CN113051728B - Traffic network anti-seismic robustness assessment method - Google Patents

Traffic network anti-seismic robustness assessment method Download PDF

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CN113051728B
CN113051728B CN202110276554.4A CN202110276554A CN113051728B CN 113051728 B CN113051728 B CN 113051728B CN 202110276554 A CN202110276554 A CN 202110276554A CN 113051728 B CN113051728 B CN 113051728B
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earthquake
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traffic network
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CN113051728A (en
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王俊彦
王乃玉
汪英俊
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Straits Innovation Internet Co ltd
Zhejiang Strait Innovation Technology Co ltd
Zhejiang University ZJU
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Zhejiang Strait Innovation Technology Co ltd
Zhejiang University ZJU
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Abstract

The invention discloses a traffic network anti-seismic robustness assessment method, which comprises the following steps: s1, initializing data; s2, earthquake risk analysis; s3, calculating the traffic network damage state; s4, calculating the normal state function level of the pre-earthquake traffic network; s5, calculating the function loss of the post-earthquake traffic network; s6, traffic network earthquake-resistant robustness assessment: and respectively calculating the earthquake-resistant robustness of the traffic cell and the earthquake-resistant robustness of the traffic network based on the traffic function indexes of the traffic cell and the traffic network before the earthquake of the target area obtained in the step S4 and the traffic function indexes of the traffic cell and the traffic network after the earthquake of the target area obtained in the step S5. The invention fully considers the influence of factors such as earthquake danger, earthquake motion correlation, earthquake vulnerability of traffic components, traffic flow distribution, traffic network function loss calculation and the like on the traffic network function, quantifies main uncertain factors in the factors, can clearly master the performance condition of the traffic network, and is beneficial to decision makers to make more effective decisions.

Description

Traffic network anti-seismic robustness assessment method
Technical Field
The invention relates to a traffic network anti-seismic robustness assessment method.
Background
The occurrence of major earthquake disasters can cause great damage to the economic and social functions of cities in disaster areas and the life and property of people. Cities with "toughness" have the ability to resist, adapt to and recover quickly from disasters. The traffic system is one of the most bottom key infrastructure systems in the city lifeline engineering, and the improvement of the earthquake resistance toughness is an important ring for improving the city disaster prevention capability.
Brunau and Reinhorn (2006) propose four dimensions of the concept of "toughness", namely robustness, redundancy, resource allowability, rapidity, and a Q-T plot of the concept of toughness. On the conceptual framework of toughness proposed by Bruneau et al, there are many scholars attempting to quantify the four-dimensional indicators of toughness. For example, Chang and Nojima (2001) use network coverage and traffic reachability to quantify post-disaster performance of traffic networks and apply to Japanese Korea's highway networks and rail systems. WH and Dingwei Wang (2011) propose a quantitative toughness evaluation method for analyzing the toughness of a traffic network. Bocchini and franopool (2011) propose a bridge network maintenance scheduling method, which combines the reliability of a single bridge and the connectivity of a network into a decision optimization formula. Morlok and Chang (2004) propose that network capacity flexibility indicators reflect the ability of a traffic system to adapt to traffic pattern changes caused by natural disasters. Henry and Ramirez-Marquez (2004) propose a time-based method for quantifying system and network toughness, describing key parameters required for system toughness analysis, such as destructive events, component recovery, and overall toughness strategy, and taking a road network as an example, illustrating the applicability of the proposed toughness index. Frangopol and Bocchini (2011) use the total travel time and the total travel distance to measure the functional condition of the traffic network, and take the total cost of the traffic system recovery after disaster as the optimization target of the recovery decision. Zhang and Wang (2017) take the total travel time as a functional index of a traffic network, and integrate a plurality of descriptive parameters such as bridge capacity grade, state grade, bridge position and the like into a global objective function of the overall network performance by utilizing a network analysis method, a structural reliability principle and a heuristic optimization algorithm.
However, many of the traffic network function indicators in the above documents do not alone reflect network toughness performance, nor do they have the ability to provide a functional recovery decision to city deciders after a disaster. Different metrics may be applicable to different decisions (e.g., renovation, repair, new construction, etc.) at different stages of the network toughness planning (e.g., pre-disaster protection, post-disaster emergency, and long-term recovery). Furthermore, the lack of uncertainty analysis found in the research work is a shortfall in many documents, and many of the documents fail to attempt to quantify the uncertainty associated with these performance indicators.
