An optimized repair method for urban flooded roads and computer-readable medium

Abstract

The present invention proposes an optimization and repair method for urban flooded roads and a computer-readable medium. The present invention constructs an undirected road network topology model; performs traffic simulation through the SUMO open source model to obtain the traffic flow set of nodes in the undirected road network topology model and the traffic flow set of edges in the undirected road network topology model; generates the shortest time point path set between each node in the undirected road network topology model and the shortest time edge path set between each node in the undirected road network topology model based on the Dijkstra algorithm; calculates the betweenness centrality of edges and the betweenness centrality of nodes in the undirected road network topology model; uses the merge sort algorithm to sort and obtain the set of edges and the set of nodes in the undirected road network topology model after descending sorting; and further repairs flooded road sections and flooded intersections. The advantage of the present invention is that it optimizes the betweenness centrality by taking into account both travel time and traffic flow, and dynamically and accurately reflects the status of the road network.

Description

Technical Field

[0001] The present invention relates to the field of data road traffic network security, and in particular to an optimization repair method for urban flooded roads and a computer-readable medium. Background Art

[0002] Heavy rain and waterlogging disasters are one of the most serious natural disasters in my country. They are characterized by suddenness, great harm and wide impact range.

[0003] Existing road traffic network resilience assessment methods are often based on static network topology indicators, which cannot reflect the complex dynamics of traffic networks under heavy rain and waterlogging disaster scenarios. They are even more difficult to meet the needs of network assessment in waterlogging scenarios and cannot provide guidance for improving traffic system resilience and emergency decision-making. This method is based on traffic simulation and takes into account travel time and flow to improve the betweenness centrality model, so that it can better reflect the importance of road traffic networks under waterlogging disaster environments. Against the backdrop of China's strengthening of high-level disaster prevention, mitigation, and relief, as well as emergency public events, improving road traffic network assessment methods and comprehensively considering road traffic network changes and route selection under heavy rain and waterlogging scenarios have important practical significance for improving urban traffic disaster prevention and mitigation and guiding waterlogging emergency decision-making.

[0004] Existing transportation network resilience assessment methods fail to accurately quantify transportation network topology indicators in flooding scenarios. Therefore, this method extracts road waterlogging, constructs real-time traffic simulations, and improves the betweenness centrality model to quantify the importance of road segments in the transportation network. This method investigates how the impact of waterlogging on the transportation network spreads, providing important guidance for emergency decision-making.

[0005] Existing betweenness centrality models for evaluating transportation networks rely on shortest path calculations, resulting in changes only when edges or nodes are disconnected. This approach is not suitable for path selection under complex traffic conditions. Therefore, this method uses the shortest time calculation method, which is more suitable for simulation of transportation networks under disaster scenarios and can dynamically reflect the importance and spatial distribution characteristics of road network nodes.

[0006] Existing betweenness centrality models for evaluating traffic networks are static models that fail to reflect the dynamic traffic flow characteristics of road traffic networks. Therefore, this method comprehensively considers traffic flow changes during calculation and uses this as the calculation indicator of betweenness centrality. Summary of the Invention

[0007] In order to solve the above technical problems, the present invention proposes an optimization repair method for urban flooded roads and a computer-readable medium.

[0008] The technical solution of the method of the present invention is an optimized repair method for urban flooded roads, and the specific steps are as follows:

[0009] Step 1: Combine road network vector data and ground water surface data to construct a road undirected network topology model;

[0010] Step 2: Based on the set of waterlogging depths and the set of design vehicle speeds in the undirected road network topology model, the design vehicle speed set for the waterlogging scenario is calculated. This design vehicle speed set for the waterlogging scenario is introduced into the SUMO open-source model for traffic simulation, resulting in the traffic flow set for the nodes and the traffic flow set for the edges in the undirected road network topology model.

