One-way street traffic network optimization method based on compatible Euler round tour

Abstract

The invention relates to the field of intelligent traffic system optimization and path planning, in particular to a one-way street traffic network optimization method based on compatible Euler tour. The method comprises the steps of collecting traffic data, constructing a traffic network diagram, and performing recursive optimization on a topological structure of the traffic network diagram to obtain a traffic network diagram after topological adjustment; and based on the traffic network diagram after topology adjustment, calculating a path correction amount through a path optimization model based on feedback correction, and introducing a real-time feedback mechanism to carry out path planning and optimization. The problems that a traditional path planning method lacks a dynamic adjustment mechanism and is difficult to cope with changes of real-time traffic flow and emergencies are solved; the dynamic change of a traffic network topology structure is mostly ignored, and continuous optimization and efficient operation of a traffic system cannot be ensured. The response speed to real-time data is slow, and it cannot be ensured that an optimal path scheduling scheme cannot be provided in different time periods and complex traffic environments.

Description

Technical Field

[0001] The present invention relates to the fields 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 Art

[0002] With the acceleration of urbanization, urban transportation networks face increasingly complex challenges. Problems such as traffic congestion, route conflicts, and uneven allocation of transportation resources have severely impacted the smoothness and efficiency of urban transportation. Existing traffic management systems are often unable to adapt to traffic fluctuations in real time, especially during peak hours and special events. This leads to inefficient road network utilization and even severe traffic congestion.

[0003] Traditional route planning methods rely on static traffic network models, ignoring factors such as dynamic traffic flow, road congestion, and real-time traffic information. These methods are unable to adapt to daily traffic changes and emergencies, resulting in inflexible adjustments to traffic management strategies and difficulty achieving real-time traffic flow optimization. Therefore, a new traffic network optimization method is urgently needed that can combine real-time traffic data with dynamic adjustment mechanisms to provide more efficient and flexible route scheduling solutions.

[0004] In summary, traditional path planning methods have the following technical problems: in traffic network optimization, they usually rely on static path planning methods, lack dynamic adjustment mechanisms, and are difficult to cope with changes in real-time traffic flow and emergencies; most of them ignore the dynamic changes in the topological structure of the traffic network and the imbalance between the in-degree and out-degree of nodes, and cannot ensure the continuous optimization and efficient operation of the transportation system; during the path optimization process, the response speed to real-time data is slow, making it difficult to adapt to traffic flow fluctuations in real time, resulting in inefficient utilization of traffic resources and an inability to ensure the provision of optimal path scheduling solutions in different time periods and complex traffic environments. Summary of the Invention

[0005] The present invention provides a one-way traffic network optimization method based on compatible Euler tours to solve the technical problems that traditional path planning methods usually rely on static path planning methods in traffic network optimization, lack a dynamic adjustment mechanism, and are difficult to cope with changes in real-time traffic flow and emergencies; most of them ignore the dynamic changes of the traffic network topology structure and the imbalance problem of node in-degree and out-degree, and cannot ensure the continuous optimization and efficient operation of the traffic system; the response speed to real-time data in the path optimization process is slow, and it is difficult to adapt to traffic flow fluctuations in real time, resulting in low utilization efficiency of traffic resources and an inability to ensure the provision of optimal path scheduling solutions in different time periods and complex traffic environments.

[0006] The present invention provides a one-way traffic network optimization method based on a compatible Euler tour, which specifically includes the following technical solutions: A one-way traffic network optimization method based on a compatible Euler tour comprises the following steps: 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 topology of the traffic network graph to obtain a topologically adjusted traffic network graph. S2. Based on the topologically adjusted traffic network diagram, the path correction amount is calculated through the feedback-correction-based path optimization model, and a real-time feedback mechanism is introduced to perform path planning and optimization.

[0007] Preferably, the S1 specifically includes: The traffic data is normalized to obtain normalized traffic data; and a traffic network diagram is constructed based on the normalized traffic data.

