Vehicle path planning method and device, electronic equipment and storage medium
By establishing an urban road network and dynamic traffic flow model, and combining a sample evaluation matrix with breadth-first search, the problem of excessive travel time in existing vehicle route planning is solved, achieving accurate route planning and time optimization.
Patent Information
- Application Number
- CN202511361791.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-23
- Publication Date
- 2026-01-23
AI Technical Summary
Existing vehicle route planning methods fail to effectively consider travel time, resulting in excessively long vehicle travel times and reduced user experience.
By acquiring urban road network data and traffic flow parameters, an urban road network model and a dynamic traffic flow model are established. The traffic flow model is optimized using a three-dimensional weight matrix. Combined with a sample evaluation matrix and a breadth-first search strategy, the path with the shortest travel time is found.
It enables accurate prediction of future traffic flow parameters, provides a comprehensive understanding of road network conditions, offers route planning with the shortest travel time, and improves user experience.
Smart Images
Figure CN121384064A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of automotive technology, and in particular to a vehicle routing method, apparatus, electronic device, and storage medium. Background Technology
[0002] Current vehicle routing methods mostly focus on minimizing the distance from the origin to the destination, without considering the travel time. This often results in longer travel times for vehicles following the planned routes, thus reducing the user experience. Summary of the Invention
[0003] This invention provides a vehicle route planning method, apparatus, electronic device, and storage medium to solve the technical problem of long travel time for planned vehicle routes in the prior art.
[0004] This invention provides a vehicle route planning method, comprising: Acquire urban road network data, actual traffic flow parameters of each road segment in the urban road network, and traffic flow parameter samples; Based on the urban road network data, the actual traffic flow parameters, and the traffic flow parameter samples, the travel time for each road segment is determined; Based on the travel time of each road segment, search the urban road network for the path with the shortest travel time from the target origin to the target destination.
[0005] According to a vehicle route planning method provided by the present invention, determining the travel time of each road segment based on the urban road network data, the actual traffic flow parameters, and the traffic flow parameter samples includes: A city road network model is established based on the aforementioned city road network data; A dynamic traffic flow model is established based on the actual traffic flow parameters. Determine the sample evaluation matrix based on the traffic flow parameter samples; The travel time for each road segment is determined based on the urban road network model, the dynamic traffic flow model, and the sample evaluation matrix.
[0006] According to a vehicle route planning method provided by the present invention, the urban road network model includes a set of intersections, a set of roads, road accessibility, and a traffic flow feature vector.
[0007] According to a vehicle routing method provided by the present invention, the step of establishing a dynamic traffic flow model based on the actual traffic flow parameters includes: An initial traffic flow model is established based on the actual traffic flow parameters. The initial traffic flow model is optimized using a three-dimensional weight matrix; The dynamic traffic flow model is obtained based on the optimized initial traffic flow model.
[0008] According to a vehicle routing method provided by the present invention, the step of determining a sample evaluation matrix based on the traffic flow parameter samples includes: The weight of each traffic flow parameter is determined based on each of the traffic flow parameter samples. The weight of each traffic flow parameter is determined based on the proportion of each traffic flow parameter and the number of traffic flow parameter samples. Obtain the congestion level membership degree of the traffic flow parameter samples, and determine the sample evaluation matrix based on the weight of each traffic flow parameter and the congestion level membership degree.
[0009] According to a vehicle route planning method provided by the present invention, the step of searching for the shortest route from the target origin to the target destination in the urban road network based on the travel time of each road segment includes: By integrating the priority queue of travel time for each road segment with a breadth-first search strategy, the path with the shortest travel time from the target starting point to the target ending point is found iteratively.
[0010] The present invention also provides a vehicle route planning device, comprising: The acquisition module is used to acquire urban road network data, actual traffic flow parameters of each road segment in the urban road network, and traffic flow parameter samples. The determination module is used to determine the travel time of each road segment based on the urban road network data, the actual traffic flow parameters, and the traffic flow parameter samples. The search module is used to search for the shortest path from the target origin to the target destination in the urban road network based on the travel time of each road segment.
[0011] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle routing method as described above.
[0012] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vehicle routing method as described above.
[0013] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the vehicle path planning method as described above.
