CN111027743B - OD optimal path searching method and device based on hierarchical road network - Google Patents
OD optimal path searching method and device based on hierarchical road network Download PDFInfo
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Abstract
The application is suitable for the technical field of traffic simulation, and provides an OD optimal path searching method based on a hierarchical road network, which comprises the following steps: determining a top layer starting point and a top layer end point corresponding to a target OD on a top layer road network, wherein the top layer road network consists of road sections with trip frequency and/or road grade higher than a set value, which are extracted from a target traffic network in advance according to historical trip track data; determining the shortest path from the top layer starting point to the top layer end point on the top layer road network as a top layer path segment; determining a shortest path from a starting point of the target OD to a starting point of the top layer on the target traffic path network as a starting point path segment; determining a shortest path from the top-layer end point to the end point of the target OD on the target traffic network as an end point path segment; and sequentially splicing the starting point path section, the top layer path section and the end point path section to obtain the optimal path of the target OD.
Description
Technical Field
The application belongs to the technical field of traffic simulation, and particularly relates to an OD optimal path searching method and device based on a layered road network.
Background
The intelligent traffic system integrates advanced technologies such as information technology, communication technology, sensing technology, control technology, computer technology and the like, can fully utilize urban resources, reduces the traffic jam condition and improves the traffic transportation efficiency. Under the background that urban traffic congestion is increasingly serious and an intelligent traffic system is continuously developed at present, the research on the path planning problem is the demand of intelligent traffic development, and the method can be convenient for the public, improve the traveling efficiency of people and promote the good development of urban traffic.
The path planning problem (also called optimal path problem) originates from operations research and aims to find a path with the shortest length between two nodes in a graph consisting of a node set and an edge set. In recent years, due to the continuous development and innovation of computer data structures, algorithms and other related scientific researches, some new theories for solving the shortest path algorithm continuously appear.
However, the existing shortest path algorithm pays too much attention to the shortest search of the path distance, the travel experience of travelers is not fully considered, and particularly in the medium-long distance path planning, the shortest path is often not the optimal path for the travelers.
Therefore, finding an optimal path search method between ODs is an urgent problem to be solved by those skilled in the art.
Disclosure of Invention
The embodiment of the application provides a method and a device for searching an optimal OD path based on a hierarchical road network, and can solve the problem that the existing shortest path cannot meet the actual travel habit of travelers easily.
In a first aspect, an embodiment of the present application provides a hierarchical road network-based OD optimal path searching method, including:
determining a top layer starting point and a top layer end point corresponding to a target OD on a top layer road network, wherein the top layer road network consists of road sections with trip frequency and/or road grade higher than a set value, which are extracted from a target traffic network in advance according to historical trip track data;
determining the shortest path from the top layer starting point to the top layer end point on the top layer road network as a top layer path segment;
determining a shortest path from a starting point of the target OD to a starting point of the top layer on the target traffic path network as a starting point path segment;
determining a shortest path from the top-layer end point to the end point of the target OD on the target traffic network as an end point path segment;
and sequentially splicing the starting point path section, the top layer path section and the end point path section to obtain the optimal path of the target OD.
In a second aspect, an embodiment of the present application provides a hierarchical road network-based OD optimal path searching apparatus, including:
the starting and end point determining module is used for determining a top layer starting point and a top layer end point corresponding to a target OD on a top layer road network, wherein the top layer road network consists of road sections of which the trip frequency and/or the road grade are higher than a set value, and the road sections are extracted from a target traffic network in advance according to historical trip track data;
a top road path segment determining module, configured to determine, on the top road network, a shortest path from the top start point to the top end point as a top road path segment;
a starting point path segment determining module, configured to determine, on the destination traffic network, a shortest path from a starting point of the destination OD to a starting point of the top layer, as a starting point path segment;
the destination path segment determining module is used for determining the shortest path from the top-level destination to the destination OD on the destination traffic network as a destination path segment;
and the path section splicing module is used for sequentially splicing the starting point path section, the top layer path section and the end point path section to obtain the optimal path of the target OD.
In a third aspect, an embodiment of the present application provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements the above hierarchical road network-based OD optimal path searching method.
In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the above hierarchical road network-based OD optimal path searching method.
It is to be understood that, for the beneficial effects of the second aspect to the fourth aspect, reference may be made to the relevant description in the first aspect, and details are not described herein again.
