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CN-121997834-A - Road network toughness evaluation and restoration optimization method and system considering cascading failure under influence of flood

CN121997834ACN 121997834 ACN121997834 ACN 121997834ACN-121997834-A

Abstract

The invention discloses a road network toughness assessment and recovery optimization method and system considering cascading failure under the influence of flood, comprising the steps of constructing a road network cascading failure model, outputting time sequence parameters by the road network cascading failure model, determining cascading failure influence indexes, network efficiency and average shortest path length based on the time sequence parameters, determining a road network toughness index according to the cascading failure influence indexes, the network efficiency and the average shortest path length, and constructing a road network toughness recovery optimization model considering cascading failure. The invention builds the urban road network cascade failure model considering the dynamic process of flood and the redistribution of local traffic flow, improves the accuracy of road network toughness assessment under the flood condition, and provides the road network toughness recovery optimization method considering cascade failure, thereby improving the emergency efficiency.

Inventors

  • XU HONGSHI
  • ZHOU YONGJIE
  • JIANG XUAN
  • CHEN YANPO
  • GENG CHUANYU

Assignees

  • 郑州大学

Dates

Publication Date
20260508
Application Date
20260130

Claims (10)

  1. 1. A road network toughness evaluation method considering cascade failure under the influence of flooding comprises the following steps: Step 100, constructing a road network cascade failure model; step 200, outputting time sequence data by the road network cascade failure model, and determining cascade failure influence indexes, network efficiency and average shortest path length based on the time sequence data; and 300, determining the road network toughness index according to the cascade failure impact index, the network efficiency and the average shortest path length.
  2. 2. The method according to claim 1, wherein the step 100 comprises: step 101, constructing a road network topology model based on GIS road network data, abstracting a road network into an undirected network graph G= { V, E }, wherein a node set V= {; Step 102, calculating the capacity C i and the initial load L i of each road based on the ML model, and setting the time step Δt.
  3. 3. The method of predicting according to claim 2, wherein said step S100 further comprises an iterative process of cascade failure simulation: Step 103, identifying submerged invalid roads, namely, according to the submerged depth hi (t) of each extracted road, judging that the road i is submerged invalid roads when hi (t) > is a first preset depth threshold value; 104, traffic flow distribution, namely calculating distribution flow of a dead road by adopting a local redistribution strategy based on betweenness centrality; Step 105, judging the road state after the flow is redistributed, if no congestion road exists in the network, directly entering the next step, if the congestion road exists, marking the congestion road as a congestion failure state, and redistributing the load of the part of the road exceeding the capacity of the road in the next iteration; And step 106, advancing the time step to t+delta t, reading flood inundation data at the next moment, repeating the steps 103-105, and simulating continuously until a set time length after the flood process is finished.
  4. 4. The prediction method according to claim 3, wherein the local reassignment strategy based on the betweenness centrality in the step S104 comprises: Calculating the median centrality of the failure edge: ; Calculating the distribution weight coefficient of the adjacent normal edges: ; Calculating flow distribution: ; Wherein: Refers to the total number of shortest paths from node s to node t, Refers to the shortest path number from node s to node t through edge i, EBC (e k ) is the median centrality of the adjacent normal edge e k , ω k is the weight coefficient of the adjacent normal edge e k , Q k is the flow allocated adjacent to normal edge e k .
  5. 5. The method according to claim 1, wherein said step 200 further comprises: the cascade failure impact index calculation formula is: ; Wherein E is the total number of network roads, E c is the number of congestion failure roads, and E f is the number of submerged failure roads.
  6. 6. The method according to claim 5, wherein said step 200 further comprises: the network efficiency calculation formula is: ; the average shortest path length calculation formula is: ; N is the total number of network nodes, and d ij is the shortest path length between node i and node j.
  7. 7. The prediction method according to claim 6, wherein: the step 300 further includes: the calculation formula of the road network toughness index is as follows: ; Wherein CFII is cascade failure influence index, NE 'and APL' are normalized network efficiency and average shortest path length respectively, and alpha 1 、α 2 、α 3 is the weight of different indexes.
  8. 8. The method of predicting according to claim 3, wherein the first predetermined depth threshold = 0.3m.
  9. 9. A road network toughness assessment system that accounts for cascading failures under the influence of flooding, comprising: the cascade failure modeling module is used for constructing a road network cascade failure model; The calculation module is used for acquiring road network toughness evaluation index parameters according to the road network cascading failure model, and determining cascading failure influence indexes, network efficiency and average shortest path length based on the road network toughness evaluation index parameters; And the toughness evaluation module is used for determining the road network toughness index according to the cascade failure influence index, the network efficiency and the average shortest path length.
  10. 10. A road network toughness recovery optimization method considering cascading failure under the influence of flood comprises the steps of constructing a road network toughness recovery optimization model considering cascading failure, maximizing accumulated road network toughness indexes as optimization targets, solving an optimal road recovery sequence through a genetic algorithm, and obtaining the road network toughness indexes according to the network toughness index formula of claim 6.