[0014] The vehicle route planning method, device, electronic equipment, and storage medium provided by this invention enable accurate prediction of future traffic flow parameters and a comprehensive understanding of the corresponding road network traffic conditions, allowing for the planning of the shortest travel time route for vehicles. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0016] Figure 1 This is a flowchart illustrating the vehicle routing method provided by the present invention.
[0017] Figure 2 This is a flowchart illustrating step S2 provided by the present invention.
[0018] Figure 3 This is a schematic diagram of the vehicle routing device provided by the present invention.
[0019] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation
[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.
[0021] It should be noted that in the description of this invention, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The terms "upper," "lower," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly, for example, as a fixed connection, a detachable connection, or an integral connection; a mechanical connection or an electrical connection; a direct connection or an indirect connection through an intermediate medium; or a connection within two elements. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.
[0022] The terms "first," "second," etc., used in this invention are used to distinguish similar objects, not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, a first object can be one or more. Furthermore, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.
[0023] The following is combined Figures 1-4 This invention describes the vehicle routing method, apparatus, electronic device, and storage medium provided by the present invention.
[0024] like Figure 1 As shown, the vehicle route planning method provided by the present invention includes steps S1-S3.
[0025] Step S1: Obtain urban road network data, actual traffic flow parameters of each road segment in the urban road network, and traffic flow parameter samples.
[0026] Urban road network data can include the coordinates of various intersections, the number of lanes, the length of each road segment, and traffic light settings. Traffic flow parameters can include parameters such as traffic volume and vehicle speed. Traffic flow parameter samples are essentially the historical traffic flow parameters for each road segment.
[0027] Urban road network data can be collected through OpenstreetMap (a free and open-source global map project collaboratively marked by users).
[0028] Step S2: Determine the travel time for each road segment based on urban road network data, actual traffic flow parameters, and traffic flow parameter samples.
[0029] In some implementations, such as Figure 2 As shown, step S2 may further include steps S21-S24.
[0030] Step S21: Establish an urban road network model based on urban road network data.
[0031] Among them, complex urban road network data can be refined into an urban road network model G = (V, E, M, N), where V is the set of intersections, E is the set of roads, M is the road accessibility, and N is the traffic flow feature vector.
[0032] For any two intersections and ,have: , For the intersection Intersection The road section between them.
[0033] Furthermore, the step of determining road accessibility M may include: if the road segment between two intersections belongs to road set E, then the road accessibility M between the two intersections is determined to be 1; otherwise, the road accessibility M between the two intersections is determined to be 0. This can be expressed by the formula: ; Furthermore, the step of determining the traffic flow feature vector N may include: obtaining first traffic flow data at multiple past times and second traffic flow data at multiple future times for the road segment between two intersections; and determining the traffic flow feature vector N based on the relationship between the first and second traffic flow data. This can be expressed as a formula: ; Where t is the current time, For the intersection Intersection Traffic flow data for the road segments between them at the current moment. For the intersection Intersection Traffic flow data for the road segment between them at the previous a time points. This is the first traffic flow data; For the intersection Intersection Traffic flow data for the road segment between them at time b in the future. This is the second traffic flow data; It is a functional relationship used to describe the connection between past and future traffic flow data, linking traffic flow data from a past time period (ta to t) with a future time period (t+1 to t+b) to analyze the changing patterns of traffic flow over time. The traffic flow feature vector N can be determined.
[0034] Step S22: Establish a dynamic traffic flow model based on actual traffic flow parameters.
[0035] Step S22 may further include: Establish an initial traffic flow model based on actual traffic flow parameters; The initial traffic flow model is optimized using a three-dimensional weight matrix; The dynamic traffic flow model is obtained based on the optimized initial traffic flow model.
