CN114528619A - Road network synthesis method under inhabitant place constraint - Google Patents

Abstract

The invention provides a road network synthesis method under the constraints of residential areas, which belongs to the technical field of road network mapping. First, the residential areas are clustered to generate settlements, and then the roads are classified according to the topological relationship between the roads and the settlements; then neurons are constructed according to the types of roads contained in the settlements, and effective paths between adjacent neurons are searched; The principle of minimum time cost simplifies the paths of adjacent neuron pairs and adjacent neuron groups forming a triangular structure in turn, so that the connectivity between settlements and the functionality of the road network can be better maintained. Compared with the existing technology The road simplification mainly relies on the semantic, geometric and topological information of the road network. The present invention takes the elements of the residential area as the comprehensive constraints of the road network, and fully considers the geographical relationship between the road and the residential area, so that the simplified road network is more suitable for the road network. actual traffic needs.

Description

A comprehensive method of road network under the constraints of residential areas

technical field

The invention relates to a road network synthesis method under the constraints of residential areas, and belongs to the technical field of road network mapping.

Background technique

Road is an important geographical element that constitutes the skeleton of the map, and it is the key research object of cartographic synthesis. The goal of road network synthesis is to retain important roads in the process of scaling down, discard redundant roads that do not meet the target scale, and maintain the overall shape and connectivity of the road network.

The current road network synthesis methods mainly include: methods based on semantic level, methods based on graph theory, methods based on Stroke, methods based on mesh density and other hybrid methods. The methods based on semantic level mainly rely on the semantic information of the road, do not establish an effective model for the road network, and ignore the topology and geometric information of the road network. The road importance evaluation method based on graph theory mainly focuses on the analysis of the connectivity and topological structure of the road network, which plays an important role in maintaining the connectivity of the road network, but ignores the semantic and geometric information of the road. The method based on Stroke can effectively simulate the influence of the visual length of the road on the road importance evaluation in the process of manual selection. While maintaining the integrity of the road in the comprehensive road selection process, it can also comprehensively consider the ductility and topology consistency between road objects. Sexual and contextual information. The method based on mesh density can better maintain the relative density of road network before and after synthesis and the consistency of road network topology and semantics. However, most of the above methods do not consider the relationship between the road network and other geographical elements, which will affect the coordination between geographical elements and even violate the laws of reality. The main purpose of roads is to serve people's lives. The connectivity between lands is deviated from the laws of reality and violates the actual needs of human beings, which in turn leads to a decrease in the connectivity between residential areas.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a road network synthesis method under the constraints of residential areas, so as to solve the problem that the connectivity between residential areas is reduced due to the failure to fully consider the geographical correlation between residential areas and roads in the existing road network synthesis process. question.

The present invention proposes a road network synthesis method under the constraint of a residential area, the method comprising the following steps:

1) Obtain residential data and road network data in the target area;

2) Clustering the acquired residential data to obtain different settlements; and classifying roads according to the topological relationship between roads and settlements;

3) Constructing the neurons of the settlement according to the road types contained in the settlement;

4) Search all valid paths between any two adjacent neurons, and find the optimal path. The optimal path refers to the path with the lowest transit time cost among all valid paths between adjacent neurons. Simplify all valid paths of adjacent neurons to obtain the simplified paths between adjacent neurons;

5) After the path simplification between all adjacent neurons is completed, all valid paths between adjacent neurons are treated as a whole, and the triangular structure formed by three adjacent settlements is searched to find the optimal value of each edge in the triangular structure. Path, judge whether the edge with the largest time cost of the optimal path in the formed triangular structure can be replaced by the combination of the other two edges, if the time cost corresponding to the edge with the largest time cost is greater than the sum of the time cost of the other two edges, delete it All valid paths corresponding to the edge with the largest time cost.

