CN116307260B - Urban road network toughness optimization method and system for disturbance of defective road sections - Google Patents
Urban road network toughness optimization method and system for disturbance of defective road sections Download PDFInfo
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Abstract
The invention relates to the technical field of urban road network characteristic analysis, in particular to an urban road network toughness optimization method and system for disturbance of a defective road section, wherein step S1 provides an urban road network toughness evaluation index, the road network toughness optimization method provided by step S3 can consider a plurality of defective road sections, macroscopically regulate and control the traffic capacity of the whole road network, thereby improving the road network toughness, step S2 provides traffic flow simulation and verification feasibility, can provide a new technology and a new method for urban road network toughness optimization, has important theoretical significance and application value in the aspects of soundness, perfection of an emergency response mechanism of the urban road network for handling sudden events and the like, and is beneficial to improving the urban road network toughness, enhancing the bearing capacity of the urban road network and the risk prevention and control capacity.
Description
Technical Field
The invention relates to the technical field of urban road network characteristic analysis, in particular to an urban road network toughness optimization method and system for disturbance of a defective road section.
Background
The urban road network has the problems of weak network resistance, long recovery time, insufficient prevention and regulation means and the like under the disturbance of risk factors. Meanwhile, the urban road network has defect road sections with different modes, and when the demand exceeds the supply capacity, the urban road network is easily broken down in cascade, so that the urban development is severely restricted. Under the background, the toughness of the urban road network is improved, the bearing capacity and the risk prevention and control capacity of the urban road network are enhanced, and the urban road network becomes an urgent requirement for current urban construction. However, the current related studies still have the following limitations:
much research focuses on robustness and redundancy of urban road networks, as well as adaptability and recoverability of urban road networks in the face of a series of bursty conditions caused by defective areas. Most of the optimization methods are mainly focused on the physical structure of the urban road network, and network toughness optimization research based on the multi-point dynamic interference of the defective road sections is not common. Moreover, the urban road network structure is complicated, the network optimization search space is huge, and the problem of solving the road network toughness optimization is more challenging.
The existing urban road network toughness optimization method and system for disturbance of a defective road section need human intervention when traffic is abnormally interrupted, most of traffic conditions are regulated and controlled according to subjective experience, a global unified optimization method is lacked, a plurality of road defect areas cannot be considered, the whole urban road network toughness optimization cannot be realized, and the use is inconvenient, so that aiming at the current situation, the development of the urban road network toughness optimization method and system for disturbance of the defective road section is urgently needed, and the defects in the current practical application are overcome.
Disclosure of Invention
The invention aims to provide an urban road network toughness optimization method and system for disturbance of a defective road section, which are used for solving the problems that when abnormal traffic interruption occurs, the traffic condition needs human intervention, is mostly regulated and controlled according to subjective experience, a global unified optimization method is lacking, and the whole urban road network toughness optimization cannot be realized in a plurality of road defective areas.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a city road network toughness optimization method facing disturbance of a defective road section comprises the following steps:
s1, setting an urban road network toughness evaluation index:
calculating the road network traffic capacity at the initial moment, the disturbance occurrence moment, the moment when the traffic capacity is reduced to the lowest degree and the moment when the traffic capacity is restored to the normal state, which are disturbed by the disturbance of the defective road section, and finally obtaining the urban road network toughness evaluation index R by comprehensively considering the ratio of the traffic capacity under the disturbance of the defective area to the traffic capacity in the normal state and the duration time of the abnormal state and the restoring state;
s2, simulating traffic flow:
assuming that the travel demand of the vehicle remains unchanged before and after disturbance of a defect area, assuming that the traffic flow distribution meets the balance condition of random users, randomly selecting N defect road sections, and constructing a traffic flow distribution method after road network optimization based on the assumption;
s3, constructing a road network toughness optimization model based on Bayesian graph optimization, and finally obtaining an optimization result.
As a further scheme of the invention: in step S1, a variable is setRespectively representing the initial moment, the disturbance occurrence moment, the moment when the traffic capacity is reduced to the lowest degree and the moment when the traffic capacity is restored to the normal state, which are disturbed by the disturbance of the defective road section;
respectively representing the road network traffic capacity at the corresponding moment.