[0036] Specifically, a ChebNet graph convolutional model, which is the initial traffic flow model, is built based on the actual traffic flow parameters: ; in, For the initial traffic flow model, K is the receptive field radius of the convolution kernel. For the k-th weight, These are the coefficients of the Chebyshev polynomial. These are actual traffic flow parameters; Let D be the Laplacian matrix of the graph, D be the degree matrix, and A be the adjacency matrix. The normalized Laplace matrix, The largest eigenvalue of the Laplace matrix. It is the identity matrix; It is the k-th order Chebyshev polynomial, where , The Chebyshev polynomial is then derived using the following formula: ; The optimization of the initial traffic flow model using a three-dimensional weight matrix further includes: if the Chebyshev polynomial is non-zero, the corresponding weight is set to 1; if the Chebyshev polynomial is zero, the corresponding weight is set to 0. This can be expressed as: ; Then, an improved convolutional neural network model is adopted, using spatiotemporal graph convolutional layers to effectively extract spatiotemporal correlations. The spatiotemporal graph convolutional layer is constructed by stacking spatial dimension graph convolutions and two temporal dimension convolutions. The final output of the model is processed through temporal dimension convolutions and fully connected layers, transforming the optimized initial traffic flow model into a multi-convolutional kernel dynamic traffic flow model. ; This invention utilizes a ChebNet graph convolutional model optimized with a three-dimensional weight matrix to capture the spatial correlation of traffic flow, and analyzes the temporal correlation through an improved multi-kernel convolutional neural network model, thereby achieving accurate prediction of future traffic flow parameters and providing reliable data support for route planning.
[0037] Step S23: Determine the sample evaluation matrix based on the traffic flow parameter samples.
[0038] Step S23 may further include: The weight of each traffic flow parameter is determined based on the samples of each traffic flow parameter. The weight of each traffic flow parameter is determined based on its proportion and the number of traffic flow parameter samples. Obtain the congestion level membership degree of traffic flow parameter samples, and determine the sample evaluation matrix based on the weights of each traffic flow parameter and the congestion level membership degree.
[0039] Specifically, each traffic flow parameter sample can be normalized, and then the i-th traffic flow parameter sample can be calculated. l The proportion of various traffic flow parameters .
[0040] The process of determining the weight of each traffic flow parameter based on its proportion and the number of traffic flow parameter samples can further include: determining the entropy value of each traffic flow parameter based on its proportion and the number of traffic flow parameter samples; determining the difference coefficient of each traffic flow parameter based on its entropy value; and determining the weight of each traffic flow parameter based on its difference coefficient and the number of types of traffic flow parameters in the traffic flow parameter samples.
[0041] Specifically, based on the weighting of traffic flow parameters The entropy value of the traffic flow parameter is determined by the number S of traffic flow parameter samples. The formula is: ; Based on the entropy value of traffic flow parameters Determine the coefficient of difference of traffic flow parameters The formula is: ; Based on the difference coefficient of traffic flow parameters The weights are determined by the number of traffic flow parameter types z in the traffic flow parameter sample. The formula is: ; This allows us to assign higher weights to traffic flow parameters with higher influencing factors using the entropy weighting method.
[0042] Then, fuzzy comprehensive judgment is used to classify the planned path, determining the classification interval for each traffic flow parameter and the congestion status classification. There are m classifications for the path. These are the corresponding critical values; the traffic flow parameters correspond to the membership degree of each congestion level. .
[0043] The process of determining the sample evaluation matrix based on the weights of each traffic flow parameter and the membership degree of the congestion level can be further summarized as follows: multiplying the weights of each traffic flow parameter by their corresponding membership degrees of the congestion level to obtain the sample evaluation matrix. This can be expressed by the formula: ; Let be the sample evaluation matrix for the i-th traffic flow parameter sample. Let be the congestion classification membership degree of the i-th traffic flow parameter sample.
[0044] By classifying traffic flow parameters into congestion levels using the entropy weight method and fuzzy comprehensive method, a comprehensive understanding of the traffic conditions of the road network can be obtained.
[0045] Step S24: Determine the travel time for each road segment based on the urban road network model, dynamic traffic flow model, and sample evaluation matrix.
[0046] Step S24 can be expressed by the formula as follows: ; T is the travel time matrix, which includes the travel time for each road segment. This is the initial time matrix.
[0047] Step S3: Based on the travel time of each road segment, search the urban road network for the shortest path from the target origin to the target destination.
[0048] Step S3 may further include: By integrating the priority queue of travel time for each road segment with a breadth-first search strategy, the path with the shortest travel time from the target origin to the target destination is found iteratively.
