CN112085949B - Road network vulnerability identification, analysis and coping method based on traffic running condition abnormality - Google Patents
Road network vulnerability identification, analysis and coping method based on traffic running condition abnormality Download PDFInfo
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Abstract
The invention relates to a road network vulnerability recognition, analysis and coping method based on traffic running condition abnormality. The invention utilizes the time sequence characteristic data of the traffic network to construct an abnormal region, analyzes and identifies the fragile region according to the quantized index change of the road network object in the abnormal region, reduces the defect of human subjectivity during traffic control, improves the analysis accuracy of the road network vulnerability, provides active countermeasures, and provides important data support for traffic control.
Description
Technical Field
The invention relates to the field of intelligent traffic, in particular to a road network vulnerability identification, analysis and coping method based on traffic running condition abnormality.
Background
The urban road transportation network is affected by human or natural random events, which can cause the road network to lose part or all of connectivity, further cause the performance and service level of the transportation network to be reduced, and even cause the situation that the transportation network is partially or completely paralyzed. The continuous decline of the performance and the service level of the transportation network not only can reduce the social and economic benefits, cause large-area and frequent traffic jams, but also can trigger a series of chain reactions.
However, most of the current identification and analysis of the vulnerable links of the road traffic network are to analyze the vulnerability of the road network on the condition that a certain road section or intersection meets the vulnerable conditions, and the situation that the vulnerable links of the road network are missed can be generated due to the limitation that the vulnerability analysis is analogically performed to the surrounding road network objects and the historical vulnerable areas.
In the aspect of model construction, the current research often simplifies an actual road network model into a graph theory network, and various characteristics of the road network can be analyzed strictly and finely by applying a small amount of road network data. However, the analysis is easy to ignore specific scenes in the real road traffic network, and the analysis result of the model is inconsistent with reality.
Therefore, a feedback mechanism and measures for analyzing, identifying and coping with the vulnerability of the road network based on the time sequence characteristic data of the road network and the topology property of the actual road network are needed to be constructed.
Disclosure of Invention
The invention aims to overcome the defects, and aims to provide a road network vulnerability identification, analysis and coping method based on traffic running condition abnormality. The traffic condition abnormal region is obtained by comprehensively utilizing the road network topology attribute and the time sequence characteristic data analysis, and the road network vulnerability links are identified and analyzed according to indexes such as service capacity, loss accumulation and the like, so that the defects of artificial subjectivity in the vulnerability link identification in a road network system are reduced, the accuracy of the vulnerability link identification is improved, active countermeasures are provided, and important data support is provided for traffic management.
The invention achieves the aim through the following technical scheme:
a method for identifying, analyzing and coping with vulnerability of road network based on abnormal traffic running condition, the method comprising the following steps:
(1) The fragile region identification based on the multi-source data is as follows:
(1.1) multisource, multi-period data preprocessing: acquiring road network time sequence characteristic data, judging according to the quality of the road network time sequence characteristic data, and performing data cleaning and data repairing by adopting a denoising, repairing and predicting mode;
(1.2) traffic flow modification characteristics and disturbance event analysis;
(1.3) road network attribute analysis;
(1.4) analysis of the evolution process of the abnormal region;
(2) The multi-index based region attribute analysis is as follows:
(2.1) establishing a multi-index evaluation system;
(2.2) quantitative analysis of regional variations;
(3) The process of constructing an active coping mechanism and establishing a daily measure and emergency mechanism is as follows:
(3.1) for the determined dominant fragile area, the dominant fragile area is regulated and controlled and treated mainly by daily measures such as daily regulation and control, area treatment, actual dredging and the like, and the change of the service capacity of the road network after treatment and the change of the actual traffic condition are optimized and iterated by methods such as artificial learning, machine learning and the like;
(3.2) for the hidden fragile area, the possible road network destructive event is prevented mainly through the establishment of a prevention scheme, the establishment of emergency facilities and the establishment of a remedy scheme, and the emergency coping capability of the emergency event can be improved through a series of simulation tests.
