A method and system for evaluating traffic flow operation status in expressway diversion areas based on self-organization theory

Abstract

The present invention relates to a method and system for evaluating the traffic flow operation status of a freeway diversion area based on self-organization theory. The method comprises: dividing the diversion area into sections and establishing a discrete dynamic traffic flow model for each section of the diversion area; collecting traffic flow operation status information in each section, including the real-time average travel speed, real-time number of vehicles, and real-time vehicle road space occupancy rate of each section; after normalizing the collected data, combining it with the overall operation status model of the diversion area, calculating the expanded entropy disorder of the traffic flow operation over a period of time, and implementing a self-organization evaluation of the traffic flow in the diversion area based on the expanded entropy disorder. The present invention can effectively reduce the computational complexity of the degree of disorder in the diversion area, and can be used as an evaluation indicator for whether the traffic flow in the diversion area can achieve self-organization, and can also be used as a basis for optimizing the traffic flow operation in the diversion area.

Description

Technical Field

[0001] The present invention relates to a method and system for evaluating the overall traffic flow operation status of a diversion zone in an expressway (highway, urban expressway, etc.) based on self-organization theory, and belongs to the research field of highway diversion zones. Background Art

[0002] Previous research on optimizing highway bottlenecks has primarily focused on merging areas, where vehicle density increases from low to high, leading to significant traffic conflicts and the potential for ramp control strategies. However, diverging areas also present traffic conflicts, but relatively little research has been conducted on traffic flow operational evaluation. Traffic flow in diverging areas can achieve self-organization to a certain extent. With the continuous improvement of infrastructure construction and the application of V2X technology, traffic flow in diverging areas can also be controlled and optimized through intelligent connected technologies.

[0003] Existing technologies evaluate highway operations primarily based on indicators such as capacity, traffic volume, occupancy, average speed, average travel time, average density, and average travel delay. However, these indicators are evaluated independently, lacking a unified approach that combines them. Furthermore, there is currently a lack of indicators for evaluating traffic conditions in diversion zones. Furthermore, the traffic environment in highway diversion zones is relatively complex, necessitating a comprehensive and integrated evaluation of all zones. Intelligent control of vehicles in diversion zones is currently possible, but constant vehicle control requires significant resources. Therefore, an indicator is needed to assess whether traffic flow has reached its self-organizing critical point. Summary of the Invention

[0004] Purpose of the invention: In response to the problems existing in the prior art, the purpose of the present invention is to provide a method and system for evaluating the traffic flow operation status in the expressway diversion area based on self-organization theory, which can better evaluate the traffic flow operation status in the diversion area and then determine the timing of intervention of external traffic control measures on the traffic flow operation in the diversion area.

[0005] Technical solution: The present invention achieves the above-mentioned purpose through the following technical solution: a method for evaluating the traffic flow operation status in the expressway diversion area based on self-organization theory, the evaluation method comprising the following steps:

[0006] Divide the diversion area into sections and establish a discrete dynamic traffic flow model for each section in the diversion area;

[0007] Collect traffic flow status information on each road section, including real-time average travel speed, real-time number of vehicles, and real-time vehicle road space occupancy rate;

[0008] The collected data are normalized and combined with the overall operation status model of the diversion area to calculate the expanded entropy chaos of traffic flow over a period of time. Based on the expanded entropy chaos, the self-organization evaluation of traffic flow in the diversion area is realized.

[0009] Preferably, the discrete dynamic traffic flow model of the road section is derived based on the Greenshields model of traffic flow. In the model, the road vehicle space occupancy rate at the next moment is related to the vehicle free travel speed of the road section under the current road conditions, the average travel speed of the traffic flow at the current moment, and the road vehicle space occupancy rate at the current moment.

