CN113778987B - Road network query method based on Beidou position service platform - Google Patents
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Abstract
The invention relates to a road network query method based on a Beidou position service platform, which aims at solving the technical problems that the road network query result in the prior art is unreliable or not an optimal path, safe and reliable position service is difficult to provide, and quick road network query is realized. The method comprises the steps of constructing a map of Thiessen polygons and a road network by means of base station and road network information, dividing the road network into initial road sections, forming calibration of an initial road section sequence, and realizing initial construction of a road network model; combining or dividing the initial road section sequence by combining the change of the track vector of the user to form a final road network model; the key point is that the method comprises the following specific steps: step one, constructing a road network model, wherein five minutes are used as observation time granularity for initial road section determination; step two, constructing a road section graph model, taking a road section as a node, taking trafficability as an edge, and constructing a road network query method; the deviation factor is obtained first, and then the final shortest path is obtained.
Description
Technical Field
The invention relates to a Beidou positioning technology, in particular to a road network query method based on a Beidou position service platform.
Background
The Beidou positioning technology is a global satellite positioning system independently developed in China, and the working principle of the Beidou positioning technology is that the distance between a satellite with a known position and a user receiver is measured, and then the specific position of the receiver is known by integrating data of a plurality of satellites. At present, road network model construction and road network query problems are generally realized by a GNSS-based method, but the road network model construction is difficult because the track of a vehicle running on a road is not necessarily kept at a certain distance from the central line of the road, namely is not necessarily completely parallel. In addition, the GNSS anchor points in the above manner are susceptible to the influence of sampling noise, and therefore the steering of the vehicle cannot be truly reflected, let alone the identification of the intersection. For this reason, as disclosed in chinese patent document application No. 201910688963.8, application publication date 2019.11.12, the invention name "a method of removing influence of road alignment on a sharp turn of a vehicle based on track data and map data"; but if the good road network model construction is not solved, the result of the road network query is unreliable or is not the optimal path.
Disclosure of Invention
In order to overcome the defects, the invention aims to provide a road network query method based on a Beidou position service platform for the field, so that the technical problems that a road network query result is unreliable or is not an optimal path, safe and reliable position service is difficult to provide and quick road network query is realized in the prior art are mainly solved. The aim is achieved by the following technical scheme.
A road network query method based on a Beidou position service platform comprises the steps of constructing a map of Thiessen polygons and a road network by means of base stations and road network information, dividing the road network into initial road sections, forming calibration of initial road section sequences, and realizing preliminary construction of a road network model; combining or dividing the initial road section sequence by combining the change of the track vector of the user to form a final road network model; the road network query method is characterized by comprising the following specific steps of:
step one, constructing a road network model; 1) Constructing a mapping diagram of Thiessen polygons and a road network by means of base station and road network information, dividing road sections of the road network, and forming calibration of an initial road section sequence; 2) Searching an initial road section with the shortest vertical distance between the Beidou track point and the initial road section sequence based on the initial road section sequence information, namely the initial road section through which the object passes; 3) Measuring the change of the vector value of the Beidou positioning point of the initial road section, taking the change as the basis of merging, merging or dividing the initial road section, and once the initial road section where the object is positioned is determined, measuring whether the initial road section has an intersection and the phenomenon of the intersection by combining a series of positioning points;
step two, constructing a road section graph model, taking a road section as a node, taking trafficability as an edge, and constructing a road network query method; the method comprises the following steps: 1) The method comprises the steps of constructing a road section graph network, taking a road section as a node, taking the position of the node as the center of the road section and the edges as connectivity among the road sections, comprehensively considering the topological structure, the speed and the congestion coefficient of the road section graph network to measure the connectivity of a road, and then starting searching from a terminal point and determining the connectivity, and providing a Dijkstra-based road network query method; 2) Measurement of road connectivity in order to effectively measure road connectivity, the road graph network is abstracted into a weighted directed network G, g= (V, E, W), where v= { V i -representing nodes in the road map network, i.e. a combination of road segments, represented by the center point of the road segment; e= { E ij I, j e V, represents the set of edges of road segments that are connected to the road segment, e ij The directed edge from the node i to the node j is understood as the directed edge from the road section i to the road section j; w= { W ij I, j epsilon V, representing the weight set of each side of the road section graph network, and representing the weight set by using average passing speed and congestion coefficients; adjacency matrix A ij Representing connectivity between nodes, expressed as:
wherein ,TTIij Is calculated by integrating the average speed and the congestion coefficient at each moment, and the calculation formula of the congestion coefficient TTI is as follows:
wherein Li Representing the total actual length of the road section obtained by inquiring from the starting point to the end point; w (W) ij This indicates which is the weight of the road segmentThe fact that the individual road sections influence the targets of users is more important, and a calculation method is given later; v ij Representing the actual speeds of road segments i to j at the current time t; v free-ij The free flow speed of the road section i to the road section j at the current time t is represented;
3) The path network query method of Dijkstra, which is an optimal path analysis model, is set as the directed path length from the starting point to the node for any node j epsilon V, and then the path length of the shortest path is the shortest path if and only if the following conditions are satisfied, and the solving process is as follows:
by solving a recursive formula of the shortest path according to formula 6:
the final shortest path is obtained.