As a further scheme of the invention: the calculation formula of the urban road network toughness evaluation index R is as follows:
；
wherein N represents the number of defective road segments;representing a defective road segment among the N defective road segments; />Is a natural constant; />Respectively represent defective road sections->The initial moment of disturbance, the moment of disturbance occurrence, the moment of capacity reduction to the lowest degree and the moment of restoration to the normal state; />For all defective road sections->The affected road section initial traffic capacity; />For all defective road sections->Influence the traffic capacity of a road section over time +.>A function of the change;indicated at defective road section->The traffic capacity of all affected road sections under disturbance is at time +.>To->Is a function of the integral of (a).
As a further scheme of the invention: in step S3, the specific steps for constructing the road network toughness optimization model based on bayesian diagram optimization are as follows:
(1) The whole urban road network is represented by G (V, E, W), wherein V represents all road sections, E represents an adjacent matrix connected between the road sections, and W is the road network structure optimization regulation weight;
is provided withRespectively represent two road sections->Representing road section->Whether or not to communicate, if so->，/>；
Respectively represent road sections->The regulation weight under disturbance has a value range of [0-1 ]]When the value is 0, the traffic is completely forbidden, and when the value is 1, the highest traffic capacity is kept;
(2) Constructing a defective area with each defective road section as a center and its K-order neighboring nodesDomain subgraph toRepresenting defective road section->Subpicture of defect area for center, +.>A set of N defective region subgraphs;
(3) For a pair ofRandomly setting m groups of weight matrixes on all road sections in the road sections, wherein the weight of the road sections in other G is set to be 1;
according to the step S2, carrying out traffic flow simulation, according to the step S1, providing the urban road network toughness evaluation index to calculate, obtaining the corresponding toughness index, and obtaining an observation set；
(4) According to observed data, optimizing a weight matrix based on a Bayesian optimization principle, emptying an observation matrix O, and performingSubgraph->Adding the m sub-graphs to the candidate set O until m sub-graphs are selected;
(5) Circularly executing the steps (3) - (4) until R is larger than a specified threshold value or the iteration number exceeds the set number, and finally obtaining an optimized result。
The system comprises an urban road network toughness evaluation index setting module, a traffic flow simulation verification module and a road network toughness optimization model construction module based on Bayesian graph optimization; the method comprises the following steps:
the urban road network toughness evaluation index setting module is used for setting urban road network toughness evaluation indexes, and specifically comprises the following steps:
calculating the road network traffic capacity at the initial moment, the disturbance occurrence moment, the moment when the traffic capacity is reduced to the lowest degree and the moment when the traffic capacity is restored to the normal state, which are disturbed by the disturbance of the defective road section, and finally obtaining the urban road network toughness evaluation index R by comprehensively considering the ratio of the traffic capacity under the disturbance of the defective area to the traffic capacity in the normal state and the duration time of the abnormal state and the restoring state;
the traffic flow simulation verification module is used for carrying out traffic flow simulation and comprises the following specific steps:
assuming that the travel demand of the vehicle remains unchanged before and after disturbance of a defect area, assuming that the traffic flow distribution meets the balance condition of random users, randomly selecting N defect road sections, and constructing a traffic flow distribution method after road network optimization based on the assumption;
the road network toughness optimization model construction module based on the Bayesian graph optimization is used for constructing a road network toughness optimization model based on the Bayesian graph optimization, and finally an optimization result is obtained.
As a further scheme of the invention: when the urban road network toughness evaluation index setting module sets the urban road network toughness evaluation index, setting variablesRespectively representing the initial moment, the disturbance occurrence moment, the moment when the traffic capacity is reduced to the lowest degree and the moment when the traffic capacity is restored to the normal state, which are disturbed by the disturbance of the defective road section;
respectively representing the road network traffic capacity at the corresponding moment.
As a further scheme of the invention: the calculation formula of the urban road network toughness evaluation index R is as follows:
；
wherein N represents the number of defective road segments;representing a defective road segment among the N defective road segments; />Is a natural constant; />Respectively represent defective road sections->The initial moment of disturbance, the moment of disturbance occurrence, the moment of capacity reduction to the lowest degree and the moment of restoration to the normal state; />For all defective road sections->The affected road section initial traffic capacity; />For all defective road sections->Influence the traffic capacity of a road section over time +.>A function of the change;indicated at defective road section->The traffic capacity of all affected road sections under disturbance is at time +.>To->Is a function of the integral of (a).