Disclosure of Invention
Aiming at the problems that the existing traffic indexes cannot truly reflect the functions of the traffic network and uncertainty related to performance indexes is not considered, the invention provides the traffic network anti-seismic robustness assessment method.
The technical scheme adopted by the invention for overcoming the technical problems is as follows:
a traffic network earthquake-resistant robustness assessment method comprises the following steps:
s1, data initialization: the data at least comprises earthquake zoning information of a target area, the field type of the target area, traffic network topology data of the target area, traffic Origin and Destination (OD) requirements of traffic of the target area and earthquake vulnerability curve data of the urban traffic component adapted to the target area;
s2, earthquake risk analysis: when a situation earthquake occurs, calculating peak ground acceleration PGA and ground permanent displacement PGD of a target area at each position of a series of traffic networks under the earthquake scene through a Monte Carlo simulation method based on earthquake motion information, target area traffic network topology information and field type information of the target area;
s3, calculating the traffic network damage state: based on the series of peak ground acceleration PGA or ground permanent displacement PGD at each part of the road network obtained in the step S2 and the seismic vulnerability data of the traffic components, obtaining the damage state of each component of the traffic network of the target area by a Monte Carlo simulation method;
s4, calculating the normal state function level of the pre-earthquake traffic network: calculating the function level of a pre-earthquake traffic cell of the target area through the node function index based on the topological data of the traffic network of the target area and the traffic OD requirement of the target area, and calculating the function level of the traffic network of the target area through the network function index;
s5, calculating the function loss of the post-earthquake traffic network: based on the damage state of each component of the target area traffic network obtained in the step S3, calculating the function level of the post-earthquake traffic cell of the target area through the node function index, and calculating the function level of the target area traffic network through the network function index;
s6, traffic network earthquake resistance robustness assessment: and respectively calculating the earthquake-resistant robustness of the traffic cell and the earthquake-resistant robustness of the traffic network based on the traffic function indexes of the traffic cell and the traffic network before the earthquake of the target area obtained in the step S4 and the traffic function indexes of the traffic cell and the traffic network after the earthquake of the target area obtained in the step S5.
Further, the step S4 may be located after the step S1 and before the step S6.
Further, in step S1, the seismic zone information of the target area at least includes longitude and latitude, seismic magnitude, and seismic source depth and longitude and latitude of the seismic zone of the target area.
Further, in step S1, the traffic OD demand of the target area refers to the traffic data actually generated in the area, and includes at least the start point, the end point and the flow rate of the traffic.
Further, in step S1, the traffic network topology data of the target area at least includes longitude and latitude of traffic components, types and grades of traffic components, free flow rate of roads, average daily traffic of roads, and traffic cell division of the traffic system, where the traffic components at least include roads and bridges.
Further, in step S1, the seismic vulnerability curve data of the urban traffic component refers to the probability that different types of traffic components are damaged to different degrees under the action of earthquakes with different intensity levels.
Further, in the step S3, the failure states of the components of the traffic network are divided into a first failure state, a second failure state, a third failure state and a fourth failure state in sequence from low to high based on the damage degree, where the first failure state is a state in which the traffic component can completely pass through, the fourth failure state is a state in which the traffic component cannot pass through, and the failure states of the traffic components in the second failure state and the third failure state are both between the first failure state and the fourth failure state.