[0010] As a preferred option, the discrete dynamic traffic flow model equation of the road section is:

[0011] In the discrete dynamic traffic flow model equation, ρ n+1 is the road vehicle space occupancy rate at the next moment, ρ n is the road vehicle space occupancy rate at the current moment, v f is the vehicle's free speed, is the average travel speed of traffic at the current moment;

[0012] As a further solution of the present invention, it also includes linear correction of the discrete dynamic traffic flow model of each road section, and applies the linear fitting optimization method to correct the discrete dynamic traffic flow model equation of each road section according to the actual operation of the traffic flow. The equation is: Where k is the slope and b is the intercept.

[0013] Preferably, the normalization process includes: dividing the road space occupancy rate of vehicles on each road section into segments of different sizes to obtain a joint distribution of the road space occupancy rate of vehicles on each road section. The normalization method is as follows:

[0014] Let the point x on the interval [a,b] k ,through Normalize the points to a fixed length of Partition A i (i=1,2,…,N), where N is the number of partitions.

[0015] As a preferred method, the overall operation state model of the diversion area is composed of the discrete dynamic traffic flow model of each road section in the form of a matrix. The proportion of the influence of each road section on the overall operation state of the diversion area is the ratio of the average number of vehicles in each road section to the total average number of vehicles during operation θ i (i=1,2,3), the overall operating state equation of the diversion area is: n+1 =F(ρ n )=[θ1f1(ρ n1 ),θ2f2(ρ n2 ),θ3f3(ρ n3 )] T ; where f i (ρ ni)(i=1,2,3) are the discrete dynamic traffic flow model equations of the three road sections.

[0016] As an example, the extended entropy disorder is obtained by summing the normalized entropy disorder of each partition. The extended entropy disorder is calculated as follows:

[0017]

[0018] Where M is all observed points, n is the time, A is all partitions, F is the overall operation state equation of the diversion area, d is the dimension (a total of 3 dimensions, each dimension represents a road section), i, j, k are the i, j, kth partitions in the three dimensions after normalization, A i,j,k represents the partitions of A with the three dimensions being the i, j, and k partitions respectively. Represents partition A i,j,k The ratio of the number of midpoints to the total number, It is partition A i,j,k The eigenvalues ​​of the variance-covariance matrix of all points on , It is partition A i,j,k All points on the n ) is the eigenvalue of the variance-covariance matrix after mapping.

[0019] Preferably, when the expanded entropy chaos degree of the diversion area is less than or equal to 0, the traffic flow inside the diversion area can achieve self-organization without the need for external information input to adjust the vehicle operation status; when the expanded entropy chaos degree of the diversion area is greater than 0, the traffic flow inside the diversion area is difficult to achieve self-organization, and external information input is required to adjust the vehicle operation status to achieve orderly operation of the traffic flow.

[0020] Beneficial Effects: Based on the concept of entropy in chemistry and the theory of self-organization in the information field, this invention proposes a method for evaluating the traffic flow status in a diversion zone, which can effectively evaluate the traffic flow status in the diversion zone. The present invention can organically combine the three parameters of free travel speed of traffic flow on each road section in the diversion zone, the average travel speed of traffic flow at each time of vehicle operation, and the road vehicle space occupancy rate, making the evaluation method more comprehensive. In addition, the present invention can determine the timing of external traffic control measures to intervene in the traffic flow operation in the diversion zone based on the calculated extended entropy chaos degree, effectively saving a large amount of computing power resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Flowchart of an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram of the diversion area definition and the division of each road section in the diversion area. DETAILED DESCRIPTION

[0023] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0024] like Figures 1 to 2 As shown, an embodiment of the present invention discloses a method for evaluating the traffic flow operation status in a freeway diversion area based on self-organization theory, and the evaluation method includes the following steps:

[0025] Step 1: Divide and establish a discrete dynamic traffic flow model for each section of the diversion area;

[0026] Step 2: Collect traffic flow status information on each road section based on intelligent transportation data collection technology;

[0027] Step 3: Linearly modify the discrete dynamic traffic flow model of each road section;

[0028] Step 4: normalize the collected data and combine it with the overall operation status model of the diversion area;

[0029] Step 5: Calculate the extended entropy chaos of traffic flow over a period of time and evaluate the self-organization of traffic flow in the diversion area.

