to be divided again, so that the inflection points between the trajectories are the same, as shown in fig. 1, the inflection points of the two trajectories are not completely the same at first, and after dynamic time warping, the two trajectories can be converted into the trajectories with the same inflection points. After the operation, the calculation of the shortest path of the track can be more accurate and convenient. Therefore, the shortest path query between the two mobile objects can be converted into a set of the shortest path query results between a group of sub-track pairs. The divided sub-track pairs satisfy two conditions: (a) the two sub-tracks have the same time period; (b) each sub-track represents motion within a certain road segment.

(2) SD point pair grouping

By judging which road sections the sub-track pairs after the dynamic time warping fall on respectively, the dynamic shortest path query problem among the road network tracks can be converted into a set of static problems and dynamic calculation. The static part is a series of SD point pairs formed by the end points of the road sections where the sub-track pairs fall, the left side of FIG. 2 is an example of corresponding the regularized sub-tracks to the corresponding road sections, and the obtained SD point pairs are shown on the right side of FIG. 2. It can be seen from the above example that there may be duplication between SD point pairs corresponding to sub-track pairs, and all SD point pairs need to be removed from duplication to form a query set QTSD ═ PO(s)₁，d₁)，PO(s₂，d₂)，...，PO(s_n，d_n) N is the number of SD point pairs, PO(s)_i，d_i) Represents a shortest path query between one SD point pair.

When the historical track of the moving object is long and spans a plurality of road segments, more than one common path may exist between query result sets of the query set QTSD, so that the query sets can be properly grouped by adopting a clustering method, and repeated calculation of the common path can be reduced as much as possible. Grouping is based on a Q-line clustering algorithm, and ensures that the point pair sets in the same group are relatively close to the starting point and the ending point.

(3) Bidirectional search based on dynamic prestoring

And after grouping, respectively executing shortest path query for the point pairs in each group. Using a two-way search method based on dynamic precomputation, first for the first pair of POs(s) in each group₁，d₁) And inquiring and storing a shortest path as a dynamically prestored content, and when the shortest path between the remaining point pairs in the group is inquired, adopting bidirectional search until the head and the tail of the shortest path are expanded to the shortest path between the first pair stored above, so that a long section of common path in the middle is not required to be repeatedly calculated.

Algorithm 1 gives pseudo code of bidirectional search based on dynamic precomputation, and the input of the algorithm is a road network G and a group of query sets QTSD_subOutputting the path set QPath corresponding to the query set_sub. Algorithm row 1 initializes the correlation values. First compute the first pair of queries PO(s)₁，d₁) Is saved as Path_preThe path is placed into the result set (lines 2-3). The queries between the remaining pairs of points are based on the query results of the first pair. The process is as follows: first from a starting point s_iExpand outwards, and put it into Path every time one edge is expanded_tempWhen the Path is extended to the shortest Path between the pre-calculated first pair of points, the Path can jump out of the while loop and the extended edge Path can be obtained by the backtracking algorithm_tempMedium reduction initiation point s_iTo connect to Path_preThe previous shortest path (lines 5-10). There is another case that the shortest path from the starting point to the ending point does not pass through the shortest path between the first pair of points, and the backtracking is from the starting point s_iTo the end point d_iOne complete Path(s) of_i，d_i) (lines 11-12). From the end point d_iThe situation when the outward expansion is started is similar to the outward expansion from the starting point (lines 13-20). If the shortest Path between the first point pair can be connected to by searching from both sides, a common shortest Path is intercepted from the middle_midPlus a starting point s_iIs connected to Path_preThe previous shortest path and end point d_iIs connected to Path_preThe shortest path of the previous segment is the most intermediate s_iTo d_iOne complete shortest Path(s) of_i，d_i) (lines 21-24), finally put the query results between each pair of points into the result set QPath_subMiddle (line 25), the whole process of the algorithm is as above.

To facilitate understanding, a simple example is given, as shown in fig. 3. Assume that the set has only two pairs of query POs(s)₁，d₁)，PO(s₂，d₂) First, s is calculated₁To d₁A shortest Path of_preThen calculates s based on the path₂To d₂Wherein the dotted line portion corresponds to the Path described in the algorithm_mid，s₂Is connected to Path_preThe previous shortest Path corresponds to Path_s，d₂Is connected to Path_preThe previous shortest Path corresponds to Path_dFinal PO(s)₂，d₂) The query result of (1) is Path_s+Path_mid+Path_mid. May also exist s₂And d₂Cannot connect to the pre-computed Path_preIn the above case, the returned result is shown by the red line in the figure.

And finally, combining the results to assist in the dynamic shortest path query among the tracks to obtain a final result.

Claims

1. A road network track bidirectional query method based on dynamic prestoring is generally characterized in that a large amount of repeated calculation exists in track dynamic shortest path query, in order to reduce repeated calculation of common paths and improve query efficiency, the invention optimizes a traditional solution based on prestoring according to dynamic time warping, point-to-cluster grouping and bidirectional search based on dynamic prestoring, and the process comprises the following three parts:

(1) dynamic time warping: performing dynamic time warping on two tracks to be queried, and dividing the two tracks into sub-track pair sets corresponding to time units;

(2) sub-track pairs are grouped: and (3) determining which road sections the sub-track pairs obtained in the step (1) respectively fall on, wherein the end points of the road sections respectively form an SD (Source-Destination) point pair set. De-duplicating the point pair sets and then grouping by using a clustering method;

(3) bidirectional search based on dynamic pre-storage: and performing bidirectional search based on dynamic pre-storage on each group of point pair set after grouping, and utilizing bidirectional search results to assist in dynamic shortest path query among tracks to obtain a final result.

2. The road network track bidirectional query method based on dynamic pre-storage as claimed in claim 1, wherein in step (1), a general dynamic time warping method is improved to ensure that the divided sub-track pairs can satisfy two conditions: the two sub-tracks have the same time period; each sub-track represents motion within a certain road segment.

3. The method according to claim 1, wherein in step (2), dynamic shortest path query among the tracks is converted into a combination of static problem and dynamic calculation, the static part is shortest path calculation among the end point pairs of the road segments, and a clustering grouping method is used to ensure that the point pair sets in the same group are relatively close to the start point and the end point after grouping.

4. The method according to claim 1, wherein in step (3), when performing bidirectional search based on dynamic pre-storage on each group of point pair sets after grouping, the shortest path between a pair of SD points is pre-calculated and recorded, the shortest path between the remaining point pairs is calculated and can be searched in bidirectional mode, when both ends of the search can be connected to the calculated shortest path, the search is stopped, and the bidirectional search result is used to assist the query of the dynamic shortest path between the tracks.