[0011] Step 3: Based on the design speed set for the waterlogging condition of the edge and the edge length set in the undirected road network topology model, the travel time set in the undirected road network topology model is obtained. Based on the Dijkstra algorithm, the shortest time point path set between each node in the undirected road network topology model and the shortest time edge path set between each node in the undirected road network topology model are generated.

[0012] Step 4: Calculate the betweenness centrality of the edges in the undirected road network topology model based on the shortest time edge path set between each node in the undirected road network topology model, the traffic flow set of the edges in the undirected road network topology model, and the edge set; calculate the betweenness centrality of the nodes in the undirected road network topology model based on the shortest time point path set between each node in the undirected road network topology model and the traffic flow set of the nodes in the undirected road network topology model;

[0013] Step 5: The edges between two nodes in the set of edges in the road undirected network topology model are sorted according to the betweenness centrality of the edges using a merge sort algorithm to obtain a set of edges in the road undirected network topology model after descending sorting, and the flooded road sections corresponding to the edges between two nodes in the set of edges in the road undirected network topology model after descending sorting are repaired in sequence; each node in the node set in the road undirected network topology model is sorted according to the betweenness centrality of the node using a merge sort algorithm to obtain a set of nodes in the road undirected network topology model after descending sorting, and the flooded intersections corresponding to each node in the set of nodes in the road undirected network topology model after descending sorting are repaired in sequence.

[0014] Preferably, the undirected road network topology model is defined in step 1, and is specifically defined as follows:

[0015]

[0016] M={m i}, 1≤i≤N

[0017] E={e i，j}, 1≤i≠j≤N

[0018] L={l i，j}, 1≤i≠j≤N

[0019] F={f i，j}, 1≤i≠j≤N

[0020] V={v i，j}, 1≤i≠j≤N

[0021]

[0022] Y={y i}, 1≤i≠j≤N

[0023] R={r i，j}, 1≤i≠j≤N

[0024] T={t i，j}, 1≤i≠j≤N

[0025] Among them, M represents the node set in the road undirected network topology model, m i It represents the i-th node in the node set in the road undirected network topology model, N represents the number of nodes in the node set in the road undirected network topology model, and is obtained through the road network vector data;

[0026] E represents the set of edges in the road undirected network topology model, e i，j Indicates whether the i-th node and the j-th node in the set of edges in the undirected road network topology model are connected, obtained through the road network vector data;

[0027] If the i-th node and the j-th node in the set of edges in the road undirected network topology model are connected, then e i，j =1, otherwise e i，j =0;

[0028] L represents the edge length set in the road undirected network topology model, l i，j Indicates the length of the edge formed by the i-th node and the j-th node in the edge length set in the road undirected network topology model, which is obtained through the road network vector data;

[0029] If the i-th node and the j-th node in the edge length set in the road undirected network topology model are connected, that is, e i，j =1, then l i，j is the length of the corresponding road; otherwise l i，j =∞;

[0030] F represents the set of water depths in the undirected road network topology model, f i，j It represents the water depth of the edge formed by the i-th node and the j-th node in the water depth set in the road undirected network topology model, which is obtained through ground water accumulation data;

[0031] If the i-th node and the j-th node in the water depth set in the road undirected network topology model are connected, that is, e i，j =1, then f i，j is the water depth of the corresponding road; otherwise f i，j =∞;

[0032] V represents the set of designed vehicle speeds in the undirected road network topology model, v i，j Indicates the design speed of the edge formed by the i-th node and the j-th node in the design speed set in the undirected road network topology model, which is obtained through the road network vector data;

[0033] If the i-th node and the j-th node in the design speed set in the road undirected network topology model are connected, that is, e i，j =1, then v i，j is the design speed of the corresponding road; otherwise v i，j =0;

[0034] V * The set of design vehicle speeds representing the flooding situation in the undirected road network topology model, The design speed for the waterlogging situation of the edge formed by the i-th node and the j-th node in the design speed set for the waterlogging situation in the undirected road network topology model is the variable to be solved; Y represents the traffic flow set of the nodes in the undirected road network topology model, y i，j It represents the traffic flow of the ith node in the traffic flow set of nodes in the road undirected network topology model, which is the variable to be solved;