Further, the step (1.2) comprises the steps of:
(1.2.1) selecting road network self time sequence traffic data and abnormal interference events, wherein the time sequence traffic data comprises road section flow, road section speed, road section saturation, road section efficiency index and road section delay index; abnormal interference events include congestion events, sudden traffic accidents, road maintenance, bad weather and human damage;
(1.2.2) researching the change characteristics of traffic flow on a continuous time axis and a discontinuous time axis according to the road section time sequence characteristic data, and acquiring a traffic running condition abnormal region or a traffic running condition abnormal road section/intersection object, wherein the preliminary candidate is traffic running condition abnormal envelope;
still further, the step (1.3) comprises the steps of:
(1.3.1) analyzing the topology attribute by improving the network efficiency principle, wherein the network efficiency and the road network efficiency are calculated as follows:
wherein C is ij D is the traffic capacity size between node i and node j ij E is the shortest path between node i and node j ij Network efficiency between node i and node j; n is the total node number in the area, C a The importance degree of the node in the network is reflected for the medium number centrality of the node;
the road network node betweenness centrality is calculated as follows:
wherein n is jk For the sum of all shortest paths between every two intersections in the network,
the number of crossing i in all shortest paths; b (B) i The intersection betweenness is N, and the total number of regional intersections is N;
(1.3.2) carrying out normalization processing on the congestion group envelope alarm data according to the alarm quantity to obtain the alarm specific gravity of each intersection/road section object in the group envelope;
(1.3.3) redefining network performance, the calculation is as follows:
and recalculating the network efficiency according to the formula, and displaying the result on a map.
Still further, the step (1.4) includes the steps of:
(1.4.1) calculating the distribution of the deviation of the position and the vulnerable road sections in different time periods of the congestion group envelope according to the step (1.3);
(1.4.2) calculating the change period/rule and the distribution sites of different time periods according to the deviation of the positions of the congestion group envelopes in different time periods and the distribution of the vulnerable road sections;
and (1.4.3) calculating the positions of the envelopes and the distribution of the fragile links under symmetrical events according to the deviation of the positions of the different time periods of the envelopes of the congestion groups and the distribution of the fragile links, wherein the symmetrical events comprise peaks in the morning and evening, opening and closing of a market and limitation of the traffic.
The process of (2.1) is as follows: according to the road network construction stage and the stable stage, respectively constructing a road network service capability change model under the two conditions of extremely fast reduction of road network service capability caused by natural disasters and reduction of road network service capability caused by aging along with infrastructure;
the step (2.1) comprises the following steps:
and (2.1.1) carrying out flow distribution calculation on the congestion envelope according to the optimal principle of the Wardrop user, wherein the calculation formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is the impedance of the kth path between the point pairs (r, s), U rs For the minimum impedance between the pairs of points (r, s),
impedance of the kth path between the pair of points (r, s);
(2.1.2) calculating the total impedance in the traffic network according to the optimal principle of the Wardrop system, wherein the calculation is shown as follows:
t a ＝t 0a (1+α(x α /k a ) β )
wherein Z is the total impedance between OD points, xa is the traffic flow on segment a, and ta is the impedance function of segment a, so t a (x a ) An impedance function taking flow as an independent variable for a road section a; q rs For the traffic flow of the OD pairs between the pairs (r, s),
as a path-dependent argument, if the section a is on the kth path between (r, s), +.>
1, otherwise 0; t is t 0a Zero flow impedance (free epidemic travel time) for road segment, x α For traffic flow, k on road section alpha in user equilibrium state a The actual traffic capacity of the road section is represented, and alpha and beta are given model parameters which are respectively 0.15 and 4;
(2.1.3) calculating the minimum value of the road network impedance according to the fastest descent method of the optimization theory, wherein the calculation steps are as follows:
(2.1.3.1): given an initial set
For initial impedance value, the error ε is allowed>0, set k=1;
(2.1.3.2): the direction of the search is calculated and,
(2.1.3.3): if d (k) ||<Epsilon, stopping; conversely, from x (k) Starting from the edge d (k) One-dimensional search is performed to find lambda k So that
(2.1.3.4): let x (k+1) ＝x (k) +λd (k) K=k+1, turn (2.2.3.2);
and (2.1.4) analyzing the change trend of the service capability of the road network according to the influence of the event on the road network, wherein the situation that the traffic capability of the road section is set to zero can be regarded as complete failure of the road section, and the effect is equivalent to removal from a map.