As a further scheme of the invention: when the road network toughness optimization model construction module based on Bayesian graph optimization constructs a road network toughness optimization model based on Bayesian graph optimization, the specific steps are as follows:
(1) The whole urban road network is represented by G (V, E, W), wherein V represents all road sections, E represents an adjacent matrix connected between the road sections, and W is the road network structure optimization regulation weight;
is provided withRespectively represent two road sections->Representing road section->Whether or not to communicate, if so->，/>；
Respectively represent road sections->The regulation weight under disturbance has a value range of [0-1 ]]When the value is 0, the traffic is completely forbidden, and when the value is 1, the highest traffic capacity is kept;
(2) Constructing a defect region subgraph by taking each defect road section as a center and K-order adjacent nodes thereof toRepresenting defective road section->Subpicture of defect area for center, +.>A set of N defective region subgraphs;
(3) For a pair ofRandomly setting m groups of weight matrixes on all road sections in the road sections, wherein the weight of the road sections in other G is set to be 1;
according to the traffic flow simulation verification module, traffic flow simulation is carried out, according to the urban road network toughness evaluation index setting module, urban road network toughness evaluation indexes are proposed for calculation, corresponding toughness indexes are obtained, and an observation set is obtained；
(4) According to observed data, optimizing a weight matrix based on a Bayesian optimization principle, emptying an observation matrix O, and performingSubgraph->Adding the m sub-graphs to the candidate set O until m sub-graphs are selected;
(5) Circularly executing the steps (3) - (4) until R is larger than a specified threshold value or the iteration number exceeds the set number, and finally obtaining an optimized result。
Compared with the prior art, the invention has the beneficial effects that:
the road network toughness evaluation index is provided in the step S1, the road network toughness optimization method provided in the step S3 can consider a plurality of defective road sections, macroscopically regulate and control the traffic capacity of the whole road network, so that the road network toughness is improved, the traffic flow simulation and the verification feasibility are provided in the step S2, a new technology and a new method can be provided for the urban road network toughness optimization, and the road network toughness optimization method has important theoretical significance and application value in the aspects of soundness, perfection of emergency response mechanisms of the urban road network for emergencies and the like, so that the road network toughness is improved, the bearing capacity and the risk prevention and control capacity of the urban road network are enhanced, and the road network toughness optimization method is worth popularizing.
Drawings
Fig. 1 is a schematic diagram of road network toughness optimization based on bayesian diagram optimization in an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Specific implementations of the invention are described in detail below in connection with specific embodiments.
Referring to fig. 1, the method for optimizing the toughness of the urban road network for disturbance of a defective road section provided by the embodiment of the invention comprises the following steps:
s1, setting an urban road network toughness evaluation index:
calculating the road network traffic capacity at the initial moment, the disturbance occurrence moment, the moment when the traffic capacity is reduced to the lowest degree and the moment when the traffic capacity is restored to the normal state, which are disturbed by the disturbance of the defective road section, and finally obtaining the urban road network toughness evaluation index R by comprehensively considering the ratio of the traffic capacity under the disturbance of the defective area to the traffic capacity in the normal state and the duration time of the abnormal state and the restoring state;
s2, simulating traffic flow:
assuming that the travel demand of the vehicle remains unchanged before and after disturbance of a defect area, assuming that the traffic flow distribution meets the balance condition of random users, randomly selecting N defect road sections, and constructing a traffic flow distribution method after road network optimization based on the assumption;
s3, constructing a road network toughness optimization model based on Bayesian graph optimization, and finally obtaining an optimization result.
Wherein in step S1, a variable is setRespectively representing the initial moment, the disturbance occurrence moment, the moment when the traffic capacity is reduced to the lowest degree and the moment when the traffic capacity is restored to the normal state, which are disturbed by the disturbance of the defective road section;
respectively representing the road network traffic capacity at the corresponding moment.
The calculation formula of the urban road network toughness evaluation index R is as follows:
；
wherein N represents the number of defective road segments;representing a defective road segment among the N defective road segments; />Is a natural constant; />Respectively represent defective road sections->The initial moment of disturbance, the moment of disturbance occurrence, the moment of capacity reduction to the lowest degree and the moment of restoration to the normal state; />For all defective road sections->The affected road section initial traffic capacity; />For all defective road sections->Influence the traffic capacity of a road section over time +.>A function of the change;indicated at defective road section->The traffic capacity of all affected road sections under disturbance is at time +.>To->Is a function of the integral of (a).
