Quepest path searching method, device and system in combination with traffic light delay and dynamic road condition

Abstract

The invention relates to the technical field of big data processing and quantum computing, in particular to a fastest path searching method, device and system in combination with traffic light delay and dynamic road conditions. According to the method, a real-time topology network is formed by combining a road network topology structure and traffic road condition data, topology solving is converted into variable solving of edges through node splitting, then constraint conditions and optimization targets are constructed by combining weights of the edges, and solving is performed by adopting a quantum algorithm. According to the method, through dynamic weight updating and quantum efficient solution, the problems of insufficient real-time performance and low calculation efficiency in traditional path planning are solved, and high-precision and low-delay technical support is provided for intelligent traffic management and vehicle-mounted navigation.

Description

Technical Field

[0001] The present invention relates to the technical fields of big data processing and quantum computing, and in particular to a fastest path search method, device and system that combine traffic light delays and dynamic road conditions. Background Art

[0002] In recent years, with the rapid development of intelligent driving technology along with the progress of intelligent electric vehicles, higher requirements have also been put forward for path planning in the process of intelligent driving. Especially in the complex urban traffic environment where road conditions are constantly changing, how to quickly and effectively find the optimal path has become a challenge. In practical applications, traditional path planning algorithms such as the enumeration method face multiple challenges such as low efficiency, high computational pressure, low accuracy, and inability to change the path according to road conditions when dealing with large-scale data. As an emerging computing model, quantum computing has great potential in future computing power and speed due to its unique quantum characteristics, providing a new perspective and possibility for solving such complex optimization problems.

[0003] However, combining quantum computing can only improve computational efficiency, and the accuracy of path planning more depends on road network modeling. Currently, road network modeling either solely considers the congestion situation at traffic light intersections or solely considers the congestion situation on road sections, with a single perspective, making it difficult for the model to accurately reflect road conditions and restricting the accuracy of path optimization. Moreover, current path optimization methods are difficult to optimize paths in combination with real-time road conditions, and traffic condition delays often occur in navigation systems, resulting in users being unable to change routes in a timely manner and unable to avoid congestion situations in a timely manner. Summary of the Invention

[0004] In order to overcome the defects of low path planning accuracy and slow response in the above-mentioned prior art in the traffic environment, the present invention proposes a fastest path search method that combines traffic light delays and dynamic road conditions, and solves the problems of insufficient real-time performance and low computational efficiency in traditional path planning through dynamic weight update and quantum efficient solution, providing high-precision and low-latency technical support for intelligent traffic management and in-vehicle navigation.

[0005] A fastest path search method that combines traffic light delays and dynamic road conditions proposed by the present invention:

[0006] Obtain a road network, use intersections as nodes, and the road sections between adjacent intersections as edges to construct an original topological network;

[0007] Update the weights of each node and edge according to traffic condition data, and split the node with a weight into a set of an edge with a weight and two weightless left and right nodes to form a real-time topological network;

[0008] Construct constraint conditions and optimization objectives; the optimization objective is to minimize the sum of the weights of the edges on the path; the constraint conditions include: the number of incoming edges and outgoing edges of the intermediate nodes on the path is equal. The incoming edge of a node refers to the edge starting from the node, and the outgoing edge of a node refers to the edge with the node as the destination. The intermediate nodes are the nodes other than the source point and the end point on the path.

[0009] Use a quantum algorithm to solve the minimization optimization objective that satisfies the constraint conditions, and obtain the combination of the edges on the path.

[0010] Preferably, update the real-time topological network periodically. Each time the real-time topological network is updated, update the source point to the current position of the navigation object and re-solve the minimization optimization objective that satisfies the constraint conditions to update the search path.

[0011] Preferably, the method for using a quantum algorithm to solve the minimization optimization objective that satisfies the constraint conditions is: construct a quantum string, make the qubits included in the quantum string correspond one-to-one with the edges in the real-time topological network, perform a unitary transformation on the optimization objective, and then measure the quantum state to solve, and obtain the solution result of the quantum string, which is the combination of the edges on the path.

[0012] Preferably, the assignment method of the weights of each node on the original topological network is: according to the traffic conditions, count the passing time of each intersection within a set time, and then perform normalization processing on the passing time of each intersection, and use the normalized value as the weight of the node corresponding to the intersection.