The initial road section determination in the first step adopts five minutes as an observation time granularity, because the time of each positioning point update is set to be within 1-5 seconds, assuming that each positioning update is five seconds, taking a positioning point of one minute to determine each vector, the five-minute observation time granularity forms five vectors, calculating an included angle cosine value of each vector to determine whether merging and fusion exist between road sections, and comparing included angle cosine values of two adjacent vectors to measure whether the road sections should be merged or not in the observation time granularity, wherein the five vectors are expressed as follows:
if the cosine value of the included angle of two adjacent vectors of different road segments is smaller than the set value theta, combining the road segments; if the cosine value of the included angle of two adjacent vectors in the same road section is smaller than the set value theta, the processing is not carried out; if the cosine value of the included angle between two adjacent vectors of the same road section is larger than the set value theta, the road section is considered to have an intersection, and the road section is segmented.
Importance W of the current road section in the formula 2 of the second step ij Representing road segments communicated from i to j and considering the speed at the current moment t more so as to predict whether a certain road segment affects the travelling process, so that the actual speeds of the previous time segments of each road segment need to be integrated, and the historical average values of the previous time segments are compared so as to measure the deviation factor of the road segment; expressed as:
wherein Ni (t) =t-1, to measure the "deviation factor" of the road segment, since the speed at the current moment has randomness, the current speed and the average speed are processed by using the mean square error, expressed as:
wherein ,an expression representing a mean square error; then the deviation factor is expressed as:
thereby deriving a deviation factor.
The specific flow of the shortest path in the second step is as follows: a) Initializing, wherein the initial node has no precursor node; b) Selecting a nearest node set from the node set Q and the starting node, wherein the nearest node is only measured by length, and the nearest node set is a candidate node set S; c) The node expansion, the actual distance from the initial node to the next node is scanned, the actual length is considered, the congestion coefficient and the speed of each intersection are considered, and the relation between the two is considered in the TTI; if the condition of the formula 7 is satisfied, leaving the node j in the set S, and eliminating other nodes; resetting the node j as a front node, repeating the second step, and continuously iterating; d) When the set Q is empty, the algorithm ends, indicating that there are no more points satisfying the condition.
The road network query method has high result reliability, can quickly query the optimal path, provides safe and reliable location service, and realizes quick road network query; the method is suitable for the rapid inquiry of the road network and the technical improvement of the similar method thereof.
Drawings
Fig. 1 is a block diagram of the working principle flow of the present invention.
Fig. 2 is a schematic diagram of the present invention based on road segment division.
Fig. 3 is a schematic diagram of the road section determination based on the shortest vertical distance of the present invention.
Fig. 4 is a schematic diagram showing movement of a display object at 008 road section according to the present invention.
FIG. 5 is a schematic diagram showing the existence of merging fusion between certain road segments according to the present invention.