The step (2.2) comprises the following steps:
(2.2.1) calculating and explaining the upper bound of the regional service capability under the three conditions of early and late peak, flat peak and simulated road section failure by taking the road network flow as the data calculation basis;
and (2.2.2) constructing a service capability upper bound change model according to the road network service capability change condition.
The step (2.2.2) comprises the following steps:
(2.2.2.1) according to the characteristics that the traffic density of the network is increased, the traffic is increased, but the overall speed is reduced, the overall impedance of the road section is increased, the actual traffic effect of the road section is greatly beyond the theoretical traffic capacity, and the like, the impedance derivative is as follows:
wherein k' a The average flow rate of each road section of the peak in the morning and evening is, therefore, the expansion amount of the total impedance of the region is shown as follows on the assumption that the impedance function is unchanged:
the increase of the road network impedance is equivalently converted into the decrease of the road network service capability according to the set conversion coefficient, so that the decrease of the road network service capability is shown as the following formula on the premise that the default impedance function is not changed:
wherein phi is the conversion coefficient of impedance and service capability;
(2.2.2.2) for the simulated road section failure situation, for the accurate OD point pair, the available road section number is reduced, the user distributes to the rest road sections according to the user optimal principle, when the road network reaches the balanced state again, the user optimal and system optimal principle can know that the minimum value of the total impedance of the traffic road network is increased compared with the original value, and the optimal travel time is increased;
similarly, on the basis that the impedance function of the road sections is unchanged and the traffic capacity of each road section is not obviously different, N is recorded as the number of original regional total road sections, N is the number of invalid road sections, namely the regional total impedance is derived as follows:
the area service capability is reduced as:
(Z(X)”-Z(X))*Φ
the step (2.2) further comprises the steps of:
(2.2.3) for determination of road network service capability, further determination may also be made using the resulting cumulative loss over the selected time period. According to the user balancing principle and the system optimal principle, when a road network reaches an equilibrium state, the total impedance and the user forming time reach the minimum value, so that the user can form a loss value in time to describe the total loss caused by the road network to a certain extent;
for a general road network system, the road network parking time in a specific time period can be used as the waste time and the lost time formed by vehicles in a road network object, and the accumulated loss of the road network is determined by comparing a series of lost time.
The invention has the beneficial effects that: the traffic condition abnormal region is obtained by comprehensively utilizing the road network topology attribute and the time sequence characteristic data analysis, and the road network vulnerability links are identified and analyzed according to indexes such as service capacity, loss accumulation and the like, so that the defects of artificial subjectivity in the vulnerability link identification in a road network system are reduced, the accuracy of the vulnerability link identification is improved, active countermeasures are provided, and important data support is provided for traffic management.
Drawings
FIG. 1 is a general technical flow diagram;
FIG. 2 is a continuous time velocity profile;
FIG. 3 is a line graph of the continuous variation of the speed at the early peak stage;
fig. 4 is a schematic diagram of congestion group envelope formation;
FIG. 5 is a schematic diagram of a road network vulnerability;
FIG. 6 is a schematic diagram of the early-late peak congestion cluster vulnerability profile;
FIG. 7 is a road network service capability change under the influence of a major disaster;
FIG. 8 is a general road network service capability degradation scenario;
fig. 9 is a detailed flow chart of the proactive approach mechanism.
