CN113393671B - Road traffic organization scheme optimization method and device - Google Patents
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Abstract
The invention discloses a road traffic organization scheme optimization method and a device, belonging to the field of road traffic organization scheme optimization; acquiring data of a scheme in multiple dimensions; then calculating data according to the data and a pre-constructed decision model to obtain a utility value of the scheme; then comparing the utility value with a preset value; and if the utility value is lower than the preset value, optimizing the scheme according to the decision model. According to the scheme, the human factor representing the degree of meeting the requirements of traffic participant behavior characteristics by traffic organization design is introduced when a road traffic organization scheme is optimized, a quantitative decision model is constructed based on an analytic hierarchy process, full-element quantitative analysis of the traffic organization scheme is realized, the traffic behavior characteristics of traffic participants can be fully reflected, the difference of the traffic participant behavior characteristics is fully considered, and the basic behavior characteristic requirements of various types of traffic participants are met. The road traffic organization is realized, and the road traffic organization is safe, orderly and smooth, meets the requirement of humanization, and meets the behavior characteristics of traffic participants.
Description
Technical Field
The invention relates to a road traffic organization scheme optimization technology, in particular to a road traffic organization scheme optimization method and a road traffic organization scheme optimization device.
Background
The road traffic organization is an important basis of public security traffic management work and is used for enabling vehicles and pedestrians on the road to pass through the road in an orderly and standard manner. Under the general condition, the current road traffic organization optimization mainly considers the traffic efficiency characterization indexes such as road traffic capacity and traffic safety, and the system design is performed by rarely integrating the microscopic behavior characteristics of the traffic participants and the influence of the environment on the traffic participants, so that the traffic organization optimization design is easy to cause that the behavior characteristics of the traffic participants are not met, the traffic order disorder is likely to be caused, the traffic illegal behaviors are highly happened, the traffic capacity is influenced, and the traffic safety is threatened. Therefore, the current road traffic organization optimization method does not fully and systematically consider road traffic influence factors, and cannot effectively optimize a road traffic scheme.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a road traffic organization scheme optimization method and a device, and aims to solve the problem that the road traffic scheme cannot be effectively optimized because road traffic influence factors are not fully and systematically considered in the current road traffic organization optimization method.
The technical scheme adopted by the invention for solving the technical problem is as follows:
on the one hand, the method comprises the following steps of,
a road traffic organization scheme optimization method comprises the following steps:
acquiring data of the road traffic organization scheme in multiple dimensions including human factor coefficients, wherein the human factor coefficients represent the degree of meeting the behavior characteristic requirements of traffic participants in traffic organization design;
calculating the data according to a pre-constructed decision model based on an analytic hierarchy process to obtain a utility value of the road traffic organization scheme;
comparing the utility value with a preset value;
and if the utility value is lower than a preset value, optimizing the road traffic organization scheme according to the decision model.
Further, the decision model comprises a plurality of primary indexes, the primary indexes are the dimensionality of the acquired data, and each primary index comprises a plurality of secondary indexes;
the primary indicator is configured to: traffic efficiency, traffic safety, operational order and human factor;
the traffic efficiency is configured to: motor vehicle delay time, motor vehicle parking times, non-motor vehicle delay time and pedestrian delay time;
the traffic safety is configured to: motor vehicle-motor vehicle conflict, motor vehicle-non-motor vehicle conflict, non-motor vehicle-non-motor vehicle conflict, motor vehicle-pedestrian conflict, and non-motor vehicle-pedestrian conflict;
the order of operation is configured to: motor vehicle violation rate, non-motor vehicle violation rate and pedestrian violation rate;
the human factor is configured to: the motor vehicles pass according to the indication, the non-motor vehicles stop according to the indication, and pedestrians pass according to the indication and wait according to the indication.
Further, the acquiring data of the road traffic organization scheme in multiple dimensions comprises:
and acquiring data of the secondary indexes in the decision model.
Further, when building the decision model, the method further comprises:
respectively constructing a primary index and a judgment matrix of a secondary index in each primary index;
and calculating to obtain the relative weight of each primary index and the relative weight of each secondary index in each primary index according to the judgment matrix.
Further, calculating the relative weight according to the decision matrix includes:
and obtaining a normalized eigenvector of the judgment matrix by a sum-product method, wherein the normalized eigenvector corresponds to the relative weight of one index.
Further, still include:
carrying out consistency detection on the judgment matrix to obtain the consistency of the judgment matrix;
and when the consistency does not meet the preset requirement, reconstructing the judgment matrix.
Further, the calculating the data according to a pre-constructed decision model based on an analytic hierarchy process to obtain the utility value of the road traffic organization scheme comprises:
carrying out normalization processing on the data to obtain a normalized value;
calculating a utility value according to the normalized value and the relative weight of the secondary index corresponding to each data; the calculation formula is as follows:
Z＝-W 1i V 1i -W 2i V 2i -W 3i V 3i -W 4i V 4i
wherein Z is the utility value, W 1i 、W 2i 、W 3i And W 4i Representing the relative weight, V, of each secondary index within each primary index 1i 、V 2i 、V 3i And V 4i Representing the normalized value of the data corresponding to each secondary index.