The invention uses the residential area elements as the constraints of road synthesis, clusters the residential areas to obtain settlements of different residential areas, classifies the roads by considering the topological relationship between the roads and the settlements, and combines the settlements and the roads that are topologically connected to the settlements to construct neurons. , search for an effective path between adjacent neurons, and according to the principle of minimum travel time cost, simplify the paths of adjacent neuron pairs and adjacent neuron groups that form a triangular structure in turn, so that the connectivity between settlements and the functionality of the road network It can be well maintained. Compared with the prior art, which mainly relies on the semantic, geometric and topological information of the road network to simplify the road, the present invention takes the elements of the residential area as the comprehensive constraints of the road network, and fully considers the relationship between the road and the residential area. The geographical relationship between them makes the simplified road network more suitable for actual traffic needs.

Further, the roads are divided into E-type roads, P-type roads, I-type roads and O-type roads; wherein E-type roads refer to one end of the road located inside the settlement of residential areas, and one end of the road located outside the settlement of residential areas. It means that the road runs through the settlement of residential areas, and both ends of the road are located outside the settlement of residential areas. Type I road means that all endpoints of the road are located inside the settlement of residential areas. Type O road means that the road is completely outside the settlement of residential areas. ;

The neuron is divided into type I neuron and type II neuron; type I neuron means that the neuron includes E-type road and type I road or only contains E-type road, and type II neuron refers to the neuron. Only P-Roads are included.

The roads are classified according to the topological relationship between roads and settlements in the above manner, which facilitates the construction of subsequent neurons and the search of effective paths.

Further, the effective path between the adjacent neurons refers to all paths formed from the E-type path of the starting neuron to the E-type path of the ending neuron, wherein, when the neuron is a type II neuron, the II The P-type road within a type neuron serves as the E-type road for that neuron.

Since the E-type road connects the inside and outside of the settlement, the P-type road runs through the settlement, and through these two types of roads, the travel path connecting two adjacent neurons can be determined, while the I-type road serves as the internal path of the neuron.

Further, the effective path refers to the path with the lowest transit time cost, and the calculation formula of the transit time cost is:

where L is the length of the road, V is the design speed of roads of different grades, and ST is the road curvature.

Since a neuron may have multiple E-type roads in different directions of the settlement, there may be multiple paths between adjacent neurons. Calculate the travel time cost between all E-type road combinations, select an effective path that meets the requirements, and further Reduce data redundancy and facilitate the simplification of subsequent paths.

Further, the simplified basis for the effective path between adjacent neurons is:

α×(κ _f +κ _s +κ _e )＜κ _t

where α is the tolerable cost ratio; κ _f is the travel time cost of the optimal path; κ _s is the internal travel time cost of the optimal path and the current path in the starting neuron; κ _e is the optimal path and the current path The internal transit time cost in the terminal neuron; _κt is the transit time cost of the current path; if the transit time cost of the current path is greater than the sum of the transit time cost of the optimal path and the internal transit time cost under the tolerable cost ratio, then Remove this path.

Further, the path simplification basis in the adjacent neuron group is:

α×(κ _e1 +κ _e2 )＜κ _em

where α is the tolerable cost ratio; κ _em is the largest transit time cost in neighboring neurons; κ _e1 and κ _e2 are the other two transit time costs.

The path simplification between adjacent neurons and adjacent neuron groups is achieved through the above methods. The core of the two simplification methods is to select the path with the smallest transit time cost, so as to realize the elimination of redundant paths.

Further, the tolerable cost ratio refers to the degree to which the service capability of the optimal path in the target area can be accepted to be weaker than that of the current path, which represents the simplification of the road network, and its value range is [0.6, 1], When the simplification of the road network is stronger, the tolerable cost is smaller.

When simplifying the route, the tolerable cost ratio can be used to adjust the simplification strength of the road network, so as to realize the route simplification under the condition that the travel time cost is not much different.

Further, before the neuron construction of the settlement, the settlement is first screened according to the importance of the settlement, and the neuron construction is carried out on the screened out settlement.

Settlements are screened by their importance, so as to eliminate a few weak and small settlements without affecting the overall road simplification, so as to enhance the restraint effect.