Detailed Description
The invention will be further described with reference to the following specific examples, but the scope of the invention is not limited thereto:
referring to fig. 1 to 9, in an actual road network system, under the influence of human beings or natural random events, the road network loses part or all of connectivity, so that the performance and service level of the traffic and transportation network are reduced, and even the traffic and transportation network is partially or completely paralyzed. Therefore, the fragile links of the current road network need to be analyzed and identified. However, the road network vulnerability analysis is performed by focusing attention on road segments or intersections, and the limitation of analogizing the vulnerability analysis to surrounding road network objects and historical vulnerable areas is lacking, so that the situation of missing the road network vulnerable links occurs. The actual road network model is simplified into a graph theory network, and various characteristics of the road network can be analyzed strictly and finely by applying a small amount of road network data. However, the analysis is easy to ignore specific scenes in the real road traffic network, and the analysis result of the model is inconsistent with reality. Based on the above situation, the invention provides a method for combining road network characteristic time sequence data and road network topology properties, aiming at the change rule and trend of road traffic network service capability, identifying the vulnerable area of the road network, analyzing the vulnerable area according to the attribute change such as the service capability and the like, and pertinently providing corresponding measures. As shown in fig. 1, a method for identifying a vulnerable area of a road network and analyzing the vulnerable area according to attribute changes such as service capacity of the vulnerable area by combining road network characteristic time sequence data and road network topology properties and aiming at the change rule and trend of the service capacity of a road traffic network, and specifically providing a countermeasure, the method comprises the following steps:
(1) The fragile region identification based on the multi-source data is as follows:
(1.1) multisource, multi-period data preprocessing: acquiring road network time sequence characteristic data, judging according to the quality of the road network time sequence characteristic data, and performing data cleaning and data repairing by adopting a denoising, repairing and predicting mode;
(1.2) traffic flow change characteristics and interference event analysis, wherein the process is as follows;
(1.2.1) selecting road network self time sequence traffic data and abnormal interference events, wherein the time sequence traffic data comprises but is not limited to: road traffic, road speed, road saturation, road efficiency index, road delay index; abnormal interference events include, but are not limited to: congestion events, sudden traffic accidents, road section maintenance, bad weather and artificial damage; for a certain fixed road network system, firstly, the traffic data produced by the road network system needs to be utilized for establishing a model. For the determined road network system or the road junction or road section individual in the system, the data change has the characteristics of continuity, non-sharp variability, predictability and the like on a continuous time axis; at the corresponding time points of the discontinuous time axis, the data change has the characteristics of regularity, similarity, periodicity and the like, the data change is shown in fig. 2 and 3, and the display objects are all speed data of a certain road section of the experimental area.
(1.2.2) researching the change characteristics of traffic flow on a continuous time axis and a discontinuous time axis according to the road section time sequence characteristic data, and acquiring a traffic running condition abnormal region or a traffic running condition abnormal road section/intersection object, wherein the preliminary candidate is traffic running condition abnormal envelope;
in this example, taking the Goldwarning data as an example, the warning data of a specific time span is selected, and the corresponding warning objects are subjected to envelope processing to be combined into a preliminary congestion envelope, as shown in fig. 4.
(1.3) road network attribute analysis, wherein the process is as follows;
(1.3.1) analyzing the topology attribute by improving the network efficiency principle, wherein the network efficiency and the road network efficiency are calculated as follows:
wherein C is ij D is the traffic capacity size between node i and node j ij E is the shortest path between node i and node j ij Network efficiency between node i and node j; for equations 1-2, N is the total number of nodes in the region, C a The degree of importance of a node in a network is reflected for the mid-level centrality of the node.
The road network node betweenness centrality is calculated as follows:
wherein n is jk For the sum of all shortest paths between every two intersections in the network,
the number of crossing i in all shortest paths; b (B) i The intersection betweenness is N, and the total number of regional intersections is N.