The OD optimal path searching method based on the hierarchical road network comprises the steps of firstly, determining a top layer starting point and a top layer end point of a target OD on a top layer road network, wherein the top layer road network consists of road sections of which the travel frequency and/or the road grade are higher than a set value, and the road sections are extracted from the target road network in advance according to historical travel track data; then, determining the shortest path from the top layer starting point to the top layer end point on the top layer road network as a top layer path segment; then, determining a shortest path from a starting point of the target OD to a starting point of the top layer on the target traffic network as a starting point path segment; determining a shortest path from the top-layer end point to the end point of the target OD on the target traffic network as an end point path segment; and finally, splicing the starting point path section, the top layer path section and the end point path section in sequence to obtain the optimal path of the target OD. Therefore, the optimal path of the target OD is respectively determined through the double-layer road networks (the top road network and the target traffic network), so that the optimal path is composed of the top road path section located in the top road network, the starting point road path section and the ending point road path section located in the target traffic network, the shorter distance of the paths between the ODs is considered, the actual travel habits of travelers are better met, and better travel experience can be brought to the travelers.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic diagram of an application environment of an OD optimal path searching method based on hierarchical road network in an embodiment of the present application;
fig. 2 is a flowchart of an OD optimal path searching method based on hierarchical road network according to an embodiment of the present application;
fig. 3 is a schematic flowchart of a top-level road network pre-generated in an application scenario by an OD optimal path search method based on hierarchical road network in an embodiment of the present application;
fig. 4 is a schematic flow chart of step 101 of an OD optimal path searching method based on a hierarchical road network in an application scenario according to an embodiment of the present application;
fig. 5 is a schematic flow chart illustrating a path distance pre-determined in an application scenario by an OD optimal path search method based on a hierarchical road network in an embodiment of the present application;
fig. 6 is a schematic flowchart illustrating a method for searching an optimal path based on an OD of a hierarchical road network in an application scenario according to an embodiment of the present application;
fig. 7 is a schematic flowchart of an application scenario of step 503 of an OD optimal path searching method based on a hierarchical road network according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an OD optimal path searching apparatus based on a hierarchical road network according to an embodiment of the present application;
FIG. 9 is a schematic diagram of a computer device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing a relative importance or importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless otherwise specifically stated.
The hierarchical road network-based OD optimal path searching method can be applied to an application environment such as that shown in FIG. 1, wherein a client can communicate with a server through a network. Wherein the client may be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server may be implemented as a stand-alone server or as a server cluster comprised of multiple servers.
In an embodiment, as shown in fig. 2, a method for searching an OD optimal path based on a hierarchical road network is provided, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps:
101. determining a top layer starting point and a top layer end point corresponding to a target OD on a top layer road network, wherein the top layer road network consists of road sections with trip frequency and/or road grade higher than a set value, which are extracted from a target traffic network in advance according to historical trip track data;
in the application, the top road network is formed by taking the target traffic road network as the bottom road network as a basis and extracting the core road section combination from the bottom road network, the core road sections are determined in the road sections with high travel frequency and high road grade according to historical travel track data, and it can be considered that the path in the top road network better meets the requirement of a traveler and is favorable for improving the travel experience of the traveler. Specifically, as shown in fig. 3, the top road network may be generated in advance through the following steps:
201. acquiring road sections with the traveling frequency and/or road grade higher than a set value from a target traffic road network according to historical traveling track data to obtain each target road section;
202. traversing each target road section on the target traffic network, analyzing road network connectivity, and determining each auxiliary road section which enables each target road section to be globally communicated;
203. and combining each target road section and each auxiliary road section to generate the top road network.
With respect to step 201, it is understood that the server may obtain, from the target traffic network, the road segments with the travel frequency and/or the road grade higher than the set value according to the historical travel track data, to obtain each target road segment. The historical travel track data can be GPS track data in the historical travel record of the trip personnel. The set value corresponding to the trip frequency and the set value corresponding to the road grade may be specifically set according to the actual situation, and are not specifically limited herein.
Specifically, in the method, a path learning method based on a generative confrontation network can be constructed based on the existing historical travel track data and an artificial intelligence technology, travel behavior habits of travelers in the road network are extracted and studied, travel paths of the travelers under different time-space conditions are estimated, and road network hierarchical division is facilitated to be better carried out. The generation of the antagonistic neural network learns the distribution of OD paths through the continuous game of the generation network G (Generator) and the discrimination network D (Discriminator), and the discrimination network D has certain capability of discriminating the authenticity of data. Based on the generation of the countermeasure network, by learning a large amount of historical travel track data in a target traffic network, an OD path is regarded as a high-dimensional vector formed by corresponding road segment codes of the starting point and the ending point of each road segment, a reasonable path vector is forged by the generation network G according to different space-time conditions (factors such as time, date, weather and road conditions), and whether the forged path is reasonable or not is judged by the judgment network D. Through continuous data game training, the OD path generated by the generated network G can meet the judgment condition for judging the network D, a reasonable path result is obtained, and the travel paths of travelers under different time-space conditions are estimated, so that the high-frequency travel road sections are extracted, each target road section is obtained, and the road network layering can be helped.
For step 202, after obtaining each target road segment, the server further needs to traverse each target road segment on the target traffic network and perform road network connectivity analysis, so as to determine each auxiliary road segment that globally connects each target road segment, which is essential for constructing a globally connected top road network.
For step 203, the top road network may be generated by combining the target road segments and the auxiliary road segments.