Further, the importance of the settlement is determined according to the area of the settlement, the density of residential land, the Voronoi area, and the road index, where the Voronoi area refers to the spatial influence range of the settlement, and the road index refers to the road connectivity of the settlement.

Four parameters are used to determine the importance of a settlement. Among them, the larger the area and the density of residential land, the more important the settlement can be; the Voronoi area represents the spatial influence range, and the larger the Voronoi area, the more representative the settlement is; the road index is also considered, In order to better ensure the connectivity of the subsequent simplified roads between settlements; these four parameters can more accurately characterize the importance of settlements.

Further, the calculation formula of the importance of the settlement is:

SP(vi )=μ×[ _{w 1} ×S(vi )+ _{w 2} ×RD(vi )+ _{w 3} ×VS(vi )]+ _{w 4} ×RI(vi ₎

In the formula, v _i (i=1,2,...,n) is the ith residential settlement, n is the total number of settlements generated, S is the settlement area, RD is the residential density, VS is the Voronoi area, and RI is the road exponent, w ₁ , w ₂ , w ₃ and w ₄ are the weights of S, RD, VS and RI respectively, and w ₁ +w ₂ +w ₃ +w ₄ =1, μ is the adjustment coefficient, when the settlement belongs to type I When a neuron, μ=1; when the colony belongs to type II neuron, μ<1.

The importance of different settlements is determined by the above formula, and the adjustment coefficients under different types of neurons are set to realize the adaptive adjustment of the importance of settlements under different types of neurons.

Description of drawings

Fig. 1 is the concrete implementation flow chart of the road network synthesis method under the restriction of the residential area of the present invention;

Figure 2(a) is an example diagram of an E-type road in the road type;

Figure 2(b) is an example diagram of a P-type road in the road type;

Figure 2(c) is an example diagram of an I-type road in the road type;

Figure 2(d) is an example diagram of an O-shaped road in the road type;

Figure 3(a) is an example diagram of a typical type I neuron;

Figure 3(b) is an example diagram of type I neurons containing P-type roads that cannot directly serve settlements;

Figure 3(c) is an example diagram of a typical type II neuron;

Figure 3(d) is an example diagram of type II neurons containing P-type roads that cannot directly serve settlements;

Fig. 4 is a simplified schematic diagram of the path between adjacent neurons;

Figure 5 is a simplified schematic diagram of the path between groups of adjacent neurons.

Detailed ways

The specific embodiments of the present invention will be further described below with reference to the accompanying drawings.

The present invention proposes a road network synthesis method under the constraints of residential areas, and the specific process of the method is shown in FIG. 1 . First, the residential data is clustered to generate settlements, and then the roads are classified according to the topological relationship between the roads and the settlements; then, neurons are constructed according to the types of roads contained in the settlements; The optimal path is found; finally, according to the principle of minimum cost of travel time, the path between adjacent neurons and the path between groups of neurons that form a triangular structure are simplified in turn.

Step 1. Get the data

The present invention first acquires the residential area data and road network data in the target area. Both kinds of data can be obtained from the established geographic database to obtain the vector data of the residential area and road network in the area. According to the actual demand for the accuracy of the road network, the data of the appropriate scale is selected from the existing database. For example, the road network data of 1:5000 needs to be generated, which can be obtained by simplifying the road network data of 1:2000. The present invention aims to simplify the subsequent path according to the geographical correlation between the road and the residential area by using the acquired residential area data as a constraint.

Step 2. Generate Settlement and Road Classification

According to the obtained residential data and road network data, the residential data is firstly clustered to generate settlements, and then the roads are classified according to the topological relationship between roads and settlements.

First, the acquired residential data is clustered by a clustering algorithm to obtain different settlements, and the outline of the settlement is extracted. In the residential clustering, the density-based clustering method is generally used. In this embodiment, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is used to cluster the residential areas, and the Ball Pivoting method is used to extract the outlines of the settlements. As other implementations, CFSFDP algorithm and K-means algorithm may also be used for clustering of residential areas. As another embodiment, the edge detection method can also be used for the settlement outline extraction.