And (1.3.2) carrying out normalization processing on the congestion group envelope alarm data according to the alarm quantity to obtain the alarm specific gravity of each intersection/road section object in the group envelope.
In this embodiment, the number of alarms is counted on the data of the selected time for all the road sections in the area, and alarm normalization processing is performed, and the processing results are shown in the following table 1:
TABLE 1
(1.3.3) redefining network performance, the calculation is as follows:
the network efficiency is recalculated according to the formula, and the result is displayed on a map, wherein the result is shown in fig. 5, and the broken line marked road section is the fragile road section of the congestion group envelope at the time.
(1.4) analysis of an abnormal region evolution process, wherein the process is as follows:
(1.4.1) calculating the distribution of the deviation of the position and the vulnerable road sections in different time periods of the congestion group envelope according to the step (1.3);
(1.4.2) calculating the change period/rule and the distribution sites of different time periods according to the deviation of the positions of the congestion group envelopes in different time periods and the distribution of the vulnerable road sections;
(1.4.3) calculating the envelope position and the fragile link distribution under the symmetrical event according to the deviation of the positions of different time segments of the envelope of the congestion group and the distribution of the fragile link, wherein the symmetrical event comprises but is not limited to: the market is opened and closed in the morning and evening, and the traffic is limited.
In the embodiment, taking the experimental place as an example, the data from 6 points of the early peak to 9 points and 16 points of the late peak to 19 points are selected to be used as envelope groups, and the two groups have no great difference in the distribution places of the groups; in the division of the fragile road section, there are cases where the road sections are symmetrical or identical. As shown in FIG. 6, the cluster division diagram has more overlapping parts of fragile parts of early and late peaks, but the fragile parts in the cluster are more easily formed by the early and late peaks and the earlier peaks, and the fragile parts are increased.
(2) The multi-index based region attribute analysis is as follows:
(2.1) establishing a multi-index evaluation system, wherein the process is as follows: according to the road network construction stage and the stable stage, road network service capability extremely-fast reduction caused by natural disasters and road network service capability reduction caused by aging along with infrastructure, respectively constructing a road network service capability change model, wherein the service capability change is shown in fig. 7 and 8. The method comprises the following steps:
and (2.1.1) carrying out flow distribution calculation on the congestion envelope according to the optimal principle of the Wardrop user, wherein the calculation formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is the impedance of the kth path between the point pairs (r, s), U rs For the minimum impedance between the pairs of points (r, s),
is the impedance of the kth path between the pair of points (r, s).
(2.1.2) calculating the total impedance in the traffic network according to the optimal principle of the Wardrop system, wherein the calculation is shown as follows:
t a ＝t 0a (1+α(x α /k a ) β )
wherein Z is the total impedance between OD points, xa is the traffic flow on segment a, and ta is the impedance function of segment a, so t a (x a ) An impedance function taking flow as an independent variable for a road section a; q rs For the traffic flow of the OD pairs between the pairs (r, s),
as a path-dependent argument, if the section a is on the kth path between (r, s), +.>
1, otherwise 0; t is t 0a Zero flow impedance (free epidemic travel time) for road segment, x α For traffic flow, k on road section alpha in user equilibrium state a The actual traffic capacity of the road section is represented, alpha and beta are given model parameters, and the general values are respectively 0.15 and 4.
(2.1.3) calculating the minimum value of the road network impedance according to the fastest descent method of the optimization theory, wherein the calculation steps are as follows:
(2.1.3.1): given an initial set
For initial impedance value, the error ε is allowed>0, set k=1;
(2.1.3.2): the direction of the search is calculated and,
(2.1.3.3): if d (k) ||<Epsilon, stopping; conversely, from x (k) Starting from the edge d (k) One-dimensional search is performed to find lambda k So that
(2.1.3.4): let x (k+1) ＝x (k) +λd (k) K=k+1, turn (2.2.3.2);
and (2.1.4) analyzing the change trend of the service capability of the road network according to the influence of the event on the road network, wherein the situation that the traffic capability of the road section is set to zero can be regarded as complete failure of the road section, and the effect is equivalent to removal from a map.