In this embodiment, when the server performs the road section planning on the target OD, in order to know the position of the target OD on the top road network in consideration of that the target OD is a node on the target traffic road network, the server may first determine a top start point and a top end point of the target OD on the top road network. Specifically, as shown in fig. 4, step 101 may include:
301. respectively determining traffic cells to which a starting point and an end point of the target OD belong on the top-level road network, and recording the traffic cells as a starting-point traffic cell and an end-point traffic cell, wherein the top-level road network is divided into a plurality of traffic cells in advance;
302. respectively extracting road network nodes in the starting point traffic cell as a top layer starting point node set and road network nodes in the terminal point traffic cell as a top layer terminal point node set from the top layer road network;
303. performing pairing calculation on nodes between the top-layer starting point node set and the top-layer end point node set one by one according to a preset distance judgment function to obtain distance judgment values corresponding to all pairs of nodes;
304. and determining a pair of nodes with the minimum distance judgment value as a top layer starting point and a top layer end point corresponding to the target OD on a top layer road network.
For step 301, the top road network is pre-divided into a plurality of traffic cells, and after obtaining the target OD, the server may first determine the traffic cells to which the start point and the end point of the target OD belong on the top road network, and mark the traffic cells as the start point traffic cell and the end point traffic cell.
For step 302, each traffic cell includes more than one node, and thus, the road network nodes in the starting traffic cell obtained on the top-level road network may be determined as a top-level starting point node set, and the road network nodes in the ending traffic cell respectively obtained on the top-level road network may be determined as a top-level ending point node set.
For step 303, the server pairs the nodes between the top-level start node set and the top-level end node set two by two, i.e., one node in each pair of nodes is from the top-level start node set and the other node is from the top-level end node set. After pairing, the server calculates a distance judgment value for each pair of nodes according to a preset distance judgment function. Wherein, in a specific application scene, the distance determination function may specifically be:
F(i，j)＝f 0 (O，i)+f * (i，j)+f d (j，D)
wherein i is a road network node in the top-level starting point node set, j is a road network node in the top-level end point node set, and f 0 (O, i) is the Euclidean distance from the starting point of the target OD to the road network node i, f d (j, D) is the Euclidean distance from the road network node j to the end point of the target OD, f * (i, j) is the shortest path distance of the road network nodes i and j on the top layer road networkF (i, j) is the distance determination value between road network nodes i and j.
For step 304, after obtaining the distance determination values corresponding to the pairs of nodes, the server may determine a pair of nodes with the smallest distance determination value as a top starting point and a top end point of the target OD corresponding to the top road network.
Considering that the hierarchical road network-based optimal OD path searching method of the present application is more advantageous and has a better and obvious effect in medium and long distance path planning, before step 101, it may be determined whether the current path planning for the target OD is a medium and long distance path planning, so as to determine whether to execute the subsequent steps. Further, as shown in fig. 5, before step 101, the method for searching for an OD optimal path based on a hierarchical road network may further include:
401. judging whether the target OD is located in the same traffic cell or not, or whether the linear distance from the starting point to the end point of the target OD is smaller than a preset distance threshold value, if so, executing step 402, otherwise, executing step 101, wherein the top-level road network is divided into a plurality of traffic cells in advance;
402. and determining the shortest path from the starting point to the end point of the target OD on the target traffic network as the optimal path of the target OD.
For the above steps 401 to 402, it can be understood that the server determines whether the target OD is a short-distance OD by determining whether the target OD is located in the same traffic cell or whether the linear distance from the starting point to the end point of the target OD is smaller than a preset distance threshold, and if so, the server may directly use a bottom-layer road network, that is, the target traffic road network to perform shortest path planning, and determine the shortest path from the starting point to the end point of the target OD as the optimal path of the target OD; on the contrary, if the target OD is not located in the same traffic cell and the linear distance from the starting point to the end point of the target OD is not less than the preset distance threshold, the target OD may be considered as the medium-and-long-distance OD, and thus the step 101 may be performed.
102. Determining the shortest path from the top layer starting point to the top layer end point on the top layer road network as a top layer path segment;
specifically, after the server generates the top-level road network, it may calculate the shortest path between any two nodes in the top-level road network in advance by using Dijstra shortest path algorithm, and store the calculated shortest path in an array form, for example, an array Distance [ o ] [ i ] is used to store the Distance from the node o to any node i, an array PreNode [ o ] [ i ] is used to store the path from the node o to any node i, and the number (front node) of the last node of any node i, and by continuously looping back, the path sequence from the node o to any node may be obtained.
It should be noted that, in the process of calculating the shortest path, since the Dijstra shortest path algorithm is a single-source shortest path algorithm, from a single point, the shortest path information from a node point to all other nodes in the road network can be calculated, and the calculation processes of the shortest paths between different single points do not affect each other. Therefore, when the Dijstra shortest path algorithm is adopted for calculation, multiple threads can be started to calculate multiple single points simultaneously, and the running speed of the shortest path algorithm is greatly increased.
