A one-way traffic network optimization method based on compatible eulerian circuit

Abstract

The application relates to the field of intelligent transportation system optimization and path planning, and particularly relates to a one-way traffic network optimization method based on compatible Euler loop tour. The content comprises the following steps: collecting traffic data, constructing a traffic network graph, and recursively optimizing the topological structure of the traffic network graph to obtain a topologically adjusted traffic network graph; based on the topologically adjusted traffic network graph, a path optimization model based on feedback correction is used to calculate a path correction amount, and a real-time feedback mechanism is introduced to perform path planning and optimization. The application solves the problems of traditional path planning methods, such as lack of dynamic adjustment mechanism, difficulty in responding to real-time traffic flow changes and emergencies, neglect of dynamic changes of the topological structure of the traffic network, inability to guarantee continuous optimization and efficient operation of the traffic system, slow response speed to real-time data, and inability to ensure the provision of the best path scheduling scheme under different time periods and complex traffic environments.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent transportation system optimization and path planning, and in particular to a one-way traffic network optimization method based on compatible Euler tour. BACKGROUND

[0002] With the acceleration of urbanization, urban transportation networks are facing increasingly complex challenges. Traffic congestion, path conflicts, and unbalanced allocation of transportation resources seriously affect the smoothness and efficiency of urban traffic flow. Especially during peak hours and special events, the existing traffic management system often cannot adapt to the fluctuations in traffic flow in real time, resulting in low utilization efficiency of road networks, and even causing serious traffic congestion.

[0003] Traditional path planning methods rely on static traffic network models, ignoring dynamic traffic flow, road congestion, and real-time traffic information, and cannot respond to daily traffic changes and unexpected situations, making it difficult to adjust traffic management strategies and achieve real-time traffic flow optimization. Therefore, a new traffic network optimization method is needed that can combine real-time traffic data and dynamic adjustment mechanisms to provide more efficient and flexible path scheduling solutions.

[0004] In summary, the traditional path planning method has the following technical problems: In traffic network optimization, it usually relies on static path planning methods, lacks dynamic adjustment mechanisms, and is difficult to respond to real-time traffic flow changes and unexpected events; most ignore the dynamic changes in the topology of the transportation network, ignore the imbalance between the in-degree and out-degree of nodes, and cannot guarantee the continuous optimization and efficient operation of the transportation system; the response speed to real-time data in the path optimization process is slow, and it is difficult to adapt to the fluctuations in traffic flow in real time, resulting in low utilization efficiency of transportation resources, and unable to ensure the best path scheduling solution in different time periods and complex traffic environments. SUMMARY

[0005] The present application provides a one-way traffic network optimization method based on compatible Euler tour to solve the technical problems of traditional path planning methods in traffic network optimization, which usually rely on static path planning methods, lack dynamic adjustment mechanisms, and are difficult to respond to real-time traffic flow changes and unexpected events; most ignore the dynamic changes in the topology of the transportation network, ignore the imbalance between the in-degree and out-degree of nodes, and cannot guarantee the continuous optimization and efficient operation of the transportation system; the response speed to real-time data in the path optimization process is slow, and it is difficult to adapt to the fluctuations in traffic flow in real time, resulting in low utilization efficiency of transportation resources, and unable to ensure the best path scheduling solution in different time periods and complex traffic environments.

[0006] The one-way traffic network optimization method based on compatible Euler tour of the present application specifically includes the following technical solutions:

[0007] A one-way traffic network optimization method based on compatible Euler tour, comprising the following steps:

[0008] S1, collect traffic data, construct a traffic network graph; based on the in-degree and out-degree of the nodes in the traffic network graph, recursively optimize the topological structure of the traffic network graph, and obtain a topologically adjusted traffic network graph;

[0009] S2, based on the topologically adjusted traffic network graph, calculate the path correction amount by a path optimization model based on feedback correction, and introduce a real-time feedback mechanism to perform path planning and optimization.

[0010] Preferably, the S1 specifically comprises:

[0011] The traffic data is normalized to obtain normalized traffic data; and the normalized traffic data is used to construct a traffic network graph.