(2.2) quantitative analysis of regional variations;
in the embodiment (2.2.1), the upper bound of the regional service capability is calculated and described for the three situations of peak, flat peak and simulated road section failure in the morning and evening by taking the traffic of the road network as the data calculation basis.
(2.2.2) constructing a service capability upper bound change model, wherein the process is as follows:
(2.2.2.1) according to the characteristics that the traffic density of the network is increased, the traffic is increased, but the overall speed is reduced, the overall impedance of the road section is increased, the actual traffic effect of the road section is greatly beyond the theoretical traffic capacity, and the like, the impedance derivative is as follows:
wherein k' a The average flow rate of each road section of the peak in the morning and evening is, therefore, the expansion amount of the total impedance of the region is shown as follows on the assumption that the impedance function is unchanged:
the increase of the road network impedance is equivalently converted into the decrease of the road network service capability according to the set conversion coefficient, so that the decrease of the road network service capability is shown as the following formula on the premise that the default impedance function is not changed:
where Φ is the scaling factor of impedance and service capability.
(2.2.2.2) for the simulated road segment failure situation, for the accurate OD point pair, the number of available road segments is reduced, users are distributed to the rest road segments according to the user optimal principle, when the road network reaches the balanced state again, the user optimal and system optimal principle can know that the minimum value of the total impedance of the traffic road network is increased compared with the original value, and the optimal travel time is increased.
Similarly, on the basis that the impedance function of the road sections is unchanged and the traffic capacity of each road section is not obviously different, N is recorded as the number of original regional total road sections, N is the number of invalid road sections, namely the regional total impedance is derived as follows:
the area service capability is reduced as:
(Z(X)”-Z(X))*Φ；
(2.2.3) for determination of road network service capability, further determination may also be made using the resulting cumulative loss over the selected time period. According to the user balancing principle and the system optimal principle, when a road network reaches an equilibrium state, the total impedance and the user forming time reach the minimum value, so that the user can form a loss value in time to describe the total loss caused by the road network to a certain extent;
for a general road network system, the road network parking time in a specific time period can be used as the waste time and the lost time formed by vehicles in a road network object, and the accumulated loss of the road network is determined by comparing a series of lost time.
(3) The process of constructing an active coping mechanism and establishing a daily measure and emergency mechanism is as follows:
(3.1) for the determined dominant fragile area, the dominant fragile area is regulated and controlled and treated mainly by daily measures such as daily regulation and control, area treatment, actual dredging and the like, the change of the service capacity of the road network after treatment and the change of the actual traffic condition are changed, the treatment content is optimized by methods such as artificial learning, machine learning and the like, and the specific implementation flow is shown in figure 9;
(3.2) for the hidden fragile area, the possible road network destructive event is prevented mainly through the establishment of a prevention scheme, the establishment of emergency facilities and the establishment of a remedy scheme, and the emergency coping capability of the emergency event can be improved through a series of simulation tests.
The foregoing is considered as illustrative of the principles of the present invention, and has been described herein before with reference to the accompanying drawings, in which the invention is not limited to the specific embodiments shown.