[0049] Specifically, weights can be assigned to each road in the road network, and Dijkstra's algorithm can be used to search for the shortest path from the target origin to the target destination. The search process is as follows: Determine the target starting point for path search Target End Point And the start time t; create an empty priority queue Q, and set the target start time... rhs (shortest time to reach a node in the current iteration) , The travel time matrix at time t is set to 0; for all nodes n in the road network, its g(shortest time to reach the node in the previous iteration) is set to 0. The initial values of ) and rhs are set to infinity; then the target starting point is set... and their corresponding heuristic factors Add to priority queue Q, heuristic factor ; Path search iteration: The minimum key value in priority queue Q is less than the target destination. key value or target endpoint If the RHS value is not equal to the G value, continuously retrieve the node n with the smallest key value from the priority queue Q; If the g value of node n is greater than the rhs value, then update the g value of node n to the rhs value, and recalculate the candidate values of rhs(q) for all nodes q that can be reached through one hop from node n. If the newly calculated rhs(q) is less than the original value, then update rhs(q) and add node q back to the priority queue Q. If the g value of node n is not greater than the rhs value, then set the g value of node n to infinity, and recalculate the rhs value for all nodes q that can be reached through one hop from node n, as well as node n itself. If the rhs value changes, add the corresponding node back to the priority queue Q, until the minimum key value of the priority queue Q is not less than the target endpoint. The key value and the target endpoint The iteration stops when the RHS value equals the g value; at this point, g (the endpoint) is the target starting point. To the destination The path with the shortest travel time is the optimal path. Node update: If node n is not the target endpoint Then calculate the value of rhs(n). Take the minimum value of g(n') + cost(n', n, g(n)) from all nodes n' that can reach n in one hop, where cost(n', n, g(n)) represents the travel time of segment (n', n); if node n is in priority queue Q, remove it from the queue; if g(n) is not equal to rhs(n), it means that g(n) has not been synchronized to the optimal in the current iteration, so add node n and its key(n) back to priority queue Q.
[0050] Through the steps described above, Dijkstra's algorithm incorporates congestion travel time to search for the optimal travel route within the road network.
[0051] Of course, this invention can also combine traffic flow trends to update road condition information in real time, providing vehicles with the latest road conditions and guiding their actual driving.
[0052] As can be seen from the above, the vehicle route planning method of the present invention can achieve accurate prediction of future traffic flow parameters and a comprehensive understanding of the corresponding road network traffic conditions, and plan the route with the shortest travel time for vehicles.
[0053] The present invention also provides a vehicle route planning device, comprising: The acquisition module is used to acquire urban road network data, actual traffic flow parameters of each road segment in the urban road network, and traffic flow parameter samples. The determination module is used to determine the travel time of each road segment based on urban road network data, actual traffic flow parameters, and traffic flow parameter samples. The search module is used to search for the shortest path from the target origin to the target destination in the urban road network based on the travel time of each road segment.
[0054] It should be noted that the vehicle route planning device provided by the present invention can execute the vehicle route planning method of any of the above embodiments during specific operation, which will not be elaborated in this embodiment.
[0055] In some implementations, the determining module can also be used for: Establish an urban road network model based on urban road network data; Establish a dynamic traffic flow model based on actual traffic flow parameters; Determine the sample evaluation matrix based on traffic flow parameter samples; The travel time for each road segment is determined based on the urban road network model, dynamic traffic flow model, and sample evaluation matrix.
[0056] In some implementations, the urban road network model may include a set of intersections, a set of roads, road accessibility, and traffic flow feature vectors.
[0057] In some implementations, the determining module can also be used for: Establish an initial traffic flow model based on actual traffic flow parameters; The initial traffic flow model is optimized using a three-dimensional weight matrix; The dynamic traffic flow model is obtained based on the optimized initial traffic flow model.
[0058] In some implementations, the determining module can also be used for: The weight of each traffic flow parameter is determined based on the samples of each traffic flow parameter. The weight of each traffic flow parameter is determined based on its proportion and the number of traffic flow parameter samples. Obtain the congestion level membership degree of traffic flow parameter samples, and determine the sample evaluation matrix based on the weights of each traffic flow parameter and the congestion level membership degree.
[0059] In some implementations, the search module can also be used for: By integrating the priority queue of travel time for each road segment with a breadth-first search strategy, the path with the shortest travel time from the target origin to the target destination is found iteratively.