[0012] Preferably, the S1 specifically comprises:

[0013] The in-degree and out-degree of the nodes in the traffic network graph are calculated, and it is determined whether the traffic network graph satisfies the Euler tour condition; when the traffic network graph does not satisfy the Euler tour condition, a correction factor is introduced to dynamically adjust the structure of the traffic network graph.

[0014] Preferably, the S1 specifically comprises:

[0015] The correction factor is calculated based on the difference between the in-degree and out-degree of the nodes and the proportion coefficient of the adjustment factor.

[0016] Preferably, the S1 specifically comprises:

[0017] Based on the correction factor and in combination with a preset adjustment threshold, the in-degree and out-degree of the nodes are recursively corrected to obtain a topologically adjusted traffic network graph.

[0018] Preferably, the S2 specifically comprises:

[0019] Based on the topologically adjusted traffic network graph, an initial planning path is calculated; when the path in the topologically adjusted traffic network graph has a road segment weight greater than a preset weight threshold, or the in-degree and out-degree of a node differ by more than a preset in-out degree difference threshold, a path optimization model based on feedback correction is constructed.

[0020] Preferably, the S2 specifically comprises:

[0021] The path optimization model based on feedback correction calculates the path correction amount based on the road segment weight and the difference between the in-degree and out-degree of the nodes, and introduces a proportion factor.

[0022] Preferably, S2 specifically includes:

[0023] A real-time feedback mechanism is introduced, and when the path correction amount is greater than the preset correction threshold, a real-time scheduling feedback correction amount is calculated to optimize the path scheduling.

[0024] Preferably, S2 specifically includes:

[0025] The real-time scheduling feedback correction amount is calculated based on the path correction amount, in combination with the traffic flow data, traffic congestion degree and travel time in the normalized traffic data.

[0026] The technical scheme of the present application has the following advantages:

[0027] 1. The present application dynamically adjusts the topology of the traffic network graph, uses real-time traffic data to optimize the traffic path in real time, fully utilizes the traffic capacity, time consumption and traffic flow information of each road section, and ensures that the traffic flow is effectively managed and scheduled; by recursively correcting the topology of the traffic network graph, it ensures that the traffic network graph can meet the Euler tour condition, so that the in-degree and out-degree of all nodes remain balanced, effectively reducing path conflicts and improving the continuity and stability of traffic flow, thereby reducing traffic bottlenecks and congestion.

[0028] 2. The path optimization method designed in the present application combines feedback mechanism and real-time dynamic adjustment, can continuously adjust the path planning according to real-time traffic flow, congestion and travel time, etc., so that the traffic system can respond to the changes of traffic flow and environment in real time, and ensures that the traffic system can always provide the best path planning scheme under different time periods and traffic conditions, thereby improving the response ability and flexibility of the traffic system.

[0029] 3. Through real-time calculation and feedback adjustment of the path correction amount, the path planning can be adjusted in time when congestion or high flow occurs, avoiding some road sections becoming bottlenecks, effectively reducing traffic congestion and improving traffic efficiency and road utilization rate; the path correction process not only depends on the road section weight, but also considers the topological structure of the node, traffic flow and other factors, so that the one-way traffic network optimization method can adapt to complex traffic environment and provide more intelligent and accurate path scheduling scheme. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 A flowchart of a one-way traffic network optimization method based on compatible Euler tour according to the present application. DETAILED DESCRIPTION

[0031] In order to further clarify the technical means and effects taken by the present application to achieve the predetermined object of the application, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0033] The specific scheme of the single-lane traffic network optimization method based on compatible Euler tour provided by the present application will be specifically described below in combination with the drawings.