Further, in the steps S4 and S5, the node function indicator is a modified independent path MIPW, and the network function indicator is a modified weighted independent path MWIPW, which are defined as follows:
Figure BDA0002976864860000031
Figure BDA0002976864860000041
in the above formula, n is the total number of nodes in the traffic topology network; i and j are node numbers; k(i,j)Is the sum of the number of independent paths between nodes i, j; w is aod(i,j)The traffic demand weight of two nodes i and j is as follows:
Figure BDA0002976864860000042
in the above formula, OD (i, j) is the traffic traveling quantity between nodes i, j;
Figure BDA0002976864860000043
the function level index for the kth independent path between nodes i, j is defined as follows:
Figure BDA0002976864860000044
in the above formula, ClIs the road grade for road segment l; q. q.saIndicating a broken state of the road section, wherein qaQ is not damageda1 is the first failure state, qaQ is a second failure stateaQ is the third failure statea4 is the fourth failure state; pk(i, j) is a set of all the sections forming the kth independent path between the nodes i, j;
Figure BDA0002976864860000045
the length-function impact factor for the kth independent path between nodes i, j is defined as follows:
Figure BDA0002976864860000046
in the above formula, wlFor the length factor of the section of road i,
Figure BDA0002976864860000047
Llfor the length of the section of road i,
Figure BDA0002976864860000048
is the length of the kth independent path between the nodes i, j; q. q.saIndicating a broken state of the link;
function level index of kth independent path between nodes i, j
Figure BDA0002976864860000049
When the temperature of the water is higher than the set temperature,
Figure BDA00029768648600000410
withe weight factor for node i is defined as:
Figure BDA00029768648600000411
Figure BDA00029768648600000412
in the formula, E belongs to V and is a point set consisting of emergency response facility points in the traffic network, N belongs to V and is a point set consisting of non-emergency response facility points in the traffic network, and V is a set of all nodes in the traffic network;
Figure BDA0002976864860000051
is the length of the kth independent path between nodes i, j, where j ∈ E.
Further, in step S6, the earthquake-resistant robustness index of the traffic zone is set as RnThe traffic network anti-seismic robustness index is RwCalculating the earthquake resistance robustness index R of the traffic districtnIs based on the traffic efficiency index MIPW of the post-earthquake traffic districtAfter earthquakePassing efficiency index MIPW of traffic district before earthquakeEarthquake frontCalculating the traffic network anti-seismic robust index RwIs a traffic network passing efficiency index MWIPW after earthquakeAfter earthquakeMWIPW (traffic efficiency index) of traffic network passing before earthquakeEarthquake frontThe ratio of (a) to (b).
The invention has the beneficial effects that:
1. the invention fully considers the influence of factors such as earthquake danger, earthquake motion correlation, earthquake vulnerability of traffic components, traffic flow distribution, traffic network function loss calculation and the like on the traffic network function, and the traffic network function index can reflect the function level of the traffic network more truly.
2. By using a method of double monte carlo simulation method, i.e. double stochastic simulation, the uncertainty of earthquake and the uncertainty of traffic component damage are quantified simultaneously.
3. The whole process from scene earthquake analysis to traffic network function analysis is completely established, so that the performance condition of the traffic network is clearly mastered, and a city manager can make a more targeted decision.
Drawings
Fig. 1 is a flowchart of a traffic network earthquake-resistant robustness assessment method according to an embodiment of the present invention.
Fig. 2 is a topological diagram of an urban transportation network in hangzhou according to an embodiment of the present invention.
FIG. 3 is a block diagram of seismic sources of a scene earthquake according to an embodiment of the invention.
Fig. 4 is a probability density distribution diagram of PGAs within the urban area of the state according to an embodiment of the present invention.
Fig. 5 is a schematic diagram illustrating probabilities of medium and higher (third failure state and fourth failure state) failure states occurring in the urban traffic network in the Hangzhou state according to the embodiment of the present invention.
Fig. 6 is a schematic diagram illustrating a road damage probability of a near earthquake region of a river in the urban area of the Hangzhou state according to an embodiment of the present invention.
Fig. 7 is a functional level distribution diagram of pre-earthquake traffic cells in the Hangzhou city according to the embodiment of the present invention.
Fig. 8 is a distribution diagram of the evaluation result of earthquake-resistant robustness of the traffic network in the urban area of the Hangzhou state according to the embodiment of the invention.
Detailed Description
In order to facilitate a better understanding of the invention for those skilled in the art, the invention will be described in further detail with reference to the accompanying drawings and specific examples, which are given by way of illustration only and do not limit the scope of the invention.
As shown in fig. 1, an embodiment of the invention discloses a traffic network earthquake-resistant robustness assessment method, which is described in detail below by taking a houndstongue urban traffic network as an example.