[0008] Preferably, the S1 specifically includes: Calculate the in-degree and out-degree of nodes in the traffic network graph and determine whether the traffic network graph meets the Euler tour condition. When the traffic network graph does not meet the Euler tour condition, introduce a correction factor to dynamically adjust the structure of the traffic network graph.

[0009] Preferably, the S1 specifically includes: The correction factor is calculated based on the difference between the in-degree and out-degree of the node and the proportional coefficient of the adjustment factor.

[0010] Preferably, the 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.

[0011] Preferably, the S2 specifically includes: Based on the topologically adjusted traffic network graph, the initial planned path is calculated; when there is a road section in the path of the topologically adjusted traffic network graph whose weight is greater than the preset weight threshold, or when there is a node whose in-degree and out-degree difference is greater than the preset in-degree and out-degree difference threshold, a path optimization model based on feedback correction is constructed.

[0012] Preferably, the S2 specifically includes: The feedback correction-based path optimization model introduces a proportional factor based on the link weight and the difference between the in-degree and out-degree of the node to calculate the path correction amount.

[0013] Preferably, the 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.

[0014] Preferably, the 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.

[0015] The beneficial effects of the technical solution of the present invention are: 1. The present invention dynamically adjusts the topological structure of the traffic network graph, uses real-time traffic data to optimize traffic paths in real time, and fully utilizes the capacity, time consumption and traffic flow information of each road section to ensure that traffic flow is effectively managed and scheduled; by recursively correcting the topological structure 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 are balanced, effectively reducing path conflicts, improving the continuity and stability of traffic flow, and thus reducing traffic bottlenecks and congestion.

[0016] 2. The path optimization method designed in this invention combines a feedback mechanism with real-time dynamic adjustment. It can continuously adjust path planning based on factors such as real-time traffic flow, congestion conditions, and travel time, enabling the transportation system to respond to changes in traffic flow and the environment in real time, ensuring that the transportation system can always provide the best path planning solution under different time periods and traffic conditions, thereby improving the responsiveness and flexibility of the transportation system.

[0017] 3. Through real-time calculation and feedback adjustment of path corrections, the route planning can be adjusted in a timely manner when congestion or high traffic occurs, preventing certain road sections from becoming bottlenecks, effectively reducing traffic congestion, and improving traffic efficiency and road utilization. The path correction process not only takes into account road section weights but also factors such as node topology and traffic flow, enabling the one-way traffic network optimization method to adapt to complex traffic environments and provide more intelligent and accurate path scheduling solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a one-way traffic network optimization method based on compatible Euler tour described in the present invention. DETAILED DESCRIPTION

[0019] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0020] Unless defined otherwise, 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 invention belongs.

[0021] The following describes in detail a specific solution of a one-way traffic network optimization method based on a compatible Euler tour provided by the present invention with reference to the accompanying drawings.