[0013] Preferably, when calculating the weight of an intersection, first filter the passing time of the intersection with a smaller value, and then perform normalization processing on the remaining passing time; the intersections that are filtered are not assigned weights.

[0014] Preferably, the assignment method of the weights of each edge on the original topological network is: according to the traffic conditions, count the passing time of each road section within a set time, and then perform normalization processing on the passing time of each road section, and use the normalized value as the weight of the edge corresponding to the road section.

[0015] Preferably, the set time is less than or equal to the update period of the real-time topological network.

[0016] A device for implementing the fastest path search method that combines traffic light delay and dynamic road conditions proposed by the present invention includes:

[0017] A road condition monitoring module, which is used to obtain real-time road conditions and calculate the weights of each intersection and road section;

[0018] A topological modeling module, which is connected to the road condition monitoring module; and the original topological network of the road network is stored in the topological modeling module. The topological modeling module is used to update the real-time topological network by combining the weight calculation results of the road condition monitoring module and the original topological network;

[0019] A quantum solution module, which is used to combine the weights of each edge on the real-time topology network and solve the optimization objective that meets the constraint conditions by using a quantum algorithm. The solution is the binary decision variable of each edge on the real-time topology network.

[0020] A fastest path search system combining traffic light delay and dynamic road conditions proposed by the present invention includes a memory and a processor. A computer program is stored in the memory. The processor is connected to the memory and is used to execute the computer program to implement the fastest path search method combining traffic light delay and dynamic road conditions.

[0021] A storage medium proposed by the present invention stores a computer program, and when the computer program is executed, it is used to implement the fastest path search method combining traffic light delay and dynamic road conditions.

[0022] The advantages of the present invention are as follows:

[0023] (1) The present invention utilizes the quantum entanglement and superposition characteristics to achieve fast parallel search in a massive solution space, and realizes a path optimization method based on quantum approximate optimization. The present invention first establishes the topological structure of the map, incorporates traffic light data and road condition data as weights into the map topology, and then uses the quantum approximate optimization algorithm to find the optimal path, overcoming multiple challenges faced by traditional path planning algorithms such as the enumeration method in dealing with large-scale data, such as low efficiency, high computational pressure, low accuracy, and inability to change the path according to road conditions. The operation speed of the quantum approximate optimization algorithm is extremely fast. Compared with classical algorithms, when facing a large number of path planning requirements simultaneously, it can significantly improve the solution speed, reduce computational pressure and costs, provide a new perspective and possibility for solving such complex optimization problems, and better and faster solve the path planning problem.

[0024] (2) The present invention converts the node weights into edge weights through node splitting, enabling the real-time topology network to fully integrate road condition characteristics, thereby further improving the accuracy of path search.

[0025] (3) The present invention dynamically reflects the changes in traffic conditions by updating the nodes and edge weights of the topology network in real time (such as traffic light delay, road section congestion degree), ensuring the timeliness and accuracy of path planning, and guaranteeing the real-time and dynamic adaptability of topological modeling. The quantum algorithm is used to efficiently solve the optimization objective, and the quantum parallelism is used to quickly process large-scale road network data, significantly shortening the complex path search time, and being applicable to the efficient navigation of urban-level road networks. By restricting the number of incoming and outgoing edges of intermediate nodes to be equal, it is possible to avoid breakpoints or loops in the path, ensuring that the generated path is a continuous and reachable optimal route.

[0026] (4) The present invention updates the source point as the current position periodically and re-solves the problem, responds to the changes in vehicle position and traffic condition fluctuations in real time, provides a dynamically optimized navigation route, and avoids detour delays caused by sudden congestion or accidents. The present invention supports continuous adjustment of the route during driving, reduces the additional time cost caused by the mismatch between the fixed route planning and the real-time traffic condition, and realizes the optimization of navigation continuity.

[0027] (5) The present invention corresponds each edge to a qubit one by one, and processes the optimization target by using unitary transformation, gives full play to the superposition and entanglement characteristics of quantum computing, and greatly reduces the time complexity of traditional algorithms. Moreover, the probability distribution characteristics of the quantum measurement results can effectively screen the global optimal solution, avoid falling into the local optimal solution, and improve the reliability of path planning.