Step S1, data initialization: the data at least comprises seismic zoning information of the target area, the field type of the target area, traffic network topology data of the target area, traffic OD requirements of the target area and seismic vulnerability curve data of the urban traffic component adapting to the target area.
Specifically, the seismic zoning information of the target area at least comprises longitude and latitude, seismic magnitude, seismic source depth and longitude and latitude of a seismic zone of the target area. The traffic network topology data of the target area at least comprises longitude and latitude of traffic components, types and grades of the traffic components, road free flow rate, road average daily flow and traffic cell division of the traffic system, wherein the traffic components at least comprise roads and bridges. The traffic origin and destination traffic OD demands of the target area refer to traffic passing data actually occurring in the area, and at least comprise a passing starting point, a passing ending point and a passing flow. The urban traffic component seismic vulnerability curve data refers to the probability that different types of traffic components are damaged in different degrees under the action of earthquakes with different intensity levels, so that the adaptive traffic component seismic vulnerability data needs to be selected according to the specific types of the components of the traffic system in the target area. These data can be found through published data or related websites.
The urban district is located in the south of Hangzhou city, the total area is about 26.06 square kilometers, the 6 streets are managed, the total number of communities is 54, and the total population is about 35.32 ten thousand. The urban traffic network comprises 72 nodes, 37 roads, 117 road segments (including main roads, secondary roads, branches and unclassified roads) and has the total length of about 62.2 kilometers. And in this example the only traffic component considered is urban roads. FIG. 2 is a topological diagram of an Hangzhou urban traffic network; the attributes of each road are detailed in table 1, wherein the unit of the free flow rate is km/h, and the unit of the average daily flow rate is pcu; the epicenter position of this example is assumed to be the location of a Dong's dam community health room (longitude: 120.297191, latitude: 30.163252) at the Schoite-ball Dipsacus asperoides crack zone; the seismic source depth is 10 kilometers; the magnitude of the moment magnitude is 6.0; the type of the upper urban area is E (according to American seismic code FEMA-450). The average distance of the source from the upper metropolitan area is about 15 kilometers and the azimuth of the source of the scene earthquake is given in fig. 3.
TABLE 1 road Attribute List
Figure BDA0002976864860000071
Figure BDA0002976864860000081
Step S2, earthquake risk analysis: when a situation earthquake occurs, peak ground acceleration PGA and ground permanent displacement PGD of the target area at each position of a series of traffic networks in the earthquake scene are calculated and obtained through a Monte Carlo simulation method based on earthquake motion information, target area traffic network topology information and field type information of the target area, and a suitable model can be selected according to geographic information of the target area.
The earthquake risk analysis is used for earthquake pre-earthquake evaluation, so the earthquake motion information refers to PGA, PGD or S which is obtained by calculation through models such as an earthquake motion attenuation model, an earthquake motion correlation model, a PGA/PGD conversion model and the like according to the assumed earthquake source position, depth and earthquake magnitudeAAnd seismic motion information is obtained. Specifically, in this embodiment, a series of spatial distribution combinations of ground peak accelerations (PGAs) at various places in the urban area are obtained sequentially through a seismic attenuation model and a seismic correlation model by using monte carlo simulation, and the ground peak accelerations (PGAs) at various places are converted into a series of combinations of ground permanent displacements (PGDs) through a PGA/PGD conversion model. FIG. 4 is a graph of probability density distribution of PGAs within Hangzhou urban areas under a situational earthquake.
Step S3, calculating the traffic network destruction state: and based on the combination of the permanent ground displacement at each position of the series of road networks obtained in the step S2 and the seismic vulnerability curve data of the traffic component, obtaining a series of combinations of the destruction states of the traffic network of the target area by a Monte Carlo simulation method again. The monte carlo simulation method is used for the second time in the present invention, and random sampling is performed once in the seismic motion correlation model in step S2, that is, the monte carlo simulation method is used once, so that the double monte carlo simulation method is used in the present invention, thereby quantifying the uncertainty of the earthquake and the uncertainty of the road damage.