[0030] For example, Figure 2 As shown, in step 1, the 100 meters upstream of the main line where the diversion area ramp is connected to the main line is regarded as section 1, the 50 meters downstream of the main line is regarded as section 2, and the 50 meters downstream of the ramp is regarded as section 3.

[0031] Based on the Greenshields traffic flow model, a discrete dynamic traffic flow model of the road section is established, and its equation is:

[0032]

[0033] In the discrete dynamic traffic flow model equation, ρ n+1 is the road vehicle space occupancy rate at the next moment, ρ n is the road vehicle space occupancy rate at the current moment, v f is the vehicle's free speed, is the average travel speed of traffic at the current moment;

[0034] The road vehicle space occupancy rates of each road section at the current moment are ρ n1 ,ρ n2 ,ρ n3 ;

[0035] The mapping of the road vehicle space occupancy rate at the current moment to the road vehicle space occupancy rate at the next moment is f1(ρ n1 ),f2(ρ n1 ),f3(ρ n1 ).

[0036] In step 2, data collection technology in the field of intelligent transportation can collect the following information: real-time average travel speed of each road section, real-time number of vehicles on each road section, and real-time vehicle road space occupancy rate of each road section.

[0037] In step 3, to further improve the accuracy of the model calculation, the linear fitting optimization method is applied to modify the discrete dynamic traffic flow model equation of each road section according to the actual operation of the traffic flow, and the improved discrete dynamic traffic flow model of the road section is obtained: Where k is the slope and b is the intercept.

[0038] In step 4, the normalization method is used: the point x on the interval [a, b] k ,through Normalize the points to a fixed length of Partition A i (i=1,2,…,N), the road space occupancy rate of vehicles on each road section is divided into segments according to size, and the joint distribution of the road space occupancy rate of vehicles on the three road sections is obtained.

[0039] In step 4, the overall operation state model of the diversion area is constructed by the discrete dynamic traffic flow model of each road section in matrix form. The proportion of the influence of each road section on the overall operation state of the diversion area is the ratio of the average number of vehicles on each road section to the total average number of vehicles during the operation, θ i (i=1,2,3), the overall operating state equation of the diversion area is: n+1 =F(ρ n )=[θ1f1(ρ n1 ),θ2f2(ρ n2 ),θ3f3(ρ n3 )] T

[0040] In step five, the extended entropy chaos degree calculation method is based on the Lyapunov index, a commonly used indicator for evaluating the degree of system chaos.

[0041] The extended entropy disorder is obtained by summing the normalized entropy disorder of each partition. The calculation method of the extended entropy disorder is as follows:

[0042]

[0043] In the extended entropy chaos degree, M represents all observed points, n represents the time, A represents all partitions, F represents the overall operation state equation of the diversion area, d represents the dimension (a total of 3 dimensions, each dimension represents a road section), i, j, k represent the i, j, kth partitions in the three dimensions after normalization, A i,j,k represents the partitions of A with the three dimensions being the i, j, and k partitions respectively. Represents partition A i,j,k The ratio of the number of midpoints to the total number, It is partition A i,j,k The eigenvalues ​​of the variance-covariance matrix of all points on , It is partition A i,j,k All points on the n ) is the eigenvalue of the variance-covariance matrix after mapping.

[0044] In step five, when the expanded entropy chaos degree of the diversion area is less than or equal to 0, the traffic flow inside the diversion area can achieve self-organization, and no external information input is required to adjust the vehicle operation status; when the expanded entropy chaos degree of the diversion area is greater than 0, the traffic flow inside the diversion area is difficult to achieve self-organization, and external information input is required to adjust the vehicle operation status to achieve orderly operation of the traffic flow.