Then, the roads are classified according to the topological relationship between the roads and the settlements, and the present invention divides the roads into E-type roads, P-type roads, I-type roads and O-type roads. Among them, the E-type road is shown in Figure 2(a). This type of road means that one end of the road is located inside the settlement and the other end is located outside the settlement, which can directly connect the inside and outside of the settlement; the P-type road is shown in Figure 2(b). Type road means that the road runs through the settlement, and both ends of the road are located outside the settlement. Also, this type of road can connect the inside and outside of the settlement. It is common in small settlements along the road, and occasionally in larger settlements; I-type roads are shown in the figure As shown in 2(c), this type of road means that each end point of the road is located inside the settlement of residential area, and its function is to connect different areas inside the settlement and E-type roads. The more complex the structure; the O-type road is shown in Figure 2(d), this type of road is separated from the target settlement, completely outside the settlement, and does not participate in the construction of subsequent neurons. In addition, the classification of roads is settlement-oriented, so the same road may have different types in different clusters.

Step 3. Settlement Screening

Settlements are screened according to the importance of the settlements. Because the present invention performs road network synthesis under the constraints of residential areas, some important settlements are selected as constraints, and some weak and small settlements, that is, settlements with low importance, are eliminated to enhance the constraint effect. The importance of the settlement is determined according to the area of the settlement, the density of residential land, the area of Voronoi, and the road index. The specific calculation formula is as follows:

SP(vi )=μ×[ _{w 1} ×S(vi )+ _{w 2} ×RD(vi )+ _{w 3} ×VS(vi )]+ _{w 4} ×RI(vi ₎ (1)

In the formula, v _i (i=1,2,...,n) is the ith residential settlement, n is the total number of settlements generated, S is the settlement area, RD is the residential density, VS is the Voronoi area, and RI is the road exponent, w ₁ , w ₂ , w ₃ and w ₄ are the weights of S, RD, VS and RI, respectively, and w ₁ +w ₂ +w ₃ +w ₄ =1, μ is the adjustment coefficient, because in the area, density When the parameters are the same, the settlements located at the intersection of the roads have more significant geographical significance than the settlements distributed along the roads, so when the settlements belong to type I neurons, μ=1; when the settlements belong to type II neurons, μ<1 . Among them, the weight values of different parameters can be adjusted according to actual application requirements.

Among them, the settlement area is the most intuitive reflection of the importance of the settlement. Generally, a settlement with a large area is more important than a settlement with a small area; the density of the residential area reflects the density of the residential areas within the settlement. The settlement area is more important, and the density of residential land is calculated as shown in formula (2); the Voronoi area refers to the spatial influence range of the settlement. The larger the Voronoi area, the more representative the settlement is, and the greater the possibility of being selected; the road index It refers to the road connectivity of the settlement. The more roads involved in the settlement and the higher the grade, the higher the importance of the settlement. The road index is determined according to the grade of the roads between settlements. The settlement mainly relies on E-type roads and P-type roads. For this reason, the road index is mainly calculated according to the number and grade of E-type roads and P-type roads. The calculation formula is shown in formula (3):

In the formula, m is the number of residential areas in the settlement vi, S(p _j ) is the area of the residential area p _j in the settlement _{vi ,} and S(v _j ) is the area of the settlement _vi .

In the formula, k is the total number of valid connected roads of the settlement v _i , Rank(r _j ) is the numerical grade of the road r _j , and the numerical grade of the road refers to the road weight value divided according to the grade of the road, as shown in Table 1. shown:

Table 1:

The importance of each settlement is determined according to formulas (1)-(3), and some settlements are eliminated according to the set proportional threshold, which can be set according to the actual situation of the obtained settlement. All subsequent processes are based on the preserved settlements. As other implementations, the importance of the settlement can also be determined according to actual needs, for example, without considering the road factor, only the area of the settlement, the density of the residential area, and the area of Voronoi can be used for determination, or the influence of other factors on the settlement, such as population density, can also be considered. , terrain, etc. As another embodiment, if the residential area data is the target scale, settlement screening may not be performed.