Further, the normalizing the data to obtain a normalized value includes:
comparing the data corresponding to the same secondary index with standard data of a preset road traffic organization scheme to obtain a normalized value, wherein the calculation formula is as follows:
K(a)＝a/(a+b)；
k (a) is a normalized value of the scheme to be optimized, and a is data corresponding to any secondary index of the scheme to be optimized; and b is standard data corresponding to the secondary indexes of the preset road traffic organization scheme which are the same as those of the a.
Further, the optimizing the road traffic organization scheme according to the decision model comprises:
acquiring a secondary index and relative weight of the secondary index, of which the data of the scheme to be optimized under each secondary index exceeds a preset range;
and modifying the road traffic organization scheme according to the acquired secondary indexes and the relative weight.
On the other hand, in the case of a system,
a road traffic organization scheme optimization apparatus, comprising:
the data acquisition module is used for acquiring data of the road traffic organization scheme in multiple dimensions including human factor coefficients, and the human factor coefficients represent the degree of meeting the behavior characteristic requirements of traffic participants in traffic organization design;
the utility value calculation module is used for calculating the data according to a decision model which is constructed in advance based on an analytic hierarchy process to obtain the utility value of the road traffic organization scheme;
the utility value comparison module is used for comparing the utility value with a preset value;
and the scheme optimization module is used for optimizing the road traffic organization scheme according to the decision model if the utility value is lower than a preset value.
This application adopts above technical scheme, possesses following beneficial effect at least:
the technical scheme of the application provides a road traffic organization scheme optimization method and device, and scheme data are obtained in multiple dimensions; then calculating data according to the data and a pre-constructed decision model to obtain a utility value of the scheme; then comparing the utility value with a preset value; and if the utility value is lower than the preset value, optimizing the scheme according to the decision model. According to the method, the human factor is introduced when the road traffic organization scheme is optimized, the quantitative decision model is constructed based on the analytic hierarchy process, the full-element quantitative analysis of the traffic organization scheme is achieved, compared with the previous method, the human factor representing the degree of meeting the traffic participant behavior characteristic requirements of traffic organization design is introduced, the traffic behavior characteristics of traffic participants can be fully reflected, the traffic participant behavior characteristic difference is fully considered, and the basic behavior characteristic requirements of various types of traffic participants are met. The road traffic organization is safe, orderly and smooth, meets the requirement of humanization, accords with the behavior characteristics of traffic participants, and can accurately and effectively optimize the road traffic organization scheme.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for optimizing a road traffic organization scheme according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a decision model according to an embodiment of the present invention;
fig. 3 is a structural diagram of a road traffic organization scheme optimization device provided in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following detailed description of the technical solutions of the present invention is provided with reference to the accompanying drawings and examples. It should be apparent that the described embodiments are only a few embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without making any creative effort, shall fall within the protection scope of the present application.
First, since the core of the scheme is to introduce human factors in traffic organization design, the human factors in traffic organization design are explained below. The human factor represents the degree to which the traffic organization design meets the requirements of the behavior characteristics of the traffic participants.
The aim of designing the human factor in the traffic organization design is to fully reflect the traffic behavior characteristics of traffic participants and fully consider the behavior characteristic difference of the traffic participants when developing the road traffic organization design, thereby meeting the basic behavior characteristic requirements of various types of traffic participants. The road traffic organization is realized, and the road traffic organization is safe, orderly and smooth, has humanization and accords with the behavior characteristics of traffic participants.
Referring to fig. 1, an embodiment of the present invention provides a road traffic organization scheme optimization method, including the following steps:
acquiring data of a road traffic organization scheme in multiple dimensions including human factor coefficients, wherein the human factor coefficients are the microscopic characteristics of the behaviors of traffic participants and influence coefficients of the environment on the traffic participants;
calculating data according to a pre-constructed decision model based on an analytic hierarchy process to obtain a utility value of the road traffic organization scheme;
comparing the utility value with a preset value;
and if the utility value is lower than the preset value, optimizing the road traffic organization scheme according to the decision model.
According to the road traffic organization scheme optimization method provided by the embodiment of the invention, data of a scheme is acquired in multiple dimensions; then calculating a scheme utility value obtained by the data according to the data and a pre-constructed decision model; then comparing the utility value with a preset value; and if the utility value is lower than the preset value, optimizing the scheme according to the decision model. According to the scheme, the human factor is introduced when the road traffic organization scheme is optimized, the quantitative decision model is constructed on the basis of the analytic hierarchy process, the full-element quantitative analysis of the traffic organization scheme is achieved, and compared with the conventional method, the method accords with the behavior characteristics of traffic participants, has more consideration factors, and can accurately and effectively optimize the road traffic organization scheme.
As a complement to the above embodiment: the decision model comprises a plurality of primary indexes, the primary indexes are the dimensionality of the acquired data, and each primary index comprises a plurality of secondary indexes;
the primary indicator is configured to: traffic efficiency, traffic safety, operational order and human factor; the traffic efficiency is configured to: motor vehicle delay time, motor vehicle parking times, non-motor vehicle delay time and pedestrian delay time; the traffic safety is configured to: motor vehicle-motor vehicle conflict, motor vehicle-non-motor vehicle conflict, non-motor vehicle-non-motor vehicle conflict, motor vehicle-pedestrian conflict, and non-motor vehicle-pedestrian conflict; the order of operation is configured to: motor vehicle law violation rate, non-motor vehicle law violation rate and pedestrian law violation rate; the human factor is configured to: the motor vehicles pass according to the indication, the non-motor vehicles stop according to the indication, pedestrians pass according to the indication and the pedestrians wait according to the indication.