[0034] Referring to the drawings Figure 1 , a flow chart of a single-lane traffic network optimization method based on compatible Euler tour provided by an embodiment of the present application is shown, and the method comprises the following steps:

[0035] S1, collect traffic data and construct a traffic network graph; based on the in-degree and out-degree of the nodes in the traffic network graph, recursively optimize the topological structure of the traffic network graph to obtain a topologically adjusted traffic network graph;

[0036] Through the city traffic monitoring system, GPS tracking device, road sensor and other equipment, the traffic data of each road section is collected in real time, including traffic flow data, road length and passing time, etc., and the traffic data is normalized to eliminate the dimension problem. The normalization method is a well-known technical means to those skilled in the art, and will not be described here. The preliminary structure of the city traffic network is extracted from the normalized traffic data to construct a traffic network graph , wherein, denotes the node set in the traffic network graph , denotes the directed edge set in the traffic network graph , denotes the node set in the traffic network graph , denotes the directed edge set in the traffic network graph It is a directed graph, where each node represents an intersection or junction, and each directed edge represents a one-way street (i.e., road segment). The edge weight (i.e., road segment weight) is obtained by weighted summation of the traffic flow data, travel time, and road segment length of the road segment, reflecting the capacity, time consumption, and distance of each one-way street on the path. Each road segment has its corresponding direction in the traffic network graph, conforming to the flow constraints of the one-way street system, ensuring that the weight of each edge in the path planning process is consistent with the actual traffic conditions. The weights used in the road segment weight calculation process are set according to specific circumstances through expert experience and are not limited here.

[0037] For each node , calculate the in-degree of the node and out-degree , and judge the traffic network diagram Whether the Euler tour condition is satisfied: If the traffic network graph There are nodes in Make , then the traffic network graph The Euler tour condition is not satisfied; in order to make the traffic network graph Satisfy the Euler tour condition, that is, ensure that the in-degree and out-degree of each node are equal, introduce the correction factor, and recursively correct the in-degree and out-degree of the nodes in the traffic network graph to improve the traffic network graph. The topological structure of the network is recursively optimized to make the traffic network graph satisfy the Euler tour condition;

[0038] Specifically, based on the basic properties of the Euler graph and combined with the flow balance problem in network flow theory, a correction factor calculation formula is constructed to obtain the correction factor. Furthermore, for each node that does not meet the Euler tour condition (i.e., the node with unequal in-degree and out-degree), a correction factor is introduced. According to the difference between the in-degree and out-degree of the node, the structure of the traffic network graph is dynamically adjusted, thereby gradually balancing the in-degree and out-degree of the node, so that the traffic network graph meets the requirements of the Euler graph. The correction factor calculation formula is:

[0039] ;

[0040] in, It is nodes Correction factor of is the proportional coefficient of the adjustment factor, which is used to adjust the influence of the difference between the in-degree and out-degree of the node on the path correction. It is selected according to the experimental or actual traffic flow conditions and the value range is [0.1,10]; is an influence coefficient for controlling the degree of non-linear correction of the difference between the in-degree and out-degree of the node, obtained by an optimization algorithm (such as gradient descent method), the value range is [0.5, 2], the gradient descent method is a technical means familiar to those skilled in the art, and will not be repeated here; and respectively represent the in-degree and out-degree of the first node , the calculation method of the in-degree and out-degree is a technical means familiar to those skilled in the art, and will not be repeated here; is the difference between the in-degree and out-degree of the first node , representing the node correction amount; is a coefficient for adjusting the normalization degree of the sum of all node correction amounts, which is adjusted by expert experience method according to the complexity of the traffic network graph, the value range is [0.1, 3];

[0041] Based on the correction factor, the in-degree and out-degree of the node are adjusted by combining the expert experience method, so as to reduce the difference between the in-degree and out-degree, and make the in-degree and out-degree of the node gradually balanced; The specific adjustment process is: if the in-degree of the node is greater than the out-degree, the edge flowing out of the node needs to be increased (i.e. increase the out-degree), if the out-degree is greater than the in-degree, the edge pointing to the node needs to be increased (i.e. increase the in-degree), the size of the correction factor determines the adjustment strength; According to the expert experience method, set the adjustment threshold, when the correction factor is greater than the adjustment threshold, increase the adjustment amplitude, when the correction factor is less than or equal to the adjustment threshold, reduce the adjustment amplitude; The specific size of the adjustment amplitude is set by expert experience method according to specific scene, which is not limited here;

[0042] After correction, the topological structure of the traffic network graph will be more in line with the conditions of Euler graph, which can make the traffic network graph more efficient in path scheduling and optimization; After multiple rounds of recursive correction, the traffic network graph satisfying the Euler tour condition (i.e. the traffic network graph after topological adjustment) , represents the node set in the traffic network graph after topological adjustment, and represents the directed edge set in the traffic network graph after topological adjustment.