In step S3, the specific steps of constructing the road network toughness optimization model based on bayesian diagram optimization are as follows:
(1) The whole urban road network is represented by G (V, E, W), wherein V represents all road sections, E represents an adjacent matrix connected between the road sections, and W is the road network structure optimization regulation weight;
is provided withRespectively represent two road sections->Representing road section->Whether or not to communicate, if so->，/>；
Respectively represent road sections->The regulation weight under disturbance has a value range of [0-1 ]]When the value is 0, the traffic is completely forbidden, and when the value is 1, the highest traffic capacity is kept;
(2) Constructing a defect region subgraph by taking each defect road section as a center and K-order adjacent nodes thereof toRepresenting defective road section->Subpicture of defect area for center, +.>A set of N defective region subgraphs;
(3) For a pair ofRandomly setting m groups of weight matrixes on all road sections in the road sections, wherein the weight of the road sections in other G is set to be 1;
according to the step S2, carrying out traffic flow simulation, according to the step S1, providing the urban road network toughness evaluation index to calculate, obtaining the corresponding toughness index, and obtaining an observation set；
(4) According to observed data, optimizing a weight matrix based on a Bayesian optimization principle, emptying an observation matrix O, and performingSubgraph->Adding the m sub-graphs to the candidate set O until m sub-graphs are selected;
(5) Circularly executing the steps (3) - (4) until R is larger than a specified threshold value or the iteration number exceeds the set number, and finally obtaining an optimized result。
The system comprises an urban road network toughness evaluation index setting module, a traffic flow simulation verification module and a road network toughness optimization model construction module based on Bayesian graph optimization; the method comprises the following steps:
the urban road network toughness evaluation index setting module is used for setting urban road network toughness evaluation indexes, and specifically comprises the following steps:
calculating the road network traffic capacity at the initial moment, the disturbance occurrence moment, the moment when the traffic capacity is reduced to the lowest degree and the moment when the traffic capacity is restored to the normal state, which are disturbed by the disturbance of the defective road section, and finally obtaining the urban road network toughness evaluation index R by comprehensively considering the ratio of the traffic capacity under the disturbance of the defective area to the traffic capacity in the normal state and the duration time of the abnormal state and the restoring state;
the traffic flow simulation verification module is used for carrying out traffic flow simulation and comprises the following specific steps:
assuming that the travel demand of the vehicle remains unchanged before and after disturbance of a defect area, assuming that the traffic flow distribution meets the balance condition of random users, randomly selecting N defect road sections, and constructing a traffic flow distribution method after road network optimization based on the assumption;
the road network toughness optimization model construction module based on the Bayesian graph optimization is used for constructing a road network toughness optimization model based on the Bayesian graph optimization, and finally an optimization result is obtained.
Wherein, when the urban road network toughness evaluation index setting module sets the urban road network toughness evaluation index, a variable is setRespectively representing the initial moment, the disturbance occurrence moment, the moment when the traffic capacity is reduced to the lowest degree and the moment when the traffic capacity is restored to the normal state, which are disturbed by the disturbance of the defective road section;
respectively representing the road network traffic capacity at the corresponding moment.
The calculation formula of the urban road network toughness evaluation index R is as follows:
；
wherein N represents the number of defective road segments;representing a defective road segment among the N defective road segments; />Is a natural constant; />Respectively represent defective road sections->The initial moment of disturbance, the moment of disturbance occurrence, the moment of capacity reduction to the lowest degree and the moment of restoration to the normal state; />For all defective road sections->The affected road section initial traffic capacity; />For all defective road sections->Influence the traffic capacity of a road section over time +.>A function of the change;indicated at defective road section->The traffic capacity of all affected road sections under disturbance is at time +.>To->Is a function of the integral of (a).
When the road network toughness optimization model construction module based on Bayesian graph optimization constructs a road network toughness optimization model based on Bayesian graph optimization, the specific steps are as follows:
(1) The whole urban road network is represented by G (V, E, W), wherein V represents all road sections, E represents an adjacent matrix connected between the road sections, and W is the road network structure optimization regulation weight;
is provided withRespectively represent two road sections->Representing road section->Whether or not to communicate, if so->，/>；
Respectively represent road sections->The regulation weight under disturbance has a value range of [0-1 ]]When the value is 0, the traffic is completely forbidden, and when the value is 1, the highest traffic capacity is kept;
(2) Constructing a defect region subgraph by taking each defect road section as a center and K-order adjacent nodes thereof toRepresenting defective road section->Subpicture of defect area for center, +.>A set of N defective region subgraphs;
(3) For a pair ofRandomly setting m groups of weight matrixes on all road sections in the road sections, wherein the weight of the road sections in other G is set to be 1;
according to the traffic flow simulation verification module, traffic flow simulation is carried out, according to the urban road network toughness evaluation index setting module, urban road network toughness evaluation indexes are proposed for calculation, corresponding toughness indexes are obtained, and an observation set is obtained；
(4) According to observed data, optimizing a weight matrix based on a Bayesian optimization principle, emptying an observation matrix O, and performingSubgraph->Adding the m sub-graphs to the candidate set O until m sub-graphs are selected;
(5) Circularly executing the steps (3) - (4) until R is larger than a specified threshold value or the iteration number exceeds the set number, and finally obtaining an optimized result。
The invention provides an urban road network toughness optimization method for disturbance of a defective road section. Firstly, considering dynamic concurrent disturbance factors of traffic defect road sections to the traffic capacity of the whole road network, and providing an urban road network toughness evaluation index; after that, a road network toughness optimization network based on Bayesian graph optimization is constructed. The method has important significance for improving the toughness of the urban road network and enhancing the bearing capacity and the risk prevention and control capacity of the urban road network.