It should be noted that acquiring data of the road traffic organization scheme in multiple dimensions includes: and acquiring data of the secondary indexes in the decision model.
As an optional implementation manner of the embodiment of the present invention, when constructing the decision model, the method further includes: respectively constructing a primary index and a judgment matrix of a secondary index in each primary index; and calculating according to the judgment matrix to obtain the relative weight of each primary index and the relative weight of each secondary index in each primary index. Wherein calculating the relative weight according to the decision matrix comprises: and obtaining a normalized eigenvector of the judgment matrix by a sum-product method, wherein the normalized eigenvector corresponds to the relative weight of one index. It can be understood that, the consistency detection is performed on the judgment matrix to obtain the consistency of the judgment matrix; and when the consistency does not meet the preset requirement, reconstructing the judgment matrix.
Optionally, the obtaining the utility value of the road traffic organization scheme according to the pre-constructed decision model calculation data includes: carrying out normalization processing on the data to obtain a normalization value; calculating a utility value according to the normalized value and the relative weight of the secondary index corresponding to each datum; the calculation formula is as follows:
Z＝-W 1i V 1i -W 2i V 2i -W 3i V 3i -W 4i V 4i
wherein Z is the utility value, W 1i 、W 2i 、W 3i And W 4i Representing the relative weight, V, of each secondary index within each primary index 1i 、V 2i 、V 3i And V 4i Representing normalization of data corresponding to each secondary indexThe value after conversion.
Wherein, normalizing the data to obtain a normalized value comprises:
comparing the data corresponding to the same secondary index with the standard data of a preset road traffic organization scheme to obtain a normalized value, wherein the calculation formula is as follows:
K(a)＝a/(a+b)；
k (a) is a normalized value of the scheme to be optimized, and a is data corresponding to any secondary index of the scheme to be optimized; and b is standard data corresponding to the secondary indexes of the preset road traffic organization scheme which are the same as those of the a.
Further, optimizing the road traffic organization scheme according to the decision model includes: acquiring secondary indexes of the scheme to be optimized under each secondary index, wherein the data of the scheme to be optimized exceed a preset range, and relative weights of the secondary indexes; and modifying the road traffic organization scheme according to the obtained secondary indexes and the relative weight.
In order to more clearly illustrate the aspects of the present invention, a specific example is provided below. The method comprises the following steps:
1. collaborative optimization decision model
1.1 Modeling method
The method utilizes an analytic hierarchy process to construct a traffic organization optimization index model as an optimization index of collaborative organization design. The analytic hierarchy process is a decision method which decomposes elements related to decision into a target, a criterion, a scheme and other layers, and carries out quantitative analysis on the basis of the target, the criterion, the scheme and the other layers, is often applied to multi-target, multi-criterion, multi-element and multi-layer unstructured complex decision problems, and can better solve the complex system decision problems of mutual correlation and mutual restriction of the multi-element.
The flow of the analytical hierarchy process is generally divided into four sections: firstly, the problem of planning decision is decomposed into a plurality of elements, and a model of a hierarchical structure is established. And secondly, determining the importance of each element in each layer, wherein the importance is relative and is obtained by comparing every two elements. Thirdly, determining importance order weights of all elements in each layer, wherein the importance is relative. And fourthly, carrying out quantitative analysis on the alternative schemes based on the weight values.
(1) Building a hierarchical model
And according to the analysis of the planning decision-making problem, hierarchically ordering the involved elements. The method is generally divided into three layers, wherein the highest layer is a target layer, the lowest layer is a scheme layer, and the middle layer is a criterion layer or an index layer.
(2) Establishing a decision matrix
Taking a certain element of the above level as a criterion, comparing every two elements of the level to construct a judgment matrix A = (a) ij ) n×n Wherein a is ij Representing element u i And u j Relative importance to the criteria. Any decision matrix should satisfy: a is a ij >0；a ii ＝1；a ij ＝1/a ji . The nine-scale method is generally used to compare each element two by two, and the specific scale and the meaning thereof are shown in table 1.
TABLE 1 judge matrix Scale and corresponding meanings
| Scale (a) ij ) | Corresponding meaning |
| 1 | Element u i And u and j the importance of the previous level factor is the same |
| 3 | Element u i Specific element u j Of slight importance |
| 5 | Element u i Specific element u j Of obvious importance |
| 7 | Element u i Element ratio u j Of strong importance |
| 9 | Element u i Biu is a ratio of j Of extreme importance |
(3) Hierarchical single ordering and consistency check
According to the formula (1), calculating the maximum eigenvalue lambda of each judgment matrix max And the corresponding characteristic vector W, the component of W is the weight of the corresponding element.
AW＝λ max W (1)
The method for calculating the maximum eigenvalue and the eigenvector mainly comprises the following steps: sum-product method, square root method, power method, etc. The sum product method is generally adopted, and the calculation steps are as follows:
the first step is as follows: normalizing the decision matrix by column, i.e.
The second step is that: adding the judgment matrixes normalized by columns according to rows to obtain
Record vector W '= [ W' 1 ,w′ 2 ，···，w′ n ] T
The third step: normalizing the vector W', i.e.