Step 4. Neuron Construction

Neurons are constructed according to the types of roads contained in the settlement, that is, combining the settlement with the roads that are topologically connected to the settlement. Neurons are divided into two categories, type I neurons and type II neurons, based on the type of road in the neuron. Among them, the I-type neuron refers to that the neuron includes E-type roads and I-type roads or only contains E-type roads, as shown in Figure 3(a); at the same time, the present invention follows the principle of "connection with the settlement core is the real connection". ” principle, that is, the target road can directly serve the settlement only if it intersects with other roads inside the settlement or the influence range of the target road covers the core area of the settlement, as shown in Figure 3(b), in Figure 3(b) There are E-type roads and P-type roads in the settlement, but the P-type road is located at the edge of the settlement and cannot directly serve the settlement. Therefore, the P-type road can be regarded as an O-type road and does not participate in the neuron’s constructed, the neuron is a type I neuron.

Among them, a type II neuron means that the neuron contains only P-type roads. The vast majority of type II neurons contain only one P-type road, and the neuron is penetrated by this P-type road, and the settlement is completely dependent on this road, as shown in Figure 3(c); a small number of type II neurons contain multiple P-type roads. However, it is also necessary to follow the principle of “connection with the settlement core is the real connection”. As shown in Figure 3(d), the settlement is penetrated by two P-type roads, but the P-type road on the right is located in the settlement. At the edge position of , it cannot directly serve the settlement, so this P-type road does not participate in the construction of this neuron.

Step 5. Valid Path Search

Search all valid paths between any two adjacent neurons, the valid path between neurons refers to the path generated from the E-type path of the starting neuron to the E-type path of the ending neuron, the starting point neuron and the ending point neuron. The other E-type roads and I-type roads in the neuron do not participate in the path construction, and the P-type road in the type II neuron that effectively connects the interior of the settlement can be regarded as the E-type road of the neuron; since there may be more than one E-shaped road in the neuron , the effective path between adjacent neurons refers to the path generated from the E-type road of the start neuron directly connected to the E-type road of the end neuron, and the E-type road connected to the external road of the neuron or connected to other neurons Does not participate in the path construction of this adjacent neuron. An efficient path refers to the path with the least travel time cost, because it is very time-consuming to search for all paths between two network nodes in a large network, and people usually choose the shortest and fastest path when traveling. Compared with the travel distance, people pay more attention to the travel time, and tend to choose the faster high-grade highway, so the travel time is used as the cost. The travel time is mainly determined by the road length, road grade and road tortuosity. The calculation formula is as follows:

where L is the length of the road, V is the design speed of roads of different grades, and ST is the road curvature.

Since neurons may have multiple E-shaped roads in different directions of the settlement, there may be multiple paths between a pair of neurons. When searching for an effective path between a pair of adjacent neurons, the E-shaped road of the starting neuron and the ending point are compared. The E-type roads of neurons are arranged and combined, and the Dijkstra algorithm is used to search for the path with the minimum travel time cost between all E-type road combinations to determine the effective path.

Step 6. Path Simplification

The path simplification of the present invention mainly includes two stages, one is the path simplification between adjacent neurons, and the other is the path simplification between adjacent neuron groups.

Path simplification between adjacent neurons:

Find the optimal path among all the generated valid paths, the optimal path refers to the path with the lowest transit time cost among all valid paths, and simplify all valid paths of two adjacent neurons according to the optimal path. The specific process is as follows:

An effective path is arbitrarily selected and compared with the optimal path. If the cost of the path is greater than the sum of the cost of the optimal path and the internal traffic cost, it is considered that the path can be replaced, which can be formally expressed as:

α×(κ _f +κ _s +κ _e )＜κ _t (5)

where α is the tolerable cost ratio; κ _f is the travel time cost of the optimal path; κ _s is the internal travel time cost of the optimal path and the current path in the starting neuron; κ _e is the optimal path and the current path The internal transit time cost in the terminal neuron; κ _t is the transit time cost of the current path; in which, the tolerable cost ratio represents the degree to which the service capability of the optimal path in the target area is weaker than that of the current path, that is, The simplification strength of the road network, its value range is [0.6, 1]. When the simplification strength of the road network is higher, the value of α is smaller, which can be selected according to the actual simplification requirements of the road network.