Specifically, the failure states of the components of the traffic network are divided into a first failure state, a second failure state, a third failure state and a fourth failure state in sequence from high to low based on the damage degree, where the first failure state is a state where the traffic component is light and can substantially meet the traffic demand (the function level is 3/4 in the normal state), the fourth failure state is a state where the traffic component is severely damaged and cannot pass (the function level is 0), the second failure state is a state where the traffic component is slightly damaged, and the third failure state is a state where the traffic component is moderately damaged, and in this embodiment, the traffic network function levels corresponding to the second failure state and the third failure state are 2/4 and 1/4 in the normal failure state, respectively.
Fig. 5 is a schematic diagram of the failure states of the urban traffic network in the state of hangzhou, wherein the higher the coloring gray scale of the road represents that the road has higher probability of causing moderate and above (the third failure state and the fourth failure state) failures, and 9.51% and 65.2% marked in the figure represent that the road has moderate and above failure states (the third failure state and the fourth failure state) probabilities. Fig. 6 shows the probability of four failure states of the roads in the near-earthquake region of the river in the urban area of the Hangzhou state in the earthquake.
Step S4, calculating the function level of the normal state of the pre-earthquake traffic network: based on the traffic network topology data of the target area and the traffic OD requirement of the target area, calculating through a node function index MIPW to obtain a traffic cell passing efficiency index before the earthquake of the target area, and calculating through a network function index MWIPW to obtain the passing efficiency of the traffic network of the target area.
FIG. 7 is a traffic function level ratio of pre-earthquake traffic cells in Hangzhou urban areas, i.e., MIPW and max { MIPW) of each traffic celliThe higher the gray level represents the lower the traffic efficiency of the traffic cell, and 100% and 29% marked in the figure represent the traffic function level ratio of 100% and 29%, respectively.
Step S5, calculating the function loss of the post-earthquake traffic network: based on the damage state of each component of the target area traffic network obtained in step S3, a traffic cell passing efficiency index after the target area earthquake is obtained through the node function index MIPW calculation, and the passing efficiency of the target area traffic network is calculated through the network function index MWIPW.
Specifically, in the steps S4 and S5, the traffic cell passing efficiency refers to a calculated value of MIPW from the traffic cell to the rest of the communities in the corresponding state; the traffic efficiency of the traffic network refers to a calculated value of a network function index MWIPW in a corresponding state.
The node function index is a modified independent path MIPW, the network function index is a modified weighted independent path MWIPW, and the node function index and the network function index are respectively defined as follows:
Figure BDA0002976864860000102
Figure BDA0002976864860000103
in the above formula, n is the total number of nodes in the traffic topology network; i and j are node numbers; k(i,j)Is the sum of the number of independent paths between nodes i, j; w is aod(i,j)The traffic demand weight of two nodes i and j is as follows:
Figure BDA0002976864860000104
in the above formula, OD (i, j) is the traffic travel amount between the nodes i, j;
Figure BDA0002976864860000105
the function level index for the kth independent path between nodes i, j is defined as follows:
Figure BDA0002976864860000111
in the above formula, ClIs the road grade for road segment l; q. q.saIndicating a broken state of the road section, wherein qaQ is not damageda1 is the first failure state, qaQ is a second failure stateaQ is the third failure statea4 is the fourth failure state; pk(i, j) is the set of all the segments between nodes i, j that form the kth independent path.
Figure BDA0002976864860000112
The length-function impact factor for the kth independent path between nodes i, j is defined as follows:
Figure BDA0002976864860000113
in the above formula, wlFor the length factor of the section of road i,
Figure BDA0002976864860000114
Llfor the length of the section of road i,
Figure BDA0002976864860000115
is the length of the kth independent path between the nodes i, j; q. q.saIndicating a broken state of the link.
In particular, the function level index of the kth independent path between nodes i, j
Figure BDA0002976864860000116
When the temperature of the water is higher than the set temperature,
Figure BDA0002976864860000117
withe weight factor for node i is defined as:
Figure BDA0002976864860000118
Figure BDA0002976864860000119
in the formula, E belongs to V and is a point set consisting of emergency response facility points in the traffic network, N belongs to V and is a point set consisting of non-emergency response facility points in the traffic network, and V is a set of all nodes in the traffic network;
Figure BDA00029768648600001110
is the length of the kth independent path between nodes i, j, where j ∈ E.