[0045] Working principle: Divide the diversion area into sections based on the traffic flow operation status and establish a discrete diversion area traffic flow operation state equation based on the vehicle road space occupancy rate. Based on roadside unit detection, obtain the status information of the traffic flow in each section of the diversion area over a certain time period. The collected data are normalized and partitioned based on the base function according to the vehicle road space occupancy rate of each section, and the discrete dynamic traffic flow model of the diversion area is fitted and optimized. Based on the improved method, the normalized entropy chaos degree of each partition is calculated and summed to obtain the overall expanded entropy chaos degree of the diversion area, and evaluate whether the traffic flow operation in the diversion area can achieve self-organization.

[0046] The present invention also discloses a system for evaluating the traffic flow status in a freeway diversion zone based on self-organization theory. The system comprises a road segmentation and modeling module for dividing the diversion zone into sections and establishing discrete dynamic traffic flow models for each section within the diversion zone; a traffic flow acquisition module for collecting traffic flow status information within each section, including the real-time average travel speed, real-time number of vehicles, and real-time vehicle road space occupancy; and a traffic flow evaluation module for normalizing the collected data and combining it with the overall traffic flow status model of the diversion zone to calculate the expanded entropy disorder of traffic flow over a period of time. This expanded entropy disorder is then used to evaluate the self-organization of traffic flow in the diversion zone. The specific implementation details of each module are described in the above-mentioned method embodiment and are not further elaborated here.

[0047] An embodiment of the present invention further discloses a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the method for evaluating the traffic flow operation status of a freeway diversion area based on self-organization theory.

[0048] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations that come within the meaning and range of equivalents of the claims be embraced therein.

[0049] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A method for evaluating the traffic flow status in a freeway diversion area based on self-organization theory, characterized in that: The steps include: The diversion area is divided into sections and a discrete dynamic traffic flow model is established for each section in the diversion area. The discrete dynamic traffic flow model for each section is derived based on the Greenshields traffic flow model. In the model, the road vehicle space occupancy rate at the next moment is related to the free travel speed of the vehicle in the section under the current road conditions, the average travel speed of the traffic flow at the current moment, and the road vehicle space occupancy rate at the current moment. Collect traffic flow status information on each road section, including real-time average travel speed, real-time number of vehicles, and real-time vehicle road space occupancy rate; The collected data is normalized and combined with the overall operation state model of the diversion area to calculate the extended entropy disorder of the traffic flow over a period of time. Based on the extended entropy disorder, the self-organization evaluation of the traffic flow in the diversion area is realized. The overall operation state model of the diversion area is composed of the discrete dynamic traffic flow models of each road section in the form of a matrix. The proportion of the influence of each road section on the overall operation state of the diversion area is the ratio of the average number of vehicles in each road section to the total average number of vehicles during operation; the expanded entropy disorder is obtained by adding the normalized entropy disorder of each partition; the expanded entropy disorder is the sum of the normalized entropy disorder of each partition. The calculation method is as follows: Where M is all observed points, n is the time, A is all partitions, F is the overall state equation of the diversion area, F(ρ n )=[θ1f1(ρ n1 ),θ2f2(ρ n2 ),θ3f3(ρ n3 )] T , d∈{1,2,3} represents the dimension, each dimension represents a road segment, θ d represents the proportion of the average number of vehicles in dimension d at time n to the average number of vehicles in the diversion area, f d (ρ nd ) represents the discrete dynamic traffic flow model equation of dimension d, i, j, k represent the i, j, kth partitions in the three dimensions after normalization, A i,j,k represents the partitions of A with the three dimensions being the i, j, and k partitions respectively. Represents partition A i,j,k The ratio of the number of midpoints to the total number, It is partition A i,j,k The eigenvalues ​​of the variance-covariance matrix of all points on , It is partition A i,j,k All points on the n ) is the eigenvalue of the variance-covariance matrix after mapping.