For example, as shown in Figure 4, there are three paths between settlement A and settlement B. Path I starts from p1 and follows road ea to p2, path II starts from p1 and follows roads ec and eb to reach p3, and path III starts from p1 and follows roads ec and eb. ed reaches p4, where path I is the optimal path between settlement A and settlement B. The starting and ending points of path II are p1 and p3, respectively, and its travel cost is 4.1. However, after starting from p1 and reaching p2 along path I, the total cost to reach p3 through internal road ib is 2.8, which is less than the cost of path II, so path II can be replaced by path I. Decreasing the value of α increases the power of simplification. For example, the cost of path III is 4.7, and the total cost from p1 to p2 along path I and then to p4 via roads ib and ia is 5.0. When α=1.0, equation (5) does not hold, and path I cannot replace path III. However, when α<0.94, equation (5) holds, and path I can replace path III.

Path simplification for adjacent groups of neurons:

All valid paths between adjacent neurons are treated as a whole, and the triangular structure formed by three adjacent settlements is searched. The path with the largest transit time cost simplifies the path in the adjacent neuron group, and judges whether the edge with the largest time cost in the formed triangle structure can be replaced by the combination of the other two edges, if the time cost corresponding to the edge with the largest time cost is greater than The sum of the time costs of the other two edges will delete the simplified path with the largest corresponding time cost. Among them, the path representing each edge of the triangle is the path with the lowest transit time cost between the two neurons on the selected corresponding edge, that is, the optimal path between the two neurons. The simplified formal expression of the adjacent neuron group path is:

α×(κ _e1 +κ _e2 )＜κ _em (6)

where α is the tolerable cost ratio; κ _em is the largest transit time cost in neighboring neurons; κ _e1 and κ _e2 are the other two transit time costs. Among them, the value range of α is consistent with that in the above-mentioned simplification of the path between adjacent neurons.

For example, as shown in Fig. 5, in Fig. 5, the neuron groups in groups A, B, and C, where the travel time cost between settlement A and settlement B is 4, and the travel time cost between settlement B and settlement C is 2, The travel time cost between settlement A and settlement C is 8. From formula (6), it can be seen that the path between settlement A and settlement C can be replaced by the path combination of settlement A and settlement B and the path combination of settlement B and settlement C , so all paths between settlement A and settlement C are deleted to simplify the path.

Through the above process, the residential area is used to constrain the synthesis of the road network, and the geographic correlation between the road and the residential area is fully considered. The comprehensive results are highly consistent with the spatial distribution of residential elements, which can ensure the connectivity between settlements and the functionality of the road network.

Claims (10)

1. A method for integrating a road network under the constraint of residential areas, characterized in that it comprises the following steps:

1) acquiring residential area data and road network data in a target area;

2) clustering the acquired residential area data to obtain different colonies; classifying the roads according to the topological relation between the roads and the colony;

3) carrying out neuron construction on the colony according to the road type contained in the colony;

4) searching all effective paths between any two adjacent neurons, finding an optimal path, wherein the optimal path is the path with the lowest transit time cost in all the effective paths between the adjacent neurons, and simplifying all the effective paths of the two adjacent neurons according to the optimal path to obtain a simplified path between the adjacent neurons;

5) after simplifying the paths between all the adjacent neurons, treating all the effective paths between the adjacent neurons as a whole, searching a triangular structure formed by three mutually adjacent colonies, finding the optimal path of each edge in the triangular structure, judging whether the edge with the maximum time cost of the optimal path in the formed triangular structure can be replaced by the combination of the other two edges, and deleting all the effective paths corresponding to the edge with the maximum time cost if the time cost corresponding to the edge with the maximum time cost is greater than the sum of the time costs of the other two edges.