Particularly, in the present application, when the optimal/shortest path between two points on the road network of different levels needs to be searched, the OD optimal path searching method may be adopted for searching, and may be applied to steps 102 to 104 and step 402 in this embodiment. In view of the fact that the traffic state of each road segment in the road network varies continuously according to the actual situation, in the process of searching for the optimal path of the OD in the road network of different levels, the weight of each road segment is the travel time of the road segment, that is, the length of the road segment is divided by the average speed of the road segment in a given time period. The average speed of the road segment at different time periods may be different according to the actual road conditions, and thus the OD optimal path search results at different time periods may also be different. The method and the device fully consider the real traffic conditions and search the dynamic OD optimal paths based on the layered road network, so that the OD optimal path set can be continuously updated, the actual situation is better met, and the real-time trip of travelers can be better helped.
For step 102, the server may directly call the pre-stored array PreNode [ o ] [ i ], so as to determine the shortest path from the top start point to the top end point on the top-level path network, and determine it as the top-level path segment.
103. Determining a shortest path from a starting point of the target OD to a starting point of the top layer on the target traffic path network as a starting point path segment;
specifically, the server may calculate the shortest path from the starting point of the destination OD to the top-level starting point using Dijstra shortest path algorithm and record it as the starting-point path segment.
104. Determining a shortest path from the top-layer end point to the end point of the target OD on the target traffic network as an end point path segment;
similarly, in particular, the server may calculate the shortest path from the top end point to the end point of the destination OD using Dijstra shortest path algorithm and mark it as an end path segment.
105. And sequentially splicing the starting point path section, the top layer path section and the end point path section to obtain the optimal path of the target OD.
After the starting point path segment, the top layer path segment, and the end point path segment are obtained, the server may sequentially splice the starting point path segment, the top layer path segment, and the end point path segment to obtain the optimal path of the target OD. It should be noted that the server may further perform path closed-loop detection on the optimal path to avoid a situation of a repeated closed-loop path, or the server may further perform path optimization on the optimal path to reduce a probability of a path detour.
In path planning, in order to make a traveler have more path options to choose from, the server may provide a plurality of planned paths to the traveler, which requires screening and determining an optimal path set. For this reason, as shown in fig. 6, in this embodiment, after sequentially splicing the starting-point path segment, the top-level path segment, and the ending-point path segment to obtain the optimal path of the target OD, the method may further include:
501. constructing an initial reasonable path set, and putting the optimal path into the reasonable path set;
502. traversing all path branches from the starting point to the end point of the latest reasonable path, and searching all candidate paths corresponding to the latest reasonable path, wherein the latest reasonable path is the latest path put into the reasonable path set;
503. performing path closed loop detection on all candidate paths corresponding to the latest reasonable path, and putting the candidate paths which pass the path closed loop detection into the reasonable path set;
504. when one candidate path is placed into the reasonable path set, respectively calculating the coincidence rate between the candidate path and each other reasonable path in the reasonable path set, and determining the maximum coincidence rate obtained by calculation as the coincidence rate value of the candidate path;
505. and putting the reasonable paths with the coincidence rate value lower than a preset coincidence threshold value in the reasonable path set into the optimal path set of the target OD, returning to execute the step of traversing all path branches from the starting point to the end point of the latest reasonable path again and searching all candidate paths corresponding to the latest reasonable path until the number of the paths in the optimal path set reaches the preset number requirement, wherein the optimal path set comprises the optimal paths of the target OD.
For step 501, the server may construct an initial reasonable path set, and put the optimal path into the reasonable path set, at this time, a parameter count may also be set to count the number of paths in the optimal path set. It can be seen that the initial value of the counter count is 1.
For step 502, the server starts to search from the starting point of the path to the end point of the path triggered by the latest reasonable path, and when a path node with multiple branches is encountered, first determines whether the branch has been traversed and searched, and if so, searches for the next branch and determines again; if the branch is not traversed, a new path to the end point of the path is sought from the branch, and the newly obtained path is put into the candidate path set. And searching from the starting point to the end point of the latest reasonable path to obtain all candidate paths which can be generated by the current reasonable path, thereby forming a new candidate path set. The latest reasonable path mentioned here is a path that is placed in the reasonable path set in the latest way, that is, every time a path is newly added in the reasonable path set, the latest reasonable path is updated at the same time.
For step 503, in order to avoid the existence of a closed-loop path in the candidate paths, the server may perform path closed-loop detection on all candidate paths corresponding to the latest reasonable path, put the candidate paths that have passed the path closed-loop detection into the reasonable path set, and delete the candidate paths that have not passed the path closed-loop detection.
Specifically, as shown in fig. 7, step 503 may specifically include:
601. carrying out closed-loop detection on all the candidate paths in sequence from short to long according to the path length;
602. for each candidate path, if closed-loop detection finds that a closed-loop path exists in each candidate path, determining that the closed-loop detection of the path of each candidate path does not pass;
603. and for each candidate path, if the closed-loop detection finds that no closed-loop path exists in each candidate path, determining that the closed-loop detection of each candidate path passes.