Description

Road network toughness evaluation and restoration optimization method and system considering cascading failure under influence of flood Technical Field The invention relates to the technical fields of urban water affairs, disaster prevention and reduction and emergency research, in particular to a road network toughness assessment and recovery optimization method and system considering cascading failure under the influence of flood Background Urban flooding easily causes road flooding failure, affects resident traveling and emergency response capability, and plays an important role in alleviating flood influence by developing road network toughness assessment and recovery strategy research under urban flooding. The traditional road network toughness evaluation method mainly adopts static indexes, such as network efficiency, betweenness centrality and the like, to evaluate the road network toughness under urban flood, does not consider the dynamic propagation process of the influence of flood on road traffic, when urban flood occurs, partial roads are interrupted to pass under the influence of ponding flooding, traffic flows originally passing through the roads are dynamically transferred to other roads, traffic jam can be caused to happen to bearing roads due to overload operation, further a larger range of chain reaction is triggered, and finally road traffic is caused to be broken down from local paralysis to integral, and the dynamic process is called cascade failure of the road network. With the continuous propagation of cascading failure, the performance of the road network can be obviously reduced, and the network toughness can be dynamically changed. However, the conventional road network toughness evaluation research ignores the cascade failure process of the road network, so that the toughness evaluation result is inaccurate. In addition, the urban flood causes the road flooding failure, seriously affects the road network service, emergency rescue and resident trip and causes great loss, and the resources and time after disaster are limited, and the optimal road recovery sequence needs to be determined, so that the efficient road network toughness recovery strategy is important to the improvement of the urban emergency management capability. However, the existing research is still insufficient when being applied to urban flood scenes (1) the insufficient consideration of the cascade failure dynamic process is lacked, so that the effect evaluation of a recovery strategy is inaccurate, and (2) the knowledge of recovery rules such as critical threshold values is insufficient, so that effective guidance is difficult to provide for emergency management. Thus, there is a need for a system and method that can finely characterize cascading failures, dynamically evaluate toughness, and optimize recovery strategies. Disclosure of Invention The invention aims to provide a road network toughness evaluation and recovery optimization method and system considering cascading failure under the influence of flooding so as to solve the problems in the background technology. In order to achieve the above objective, an embodiment of a first aspect of the present application provides a road network toughness evaluation method considering cascade failure under the influence of flooding, including: Step 100, constructing a road network cascade failure model; step 200, outputting time sequence data by a road network cascade failure model, and determining a cascade failure impact index, network efficiency and average shortest path length based on road network toughness evaluation index parameters; and 300, determining the road network toughness index according to the cascade failure impact index, the network efficiency and the average shortest path length. According to one embodiment of the present application, the step 100 includes: step 101, constructing a road network topology model based on GIS road network data, and abstracting an urban road network into an undirected network graph G= { V, E }, wherein the node set Representing an intersection, and obtaining road attribute information by representing a road by an edge set E= { E ij }; and 102, calculating the capacity Ci and the initial load Li of each road based on the ML model, initializing all the roads to be in a normal state, and setting a time step delta t. According to one embodiment of the present application, the step S100 further includes an iterative process of cascade failure simulation: Identifying inundation failure roads, wherein the inundation failure roads comprise inundation depth hi (t) of each road is extracted, and when hi (t) > is a first preset depth threshold value, the road i is judged to be inundation failure road; 104, traffic flow distribution, namely calculating distribution flow of a dead road by adopting a local redistribution strategy based on betweenness centrality; Step 105, the propagation of the road failure, which comprises judging