Normalized vector W = [ W = 1 ,w 2 ,···,w n ] T I.e. the eigenvectors of the matrix.
The fourth step: calculating the maximum eigenvalue
In the formula: (AW) i To determine the ith term component of the product of the matrix A and the feature vector W.
The consistency of the judgment matrix is checked by using the formula (6).
The average random consistency index RI, shown in table 2, is used to check whether the decision matrix has satisfactory consistency.
TABLE 2 average random consistency index
| Order of the order | 1/2 | 3 | 4 | 5 | 6 | 7 | 8 |
| RI | 0 | 0.52 | 0.89 | 1.12 | 1.25 | 1.35 | 1.42 |
| Order of the scale | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
| RI | 1.46 | 1.49 | 1.52 | 1.54 | 1.56 | 1.58 | 1.59 |
The random consistency ratio can be derived from the values of CI and RI, as
When CR <0.1, the judgment matrix has satisfactory consistency, otherwise, the judgment matrix needs to be corrected.
(4) Hierarchical gross ordering and consistency check
The overall hierarchical ordering needs to be performed layer by layer from top to bottom, and consistency checks need to be performed as well. Now suppose AThe layer has m elements, and the obtained single-layer sequencing result is a 1 ,a 2 ,···,a m The next level B has n elements, which are for A j Is b 'as the hierarchical single-sort result' 1 ，b′ 2 ，···，b′ n Then the random consistency ratio of the total ordering of the B level is
CI j Representing the consistency index of the judgment matrix in the layer B corresponding to the factors in the layer A; RI (Ri) j The average random consistency of the decision matrices in layer B corresponding to the factors in layer a is shown.
Similarly, when CR <0.1, the consistency of the overall ordering of the hierarchy is satisfactory, and the inconsistency degree is acceptable.
1.2 decision model
The traffic organization design scheme is optimized, and is related to the road traffic efficiency, the road traffic safety and the like, and also related to the human factor characteristics of the traffic participants, and the traffic organization optimization decision model should include 4-dimensional indexes of the traffic efficiency, the traffic safety, the operation order, the human factor characteristics and the like, which are specifically shown in table 3.
(1) Efficiency of passage
In the research range, the traffic efficiency of motor vehicles, non-motor vehicles and pedestrians is represented, and the traffic delay of the motor vehicles, the parking times of the motor vehicles, the traffic efficiency of the non-motor vehicles and the traffic efficiency of the pedestrians are mainly related.
(2) Traffic safety
Within the scope of the study, the number of traffic conflicts between motor vehicles, non-motor vehicles and pedestrians in the temporal and spatial dimensions is characterized.
(3) Order of operation
In the research range, the method represents the operation order conditions of motor vehicles, non-motor vehicles and pedestrians, and mainly relates to the traffic violation rate of motor vehicles, the traffic violation rate of non-motor vehicles and the traffic violation rate of pedestrians.
(4) Human factor coefficient
In the research range, the matching degree of the traffic organization design and the traffic behavior characteristics of traffic participants of various traffic modes is represented, and the matching degree mainly relates to whether motor vehicles pass according to a lane guide line, whether non-motor vehicles pass according to lane indication, whether non-motor vehicles stop according to a parking indication and whether pedestrians pass according to the indication of a sidewalk and a pedestrian crossing.
TABLE 3 decision model indices
1.3 case analysis
Based on the principle of an analytic hierarchy process, a traffic organization optimization decision model shown in fig. 2 is constructed, wherein 4 first-level indexes comprise traffic efficiency, traffic safety, operation order, human factor and the like, which are respectively represented as t 1 、t 2 、t 3 、t 4 . The two-stage indexes comprise 17, the passing efficiency is mainly divided into 4 indexes of motor vehicle delay time, motor vehicle stopping times, non-motor vehicle delay time and pedestrian delay time which are respectively represented as t 11 、t 12 、t 13 、t 14 (ii) a The traffic safety is mainly divided into 5 aspects of motor vehicle-motor vehicle conflict, motor vehicle-non-motor vehicle conflict, non-motor vehicle-non-motor vehicle conflict, motor vehicle-pedestrian conflict and non-motor vehicle-pedestrian conflict, which are respectively represented as t 21 、t 22 、t 23 、t 24 、t 25 (ii) a The order of operation is mainlyIs divided into 3 aspects of motor vehicle illegal rate, non-motor vehicle illegal rate and pedestrian illegal rate which are respectively expressed as t 31 、t 32 、t 33 (ii) a The human factor is mainly divided into t for motor vehicle passing according to indication, non-motor vehicle stopping according to indication, pedestrian passing according to indication and pedestrian waiting according to indication 41 、t 42 、t 43 、t 44 、t 45 。
Constructing a corresponding judgment matrix according to the importance degree of the 4 indexes associated with the layer A (namely, the first level):
the numerical values in the matrix are relative importance of traffic efficiency, traffic safety, operation order and human factor indexes. And (3) obtaining a normalized eigenvector of the matrix A by using a sum-product method:
W＝[0.0454,0.5566,0.1135，0.2845] T
the maximum eigenvalues are:
λ max ＝4.2051
and carrying out consistency judgment on the judgment matrix, wherein the calculation result is as follows: CI =0.6837, ri =0.89, cr =0.0768<0.10, and thus the matrix a is judged to have satisfactory consistency, the degree of inconsistency of which is acceptable. Each first-level index t obtained after calculation 1 、t 2 、t 3 、t 4 The weights are shown in Table 4.