For the above steps 601-603, it can be understood that the server may arrange the candidate paths from short to long according to the path lengths, then sequentially perform path closed-loop detection, if a closed-loop path exists in the candidate path, the candidate path does not meet the condition of a reasonable path, and then continue to perform path closed-loop detection on the next candidate path; if the closed-loop path does not exist in the candidate path, the candidate path meets the condition of a reasonable path and is placed in a reasonable path set.
For step 504, when a new reasonable path is obtained, that is, when each candidate path is put into the reasonable path set, the server may compare the new reasonable path with the rest of the reasonable paths in the reasonable path set, so as to obtain the Overlap Ratio overlay _ Ratio of the reasonable path. In this embodiment, the method for calculating the coincidence rate may be to successively compare the current reasonable path with the road segment collection in each of the other reasonable paths in the reasonable path set, calculate the coincidence rate between the new reasonable path and each of the other reasonable paths, and finally select the maximum value of the coincidence rate as the coincidence rate value of the new reasonable path.
With respect to step 505, it is appreciated that the server may preset a coincidence threshold as the highest acceptable repetition rate value (Given _ Ratio). The server compares the calculated coincidence rate value of each reasonable path with the preset coincidence threshold value to judge whether the reasonable path can meet the coincidence rate requirement, if the coincidence rate is lower than the preset coincidence threshold value Given _ Ratio, the reasonable path meets the condition of the first k paths, the numerical value of the counter count is added with 1, and the reasonable path is put into an optimal path set; if the value is higher than the preset coincidence threshold value Given _ Ratio, the reasonable path does not meet the condition of the previous k paths, and the steps are continuously repeated to screen the paths. When the value of the counter count is equal to the predetermined k value, the previous k optimal paths are searched, and the obtained optimal path set includes k paths, and particularly, one of the k paths is the optimal path of the target OD.
In the embodiment, firstly, a top layer starting point and a top layer terminal point corresponding to a target OD on a top layer road network are determined, wherein the top layer road network consists of road sections of which the travel frequency and/or the road grade are higher than a set value, and the road sections are extracted from a target traffic road network in advance according to historical travel track data; then, determining the shortest path from the top layer starting point to the top layer end point on the top layer road network as a top layer path segment; then, determining a shortest path from a starting point of the target OD to a starting point of the top layer on the target traffic network as a starting point path segment; determining a shortest path from the top-layer terminal to the terminal of the destination OD on the destination traffic network as a terminal path segment; and finally, splicing the starting point path section, the top layer path section and the end point path section in sequence to obtain the optimal path of the target OD. Therefore, the optimal path of the target OD is respectively determined through the double-layer road networks (the top road network and the target traffic network), so that the optimal path is composed of the top road path section located in the top road network, the starting point road path section and the ending point road path section located in the target traffic network, the shorter distance of the paths between the ODs is considered, the actual travel habits of travelers are better met, and better travel experience can be brought to the travelers. Further, when the optimal path is calculated, the calculation of the paths in different layers and the connection of the paths among different layers are carried out simultaneously by establishing the layered database, so that the calculation and search speed of the shortest path is greatly improved. Meanwhile, based on the optimal path among the target ODs, a user can set the number and repetition rate of the paths in the path set, continuously obtain new paths by using the idea of tree branching, and finally obtain a reasonable path set of the road network.
In order to intuitively explain the advantages of the OD optimal path searching method based on the hierarchical road network, which is provided by the application, compared with the prior art, the Shenzhen road network is taken as an example, the application is practically applied, and the four aspects of different spatial distances, different path set path numbers, different minimum coincidence rate standards and comparison with the traditional shortest path algorithm are respectively tested, so that the beneficial effects brought by the method can be obtained through comparison:
1. in a large-scale road network, the path collection obtained by the method can effectively reflect the actual travel habits of people;
2. compared with a traditional path set generation algorithm, the calculation operation efficiency can be improved by 5 times;
3. the method has the advantages that the operation efficiency of the path set generation among different distances OD is approximately the same;
4. the method and the device can support the requirements of different path numbers and different path coincidence rates in the path set.
The following is a specific test case result, and the application environment for testing is an Inter (R) Core (TM) i7-7700 CPU @3.60GHz processor with 8-Core CPU and 16G running memory.
The following calculation results can be obtained by performing the k-path calculation on OD pairs at different spatial distances, see table 1.
TABLE 1
| Numbering | Number of front k paths | OD to empty space distance (\ m) | Runtime (\ s) | Rate of coincidence |
| 1 | 4 | 2700 | 0.787 | 0.8 |
| 2 | 4 | 13600 | 0.754 | 0.8 |
| 3 | 4 | 19500 | 0.748 | 0.8 |
| 4 | 4 | 44160 | 0.878 | 0.8 |
According to the test case, the running time of the front k path algorithm basically fluctuates little for the distance in space, and the method has the advantage of operation time.