[0028] (6) The present invention is based on the statistics of historical and real-time traffic data (such as intersection passing time, road section congestion duration), converts multi-dimensional indexes into unified weights through normalization processing, and enhances the scientificity and comparability of weight assignment. Filter the smaller values of the intersection passing time, and eliminate abnormal or instantaneous interference data (such as temporary construction), avoid its negative impact on path planning, and ensure that the weights reflect the real traffic state.

[0029] (7) The setting time of the weights calculated by the present invention matches the topology update period, ensures the timeliness and continuity of the weight data, and avoids path deviation caused by data lag.

[0030] (8) For the device provided by the present invention, the road condition monitoring, topology modeling and quantum solution modules have clear division of labor, support flexible access to multi-source traffic data (such as cameras, floating vehicle GPS), adapt to the integration requirements of different urban traffic management systems, and have a good modular design to improve scalability. Moreover, the quantum algorithm is implemented by using a dedicated hardware or a quantum simulator, which significantly improves the computing throughput under a complex road network and meets the computing power requirements of real-time navigation.

[0031] (9) The collaborative design of the system and storage medium provided by the present invention with the processor reduces the dependence on the cloud, reduces the data transmission delay and bandwidth occupation, and improves the system response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flowchart of the fastest path search method combining traffic light delay and dynamic road conditions;

[0033] Figure 2 It is a 6-node topology graph;

[0034] Figure 3 It is a schematic diagram of the quantum circuit model of the 6-node system. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0036] Referring to Figure 1 、 Figure 2 , a fastest path search method combining traffic light delay and dynamic road conditions proposed in this embodiment includes the following steps:

[0037] S1. Obtain a road network, use intersections as nodes, and the road sections between adjacent intersections as edges to construct an original topological network.

[0038] S2. Update the weights of each node and edge according to traffic condition data, use the nodes with weights as replacement nodes, and replace the replacement nodes with an edge without weights at both ends to form a real-time topological network; the weight of the edge after the replacement of the replacement node is the weight of the replacement node.

[0039] The weights of the nodes are set by comprehensively considering traffic light delay, traffic flow, and traffic signal delay, and the weights of the edges in the original topology are considered by comprehensively considering road section length, traffic flow, and weather conditions. Specifically in implementation, after obtaining data such as road network, traffic flow, traffic signals, and weather conditions, preprocess the collected data, including data cleaning, data conversion, data normalization, etc.; according to the extracted features, establish an original topological network, on which each node represents an intersection and each edge represents a road section.

[0040] In the real-time topological network, the nodes do not have weights and the edges have weights, thus transforming the path search problem on the original topological network into the shortest path problem on the real-time topological network.

[0041] In this step, existing traffic data models can be used to assign values to intersections and road sections, so as to obtain the assignments of nodes and edges in the original topological network. In this embodiment, weight assignment is performed through data screening and normalization.

[0042] Specifically, for intersections, first, for each intersection, calculate the passing time of vehicles passing through the intersection within a set time, and then filter the intersections with passing time less than the set threshold, and these filtered intersections have no weights; for the remaining intersections, normalize their passing times, and the obtained normalized values are the weights of each intersection, that is, the weights of each node on the original topological network.

[0043] For each road segment, based on the traffic conditions, the travel time of each road segment within a set time is statistically calculated, and then the travel times of all road segments are normalized. The normalized value is used as the weight of the edge corresponding to the road segment.

[0044] S3. Construct the constraint conditions and the optimization objective;

[0045] The optimization objective is to minimize the sum of the weights of the edges on the path;

[0046] The constraint conditions are as follows:

[0047] 1) Only one edge on the path passes through the source point;

[0048] 2) Only one edge passes through the end point;

[0049] 3) The number of incoming edges and outgoing edges of each intermediate node is equal. The incoming edge of a node refers to the edge starting from the node, and the outgoing edge of a node refers to the edge ending at the node. An intermediate node refers to a node other than the source point and the end point on the path.