It should be noted that, in the present invention, it should be understood that, although the present disclosure describes embodiments, not every embodiment includes only a single embodiment, and this description is for clarity only, and those skilled in the art should consider the present disclosure as a whole, and the embodiments of the present disclosure may be combined appropriately to form other embodiments that can be understood by those skilled in the art.
Claims (6)
1. A city road network toughness optimization method facing disturbance of a defective road section is characterized by comprising the following steps:
s1, setting an urban road network toughness evaluation index:
calculating the road network traffic capacity at the initial moment, the disturbance occurrence moment, the moment when the traffic capacity is reduced to the lowest degree and the moment when the traffic capacity is restored to the normal state, which are disturbed by the disturbance of the defective road section, and finally obtaining the urban road network toughness evaluation index R by comprehensively considering the ratio of the traffic capacity under the disturbance of the defective area to the traffic capacity in the normal state and the duration time of the abnormal state and the restoring state;
s2, simulating traffic flow:
assuming that the travel demand of the vehicle remains unchanged before and after disturbance of a defect area, assuming that the traffic flow distribution meets the balance condition of random users, randomly selecting N defect road sections, and constructing a traffic flow distribution method after road network optimization based on the assumption;
s3, constructing a road network toughness optimization model based on Bayesian graph optimization, and finally obtaining an optimization result;
in step S3, the specific steps for constructing the road network toughness optimization model based on bayesian diagram optimization are as follows:
(1) The whole urban road network is represented by G (V, E, W), wherein V represents all road sections, E represents an adjacent matrix connected between the road sections, and W is the road network structure optimization regulation weight;
is provided with，/>Respectively represent two road sections->，/>Representing road section->Whether or not to communicate, if so=1, otherwise->=0；
，/>Respectively represent road sections->The regulation weight under disturbance has a value range of [0,1 ]]When the value is 0, the traffic is completely forbidden, and when the value is 1, the highest traffic capacity is kept;
(2) Constructing a defect region subgraph by taking each defect road section as a center and K-order adjacent nodes thereof toRepresenting defective road section->Defects as the centerRegional subgraph->A set of N defective region subgraphs;
(3) For a pair ofRandomly setting m groups of weight matrixes on all road sections in the road sections, wherein the weight of the road sections in other G is set to be 1;
according to the step S2, carrying out traffic flow simulation, according to the step S1, providing the urban road network toughness evaluation index to calculate, obtaining the corresponding toughness index, and obtaining an observation set；
(4) According to observed data, optimizing a weight matrix based on a Bayesian optimization principle, emptying an observation set O, and performingSubgraph->Adding to the observation set O until m subgraphs are selected again, wherein +.>Subgraph after updating weight matrix +.>Is an urban road network toughness evaluation index;
(5) Circularly executing the steps (3) - (4) until R is larger than a specified threshold value or the iteration number exceeds the set number, and finally obtaining an optimized result。
2. Urban road for disturbance of defective road section according to claim 1A method for optimizing the toughness of a web, characterized in that in step S1, variables are setRespectively representing the initial moment, the disturbance occurrence moment, the moment when the traffic capacity is reduced to the lowest degree and the moment when the traffic capacity is restored to the normal state, which are disturbed by the disturbance of the defective road section;
respectively representing the road network traffic capacity at the corresponding moment.
3. The urban road network toughness optimization method for disturbance of defective road segments according to claim 2, wherein the calculation formula of the urban road network toughness evaluation index R is as follows:
；
wherein ,representing the number of defective road segments; />Representing a defective road segment among the N defective road segments; />Is a natural constant;respectively represent defective road sections->The initial moment of disturbance, the moment of disturbance occurrence, the moment of capacity reduction to the lowest degree and the moment of restoration to the normal state; />For all defective road sections->The affected road section initial traffic capacity; />For all defective road sections->Influence the traffic capacity of a road section over time +.>A function of the change; />Indicated at defective road section->The traffic capacity of all affected road sections under disturbance is at time +.>To->Is a function of the integral of (a).