The following calculation results can be obtained by performing the k-first path calculation for different numbers of OD pairs of k-first paths, see table 2:
TABLE 2
| Numbering | Number of front k paths | OD to empty space distance (\ m) | Runtime (\ s) | Rate of coincidence |
| 1 | 1 | 67000 | 0.614 | 0.8 |
| 2 | 2 | 67000 | 0.643 | 0.8 |
| 3 | 3 | 67000 | 0.717 | 0.8 |
| 4 | 4 | 67000 | 0.938 | 0.8 |
The following calculation results can be obtained by performing the k-path calculation for OD pairs of different minimum coincidence standards, see table 3.
TABLE 3
The improved algorithm is compared with the path set obtained by the traditional front k shortest path algorithm. The results are shown in Table 4.
TABLE 4
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by functions and internal logic of the process, and should not constitute any limitation to the implementation process of the embodiments of the present application.
In an embodiment, a hierarchical road network based OD optimal path searching apparatus is provided, which is in one-to-one correspondence with the hierarchical road network based OD optimal path searching method in the above embodiments. As shown in fig. 8, the hierarchical road network-based OD optimal path searching apparatus includes a starting and ending point determining module 701, a top-level path segment determining module 702, a starting point path segment determining module 703, an ending point path segment determining module 704, and a path segment splicing module 705. The functional modules are explained in detail as follows:
a start-end point determining module 701, configured to determine a top-level start point and a top-level end point corresponding to a target OD on a top-level road network, where the top-level road network is composed of road segments with trip frequencies and/or road grades higher than a set value, and the road segments are extracted from a target traffic network in advance according to historical trip track data;
a top-level path segment determining module 702, configured to determine a shortest path from the top-level starting point to the top-level end point on the top-level road network as a top-level path segment;
a starting point path segment determining module 703, configured to determine, on the destination transit network, a shortest path from a starting point of the destination OD to the top-level starting point, as a starting point path segment;
an end-point path segment determining module 704, configured to determine, on the destination traffic network, a shortest path from the top-level end point to an end point of the destination OD, as an end-point path segment;
and a path segment splicing module 705, configured to splice the starting point path segment, the top layer path segment, and the end point path segment in sequence, so as to obtain an optimal path of the target OD.
Further, the hierarchical road network based OD optimal path searching apparatus may further include:
the judging module is used for judging whether the target OD is positioned in the same traffic cell or not, or whether the linear distance from the starting point to the end point of the target OD is smaller than a preset distance threshold value, wherein the top road network is divided into a plurality of traffic cells in advance;
the first optimal path determining module is used for determining a shortest path from a starting point to a destination of the target OD on the target traffic network as an optimal path of the target OD if the judging result of the judging module is positive;
and the triggering module is used for triggering the starting point and end point determining module if the judgment result of the judging module is negative and the linear distance from the starting point to the end point of the target OD is not less than a preset distance threshold.
Further, the starting and ending point determination module may include:
a traffic cell determining unit, configured to determine traffic cells to which a start point and an end point of the target OD belong on the top-level road network, and mark the traffic cells as a start-point traffic cell and an end-point traffic cell, where the top-level road network is pre-divided into a plurality of traffic cells;
a node set extracting unit, configured to extract, from the top-level road network, road network nodes in the starting point traffic cell as a top-level starting point node set and road network nodes in the end point traffic cell as a top-level end point node set;
the judgment value calculation unit is used for performing pairing calculation on nodes between the top-layer starting point node set and the top-layer end point node set one by one according to a preset distance judgment function to obtain distance judgment values corresponding to all pairs of nodes;
and the top starting point and end point determining unit is used for determining a pair of nodes with the minimum distance judgment value as a top starting point and a top end point corresponding to the target OD on the top road network.
Further, the distance decision function may be:
F(i，j)＝f 0 (O，i)+f * (i，j)+f d (j，D)
wherein i is a road network node in the top-level starting point node set, j is a road network node in the top-level end point node set, and f 0 (O, i) is the Euclidean distance from the starting point of the target OD to the road network node i, f d (j, D) is the Euclidean distance from the road network node j to the end point of the target OD, f * And (i, j) is the shortest path distance of the road network nodes i and j on the top road network, and F (i, j) is the distance judgment value between the road network nodes i and j.
Further, the top road network may be generated in advance through the following modules:
the target road section extraction module is used for acquiring road sections with the traveling frequency and/or the road grade higher than a set value from a target traffic network according to historical traveling track data to obtain each target road section;
an auxiliary road section determining module, configured to traverse the target road sections on the target traffic network, perform road network connectivity analysis, and determine auxiliary road sections that enable the target road sections to be globally connected;
and the top road network generating module is used for combining each target road section and each auxiliary road section to generate the top road network.