[0060] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 4 As shown, the electronic device may include a processor, a communications interface, memory, and a communication bus, wherein the processor, communications interface, and memory communicate with each other via the communication bus. The processor can call logical instructions in the memory to execute a vehicle route planning method, which includes: acquiring urban road network data, actual traffic flow parameters of each road segment in the urban road network, and traffic flow parameter samples; determining the travel time of each road segment based on the urban road network data, actual traffic flow parameters, and traffic flow parameter samples; and searching for the shortest path from the target origin to the target destination in the urban road network based on the travel time of each road segment.
[0061] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0062] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute the vehicle route planning method provided in the above embodiments, the method comprising: acquiring urban road network data, actual traffic flow parameters of each road segment in the urban road network, and traffic flow parameter samples; determining the travel time of each road segment based on the urban road network data, actual traffic flow parameters, and traffic flow parameter samples; and searching for the path with the shortest travel time from the target origin to the target destination in the urban road network based on the travel time of each road segment.
[0063] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program is implemented to perform the vehicle route planning method provided in the above embodiments. The method includes: acquiring urban road network data, actual traffic flow parameters of each road segment in the urban road network, and traffic flow parameter samples; determining the travel time of each road segment based on the urban road network data, actual traffic flow parameters, and traffic flow parameter samples; and searching for the path with the shortest travel time from the target origin to the target destination in the urban road network based on the travel time of each road segment.
[0064] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0065] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A vehicle route planning method, characterized in that, include: Acquire urban road network data, actual traffic flow parameters of each road segment in the urban road network, and traffic flow parameter samples; Based on the urban road network data, the actual traffic flow parameters, and the traffic flow parameter samples, the travel time for each road segment is determined; Based on the travel time of each road segment, search the urban road network for the path with the shortest travel time from the target origin to the target destination.
2. The vehicle routing method according to claim 1, characterized in that, The step of determining the travel time for each road segment based on the urban road network data, the actual traffic flow parameters, and the traffic flow parameter samples includes: A city road network model is established based on the aforementioned city road network data; A dynamic traffic flow model is established based on the actual traffic flow parameters. Determine the sample evaluation matrix based on the traffic flow parameter samples; The travel time for each road segment is determined based on the urban road network model, the dynamic traffic flow model, and the sample evaluation matrix.
3. The vehicle routing method according to claim 2, characterized in that, The urban road network model includes a set of intersections, a set of roads, road accessibility, and traffic flow feature vectors.
4. The vehicle routing method according to claim 2, characterized in that, The step of establishing a dynamic traffic flow model based on the actual traffic flow parameters includes: An initial traffic flow model is established based on the actual traffic flow parameters. The initial traffic flow model is optimized using a three-dimensional weight matrix; The dynamic traffic flow model is obtained based on the optimized initial traffic flow model.
5. The vehicle routing method according to claim 2, characterized in that, The step of determining the sample evaluation matrix based on the traffic flow parameter samples includes: The weight of each traffic flow parameter is determined based on each of the traffic flow parameter samples. The weight of each traffic flow parameter is determined based on the proportion of each traffic flow parameter and the number of traffic flow parameter samples. Obtain the congestion level membership degree of the traffic flow parameter samples, and determine the sample evaluation matrix based on the weight of each traffic flow parameter and the congestion level membership degree.
6. The vehicle routing method according to claim 1, characterized in that, The process of searching the urban road network for the shortest path from the target origin to the target destination based on the travel time of each road segment includes: By integrating the priority queue of travel time for each road segment with a breadth-first search strategy, the path with the shortest travel time from the target starting point to the target ending point is found iteratively.
7. A vehicle route planning device, characterized in that, include: The acquisition module is used to acquire urban road network data, actual traffic flow parameters of each road segment in the urban road network, and traffic flow parameter samples. The determination module is used to determine the travel time of each road segment based on the urban road network data, the actual traffic flow parameters, and the traffic flow parameter samples. The search module is used to search for the shortest path from the target origin to the target destination in the urban road network based on the travel time of each road segment.
8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the vehicle path planning method as described in any one of claims 1 to 6.
9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the vehicle path planning method as described in any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the vehicle path planning method as described in any one of claims 1 to 6.