Step S6, traffic network earthquake-resistant robustness assessment: based on the traffic function indexes of the traffic cells and the traffic network before the earthquake of the target area obtained in the step S4 and the traffic function indexes of the traffic cells and the traffic network after the earthquake of the target area obtained in the step S5, the earthquake-resistant robustness index R of the traffic cell is obtainednAnd traffic network anti-seismic robustness index RwRespectively representing the earthquake-resistant robustness of the traffic cell and the whole traffic network. Specifically, calculating the traffic cell earthquake-resistant robustness index RnIs based on the traffic efficiency index MIPW of the post-earthquake traffic districtAfter earthquakePassing efficiency index MIPW of traffic district before earthquakeEarthquake frontThe ratio of (a) to (b). Computing traffic network anti-seismic robust index RwIs a traffic network passing efficiency index MWIPW after earthquakeAfter earthquakeMWIPW (traffic efficiency index) of traffic network passing before earthquakeEarthquake frontThe ratio of (a) to (b).
The evaluation result of the earthquake-resistant robustness of the traffic network in the urban areas in the Hangzhou state obtained by the method of the embodiment is shown in FIG. 8 and Table 2. In fig. 8, the higher the gray level represents the higher the influence degree of the post-earthquake traffic efficiency of the community, and the 12.89% and 51.44% marked in the figure each represent the ratio R of the function losse(ii) a As can be seen from table 2, the probability that the entire function of the urban traffic network in the state of hangzhou is lost 38.97% after earthquake and the function of the traffic network is lost more than 60% is 26.64%.
TABLE 2 Hangzhou urban area traffic network earthquake robustness assessment results
Figure BDA0002976864860000121
In the present embodiment, the step S4 may be located after the step S1 and before the step S6, that is, the step S4 may be located between the steps S1 and S2, between the steps S2 and S3, between the steps S3 and S5, or between the steps S5 and S6, and is not limited to being located after the step S3 and before the step S5. The present embodiment preferably has step S4 located between step S3 and step S5.
The foregoing merely illustrates the principles and preferred embodiments of the invention and many variations and modifications may be made by those skilled in the art in light of the foregoing description, which are within the scope of the invention.

Claims (8)

1. A traffic network earthquake-resistant robustness assessment method is characterized by comprising the following steps:
s1, data initialization: the data at least comprises earthquake zoning information of a target area, the field type of the target area, traffic network topology data of the target area, traffic Origin and Destination (OD) requirements of traffic of the target area and earthquake vulnerability curve data of the urban traffic component adapted to the target area;
s2, earthquake risk analysis: when a situation earthquake occurs, calculating peak ground acceleration PGA and ground permanent displacement PGD of a target area at each position of a series of traffic networks in the earthquake scene through a Monte Carlo simulation method based on earthquake motion information, target area traffic network topology information and field type information of the target area;
s3, calculating the traffic network damage state: based on the series of peak ground acceleration PGA or ground permanent displacement PGD and the traffic component seismic vulnerability data obtained at each position of the road network in the step S2, obtaining the damage state of each component of the traffic network of the target area by a Monte Carlo simulation method;
s4, calculating the normal state function level of the pre-earthquake traffic network: calculating the function level of a pre-earthquake traffic cell of the target area through the node function index based on the topological data of the traffic network of the target area and the traffic OD requirement of the target area, and calculating the function level of the traffic network of the target area through the network function index;
s5, calculating the function loss of the post-earthquake traffic network: based on the damage state of each component of the target area traffic network obtained in the step S3, calculating the function level of the post-earthquake traffic cell of the target area through the node function index, and calculating the function level of the target area traffic network through the network function index;
s6, traffic network earthquake-resistant robustness assessment: respectively calculating the earthquake-resistant robustness of the traffic cell and the earthquake-resistant robustness of the traffic network based on the traffic function indexes of the traffic cell and the traffic network before the earthquake of the target area obtained in the step S4 and the traffic function indexes of the traffic cell and the traffic network after the earthquake of the target area obtained in the step S5;
in the steps S4 and S5, the node function index is a modified independent path MIPW, the network function index is a modified weighted independent path MWIPW, and the node function index and the network function index are defined as follows:
Figure FDA0003573558810000011
Figure FDA0003573558810000021
in the above formula, n is the total number of nodes in the traffic topology network; i and j are node numbers; k(i,j)Is the sum of the number of independent paths between nodes i, j; w is aod(i,j)The traffic demand weight of two nodes i and j:
Figure FDA0003573558810000022
in the above formula, OD (i, j) is the traffic traveling quantity between nodes i, j;
Figure FDA0003573558810000023
the function level index for the kth independent path between nodes i, j is defined as follows:
Figure FDA0003573558810000024
in the above formula, ClIs the road grade for road segment l;qaindicating a broken state of the road section, wherein qaQ is not destroyeda1 is the first failure state, qaQ is a second failure stateaQ is the third failure statea4 is the fourth failure state; pk(i, j) is a set of all road sections forming the kth independent path between the nodes i, j;
Figure FDA0003573558810000025
the length-function impact factor for the kth independent path between nodes i, j is defined as follows:
Figure FDA0003573558810000026
in the above formula, wlFor the length factor of the section of road i,
Figure FDA0003573558810000027
Llfor the length of the section of road i,
Figure FDA0003573558810000028
is the length of the kth independent path between the nodes i, j; q. q.saIndicating a broken state of the link;
function level index of kth independent path between nodes i, j
Figure FDA0003573558810000029
When the temperature of the water is higher than the set temperature,
Figure FDA00035735588100000210
withe weight factor for node i is defined as:
Figure FDA00035735588100000211
Figure FDA00035735588100000212
in the formula, E belongs to V and is a point set consisting of emergency response facility points in the traffic network, N belongs to V and is a point set consisting of non-emergency response facility points in the traffic network, and V is a set of all nodes in the traffic network;
Figure FDA0003573558810000031
is the length of the kth independent path between nodes i, j, where j ∈ E.
2. The method for traffic network earthquake robustness assessment according to claim 1, wherein said step S4 is located after step S1 and before step S6.
3. The method for evaluating traffic network earthquake robustness according to claim 1, wherein in the step S1, the seismic zone information of the target area at least includes longitude and latitude, earthquake magnitude, and earthquake source depth and longitude and latitude of the earthquake zone of the target area.
4. The method for evaluating earthquake-resistant robustness of a traffic network according to claim 1, wherein in step S1, the OD demand of the traffic at the start point and the end point of the target area refers to the traffic data actually occurring in the area, and includes at least the start point, the end point and the flow rate of the traffic.
5. The method for evaluating earthquake-resistant robustness of traffic network according to claim 1, wherein in step S1, the topology data of traffic network in the target area at least includes longitude and latitude of traffic components, types and grades of traffic components, free flow rate of road, average daily traffic of road, and traffic cell division of the traffic network, wherein the traffic components at least include road and bridge.
6. The method for assessing earthquake-resistant robustness of a traffic network according to claim 5, wherein in the step S1, the earthquake vulnerability curve data of the urban traffic components refers to the probability of different degrees of damage of different types of traffic components under the action of earthquakes with different intensity levels.
7. The method for evaluating earthquake-resistant robustness of the traffic network according to claim 5, wherein in step S3, the failure states of the components of the traffic network are divided into a first failure state, a second failure state, a third failure state and a fourth failure state in sequence from low to high based on the damage degree, wherein the first failure state is a state in which the traffic component can completely pass through, the fourth failure state is a state in which the traffic component cannot pass through, and the failure states of the traffic components in the second failure state and the third failure state are both between the first failure state and the fourth failure state.
8. The method for evaluating traffic network earthquake-resistant robustness according to claim 7, wherein in step S6, traffic cell earthquake-resistant robustness indexes are respectively set as RnThe traffic network earthquake-resistant robustness index is RwCalculating the earthquake resistance robustness index R of the traffic districtnIs based on the traffic efficiency index MIPW of the post-earthquake traffic districtAfter earthquakePassing efficiency index MIPW of traffic district before earthquakeEarthquake frontCalculating the traffic network anti-seismic robust index RwIs a traffic network passing efficiency index MWIPW after earthquakeAfter earthquakeTraffic efficiency index MWIPW of traffic network before earthquakeEarthquake frontThe ratio of (a) to (b).
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