Further, the hierarchical road network based OD optimal path searching apparatus may further include:
the reasonable path set building module is used for building an initial reasonable path set and putting the optimal path into the reasonable path set;
the candidate path searching module is used for traversing all path branches from the starting point to the end point of the latest reasonable path and searching all candidate paths corresponding to the latest reasonable path, wherein the latest reasonable path is the latest path which is put into the reasonable path set;
the closed loop detection module is used for carrying out path closed loop detection on all candidate paths corresponding to the latest reasonable path and putting the candidate paths passed by the path closed loop detection into the reasonable path set;
the coincidence rate calculation module is used for respectively calculating the coincidence rate between one candidate path and each other reasonable path in the reasonable path set when the candidate path is placed into the reasonable path set, and determining the maximum coincidence rate obtained by calculation as the coincidence rate value of the candidate path;
and the optimal path set module is used for putting the reasonable paths with the coincidence rate values lower than a preset coincidence threshold value in the reasonable path set into the optimal path set of the target OD, returning to execute the step of traversing all path branches from the starting point to the end point of the latest reasonable path again, and searching all candidate paths corresponding to the latest reasonable path until the number of the paths in the optimal path set reaches the preset number requirement, wherein the optimal path set comprises the optimal paths of the target OD.
Further, the closed loop detection module may include:
the closed loop detection unit is used for carrying out closed loop detection on all the candidate paths in sequence from short to long according to the path length;
a detection failure unit, configured to determine, for each candidate path, that the closed-loop detection of the path of each candidate path fails if the closed-loop detection finds that a closed-loop path exists in the candidate path;
and the detection passing unit is used for determining that the closed-loop detection of each candidate path passes if the closed-loop detection finds that no closed-loop path exists in each candidate path aiming at each candidate path.
For specific definition of the hierarchical road network based OD optimal path searching apparatus, reference may be made to the above definition of the hierarchical road network based OD optimal path searching method, and details are not repeated here. All or part of each module in the above hierarchical road network based OD optimal path searching apparatus may be implemented by software, hardware, or a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, as shown in fig. 9, and includes a memory, a processor and a computer program stored in the memory and executable on the processor, where the processor when executing the computer program implements the steps of the method for searching for an optimal path based on OD of a hierarchical road network in the above embodiments, for example, steps 101 to 105 shown in fig. 2. Alternatively, the processor, when executing the computer program, implements the functions of each module/unit of the OD optimal path searching apparatus based on the hierarchical road network in the above embodiments, for example, the functions of the modules 701 to 705 shown in fig. 8. To avoid repetition, further description is omitted here.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and the computer program is executed by a processor to implement the steps of the method for searching for an optimal path based on OD of hierarchical road network in the above embodiment, for example, steps 101 to 105 shown in fig. 2. Alternatively, the computer program is executed by a processor to implement the functions of each module/unit of the OD optimal path searching apparatus based on hierarchical road network in the above embodiment, for example, the functions of the modules 701 to 705 shown in fig. 8. To avoid repetition, further description is omitted here.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above may be implemented by instructing relevant hardware by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the embodiments of the methods described above may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other ways. For example, the above-described apparatus/network device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (9)
1. An OD optimal path searching method based on a layered road network is characterized by comprising the following steps:
determining a top layer starting point and a top layer terminal point of a target OD corresponding to a top layer road network, wherein the top layer road network consists of road sections of which the travel frequency and/or road grade are higher than a set value, and the road sections are extracted from the target road network in advance according to historical travel track data;
determining the shortest path from the top layer starting point to the top layer end point on the top layer road network as a top layer path segment;
determining a shortest path from a starting point of the destination OD to a starting point of the top layer on the destination traffic network as a starting point path segment;
determining a shortest path from the top-layer end point to the end point of the target OD on the target traffic network as an end point path segment;
sequentially splicing the starting point path section, the top layer path section and the end point path section to obtain the optimal path of the target OD;
constructing an initial reasonable path set, and putting the optimal path into the reasonable path set;
traversing all path branches from the starting point to the end point of the latest reasonable path, and searching out all candidate paths corresponding to the latest reasonable path, wherein the latest reasonable path is the latest path put into the reasonable path set;
performing path closed loop detection on all candidate paths corresponding to the latest reasonable path, and putting the candidate paths passed by the path closed loop detection into the reasonable path set;
when one candidate path is placed into the reasonable path set, respectively calculating the coincidence rate between the candidate path and each other reasonable path in the reasonable path set, and determining the maximum coincidence rate obtained by calculation as the coincidence rate value of the candidate path;
and putting the reasonable paths with the coincidence rate value lower than a preset coincidence threshold value in the reasonable path set into the optimal path set of the target OD, returning to execute the step of traversing all path branches from the starting point to the end point of the latest reasonable path again and searching all candidate paths corresponding to the latest reasonable path until the number of the paths in the optimal path set reaches the preset number requirement, wherein the optimal path set comprises the optimal paths of the target OD.
2. The hierarchical road network-based OD optimal path searching method as claimed in claim 1, further comprising, before determining the top layer start point and the top layer end point of the target OD on the top layer road network, the following steps:
judging whether the target OD is located in the same traffic cell or not, or judging whether the linear distance from the starting point to the end point of the target OD is smaller than a preset distance threshold, wherein the top road network is divided into a plurality of traffic cells in advance;
if the target OD is located in the same traffic cell or the linear distance from the starting point to the end point of the target OD is smaller than a preset distance threshold, determining a shortest path from the starting point to the end point of the target OD on the target traffic network as an optimal path of the target OD;
and if the target OD is not located in the same traffic cell and the linear distance from the starting point to the end point of the target OD is not smaller than a preset distance threshold, executing the step of determining the corresponding top layer starting point and top layer end point of the target OD on the top layer road network.