TABLE 4 relative importance and weight of traffic organization optimization decision primary index
| t | t 1 | t 2 | t 3 | t 4 | W |
| t 1 | 1 | 1/8 | 1/4 | 1/6 | 0.0454 |
| t 2 | 8 | 1 | 5 | 3 | 0.5566 |
| t 3 | 4 | 1/5 | 1 | 1/4 | 0.1135 |
| t 4 | 6 | 1/3 | 4 | 1 | 0.2845 |
The processing of each secondary index judgment matrix is the same as above, and the results are shown in tables 5 to 8.
TABLE 5 decision matrix t 1 -t 1i
TABLE 6 decision matrix t 2 -t 2i
| t 2 | t 21 | t 22 | t 23 | t 24 | t 25 | W | Index (es) |
| t 21 | 1 | 1/4 | 2 | 1/6 | 4 | 0.1098 | λ max ＝5.3375 |
| t 22 | 4 | 1 | 4 | 1/4 | 5 | 0.2463 | CI＝0.0844 |
| t 23 | 1/2 | 1/4 | 1 | 1/6 | 1 | 0.0593 | RI＝1.12 |
| t 24 | 6 | 4 | 6 | 1 | 6 | 0.5328 | CR＝0.0753 |
| t 25 | 1/4 | 1/5 | 1 | 1/6 | 1 | 0.0518 | CR<0.10 |
TABLE 7 decision matrix t 3 -t 3i
TABLE 8 decision matrix t 4 -t 4i
The consistency check of the total sorting is carried out according to the formula (8), and the result is 0.0782 (< 0.10), namely, the hierarchical total sorting has satisfactory consistency, the inconsistency degree is acceptable, and the weight after the weighting processing of each secondary index is shown in table 9.
TABLE 9 weight of each index of traffic organization optimization decision-making hierarchical analysis system
Through the data analysis, the most important factor in the first-level indexes in the traffic organization optimization decision indexes is traffic safety; secondly, human factor coefficient and operation order; of relatively low importance is "traffic efficiency". In the secondary indexes of "traffic safety", the status of "motor vehicle-pedestrian conflict" and "motor vehicle-non-motor vehicle conflict" is relatively prominent, the "motor vehicle-motor vehicle conflict" and "non-motor vehicle-non-motor vehicle conflict" are second, and the importance of "non-motor vehicle-pedestrian conflict" is relatively weak. In the secondary indexes of the human factor, the 'motor vehicle passing according to indication' is most important, the 'non-motor vehicle passing according to indication' and the 'non-motor vehicle stopping according to indication' are the second most important, and the 'pedestrian passing according to indication' and the 'pedestrian waiting according to indication' are the weakest. In the secondary index of the 'operation order', the importance of the 'motor vehicle violation rate' and the 'pedestrian violation rate' is obviously greater than the 'non-motor vehicle violation rate'. In the secondary indexes of the traffic efficiency, the pedestrian delay time is the most important, the non-motor vehicle delay time is the next, the motor vehicle delay time and the motor vehicle parking times are the least important, and the difference between the motor vehicle delay time and the motor vehicle parking times is small.
Taking two optional organization schemes a and B at a certain intersection as an example, the above analytic hierarchy process is used for optimization, wherein the scheme B is equivalent to a preset scheme, as shown in table 10.
TABLE 10 traffic organization schemes A, B
Since each index belongs to different dimensions, normalization is required, and the results are shown in table 11.
TABLE 11 traffic organization schemes A, B normalization index
The goals of traffic organization optimization are shorter delay times, fewer collision points and parking times, lower violation rates, and better traffic order. Therefore, Z is not set to the utility function of each organization scheme
Z＝-W 1i V 1i -W 2i V 2i -W 3i V 3i -W 4i V 4i (9)
The utility values of the schemes A and B are obtained according to the formula
Z A ＝-0.5179*0.0056-0.4855*0.0031-0.4759*0.0131-0.4527*0.0236-0.5714*0.0611-0.5714*0.1371-0.6667*0.0330-0.6667*0.2966-0.3333*0.0288-0.6154*0.1591-0.5789*0.0252-0.6327*0.1002-0.3306*0.1321-0.4818*0.0439-0.1184*0.068-0.2696*0.0247-0.3577*0.0149＝-0.6248
Z B ＝-0.4821*0.0056-0.5145*0.0031-0.5241*0.0131-0.5473*0.0236-0.4286*0.0611-0.4286*0.1371-0.3333*0.0330-0.3333*0.2966-0.6667*0.0288-0.3846*0.1591-0.4211*0.0252-0.3673*0.1002-0.6694*0.1321-0.5182*0.0439-0.8816*0.0689-0.7304*0.0247-0.6423*0.0149＝-0.5462
For secondary indexes such as delay time, parking times, collision points, illegal rates and the proportion of traffic behaviors which are not indicated according to the instructions, the larger the numerical value is, the worse the effect of the traffic organization scheme is, the more the traffic organization scheme is contradictory to the goal of traffic organization optimization, and negative effect is brought, so that the utility function Z is represented by a negative value, and the larger the utility value Z is, the better the optimization effect of the organization scheme is. The comparison shows that the utility value of the scheme B is greater than that of the scheme A, so that the scheme B is better in the alternative traffic organization optimization scheme of the intersection, and the fact that the scheme B is obviously better than the scheme A in traffic safety indexes is easy to see, which also causes the fact that Z is B >Z A Is a key factor of (1). By utilizing an analytic hierarchy process, on the basis of constructing a traffic organization optimization index model, the advantages and disadvantages of all schemes can be compared by comparing the utility value Z, reference is provided for optimization selection work of the traffic organization optimization scheme in the future, and meanwhile, the traffic safety index is found to have a prominent position in the formulation of the traffic organization optimization scheme.