[0022] Refer to the attached Figure 1 , which shows a flow chart of a one-way traffic network optimization method based on a compatible Euler tour provided by an embodiment of the present invention, the method comprising the following steps: 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 topology of the traffic network graph to obtain a topologically adjusted traffic network graph. Through urban traffic monitoring systems, GPS tracking devices, road sensors and other equipment, traffic data of each road section is collected in real time, including traffic flow data, section length and travel time, etc., and the traffic data is normalized to obtain normalized traffic data to eliminate dimensionality problems. The normalization method is a technical means well known to those skilled in the art and will not be described in detail here. The preliminary structure of the urban traffic network is extracted from the normalized traffic data to construct a traffic network map. ,in, Representing a traffic network graph The node set in Representing a traffic network graph 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. 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; 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: ; in, It is nodes Correction factor for 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]; The influence coefficient is used to control the nonlinear correction degree of the difference between the in-degree and out-degree of the node. It is obtained by an optimization algorithm (such as the gradient descent method) and has a value range of [0.5, 2]. The gradient descent method is a technical means well known to those skilled in the art and will not be described in detail here. and Respectively represent nodes The calculation method of the in-degree and out-degree is a well-known technical means for those skilled in the art and will not be described in detail here; It is nodes The difference between the in-degree and out-degree of represents the node correction amount; It is a coefficient used to adjust the normalization degree of the sum of all node corrections. It is adjusted according to the complexity of the traffic network graph through expert experience and its value range is [0.1, 3]. Based on the correction factor and combined with expert experience, the in-degree and out-degree of the node are adjusted to reduce the difference between the in-degree and out-degree, so that the in-degree and out-degree of the node gradually tend to be balanced. The specific adjustment process is: if the in-degree of the node is greater than the out-degree, it is necessary to increase the edges flowing out of the node (i.e., increase the out-degree); if the out-degree is greater than the in-degree, it is necessary to increase the edges pointing to the node (i.e., increase the in-degree). The size of the correction factor determines the intensity of the adjustment. The adjustment threshold is set according to the expert experience. When the correction factor is greater than the adjustment threshold, the adjustment range is increased. When the correction factor is less than or equal to the adjustment threshold, the adjustment range is reduced. The specific size of the adjustment range is set according to the specific scenario through expert experience and is not limited here. After the correction, the topology of the traffic network graph will be more consistent with the conditions of the Euler diagram, which will enable the traffic network graph to be more efficiently scheduled and optimized. After multiple rounds of recursive correction, the traffic network graph that meets the Euler tour conditions is finally obtained (that is, the traffic network graph after topology adjustment). , It represents the node set in the transportation network graph after topology adjustment, and it represents the directed edge set in the transportation network graph after topology adjustment.

[0023] S2. Based on the topologically adjusted traffic network diagram, the path correction amount is calculated through a feedback-correction-based path optimization model, and a real-time feedback mechanism is introduced to perform path planning and optimization. Based on the topologically adjusted traffic network diagram, a feedback-correction-based path optimization model is constructed to calculate the path correction amount. A real-time feedback mechanism is introduced to perform path planning and optimization to ensure the real-time adaptability and optimality of the path. The specific optimization process is as follows: First, based on the road section weights in the topologically adjusted traffic network graph, the known paths are calculated using the shortest path algorithm. The shortest path from the starting point to the end point is used as the initial planned path; the shortest path algorithm (such as Dijkstra algorithm, A* algorithm, etc.) is a technical means well known to those skilled in the art and will not be described in detail here; Furthermore, based on the road segment weights and the traffic network diagram after topology adjustment, the initially planned 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 travel cost of the road segment exceeds expectations and the path needs to be adjusted to prevent the road segment from becoming a bottleneck. At the same time, in path planning, the topological structure of the nodes determines the choice of path and flow direction. The path correction amount needs to consider the topological structure of the nodes, especially the difference between the in-degree and out-degree of the nodes. If the in-degree and out-degree mismatch of some nodes in the path (such as intersections 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. Therefore, the final path correction is the result of the combined influence of the link weight and the node topology. When the link weight in the path is greater than the weight threshold, or the difference between the in-degree and out-degree of the node is greater than the in-degree difference threshold, the link weight and the node topology are combined to construct a path optimization model based on feedback correction. The specific expression is as follows: ; in, Is the path In time The path correction amount; It is a proportional factor used to control the effect of the road segment weight on the path correction. It determines the importance of the road segment weight in the path correction. It is adjusted by gradient descent method based on the historical traffic flow data from the existing database and has a value range of [0.1, 10]. It's a road section The weight of nodes To nodes The weighted sum of the traffic flow data, travel time and section length of the road section between reflects the travel time, distance and traffic flow of the road section; Indicates the path the cost of travel; It is a proportional factor that controls the influence of the node topology structure and determines the importance of the difference in node in-degree and out-degree in the path correction. It is experimentally determined that its value range is [0.1, 10]. path Any section of the road; Is the path Any node in ; It is nodes The in-degree of It is nodes By comprehensively considering the travel cost of each section on the path and the difference between the in-degree and out-degree of each node on the path, the travel efficiency and structural rationality of the path can be optimized simultaneously during path correction. Furthermore, a real-time feedback mechanism is introduced to correct the path based on the output , calculates real-time scheduling feedback corrections and optimizes path scheduling; the real-time scheduling feedback corrections draw on concepts from dynamic programming and feedback control theory, especially the idea of adjusting and optimizing paths based on real-time data. It not only relies on the path corrections, but also involves multiple factors such as real-time traffic flow data, path congestion level, and travel time; Specifically, under the influence of the path correction amount, traffic flow data and path congestion levels will change dynamically. When the path correction amount exceeds the preset correction threshold, it means that the current path needs to be adjusted and optimized to ensure path efficiency. The specific real-time feedback correction amount calculation formula is as follows: ; in, Indicates the path In time Real-time scheduling feedback correction on ; and is the weight factor used to balance the path correction amount in feedback correction Traffic flow data and path congestion The influence between them is obtained through regression analysis, and the value range is [0,1]; and is a sensitivity adjustment parameter used to control traffic flow data and travel time The influence intensity of is obtained through regression analysis and is a positive real number; Is the path In time Traffic flow data, which represents the traffic volume or traffic density per unit time on the path; Is the path In time the degree of traffic congestion; Is the path In time The traffic flow data, traffic congestion level and travel time are all derived from normalized traffic data; the regression analysis is a technical means well known to those skilled in the art and will not be described in detail here; Correction based on real-time scheduling feedback , to ensure real-time and optimal scheduling, allowing for real-time corrections to achieve optimal routing in a dynamic traffic environment. Through a real-time feedback mechanism, path corrections are closely integrated with real-time scheduling, forming an efficient and intelligent traffic scheduling system that ensures optimal routing and scheduling efficiency in a dynamically changing traffic environment.