3. The hierarchical road network-based OD optimal path searching method as claimed in claim 1, wherein said determining the top layer starting point and the top layer end point of the target OD on the top layer road network comprises:
respectively determining traffic cells to which a starting point and an end point of the target OD belong on the top-level road network, and recording the traffic cells as a starting-point traffic cell and an end-point traffic cell, wherein the top-level road network is divided into a plurality of traffic cells in advance;
respectively extracting road network nodes in the starting point traffic cell as a top layer starting point node set and road network nodes in the terminal point traffic cell as a top layer terminal point node set from the top layer road network;
performing pairing calculation on nodes between the top-layer starting point node set and the top-layer end point node set one by one according to a preset distance judgment function to obtain distance judgment values corresponding to all pairs of nodes;
and determining a pair of nodes with the minimum distance judgment value as a top layer starting point and a top layer end point of the target OD on a top layer road network.
4. The hierarchical road network-based OD optimal path search method according to claim 3, wherein said distance decision function is:
F(i，j)＝f 0 (O，i)+f * (i，j)+f d (j，D)
wherein i is a road network node in the top-level starting point node set, j is a road network node in the top-level end point node set, and f 0 (O, i) is the Euclidean distance from the starting point of the target OD to the road network node i, f d (j, D) is the Euclidean distance from the road network node j to the end point of the target OD, f * And (i, j) is the shortest path distance of the road network nodes i and j on the top road network, and F (i, j) is the distance judgment value between the road network nodes i and j.
5. The hierarchical road network-based OD optimal path search method according to claim 1, wherein said top level road network is generated in advance by the steps of:
acquiring road sections with the traveling frequency and/or the road grade higher than a set value from a target traffic network according to historical traveling track data to obtain each target road section;
traversing each target road segment on the target traffic network, analyzing road network connectivity, and determining each auxiliary road segment which enables each target road segment to be globally communicated;
and combining each target road section and each auxiliary road section to generate the top road network.
6. The hierarchical road network based OD optimal path searching method according to claim 1, wherein said performing the closed loop detection of the paths on all candidate paths corresponding to the latest reasonable path comprises:
carrying out closed-loop detection on all the candidate paths in sequence from short to long according to the path length;
for each candidate path, if closed-loop detection finds that a closed-loop path exists in each candidate path, determining that the closed-loop detection of the path of each candidate path does not pass;
and for each candidate path, if the closed-loop detection finds that no closed-loop path exists in each candidate path, determining that the closed-loop detection of each candidate path passes.
7. An OD optimal path searching device based on a hierarchical road network is characterized by comprising the following components:
the starting and end point determining module is used for determining a top layer starting point and a top layer end point corresponding to a target OD on a top layer road network, wherein the top layer road network consists of road sections of which the trip frequency and/or the road grade are higher than a set value, and the road sections are extracted from a target traffic network in advance according to historical trip track data;
a top road path segment determining module, configured to determine, on the top road network, a shortest path from the top start point to the top end point as a top road path segment;
a starting point path segment determining module, configured to determine, on the destination transit network, a shortest path from a starting point of the destination OD to the top-level starting point, as a starting point path segment;
the destination path segment determining module is used for determining a shortest path from the top-layer destination to a destination OD destination on the destination traffic network as a destination path segment;
the path segment splicing module is used for sequentially splicing the starting point path segment, the top layer path segment and the end point path segment to obtain the optimal path of the target OD;
the reasonable path set building module is used for building an initial reasonable path set and putting the optimal path into the reasonable path set;
the candidate path searching module is used for traversing all path branches from the starting point to the end point of the latest reasonable path and searching all candidate paths corresponding to the latest reasonable path, wherein the latest reasonable path is the latest path which is put into the reasonable path set;
the closed loop detection module is used for carrying out path closed loop detection on all candidate paths corresponding to the latest reasonable path and putting the candidate paths which pass the path closed loop detection into the reasonable path set;
the coincidence rate calculation module is used for respectively calculating the coincidence rate between one candidate path and each other reasonable path in the reasonable path set when the candidate path is placed into the reasonable path set, and determining the maximum coincidence rate obtained by calculation as the coincidence rate value of the candidate path;
and the optimal path set module is used for placing the reasonable paths with the coincidence rate values lower than a preset coincidence threshold value in the reasonable path set into the optimal path set of the target OD, returning to execute the step of traversing all path branches from the starting point to the end point of the latest reasonable path again and searching all candidate paths corresponding to the latest reasonable path until the number of the paths in the optimal path set reaches the preset number requirement, wherein the optimal path set comprises the optimal paths of the target OD.
8. A computer device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, wherein said processor when executing said computer program implements a hierarchical road network based OD optimal path search method according to any of claims 1 to 6.
9. A computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the hierarchical road network based OD optimal path searching method according to any one of claims 1 to 6.
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