For the optimization aspect, the simplest optimization is to change the scheme A into the scheme B; or because the scheme B traffic safety index is obviously superior to the scheme A, the scheme A traffic safety can be optimized, for example, a monitoring device or a prompting device is added, the secondary indexes, which are superior to the scheme A, in the traffic safety index in the scheme B can be obtained specifically, and the optimization is carried out according to the relative weight of the secondary indexes, namely, the optimization sequence is positively correlated to the relative weight; detailed optimization means can be set by those skilled in the art according to actual situations, and the embodiment of the present invention only provides an optimization direction, and the detailed means are not described in detail herein.
The scheme provided by the embodiment of the invention deeply considers the behavior characteristics of the traffic participants and the influence of the environment on the traffic participants. And establishing a traffic organization optimization decision model with the cooperation of human demand and traffic capacity, traffic safety and traffic order of traffic participants, and constructing a set of new traffic organization optimization design method flow. The conventional traffic organization optimization process is to investigate the current situation, take the optimal traffic efficiency as a target, consider traffic safety, and rarely relate to quantitative analysis of traffic order, especially lack quantitative analysis of traffic organization designer factor characteristic matching. The optimized design flow innovatively reproduces the organization design process, increases the traffic safety, the traffic order degree and the quantitative analysis of human factor indexes, implements dynamic optimized design, improves the overall efficiency of traffic organization design, promotes the convenience and the comfort of traffic participants, and meets the target requirement of modern road traffic control.
In one embodiment, the present invention further provides a road traffic organization scheme optimizing apparatus, as shown in fig. 3, including:
the data acquisition module 31 is configured to acquire data of the road traffic organization scheme in multiple dimensions including human factor coefficients, where the human factor coefficients represent a degree to which a traffic organization design meets a requirement of a traffic participant behavior feature; specifically, the data obtaining module 31 obtains data of the secondary index in the decision model.
The utility value calculation module 32 is used for calculating the data according to a decision model which is pre-constructed based on an analytic hierarchy process to obtain a utility value of the road traffic organization scheme; specifically, the utility value calculation module 32 normalizes the data to obtain a normalized value; calculating a utility value according to the normalized value and the relative weight of the secondary index corresponding to each datum; the calculation formula is as follows:
Z＝-W 1i V 1i -W 2i V 2i -W 3i V 3i -W 4i V 4i
wherein Z is the utility value, W 1i 、W 2i 、W 3i And W 4i Representing the relative weight, V, of each secondary index within each primary index 1i 、V 2i 、V 3i And V 4i Representing the normalized value of the data corresponding to each secondary index. Further, normalizing the data to obtain a normalized value includes: comparing the data corresponding to the same secondary index with the standard data of a preset road traffic organization scheme to obtain a normalized value, wherein the calculation formula is as follows:
K(a)＝a/(a+b)；
k (a) is a normalized value of the scheme to be optimized, and a is data corresponding to any secondary index of the scheme to be optimized; and b is standard data corresponding to the secondary indexes of the preset road traffic organization scheme which are the same as those of the a.
A utility value comparison module 33, configured to compare the utility value with a preset value;
and the scheme optimization module 34 is configured to optimize the road traffic organization scheme according to the decision model if the utility value is lower than the preset value. Specifically, the scheme optimization module 34 obtains the secondary indexes of the to-be-optimized scheme under each secondary index, where the data of the to-be-optimized scheme exceeds the preset range, and the relative weights of the secondary indexes; and modifying the road traffic organization scheme according to the obtained secondary indexes and the relative weight.
The decision model comprises a plurality of primary indexes, the primary indexes are the dimensionality of acquired data, and each primary index comprises a plurality of secondary indexes; the primary indicator is configured to: traffic efficiency, traffic safety, operational order and human factor; the traffic efficiency is configured to: motor vehicle delay time, motor vehicle parking times, non-motor vehicle delay time and pedestrian delay time; the traffic safety is configured to: vehicle-vehicle conflict, vehicle-non-vehicle conflict, non-vehicle-non-vehicle conflict, vehicle-pedestrian conflict, and non-vehicle-pedestrian conflict; the order of operation is configured to: motor vehicle law violation rate, non-motor vehicle law violation rate and pedestrian law violation rate; the human factor is configured to: the motor vehicles pass according to the indication, the non-motor vehicles stop according to the indication, and pedestrians pass according to the indication and wait according to the indication.