[0050] Constraint condition 3) stipulates that each intermediate node has both incoming and outgoing edges, thus limiting the connection relationship of intermediate nodes. Combining constraint conditions 1) and 2), it is stipulated that the solution result of the optimization objective must be a path with the source point and the end point as endpoints and passing through each intermediate node. During the optimization solution process, to ensure the minimum value, the situation of passing through the same intermediate node repeatedly will be avoided, so as to ensure that the finally obtained path is a path with the source point and the end point as endpoints and passing through each intermediate node in sequence.

[0051] It should be noted that the weight of the intersection node represents the time-consuming situation of passing through the intersection. Therefore, the source point and the end point do not need to be assigned weights, that is, the source point and the end point do not need to be split into edges and left and right nodes, thus avoiding the problem of non-uniqueness of the source point and the end point in the constraint conditions.

[0052] Let the original topological network contain n nodes and m edges. Assume that n* nodes among the n nodes have weights. Then the real-time topological network has n + n* nodes and m + n* edges. Let the node set on the real-time topological network be denoted as V, and the edge set be denoted as E. Then the real-time topological network is denoted as G=(V, E). Let node i and node j be two adjacent nodes in the real-time topological network, that is, there is a connection between node i and node j. Then the edge between node i and node j is denoted as v ij , v ij ∈V, (i, j)∈E, and set x ij to represent the binary decision variable of edge v ij . When the path passes through edge v ij , then x ij = 1; conversely, when the path does not pass through v ij , then x ij= 0; Let w ij represent the weight of edge v ij .

[0053] The present invention needs to convert the shortest path optimization problem into a combinatorial optimization problem. The goal of the combinatorial optimization problem is to find the optimal solution from the feasible solution space of the combinatorial problem, which is mainly composed of constraints and objective functions.

[0054] The objective function is:[[]]

[0055]

[0056] The constraint function formula is expressed as:[[]]

[0057]

[0058] Among them, x sj , x id , x ik and x kj are all binary decision variables; k≠s, d; v sj represents the edge from the source point s to the node j. If the search path includes v sj , then x sj = 1, otherwise x sj = 0; v id represents the edge from the node i to the end point d; if the search path includes v id , then x id = 1, otherwise x id = 0; v ik represents the edge from the node i to the intermediate node k; if the search path includes v ik , then x ik = 1, otherwise x ik = 0; v kj represents the edge from the intermediate node k to the node j. If the search path includes v kj , then x kj = 1, otherwise x kj = 0.

[0059] This step needs to construct an optimization function that minimizes the objective function while satisfying the constraint conditions. Considering that both the minimum value of the objective function and the satisfaction of the constraint conditions need to be ensured, the form of the least squares sum is adopted. Only when all the constraint conditions are satisfied can the term in the penalty factor of the optimization function be 0. Adding multiple constraint conditions and multiplying by the set penalty factor M, the larger M is, the stronger the constraint. Only when all the constraint conditions are satisfied and the objective function is minimized can the optimization function be minimized, that is, the minimum optimization goal is achieved. Therefore, the optimization function formula is as follows:[[]]

[0060]

[0061] In the formula, C represents the objective function. That is, when all constraint conditions are satisfied, the influence of the penalty factor on F is 0, so the optimization objective is obtained as follows:

[0062]

[0063] S4. Use the quantum algorithm to solve the minimization optimization objective that satisfies the constraint conditions, list the solution results {x ij |(i, j) ∈ E}, where E is the edge set in the real-time topology network; and extract the x with a value of 1 ij to form the target path.

[0064] Refer to Figure 3 , in this step, construct a quantum string, and make the qubits included in the quantum string correspond one-to-one with the binary decision variables x of each edge in the real-time topology network ij , perform a unitary transformation on the optimization objective, and then measure the quantum state to solve, obtain the solution result of the quantum string, that is, obtain the solution result of x ij ; due to the constraint conditions, the edges with x ij = 1 in each solution must be sequentially connected to form a path from the source point to the end point.

[0065] S5. Refresh the real-time topology network according to the set time interval. Then, when the navigation object reaches the end point of the current road section, update the source point to the real-time position of the navigation object, and then return to step S2 for loop calculation until the navigation object reaches the end point.

[0066] Specifically, the time interval should be greater than the set time for calculating the weights of intersections and road sections in S2, so as to ensure the real-time performance of the dynamic update of the topology network.