Claims (3)
1. The road network vulnerability identification, analysis and response method based on traffic running condition abnormality is characterized by comprising the following steps:
(1) The fragile region identification based on the multi-source data is as follows:
(1.1) multisource, multi-period data preprocessing: acquiring road network time sequence characteristic data, judging according to the quality of the road network time sequence characteristic data, and performing data cleaning and data repairing by adopting a denoising, repairing and predicting mode;
(1.2) traffic flow modification characteristics and disturbance event analysis;
(1.3) road network attribute analysis;
(1.4) analysis of the evolution process of the abnormal region;
(2) The multi-index based region attribute analysis is as follows:
(2.1) establishing a multi-index evaluation system;
(2.2) quantitative analysis of regional variations;
(3) The process of constructing an active coping mechanism and establishing a daily measure and emergency mechanism is as follows:
(3.1) for the determined dominant fragile area, the dominant fragile area is regulated and controlled and treated mainly by daily regulation and control, area treatment and daily measure of actual dredging, and the change of the service capacity of the road network after treatment and the change of the actual traffic condition are treated, and the treatment content is optimized and iterated by a manual learning and machine learning method;
(3.2) for the hidden fragile area, the road network destructive event which possibly occurs is prevented mainly through the establishment of a prevention scheme, the establishment of emergency facilities and the establishment of a remedy scheme, and the emergency coping capability of the emergency event can be improved through a series of simulation tests;
the step (1.3) comprises the following steps:
(1.3.1) analyzing the topology attribute by improving the network efficiency principle, wherein the network efficiency and the road network efficiency are calculated as follows:
wherein C is ij D is the traffic capacity size between node i and node j ij E is the shortest path between node i and node j ij Network efficiency between node i and node j; n is the total node number in the area, C a The importance degree of the node in the network is reflected for the medium number centrality of the node;
the road network node betweenness centrality is calculated as follows:
wherein n is jk For the sum of all shortest paths between every two intersections in the network,
the number of crossing i in all shortest paths; b (B) i The intersection betweenness is N, and the total number of regional intersections is N;
(1.3.2) carrying out normalization processing on the congestion group envelope alarm data according to the alarm quantity to obtain the alarm specific gravity of each intersection/road section object in the group envelope;
(1.3.3) redefining network performance, the calculation is as follows:
recalculating the network efficiency according to the formula, and displaying the result on a map;
the step (1.4) comprises the following steps:
(1.4.1) calculating the distribution of the deviation of the position and the vulnerable road sections in different time periods of the congestion group envelope according to the step (1.3);
(1.4.2) calculating the change period/rule and the distribution sites of different time periods according to the deviation of the positions of the congestion group envelopes in different time periods and the distribution of the vulnerable road sections;
(1.4.3) calculating the envelope position and the fragile link distribution under the symmetrical event according to the deviation of the positions of different time segments of the envelope of the congestion group and the distribution of the fragile link, wherein the symmetrical event comprises but is not limited to: the market is opened and closed at the peak in the morning and evening, and the traffic is limited;
the process of (2.1) is as follows: according to the road network construction stage and the stable stage, respectively constructing a road network service capability change model under the two conditions of extremely fast reduction of road network service capability caused by natural disasters and reduction of road network service capability caused by aging along with infrastructure;
the step (2.1) comprises the following steps:
and (2.1.1) carrying out flow distribution calculation on the congestion envelope according to the optimal principle of the Wardrop user, wherein the calculation formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is the impedance of the kth path between the point pairs (r, s), U rs Is the minimum impedance between the point pairs (r, s), is->
Impedance of the kth path between the pair of points (r, s);
(2.1.2) calculating the total impedance in the traffic network according to the optimal principle of the Wardrop system, wherein the calculation is shown as follows:
t a ＝t 0a (1+α(x α /k a ) β )
wherein Z is the total impedance between the OD points, x a For traffic flow on road section a, t a As a function of the impedance of segment a, t a (x a ) An impedance function taking flow as an independent variable for a road section a; q rs For the traffic flow of the OD pairs between the pairs (r, s),
as a path-dependent argument, if the section a is on the kth path between (r, s), +.>