Detailed Description
The steps of the present invention will be described in further detail with reference to the accompanying drawings. 1-5, the road network query method provides safe and reliable location service in service applications such as emergency rescue, personal positioning, vehicle navigation, vehicle scheduling, emergency command and the like; therefore, the road network query method focuses on solving the problem of road network model construction, proposes to construct a map of Thiessen polygons and a road network by means of base station and road network information, divides the road network into initial road sections, forms calibration of initial road section sequences, and realizes the initial construction of the road network model; and combining or dividing the initial road segment sequences by combining the change of the track vector of the user to form a final road network model. On the basis, the starting point and the end point are combined to realize the rapid road network query, and the flow chart of the road network query method is shown in figure 1.
Step one, constructing a road network model.
1) And constructing a mapping diagram of the Thiessen polygon and the road network by means of the base station and the road network information, and dividing the road network to form the calibration of the initial road section sequence. As shown in fig. 2, based on a schematic diagram of road segment division, a mapping relation layer based on intersection of a Thiessen polygon and a road network is shown in the diagram, and initial road segment division is performed on the road network based on the two types of layers, wherein each initial road segment is the length covered by the Thiessen polygon; and forming a calibration of the initial road segment sequence based on the division of each initial road segment.
2) And searching an initial road section with the shortest vertical distance between the Beidou track point and the initial road section sequence based on the initial road section sequence information, namely the initial road section through which the object passes. As shown in fig. 3, the determination is based on the road section with the shortest vertical distance.
3) Measuring the change of the vector value of the Beidou positioning point of the initial road section, taking the change as the basis of merging, merging or dividing the initial road section, and once the initial road section where the object is located is determined, measuring whether the initial road section has an intersection and the phenomenon of the intersection by combining a series of positioning points. Because the Beidou positioning point has a drifting phenomenon, a series of track points are combined to determine an initial road section where the object is located, and the lower diagram shows that the object moves on the 008 road section, as shown in fig. 4.
Five minutes are adopted as one observation time granularity, and since the time of each positioning point update is set to be within 1-5 seconds, if each positioning update is five seconds, a positioning point of one minute is taken to determine each vector, then five vectors are formed by the five-minute observation time granularity, and the cosine value of the included angle of each vector needs to be calculated to determine whether merging exists between road sections. As shown in fig. 5, the upper graph has five vectors from 008 road segments to 010 road segments, and in the granularity of the observation time, the cosine values of the included angles of two adjacent vectors need to be compared to measure whether the road segments should be combined, which is expressed as:
if the cosine value of the included angle of two adjacent vectors of different road segments is smaller than the set value theta, combining the road segments; if the cosine value of the included angle of two adjacent vectors in the same road section is smaller than the set value theta, the processing is not carried out; if the cosine value of the included angle between two adjacent vectors of the same road section is larger than the set value theta, the road section is considered to have an intersection, and the road section is segmented.
And step two, constructing a road section graph model, and constructing a road network query method by taking a road section as a node and taking trafficability as an edge.
1) In order to simplify a road network model, the method constructs a road section graph network, a road section is taken as a node, the position of the node is the center of the road section, and the edges are the connectivity among the road sections. If the road sections are not communicated, no edge exists; if the road sections are communicated, the average passing speed and the congestion coefficient of the road in each time period are comprehensively considered to measure; whereas connectivity is considered the weight of an edge. Therefore, the method comprehensively considers the topological structure, the speed and the congestion coefficient of the road map network to measure the connectivity of the road, and then provides a Dijkstra-based road network query method from two aspects of searching from the end point and determining the connectivity.