[0043] S2, based on the traffic network graph after topological adjustment, a path optimization model based on feedback correction is used to calculate the path correction amount, and a real-time feedback mechanism is introduced to perform path planning and optimization;

[0044] Based on the traffic network graph after topological adjustment, a path optimization model based on feedback correction is constructed to calculate the path correction amount, and a real-time feedback mechanism is introduced to perform path planning and optimization, so as to ensure the real-time adaptability and optimality of the path; The specific optimization process is as follows:

[0045] Firstly, based on the weight of the road segment in the topology-adjusted traffic network graph, the known path is calculated by the shortest path algorithm The shortest path from the starting point to the ending point is the initial planning path; the shortest path algorithm (such as Dijkstra algorithm, A* algorithm, etc.) is a technical means familiar to those skilled in the art, and will not be described here;

[0046] Further, based on the weight of the road segment and the topology-adjusted traffic network graph, the initial planning path is corrected; specifically, after each path selection, the path correction amount is adjusted according to the weight of each road segment on the selected path: when the weight of some road segments in the path is greater than the weight threshold preset according to the expert experience method, it means that the traffic cost of the road segment exceeds the expectation, and the path needs to be adjusted to avoid the road segment becoming a bottleneck; at the same time, in the path planning, the topology structure of the node determines the selection and flow direction of the path, and the path correction amount needs to consider the topology structure of the node, especially the difference between the in-degree and out-degree of the node: if the difference between the in-degree and out-degree of some nodes in the path (such as the intersection with high traffic flow) exceeds the in-degree and out-degree difference threshold set according to the expert experience method, the node may cause path conflict or traffic bottleneck;

[0047] Therefore, the final path correction amount is the result of the joint influence of the road segment weight and the node topology structure; when there is a road segment weight greater than the weight threshold, or the difference between the in-degree and out-degree of the node is greater than the in-degree and out-degree difference threshold, a path optimization model based on feedback correction is constructed combining the road segment weight and the node topology structure, and the specific expression is as follows:

[0048] ;

[0049] Among them, is the path correction amount at time ; is a proportional factor for controlling the influence of the road segment weight on the path correction, which determines the importance of the road segment weight part in the path correction amount, and is adjusted by gradient descent method according to the historical traffic flow data from the existing database, with a value range of [0.1, 10]; is the weight of the road segment , which represents the weighted sum of the traffic flow data, travel time and road length between the th node and the th node , reflecting the travel time, distance and traffic flow of the road segment; represents the travel cost of the path ; is a scale factor of the influence of the node topology structure, which determines the importance of the node in-out degree difference part in the path correction amount, and the value range is [0.1, 10] through experiments; path ; is any one node in the path ; is the in-degree of the th node ; is the out-degree of the th node ; by comprehensively considering the passing cost of each section in the path and the in-degree and out-degree difference of each node in the path, the passing efficiency and structural rationality of the path can be optimized at the same time when the path is corrected;

[0050] Further, a real-time feedback mechanism is introduced, and based on the output path correction amount , a real-time scheduling feedback correction amount is calculated to optimize the path scheduling; the real-time scheduling feedback correction amount draws on the concepts in dynamic programming and feedback control theory, especially the idea of adjusting and optimizing the path based on real-time data, which not only depends on the path correction amount, but also involves multiple factors such as real-time traffic flow data, path congestion degree and passing time;

[0051] Specifically, under the influence of the path correction amount, traffic flow data and path congestion degree will dynamically change, and when the path correction amount is greater than the preset correction threshold, it indicates that the current path needs to be adjusted and optimized to ensure the path efficiency. The specific real-time feedback correction amount calculation formula is as follows:

[0052] ;