[0024] In summary, a one-way traffic network optimization method based on compatible Euler tour was completed.

[0025] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0026] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0027] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

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. Based on the in-degree and out-degree of the nodes in the traffic network graph, recursively optimize the topology of the traffic network graph to obtain a topologically adjusted traffic network graph. S2. Based on the topologically adjusted traffic network diagram, the path correction amount is calculated through the feedback-correction-based path optimization model, and a real-time feedback mechanism is introduced to perform path planning and optimization.

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; and a traffic network diagram is constructed based on the normalized traffic data.

3. The one-way traffic network optimization method based on compatible Euler tour according to claim 2 is characterized in that: Said S1 specifically includes: Calculate the in-degree and out-degree of nodes in the traffic network graph and determine whether the traffic network graph meets the Euler tour condition. When the traffic network graph does not meet the Euler tour condition, introduce a correction factor to dynamically adjust the structure of the traffic network graph.

4. The one-way traffic network optimization method based on compatible Euler tour according to claim 3 is characterized in that: Said S1 specifically includes: The correction factor is calculated based on the difference between the in-degree and out-degree of the node and the proportional coefficient of the adjustment factor.

5. The one-way traffic network optimization method based on compatible Euler tour according to claim 4 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.

6. The one-way traffic network optimization method based on compatible Euler tour according to claim 5, characterized in that: Said S2 specifically includes: Based on the topologically adjusted traffic network graph, the initial planned path is calculated; when there is a road section in the path of the topologically adjusted traffic network graph whose weight is greater than the preset weight threshold, or when there is a node whose in-degree and out-degree difference is greater than the preset in-degree and out-degree difference threshold, a path optimization model based on feedback correction is constructed.

7. The one-way traffic network optimization method based on compatible Euler tour according to claim 6 is characterized in that: Said S2 specifically includes: The feedback correction-based path optimization model introduces a proportional factor based on the link weight and the difference between the in-degree and out-degree of the node to calculate the path correction amount.

8. The one-way traffic network optimization method based on compatible Euler tour according to claim 7 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.

9. The one-way traffic network optimization method based on compatible Euler tour according to claim 8, 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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