When the decision model is built, the method further comprises the following steps: respectively constructing a primary index and a judgment matrix of a secondary index in each primary index; and calculating according to the judgment matrix to obtain the relative weight of each primary index and the relative weight of each secondary index in each primary index. Wherein calculating the relative weight according to the decision matrix comprises: and obtaining a normalized eigenvector of the judgment matrix by a sum-product method, wherein the normalized eigenvector corresponds to the relative weight of one index. It can be understood that, the consistency detection is performed on the judgment matrix to obtain the consistency of the judgment matrix; and when the consistency does not meet the preset requirement, reconstructing a judgment matrix.
In the road traffic organization scheme optimizing device provided by the embodiment of the invention, the data acquisition module acquires data of the road traffic organization scheme in multiple dimensions, and the multiple dimensions comprise: traffic efficiency, traffic safety, operational order and human factor; the utility function calculation module calculates data according to a pre-constructed decision model to obtain a utility function of the road traffic organization scheme; the utility function comparison module compares the utility function with a preset value; and if the utility function value is lower than the preset value, the scheme optimization module optimizes the road traffic organization scheme according to the decision model. The traffic efficiency, traffic safety, operation order and human factor are comprehensively considered, a quantitative decision model is constructed, full-element quantitative analysis of the traffic organization scheme is achieved, and compared with the traditional method, the method accords with the behavior characteristics of traffic participants, has more considered factors, and can accurately and effectively optimize the road traffic organization scheme.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present application, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried out in the method of implementing the above embodiments may be implemented by hardware associated with program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program may include one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (6)
1. A road traffic organization scheme optimization method is characterized by comprising the following steps:
acquiring data of the road traffic organization scheme in multiple dimensions including human factor coefficients, wherein the human factor coefficients represent the degree of meeting the behavior characteristic requirements of traffic participants in traffic organization design;
calculating the data according to a decision model pre-constructed based on an analytic hierarchy process to obtain a utility value of the road traffic organization scheme;
comparing the utility value with a preset value;
if the utility value is lower than a preset value, optimizing the road traffic organization scheme according to the decision model;
the decision model comprises a plurality of primary indexes, the primary indexes are the dimensionality of the acquired data, and each primary index comprises a plurality of secondary indexes;
the primary indicator is configured to: traffic efficiency, traffic safety, operational order and human factor;
the traffic efficiency is configured to: motor vehicle delay time, motor vehicle parking times, non-motor vehicle delay time and pedestrian delay time;
the traffic safety is configured to: vehicle-vehicle conflict, vehicle-non-vehicle conflict, non-vehicle-non-vehicle conflict, vehicle-pedestrian conflict, and non-vehicle-pedestrian conflict;
the order of operation is configured to: motor vehicle violation rate, non-motor vehicle violation rate and pedestrian violation rate;
the human factor is configured to: the motor vehicles pass according to the indication, the non-motor vehicles stop according to the indication, pedestrians pass according to the indication and the pedestrians wait according to the indication;
acquiring data of the road traffic organization scheme in multiple dimensions comprises the following steps:
acquiring data of a secondary index in the decision model;
the method also comprises the following steps of:
respectively constructing a primary index and a judgment matrix of a secondary index in each primary index;
calculating to obtain the relative weight of each primary index and the relative weight of each secondary index in each primary index according to the judgment matrix;
the step of calculating the data according to a decision model pre-constructed based on an analytic hierarchy process to obtain the utility value of the road traffic organization scheme comprises the following steps:
carrying out normalization processing on the data to obtain a normalized value;
calculating a utility value according to the normalized value and the relative weight of the secondary index corresponding to each data; the calculation formula is as follows:
Z＝-t 11 V 11 -t 12 V 12 -t 13 V 13 -t 14 V1 4 -t 21 V 21 -t 22 V 22 -t 23 V 23 -t 24 V 24 -t 25 V 25 -t 31 V 31 -t 32 V 32 -t 33 V 33 -t 41 V 41 -t 42 V 42 -t 43 V 43 -t 44 V 44 -t 45 V 45 ；
wherein Z is the utility value, t 11 Delay time for motor vehicle, t 12 Number of motor vehicle stops, t 13 For time delay of non-motor vehicles, t 14 Delaying the time for the pedestrian; t is t 21 For a motor vehicle-motor vehicle collision, t 22 For motor vehicle-non-motor vehicle collision, t 23 For non-motor vehicle-non-motor vehicle collision, t 24 For a motor vehicle-pedestrian conflict, t 25 Is a non-motor-vehicle-pedestrian conflict; t is t 31 As rate of violation of the motor vehicle, t 32 As rate of violation of non-motor vehicles, t 33 Is the pedestrian violation rate, t 41 For motor vehicles to pass by as indicated, t 42 For non-motor vehicles to pass by as indicated, t 43 For non-motor vehicles stopping as indicated, t 44 For the pedestrian to pass according to the indication t 45 Waiting for the pedestrian according to the indication; v 11 ，V 12 ，V 13 ，V 14 ，V 21 ，V 22 ，V 23 ，V 24 ，V 25 ，V 31 ，V 32 ，V 33 ，V 41 ，V 42 ，V 43 ，V 44 And V 45 Respectively correspond to the above t 11 ，t 12 ，t 13 ，t 14 ，t 21 ，t 22 ，t 23 ，t 24 ，t 25 ，t 31 ，t 32 ，t 33 ，t 41 ，t 42 ，t 43 ，t 44 And t 45 Normalized secondary index value of (1).