[0067] Of course, for those skilled in the art, the present invention is not limited to the details of the above exemplary embodiments, but also includes the same or similar structures that can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

[0068] In addition, it should be understood that although this specification is described in terms of embodiments, not every embodiment contains only an independent technical solution. This narrative style of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

[0069] The technologies, shapes, and structures not described in detail in the present invention are all well-known technologies.

Claims

1. A fastest path search method combining traffic light delay and dynamic road conditions, characterized in that: Obtain a road network, use intersections as nodes, and the sections between adjacent intersections as edges to construct an original topological network; Update the weights of each node and edge according to traffic condition data, and split the node with a weight into a set of an edge with a weight and two non-weighted left and right nodes to form a real-time topological network; Construct constraint conditions and optimization objectives; The optimization objective is to minimize the sum of the weights of the edges on the path; the constraint conditions include: the number of incoming edges and outgoing edges of the intermediate nodes on the path is equal. The incoming edge of a node refers to the edge starting from the node, and the outgoing edge of a node refers to the edge with the node as the destination. The intermediate node is a node other than the source point and the end point on the path; Use a quantum algorithm to solve the minimization optimization objective that satisfies the constraint conditions to obtain the combination of edges on the path.

2. The fastest path search method combining traffic light delay and dynamic road conditions according to claim 1, wherein Periodically update the real-time topological network. Each time the real-time topological network is updated, update the source point to the current position of the navigation object and re-solve the minimization optimization objective that satisfies the constraint conditions to update the search path.

3. The fastest path search method combining traffic light delay and dynamic road conditions according to claim 1, characterized in that, The method of using a quantum algorithm to solve the minimization optimization objective that satisfies the constraint conditions is: construct a quantum string, make the qubits included in the quantum string correspond one by one to the edges in the real-time topological network, perform a unitary transformation on the optimization objective, and then measure the quantum state to solve, and obtain the solution result of the quantum string, which is the combination of the edges on the path.

4. The fastest path search method combining traffic light delay and dynamic road conditions according to claim 1 or 2 or 3, characterized in that The assignment method of the weights of each node on the original topological network is: according to the traffic conditions, statistically calculate the passing time of each intersection within a set time, and then normalize the passing time of each intersection, and use the normalized value as the weight of the node corresponding to the intersection.

5. The fastest path search method combining traffic light delay and dynamic road conditions according to claim 4, wherein When calculating the weight of an intersection, first filter the passing time of the intersection with a smaller value, and then normalize the remaining passing time; the intersections that are filtered are not assigned weights.

6. The fastest path search method combining traffic light delay and dynamic road conditions according to claim 4, characterized in that, The assignment method of the weights of each edge on the original topological network is: according to the traffic conditions, statistically calculate the passing time of each section within a set time, and then normalize the passing time of each section, and use the normalized value as the weight of the edge corresponding to the section.

7. The fastest path search method combining traffic light delay and dynamic road conditions according to claim 6, characterized in that The set time is less than or equal to the update period of the real-time topological network.

8. An apparatus for implementing a method for searching the fastest path by combining traffic light delays and dynamic road conditions, characterized in that, It includes: A road condition monitoring module for obtaining real-time road conditions and calculating the weights of each intersection and section; A topological modeling module, connected to the road condition monitoring module; and the original topological network of the road network is stored in the topological modeling module. The topological modeling module is used to update the real-time topological network by combining the weight calculation results of the road condition monitoring module and the original topological network; A quantum solution module for combining the weights of each edge on the real-time topological network and using a quantum algorithm to solve the optimization objective that satisfies the constraint conditions. The solution is the binary decision variable of each edge on the real-time topological network.

9. A fastest path search system that combines traffic light delays and dynamic road conditions, characterized in that, It includes a memory and a processor. A computer program is stored in the memory, and the processor is connected to the memory. The processor is used to execute the computer program to implement the fastest path search method combining traffic light delay and dynamic road conditions as described in any one of claims 1-7.

10. A storage medium, characterized in that, A computer program is stored, and when the computer program is executed, it is used to implement the fastest path search method combining traffic light delay and dynamic road conditions as described in any one of claims 1-7.

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