2. The method of integrating road networks under inhabitant's constraints of claim 1, wherein said roads are classified into E-type roads, P-type roads, I-type roads and O-type roads; the E-shaped road is characterized in that one end of the road is positioned inside the residential area settlement, the other end of the road is positioned outside the residential area settlement, the P-shaped road is characterized in that the road penetrates through the residential area settlement, the two ends of the road are positioned outside the residential area settlement, the I-shaped road is characterized in that all end points of the road are positioned inside the residential area settlement, and the O-shaped road is completely positioned outside the residential area settlement;

the neurons are classified into type I neurons and type II neurons; type I neurons refer to neurons that include both E-type and I-type pathways or only E-type pathways, and type II neurons refer to neurons that include only P-type pathways.

3. The city-restricted road network integration method according to claim 2, wherein said valid path between adjacent neurons is all the path from the E-type road of the starting neuron to the E-type road of the ending neuron, and when the neuron is a type II neuron, the P-type road in the type II neuron is the E-type road of the neuron.

4. The method of integrating a road network under inhabitant's constraints as claimed in claim 3, wherein said valid path is the path with the lowest transit time cost, which is calculated by the formula:

wherein L is the length of the road, V is the designed speed of the road with different grades, and ST is the curvature of the road.

5. The residential neighborhood constrained road network integration method according to claim 1, wherein the effective path reduction among neighboring neurons is based on:

α×(κ_f+κ_s+κ_e)＜κ_t

in the formula, alpha is a tolerable cost ratio; kappa_fTransit time cost for optimal path; kappa type_sInternal transit time costs for the optimal path and the current path in the starting neuron; kappa_eInternal transit time cost of the optimal path and the current path in the destination neuron; kappa_tThe transit time cost for the current path; if the transit time cost of the current path is greater than the sum of the optimal path transit time cost and the internal transit time cost at the tolerable cost ratio, the path is removed.

6. The residential neighborhood constrained road network integration method of claim 1, wherein said reduction of paths in said set of adjacent neurons is based on the following:

α×(κ_e1+κ_e2)＜κ_em

in the formula, alpha is a tolerable cost ratio; kappa_emThe largest time-to-flight cost among neighboring neurons; kappa_e1And kappa_e2Two additional transit time costs.

7. The method according to claim 5 or 6, wherein said tolerable cost ratio is a degree that the service capability of the acceptable optimal path in the target area is weaker than that of the current path, and represents a simplification degree of the road network, and the value range is [0.6,1], and the tolerable cost ratio is smaller when the simplification degree of the road network is stronger.

8. The method according to any one of claims 1 to 6, wherein before the construction of neurons in the colony, the colony is screened according to the importance of the colony, and the neuron construction is performed on the screened colony.

9. The method of claim 8, wherein the importance of said colony is determined by the area of colony, the density of colony, the Voronoi area, and the road index, wherein the Voronoi area is the spatial influence range of colony, and the road index is the road connectivity of colony.

10. The method of integrating road networks under residential constraints as claimed in claim 9, wherein said calculation formula of the importance of said settlement is:

SP(v_i)＝μ×[w₁×S(v_i)+w₂×RD(v_i)+w₃×VS(v_i)]+w₄×RI(v_i)

in the formula, v_i(i ═ 1,2, …, n) is the ith residential colony, n is the total number of generated colonies, S is the colony area, RD is the density of the residences, VS is the Voronoi area, RI is the road index, w is the road index₁、w₂、w₃And w₄Are the weights of S, RD, VS and RI, respectively, and w₁+w₂+w₃+w₄μ is 1, and μ is a regulatory factor, when the colony belongs to a type I neuron, μ is 1; when the colony belongs to a type II neuron, mu<1。

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