2) Measurement of road connectivity in order to effectively measure road connectivity, the road graph network is abstracted into a weighted directed network G, g= (V, E, W), where v= { V i -representing nodes in the road map network, i.e. a combination of road segments, represented by the center point of the road segment; e= { E ij I, j e V, represents the set of edges of road segments that are connected to the road segment, e ij The directed edge from the node i to the node j is understood as the directed edge from the road section i to the road section j; w= { W ij I, j e V, representing the weight set of each side of the road map network, and representing the weight set by using average passing speed and congestion coefficient. Adjacency matrix A ij Representing connectivity between nodes, expressed as:
wherein ,TTIij Is calculated by integrating the average speed and the congestion coefficient at each moment, and the calculation formula of the congestion coefficient TTI is as follows:
wherein Li Representing the total actual length of the road section obtained by inquiring from the starting point to the end point; w (W) ij For the weight of the road section, it is more important that this represents which road section affects the user's goal, and a calculation method is given later; v ij Representing the actual speeds of road segments i to j at the current time t; v free-ij The free flow speed of road segment i to road segment j at the current time t is represented. Since the current speed is affected by many factors, the importance W of the current road segment ij The road segments that are communicated from i to j are represented, and the speed at the current moment t is considered more so as to predict whether a certain road segment will affect the travelling process, so that the actual speeds of the previous time segments of each road segment need to be integrated, and the historical average values of the previous time segments are compared so as to measure the 'deviation factor' of the road segments. Expressed as:
wherein Ni (t) =t-1, to measure the "deviation factor" of the road segment, since the speed at the current moment has randomness, the current speed and the average speed are processed by using the mean square error, expressed as:
wherein ,an expression representing the mean square error. Then the deviation factor is expressed as:
3) And an optimal path analysis model-Dijkstra's road network query method.
For any one node j e V, set to the directed path length from the start point to the node, then the solution process is as follows, for the shortest path length, if and only if the following conditions are satisfied:
by solving a recursive formula of the shortest path according to formula 6:
the specific flow of the shortest path is as follows: a) Initializing, wherein the initial node has no precursor node; b) Selecting a nearest node set from the node set Q and the starting node, wherein the nearest node is only measured by length, and the nearest node set is a candidate node set S; c) The node expansion, the actual distance from the initial node to the next node is scanned, the actual length is considered, the congestion coefficient and the speed of each intersection are considered, and the relation between the two is considered in the TTI; if the condition of the formula 7 is satisfied, leaving the node j in the set S, and eliminating other nodes; resetting the node j as a front node, repeating the second step, and continuously iterating; d) When the set Q is empty, the algorithm ends, indicating that there are no more points satisfying the condition.
The innovation points of the method are as follows: 1. the road network model, namely the road section graph model, is constructed by combining the base station, the road network information and the user track information, and the road network query method adopts the real track to divide the road sections so as to more reasonably reflect the travel rule of the user. 2. Connectivity is considered by integrating the current speed and the congestion coefficient of the road section, and the calculation of the connectivity is based on the current moment, so that the connectivity of different roads at different moments can be measured more conveniently; because the speed at the current moment has randomness, the current speed and the average speed are processed by adopting the mean square error, and the deviation factor for measuring the road section is adopted, so that the traffic situation of the real road is reflected more accurately.
Claims (4)
1. A road network query method based on a Beidou position service platform comprises the steps of constructing a map of Thiessen polygons and a road network by means of base stations and road network information, dividing the road network into initial road sections, forming calibration of initial road section sequences, and realizing preliminary construction of a road network model; combining or dividing the initial road section sequence by combining the change of the track vector of the user to form a final road network model; the road network query method is characterized by comprising the following specific steps of:
step one, constructing a road network model; 1) Constructing a mapping diagram of Thiessen polygons and a road network by means of base station and road network information, dividing road sections of the road network, and forming calibration of an initial road section sequence; 2) Searching an initial road section with the shortest vertical distance between the Beidou track point and the initial road section sequence based on the initial road section sequence information, namely the initial road section through which the object passes; 3) Measuring the change of the vector value of the Beidou positioning point of the initial road section, taking the change as the basis of merging, merging or dividing the initial road section, and once the initial road section where the object is positioned is determined, measuring whether the initial road section has an intersection and the phenomenon of the intersection by combining a series of positioning points;