2. The method for evaluating the traffic flow operation status in the expressway diversion area based on self-organization theory according to claim 1 is characterized in that: The discrete dynamic traffic flow model equation is: where ρ n+1 is the road vehicle space occupancy rate at the next moment, ρ n is the road vehicle space occupancy rate at the current moment, v f is the vehicle's free speed, is the average travel speed of the traffic at the current moment.

3. The method for evaluating the traffic flow operation status of a freeway diversion area based on self-organization theory according to claim 1 is characterized in that: It also includes linear correction of the discrete dynamic traffic flow model of each road section, and the application of linear fitting optimization method to correct the discrete dynamic traffic flow model equation of each road section.

4. The method for evaluating the traffic flow operation status of a freeway diversion area based on self-organization theory according to claim 1 is characterized in that: The normalization process includes: dividing the road space occupancy rate of vehicles on each road section into segments according to size, and obtaining the joint distribution of the road space occupancy rate of vehicles on each road section; the normalization method is as follows: Assume x k is a point on the interval [a,b], which is obtained by Normalize the points to a fixed length of On the partitions, N is the number of partitions.

5. The method for evaluating the traffic flow operation status in the expressway diversion area based on self-organization theory according to claim 1 is characterized in that: When the expanded entropy chaos degree of the diversion area is less than or equal to 0, the traffic flow within the diversion area can achieve self-organization and no external information input is required to adjust the vehicle operation status; when the expanded entropy chaos degree of the diversion area is greater than 0, the traffic flow within the diversion area is difficult to achieve self-organization and external information input is required to adjust the vehicle operation status to achieve orderly operation of the traffic flow.

6. A traffic flow operation status evaluation system for expressway diversion areas based on self-organization theory, characterized by: include: The road segmentation and modeling module is used to divide the diversion area into sections and establish discrete dynamic traffic flow models for each section in the diversion area. The discrete dynamic traffic flow model for the section is derived based on the Greenshields model of traffic flow. In the model, the road vehicle space occupancy rate at the next moment is related to the free travel speed of the vehicle in the section under the current road conditions, the average travel speed of the traffic flow at the current moment, and the road vehicle space occupancy rate at the current moment. The operation status acquisition module is used to collect the traffic operation status information of each road section, including the real-time average travel speed, real-time number of vehicles and real-time vehicle road space occupancy rate of each road section; The operation status evaluation module is used to normalize the collected data and combine it with the overall operation status model of the diversion area to calculate the expanded entropy disorder of the traffic flow over a period of time. Based on the expanded entropy disorder, the self-organization evaluation of the traffic flow in the diversion area is realized; The overall operation state model of the diversion area is composed of the discrete dynamic traffic flow models of each road section in the form of a matrix. The proportion of the influence of each road section on the overall operation state of the diversion area is the ratio of the average number of vehicles in each road section to the total average number of vehicles during operation; the expanded entropy disorder is obtained by adding the normalized entropy disorder of each partition; the expanded entropy disorder is the sum of the normalized entropy disorder of each partition. The calculation method is as follows: Where M is all observed points, n is the time, A is all partitions, F is the overall state equation of the diversion area, F(ρ n )=[θ1f1(ρ n1 ),θ2f2(ρ n2 ),θ3f3(ρ n3 )] T , d∈{1,2,3} represents the dimension, each dimension represents a road segment, θ d represents the proportion of the average number of vehicles in dimension d at time n to the average number of vehicles in the diversion area, f d (ρ nd ) represents the discrete dynamic traffic flow model equation of dimension d, i, j, k represent the i, j, kth partitions in the three dimensions after normalization, A i,j,k represents the partitions of A with the three dimensions being the i, j, and k partitions respectively. Represents partition A i,j,k The ratio of the number of midpoints to the total number, It is partition A i,j,k The eigenvalues ​​of the variance-covariance matrix of all points on , It is partition A i,j,k All points on the n ) is the eigenvalue of the variance-covariance matrix after mapping.

7. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the method for evaluating the traffic flow operation status of an expressway diversion area based on self-organization theory according to any one of claims 1 to 5 are implemented.

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