[0035] R represents the traffic flow set of the edges in the road undirected network topology model, r i，i The traffic flow of the edge formed by the i-th node and the j-th node in the traffic flow set of the edge in the road undirected network topology model is the variable to be solved;

[0036] If the traffic flow set of the edge in the road undirected network topology model is connected between the i-th node and the j-th node, that is, e i，j =1, then r i，j is the traffic flow of the corresponding road; otherwise r i，j =0;

[0037] T represents the travel time set in the undirected road network topology model, t i，j It represents the travel time of the edge formed by the i-th node and the j-th node in the travel time set in the road undirected network topology model, which is the variable to be solved;

[0038] If the i-th node and the j-th node in the travel time set in the road undirected network topology model are connected, that is, e i,j =1, then ti，j is the travel time of the corresponding road; otherwise t i,j =∞;

[0039] Preferably, the design vehicle speed set for calculating the waterlogging situation in step 2 is specifically:

[0040]

[0041] 1≤i≠j≤N

[0042] in, is the design vehicle speed for the waterlogging condition on the edge formed by the i-th node and the j-th node in the node set in the undirected road network topology model, v i，j is the design speed of the edge formed by the i-th node and the j-th node in the node set in the road undirected network topology model, f i，j is the water depth of the edge formed by the i-th node and the j-th node in the node set of the road undirected network topology model, a is the critical water depth at which the vehicle is forced to stop, b is the attenuation exponent, tanh represents the hyperbolic tangent function, and N represents the number of nodes in the node set of the road undirected network topology model;

[0043] Preferably, the travel time set in the undirected road network topology model in step 3 is specifically:

[0044]

[0045] 1≤i≠j≤N

[0046] Where, t i，j is the travel time of the edge formed by the i-th node and the j-th node in the node set of the road undirected network topology model, l i，j is the length of the edge formed by the i-th node and the j-th node in the node set of the road undirected network topology model, e i，j Is the connection between the i-th node and the j-th node in the node set of the road undirected network topology model, N is the number of nodes in the node set of the road undirected network topology model;

[0047] Step 3 generates the shortest time point path set between nodes of the road undirected network topology model based on the Dijkstra algorithm, as follows:

[0048] Step 3.1: Introduce node set M = {m i}0≤i≤N, let the initial node count set The shortest time point path set between nodes in the road undirected network topology model

[0049] Step 3.2: Select any two nodes m from M o、 md As the path starting node and ending node respectively, and m o 、m d join in In the undirected road network topology model, the travel time set is used as the weight, and the Dijkstra algorithm is used to obtain the node m o 、m d The shortest path between them, represented as a node sequence

[0050] Add the node sequence to S;

[0051] Step 3.3: Repeat step 3.2 until all nodes in the initial set M are added And when each node is N-1, the iteration ends

[0052] Thus, the shortest time point path set between each node of the road undirected network topology model is obtained, which is as follows:

[0053] S={s1,s2,…,s N*(N-1) / 2}

[0054] Among them, s q represents the qth node sequence in the shortest time point path set between nodes in the road undirected network topology model, N*(N-1) / 2 represents the number of node sequences in the shortest time point path set between nodes in the road undirected network topology model, q∈[1, N*(N-1) / 2];

[0055] Each node sequence in the shortest time point path set between nodes of the road undirected network topology model is sequentially converted through the corresponding edges of two adjacent nodes to obtain the edge sequence corresponding to each node sequence in the shortest time point path set between nodes of the road undirected network topology model;

[0056] Construct the shortest time edge path set between each node in the road undirected network topology model through the edge sequence corresponding to each node sequence in the shortest time point path set between each node in the road undirected network topology model;

[0057] The shortest time edge path set between nodes in the road undirected network topology model is as follows:

[0058] P={p1,P2,…,P N*(N-1) / 2}

[0059] Among them, s q Represents the qth time series in the set of the shortest time edge paths between nodes in the road undirected network topology model, q∈[1, N*(N-1) / 2];

[0060] Preferably, the betweenness centrality of the edges in the undirected road network topology model is calculated in step 4 as follows:

[0061]

[0062] Among them, EB i,j is the betweenness centrality of the edge formed by the i-th node and the j-th node in the node set of the road undirected network topology model; r i，j is the traffic flow of the edge formed by the i-th node and the j-th node in the node set of the road undirected network topology model, σ i，j is the number of edges between the i-th node and the j-th node in the node set of the road undirected network topology model that appear in the subset of the shortest time edge path set between the nodes of the road undirected network topology model; N is the number of nodes in the node set of the road undirected network topology model;

[0063] Step 4 calculates the betweenness centrality of nodes in the road undirected network topology model as follows:

[0064]

[0065] Among them, EB i is the betweenness centrality of the i-th node in the node set of the road undirected network topology model; y i is the traffic flow of the i-th node in the node set of the road undirected network topology model, σ i is the number of nodes in the node set of the road undirected network topology model that appear in the shortest time point path set subset between the nodes of the road undirected network topology model; N is the number of nodes in the node set of the road undirected network topology model;

[0066] The present invention also provides a computer-readable medium, which stores a computer program executed by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of the method for optimizing the repair of urban flooded roads.

[0067] The advantages of the present invention are:

[0068] The road traffic network topology model is more accurate. Betweenness centrality is introduced as a network evaluation metric and optimized based on time-based path measurement according to the characteristics of rainstorm and waterlogging disaster scenarios, making it more able to reflect the road traffic network topology indicators under waterlogging disaster environments.

[0069] The network indicator evaluation model is more scalable. The improved betweenness centrality combines road network traffic flow and link connectivity, reflecting the importance and spatial distribution characteristics of road links and nodes in the transportation network, providing a reference for pre-warning, post-disaster restoration, and emergency command decision-making in urban flooding. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 : A flow chart of a method according to an embodiment of the present invention.

[0071] Figure 2 : Schematic diagram of the shortest time improved betweenness centrality model according to an embodiment of the present invention.

[0072] Figure 3 : Schematic diagram of the traffic flow improved betweenness centrality model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0073] In order to reflect the complex changes of the traffic network under waterlogging conditions, the betweenness centrality model is optimized by taking into account travel time and traffic flow, so that it can dynamically and accurately reflect the changes in road network conditions, and thus realize the priority estimation of urban flooded road repair.

[0074] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0075] In specific implementation, the method proposed in the technical solution of the present invention can be automatically run by those skilled in the art using computer software technology. System devices that implement the method, such as computer-readable storage media that store the corresponding computer program of the technical solution of the present invention and computer equipment that runs the corresponding computer program, should also be within the scope of protection of the present invention.

[0076] The following combination Figure 1-3 The technical solution of the method of the embodiment of the present invention is an optimized repair method for urban flooded roads, which is specifically as follows:

[0077] like Figure 1 Shown is a flow chart of the method of the present invention.

[0078] Step 1: Combine road network vector data and ground water surface data to construct a road undirected network topology model;

[0079] The road undirected network topology model is defined in step 1. The specific definition is as follows:

[0080]

[0081] M={m i}, 1≤i≤N

[0082] E={e i，j}, 1≤i≠j≤N

[0083] L={l i，j}, 1≤i≠j≤N

[0084] F={f i，j}, 1≤i≠j≤N

[0085] V={v i，j}, 1≤i≠j≤N

[0086]

[0087] Y={y i}, 1≤i≠j≤N

[0088] R={r i，j}, 1≤i≠j≤N

[0089] T={t i，j}, 1≤i≠j≤N

[0090] Among them, M represents the node set in the road undirected network topology model, m i represents the i-th node in the node set in the road undirected network topology model, N=100 represents the number of nodes in the node set in the road undirected network topology model, and is obtained through the road network vector data;