1, otherwise 0; t is t 0a For zero flow impedance of road section, i.e. free time of flight, x α For traffic flow, k on road section alpha in user equilibrium state a The actual traffic capacity of the road section is represented, and alpha and beta are given model parameters which are respectively 0.15 and 4;
(2.1.3) calculating the minimum value of the road network impedance according to the fastest descent method of the optimization theory, wherein the calculation steps are as follows:
(2.1.3.1): given an initial set
For initial impedance value, the error ε is allowed>0, set k=1;
(2.1.3.2): the direction of the search is calculated and,
(2.1.3.3): if d (k) ||<Epsilon, stopping; conversely, from x (k) Starting from the edge d (k) One-dimensional search is performed to find lambda k So that
(2.1.3.4): let x (k+1) ＝x (k) +λd (k) K=k+1, turn (2.2.3.2);
(2.1.4) analyzing the change trend of the service capability of the road network according to the influence of the event on the road network, wherein the situation that the traffic capability of the road section is set to zero can be regarded as complete failure of the road section, and the effect is equivalent to removal from a map;
the step (2.2) comprises the following steps:
(2.2.1) calculating and explaining the upper bound of the regional service capability under the three conditions of early and late peak, flat peak and simulated road section failure by taking the road network flow as the data calculation basis;
(2.2.2) constructing a service capability upper bound change model according to the road network service capability change condition;
the step (2.2.2) comprises the following steps:
(2.2.2.1) according to the characteristics that the traffic density of the network is increased, the traffic is increased, but the overall speed is reduced and the overall impedance of the road section is increased, the actual traffic effect of the road section is greatly beyond the theoretical traffic capacity, and the impedance derivative is as follows:
wherein k' a The average flow rate of each road section of the peak in the morning and evening is, therefore, the expansion amount of the total impedance of the region is shown as follows on the assumption that the impedance function is unchanged:
the increase of the road network impedance is equivalently converted into the decrease of the road network service capability according to the set conversion coefficient, so that the decrease of the road network service capability is shown as the following formula on the premise that the default impedance function is not changed:
wherein phi is the conversion coefficient of impedance and service capability;
(2.2.2.2) for the simulated road section failure situation, for the accurate OD point pair, the available road section number is reduced, the user distributes to the rest road sections according to the user optimal principle, when the road network reaches the balanced state again, the user optimal and system optimal principle can know that the minimum value of the total impedance of the traffic road network is increased compared with the original value, and the optimal travel time is increased;
similarly, on the basis that the impedance function of the road sections is unchanged and the traffic capacity of each road section is not obviously different, N is recorded as the number of original regional total road sections, N is the number of invalid road sections, namely the regional total impedance is derived as follows:
the area service capability is reduced as:
(Z(X)”-Z(X))*Φ；
(2.2.3) for judging the service capability of the road network, the accumulated loss caused in the selected time can be used for further judging, and the user balancing principle and the system optimizing principle can know that when the road network reaches the balanced state, the total impedance and the user forming time reach the minimum value, so that the user can form the loss value in time to describe the total loss caused by the road network to a certain extent.
2. The road network vulnerability identification, analysis and response method based on abnormal traffic operation condition according to claim 1, wherein the method comprises the following steps: the step (1.2) comprises the following steps:
(1.2.1) selecting road network self time sequence traffic data and abnormal interference events, wherein the time sequence traffic data comprises road section flow, road section speed, road section saturation, road section efficiency index and road section delay index; abnormal interference events include congestion events, sudden traffic accidents, road maintenance, bad weather and human damage;
and (1.2.2) researching the change characteristics of traffic flow on a continuous time axis and a discontinuous time axis according to the road section time sequence characteristic data, and acquiring a traffic running condition abnormal region or a traffic running condition abnormal road section/intersection object, wherein the preliminary candidate is a traffic running condition abnormal envelope.
3. The road network vulnerability recognition, analysis and response method based on abnormal traffic operation condition according to claim 1, wherein the method comprises the following steps: in the above (2.2.3), for a general road network system, the road network parking time in a specific time period can be used as "waste" time and lost time formed by vehicles in the road network object, and the accumulated loss of the road network is determined by comparing a series of lost times.
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