[0053] wherein, represents the real-time scheduling feedback correction amount of the path at time ; and are weight factors for balancing the influence between the path correction amount and the traffic flow data and the path congestion degree in feedback correction, and the value range is [0, 1] through regression analysis; and are sensitivity adjustment parameters for controlling the influence strength of the traffic flow data and the passing time , and the value is a positive real number through regression analysis; is the passing time of the path at time traffic flow data representing the traffic flow or traffic density on the path per unit time; is the path at time traffic congestion degree; is the path at time passage time; the traffic flow data, traffic congestion degree and passage time are all from normalized traffic data; the regression analysis is a technique well known to those skilled in the art and will not be described here;

[0054] based on the real-time scheduling feedback correction amount , the path scheduling is performed to ensure the real-time and optimality of the scheduling, so that the path scheduling can be corrected in real time in the dynamic traffic environment to achieve the optimal state. Through the real-time feedback mechanism, the path correction is closely connected with the real-time scheduling, forming an efficient and intelligent traffic scheduling system, which ensures that the traffic network can maintain the optimal path planning and scheduling efficiency in the dynamically changing environment.

[0055] In summary, a one-way traffic network optimization method based on compatible Euler tour is completed.

[0056] The order of the embodiments of the application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or can be advantageous.

[0057] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0058] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit the same; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A one-way traffic network optimization method based on compatible Euler tour, characterized in that: The following steps are involved: S1. Collect traffic data and construct a traffic network graph. A traffic network graph is a directed graph where each node represents a road junction or intersection, and each directed edge represents a one-way street, i.e., a road segment. For each node in the traffic network graph, calculate the node's in-degree and out-degree, and determine whether the traffic network graph satisfies the Euler tour condition. When the traffic network graph does not meet the Euler tour condition, the node correction factor is generated based on the difference between the in-degree and out-degree of the node and the proportional coefficient of the adjustment factor. The topological structure of the traffic network graph is recursively optimized to obtain the traffic network graph with topological adjustment. The correction factor calculation formula is: ； in, It is nodes Correction factor for is the proportional coefficient of the adjustment factor; is the influence coefficient; and Respectively represent nodes The in-degree and out-degree of It is nodes The difference between the in-degree and out-degree of represents the node correction amount; is the coefficient used to adjust the normalization degree of the sum of all node corrections; Representing a traffic network graph The set of nodes in ; S2. Based on the topologically adjusted traffic network graph, a feedback-corrected path optimization model is used. A scaling factor is introduced based on the road segment weights and the difference between the in-degree and out-degree of the nodes to calculate the path correction amount. A real-time feedback mechanism is also introduced to calculate the real-time scheduling feedback correction amount. Path planning and optimization are performed based on real-time scheduling feedback corrections.

2. The one-way traffic network optimization method based on compatible Euler tour according to claim 1 is characterized in that: Said S1 specifically includes: The traffic data is normalized to obtain normalized traffic data; the preliminary structure of the urban traffic network is extracted from the normalized traffic data to construct a traffic network diagram.

3. The one-way traffic network optimization method based on compatible Euler tour according to claim 1 is characterized in that: Said S1 specifically includes: Based on the correction factor and the preset adjustment threshold, the in-degree and out-degree of the node are recursively corrected to obtain the transportation network graph after topology adjustment.

4. The one-way traffic network optimization method based on compatible Euler tour according to claim 3 is characterized in that: Said S2 specifically includes: Based on the topologically adjusted traffic network graph, the initial planned path is calculated using the shortest path algorithm. When the weight of a section in the topologically adjusted traffic network graph is greater than a preset weight threshold, or the difference between the in-degree and out-degree of a node is greater than a preset in-degree and out-degree difference threshold, a path optimization model based on feedback correction is constructed.

5. The one-way traffic network optimization method based on compatible Euler tour according to claim 1 is characterized in that: Said S2 specifically includes: A real-time feedback mechanism is introduced. When the path correction amount is greater than the preset correction threshold, the real-time scheduling feedback correction amount is calculated to optimize the path scheduling.

6. The one-way traffic network optimization method based on compatible Euler tour according to claim 5, characterized in that: Said S2 specifically includes: The real-time scheduling feedback correction amount is calculated based on the path correction amount and in combination with the traffic flow data, traffic congestion level and travel time in the normalized traffic data.

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