2. The method of claim 1, wherein: calculating the relative weight according to the decision matrix includes:
and obtaining a normalized eigenvector of the judgment matrix by a sum-product method, wherein the normalized eigenvector corresponds to the relative weight of one index.
3. The method of claim 2, further comprising:
carrying out consistency detection on the judgment matrix to obtain the consistency of the judgment matrix;
and when the consistency is not in the preset requirement, reconstructing the judgment matrix.
4. The method of claim 1, wherein: the normalization processing of the data to obtain a normalization value includes:
comparing the data corresponding to the same secondary index with standard data of a preset road traffic organization scheme to obtain a normalized value, wherein the calculation formula is as follows:
K(a)＝a/(a+b)；
k (a) is a normalized value of the scheme to be optimized, and a is data corresponding to any secondary index of the scheme to be optimized; and b is standard data corresponding to the secondary indexes of the preset road traffic organization scheme which are the same as those of the a.
5. The method of claim 4, wherein: said optimizing said road traffic organization scheme according to said decision model comprises:
acquiring a secondary index and relative weight of the secondary index, of which the data of the scheme to be optimized under each secondary index exceeds a preset range;
and modifying the road traffic organization scheme according to the acquired secondary indexes and the relative weight.
6. A road traffic organization scheme optimization apparatus, comprising:
the data acquisition module is used for acquiring data of the road traffic organization scheme in multiple dimensions including human factor coefficients, and the human factor coefficients represent the degree of meeting the behavior characteristic requirements of traffic participants in traffic organization design;
the utility value calculation module is used for calculating the data according to a pre-constructed decision model based on an analytic hierarchy process to obtain the utility value of the road traffic organization scheme; the decision model comprises a plurality of primary indexes, the primary indexes are the dimensionality of the acquired data, and each primary index comprises a plurality of secondary indexes; the primary indicator is configured to: traffic efficiency, traffic safety, operational order and human factor; the traffic efficiency is configured to: motor vehicle delay time, motor vehicle parking times, non-motor vehicle delay time and pedestrian delay time; the traffic safety is configured to: vehicle-vehicle conflict, vehicle-non-vehicle conflict, non-vehicle-non-vehicle conflict, vehicle-pedestrian conflict, and non-vehicle-pedestrian conflict; the order of operation is configured to: motor vehicle law violation rate, non-motor vehicle law violation rate and pedestrian law violation rate; the human factor is configured to: the motor vehicles pass according to the indication, the non-motor vehicles stop according to the indication, and pedestrians pass according to the indication and wait according to the indication; acquiring data of the road traffic organization scheme in multiple dimensions comprises the following steps: acquiring data of a secondary index in the decision model; when the decision model is constructed, the method further comprises the following steps: respectively constructing a primary index and a judgment matrix of a secondary index in each primary index; calculating to obtain the relative weight of each primary index and the relative weight of each secondary index in each primary index according to the judgment matrix; the step of calculating the data according to a pre-constructed decision model based on an analytic hierarchy process to obtain the utility value of the road traffic organization scheme comprises the following steps: carrying out normalization processing on the data to obtain a normalized value; calculating a utility value according to the normalized value and the relative weight of the secondary index corresponding to each data; the calculation formula is as follows:
Z＝-t 11 V 11 -t 12 V 12 -t 13 V 13 -t 14 V1 4 -t 21 V 21 -t 22 V 22 -t 23 V 23 -t 24 V 24 -t 25 V 25 -t 31 V 31 -t 32 V 32 -t 33 V 33 -t 41 V 41 -t 42 V 42 -t 43 V 43 -t 44 V 44 -t 45 V 45 ；
wherein Z is the utility value, t 11 Delay time for motor vehicle, t 12 Number of motor vehicle stops, t 13 For time delay of non-motor vehicles, t 14 Delaying the time for the pedestrian; t is t 21 For a motor vehicle-motor vehicle collision, t 22 For motor vehicle-non-motor vehicle conflicts, t 23 For non-motor-non-motor conflict, t 24 For a motor vehicle-pedestrian conflict, t 25 Is a non-motor-vehicle-pedestrian conflict; t is t 31 As rate of violation of the motor vehicle, t 32 As illegal rate of non-motor vehicles, t 33 Is the pedestrian violation rate, t 41 For motor vehicles to pass by as indicated, t 42 For non-motor vehicles to pass by as indicated, t 43 For non-motor vehicles stopping as indicated, t 44 For the pedestrian to pass according to the indication t 45 Waiting for the pedestrian according to the indication; v 11 ，V 12 ，V 13 ，V 14 ，V 21 ，V 22 ，V 23 ，V 24 ，V 25 ，V 31 ，V 32 ，V 33 ，V 41 ，V 42 ，V 43 ，V 44 And V 45 Respectively correspond to the above t 11 ，t 12 ，t 13 ，t 14 ，t 21 ，t 22 ，t 23 ，t 24 ，t 25 ，t 31 ，t 32 ，t 33 ，t 41 ，t 42 ，t 43 ，t 44 And t 45 Normalized secondary index values of (a);
the utility value comparison module is used for comparing the utility value with a preset value;
and the scheme optimization module is used for optimizing the road traffic organization scheme according to the decision model if the utility value is lower than a preset value.
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