4. The urban road network toughness optimization system for the disturbance of the defective road section is characterized by comprising an urban road network toughness evaluation index setting module, a traffic flow simulation verification module and a road network toughness optimization model construction module based on Bayesian graph optimization; the method comprises the following steps:
the urban road network toughness evaluation index setting module is used for setting urban road network toughness evaluation indexes, and specifically comprises the following steps:
calculating the road network traffic capacity at the initial moment, the disturbance occurrence moment, the moment when the traffic capacity is reduced to the lowest degree and the moment when the traffic capacity is restored to the normal state, which are disturbed by the disturbance of the defective road section, and finally obtaining the urban road network toughness evaluation index R by comprehensively considering the ratio of the traffic capacity under the disturbance of the defective area to the traffic capacity in the normal state and the duration time of the abnormal state and the restoring state;
the traffic flow simulation verification module is used for carrying out traffic flow simulation and comprises the following specific steps:
assuming that the travel demand of the vehicle remains unchanged before and after disturbance of a defect area, assuming that the traffic flow distribution meets the balance condition of random users, randomly selecting N defect road sections, and constructing a traffic flow distribution method after road network optimization based on the assumption;
the road network toughness optimization model construction module based on Bayesian graph optimization is used for constructing a road network toughness optimization model based on Bayesian graph optimization, and finally an optimization result is obtained;
when the road network toughness optimization model construction module based on Bayesian graph optimization constructs a road network toughness optimization model based on Bayesian graph optimization, the specific steps are as follows:
(1) The whole urban road network is represented by G (V, E, W), wherein V represents all road sections, E represents an adjacent matrix connected between the road sections, and W is the road network structure optimization regulation weight;
is provided with，/>Respectively represent two road sections->Representing road section->Whether or not to communicate, if so=1, otherwise->=0；
Respectively represent road sections->The regulation weight under disturbance has a value range of [0,1 ]]When the value is 0, the traffic is completely forbidden, and when the value is 1, the highest traffic capacity is kept;
(2) Constructing a defect region subgraph by taking each defect road section as a center and K-order adjacent nodes thereof toRepresenting defective road section->Subpicture of defect area for center, +.>A set of N defective region subgraphs;
(3) For a pair ofRandomly setting m groups of weight matrixes on all road sections in the road sections, wherein the weight of the road sections in other G is set to be 1;
according to the traffic flow simulation verification module, traffic flow simulation is carried out, according to the urban road network toughness evaluation index setting module, urban road network toughness evaluation indexes are proposed for calculation, corresponding toughness indexes are obtained, and an observation set is obtained；
(4) According to observed data, optimizing a weight matrix based on a Bayesian optimization principle, emptying an observation set O, and performingSubgraph->Adding to the observation set O until m subgraphs are selected again, wherein +.>Subgraph after updating weight matrix +.>Is an urban road network toughness evaluation index;
(5) Circularly executing the steps (3) - (4) until R is larger than a specified threshold value or the iteration number exceeds the set number, and finally obtaining an optimized result。
5. The system for optimizing urban road network toughness for disturbance of defective road segment according to claim 4, wherein the urban road network toughness evaluation index setting module sets a variable when setting an urban road network toughness evaluation indexRespectively representing the initial moment, the disturbance occurrence moment, the moment when the traffic capacity is reduced to the lowest degree and the moment when the traffic capacity is restored to the normal state, which are disturbed by the disturbance of the defective road section;
respectively representing the road network traffic capacity at the corresponding moment.
6. The urban road network toughness optimization system for disturbance on defective road segments according to claim 5, wherein the calculation formula of the urban road network toughness evaluation index R is as follows:
；
wherein ,representing the number of defective road segments; />Representing a defective road segment among the N defective road segments; />Is a natural constant;respectively represent defective road sections->The initial moment of disturbance, the moment of disturbance occurrence, the moment of capacity reduction to the lowest degree and the moment of restoration to the normal state; />For all defective road sections->The affected road section initial traffic capacity; />For all defective road sections->Influence the traffic capacity of a road section over time +.>A function of the change; />Indicated at defective road section->The traffic capacity of all affected road sections under disturbance is at time +.>To->Is a function of the integral of (a).
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