step two, constructing a road section graph model, taking a road section as a node, taking trafficability as an edge, and constructing a road network query method; the method comprises the following steps: 1) The method comprises the steps of constructing a road section graph network, taking a road section as a node, taking the position of the node as the center of the road section and the edges as connectivity among the road sections, comprehensively considering the topological structure, the speed and the congestion coefficient of the road section graph network to measure the connectivity of a road, and then starting searching from a terminal point and determining the connectivity, and providing a Dijkstra-based road network query method; 2) Measurement of road connectivity for effective measurementConnectivity of roads abstracts the road graph network into a weighted directed network G, g= (V, E, W), where v= { V i -representing nodes in the road map network, i.e. a combination of road segments, represented by the center point of the road segment; e= { E ij I, j e V, represents the set of edges of road segments that are connected to the road segment, e ij The directed edge from the node i to the node j is understood as the directed edge from the road section i to the road section j; w= { W ij I, j epsilon V, representing the weight set of each side of the road section graph network, and representing the weight set by using average passing speed and congestion coefficients; adjacency matrix A ij Representing connectivity between nodes, expressed as:
wherein ,TTIij Is calculated by integrating the average speed and the congestion coefficient at each moment, and the calculation formula of the congestion coefficient TTI is as follows:
wherein Li Representing the total actual length of the road section obtained by inquiring from the starting point to the end point; w (W) ij For the weight of the road section, it is more important that this represents which road section affects the user's goal, and a calculation method is given later; v ij Representing the actual speeds of road segments i to j at the current time t; v free-ij The free flow speed of the road section i to the road section j at the current time t is represented;
3) The path network query method of Dijkstra, which is an optimal path analysis model, is set as the directed path length from the starting point to the node for any node j epsilon V, and then the path length of the shortest path is the shortest path if and only if the following conditions are satisfied, and the solving process is as follows:
by solving a recursive formula of the shortest path according to formula 6:
the final shortest path is obtained.
2. The road network query method based on the Beidou position service platform according to claim 1, wherein the initial road section determination in the step one adopts five minutes as an observation time granularity, and because the time of each positioning point update is set to be 1-5 seconds, assuming that each positioning point update is five seconds, taking one minute of positioning point to determine each vector, the five minute observation time granularity forms five vectors, and calculating an included angle cosine value of each vector is needed to determine whether merging and fusion exist between road sections, and comparing included angle cosine values of two adjacent vectors is needed to measure whether the road sections should be merged in the observation time granularity, wherein the method is characterized in that:
if the cosine value of the included angle of two adjacent vectors of different road segments is smaller than the set value theta, combining the road segments; if the cosine value of the included angle of two adjacent vectors in the same road section is smaller than the set value theta, the processing is not carried out; if the cosine value of the included angle between two adjacent vectors of the same road section is larger than the set value theta, the road section is considered to have an intersection, and the road section is segmented.
3. The road network query method based on Beidou position service platform according to claim 1, wherein the importance W of the current road section in the formula 2 in the step two is characterized in that ij Representing road segments communicating from i to j and taking more into account the speed at the current time t in order to predict whether a certain road segment will affect traveling throughThe process therefore requires integrating the actual speeds of the first few time segments of each road segment, comparing the historical average of the first few time segments in order to measure the "deviation factor" of the road segment; expressed as:
wherein Ni (t) =t-1, to measure the "deviation factor" of the road segment, since the speed at the current moment has randomness, the current speed and the average speed are processed by using the mean square error, expressed as:
wherein ,an expression representing a mean square error; then the deviation factor is expressed as:
thereby deriving a deviation factor.
4. The road network query method based on the Beidou position service platform of claim 1, wherein the specific flow of the shortest path in the step two is as follows: a) Initializing, wherein the initial node has no precursor node; b) Selecting a nearest node set from the node set Q and the starting node, wherein the nearest node is only measured by length, and the nearest node set is a candidate node set S; c) The node expansion, the actual distance from the initial node to the next node is scanned, the actual length is considered, the congestion coefficient and the speed of each intersection are considered, and the relation between the two is considered in the TTI; if the condition of the formula 7 is satisfied, leaving the node j in the set S, and eliminating other nodes; resetting the node j as a front node, repeating the second step, and continuously iterating; d) When the set Q is empty, the algorithm ends, indicating that there are no more points satisfying the condition.
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