[0091] E represents the set of edges in the road undirected network topology model, e i，j Indicates whether the i-th node and the j-th node in the set of edges in the undirected road network topology model are connected, obtained through the road network vector data;

[0092] If the i-th node and the j-th node in the set of edges in the road undirected network topology model are connected, then e i，j =1, otherwise e i，j =0;

[0093] L represents the edge length set in the road undirected network topology model, l i，j Indicates the length of the edge formed by the i-th node and the j-th node in the edge length set in the road undirected network topology model, which is obtained through the road network vector data;

[0094] If the i-th node and the j-th node in the edge length set in the road undirected network topology model are connected, that is, e i，j =1, then l i，j is the length of the corresponding road; otherwise l i，j =∞;

[0095] F represents the set of water depths in the undirected road network topology model, f i，jIt represents the water depth of the edge formed by the i-th node and the j-th node in the water depth set in the road undirected network topology model, which is obtained through ground water accumulation data;

[0096] If the i-th node and the j-th node in the water depth set in the road undirected network topology model are connected, that is, e i，j =1, then f i，j is the water depth of the corresponding road; otherwise f i，j =∞;

[0097] V represents the set of designed vehicle speeds in the undirected road network topology model, v i，j Indicates the design speed of the edge formed by the i-th node and the j-th node in the design speed set in the undirected road network topology model, which is obtained through the road network vector data;

[0098] If the i-th node and the j-th node in the design speed set in the road undirected network topology model are connected, that is, e i，j =1, then v i，j is the design speed of the corresponding road; otherwise v i，j =0;

[0099] V * The set of design vehicle speeds representing the flooding situation in the undirected road network topology model, The design speed for the waterlogging situation of the edge formed by the i-th node and the j-th node in the design speed set for the waterlogging situation in the undirected road network topology model is the variable to be solved; Y represents the traffic flow set of the nodes in the undirected road network topology model, y i，j It represents the traffic flow of the ith node in the traffic flow set of nodes in the road undirected network topology model, which is the variable to be solved;

[0100] R represents the traffic flow set of the edges in the road undirected network topology model, r i，j The traffic flow of the edge formed by the i-th node and the j-th node in the traffic flow set of the edge in the road undirected network topology model is the variable to be solved;

[0101] If the traffic flow set of the edge in the road undirected network topology model is connected between the i-th node and the j-th node, that is, e i，j =1, then r i，j is the traffic flow of the corresponding road; otherwise r i，j =0;

[0102] T represents the travel time set in the undirected road network topology model, t i，j It represents the travel time of the edge formed by the i-th node and the j-th node in the travel time set in the road undirected network topology model, which is the variable to be solved;

[0103] If the i-th node and the j-th node in the travel time set in the road undirected network topology model are connected, that is, e i,j =1, then t i，j is the travel time of the corresponding road; otherwise t i,j =∞;

[0104] Step 2: Based on the set of waterlogging depths and the set of design vehicle speeds in the undirected road network topology model, the design vehicle speed set for the waterlogging scenario is calculated. This design vehicle speed set for the waterlogging scenario is introduced into the SUMO open-source model for traffic simulation, resulting in the traffic flow set for the nodes and the traffic flow set for the edges in the undirected road network topology model.

[0105] The design speed for the flooded situation of the edge formed by the i-th node and the j-th node in the design speed set for the flooded situation in the undirected road network topology model described in step 2 is specifically:

[0106]

[0107] 1≤i≠j≤N

[0108] in, is the design vehicle speed for the waterlogging condition on the edge formed by the i-th node and the j-th node in the node set in the undirected road network topology model, v i，j is the design speed of the edge formed by the i-th node and the j-th node in the node set in the road undirected network topology model, f i，j is the water depth of the edge formed by the i-th node and the j-th node in the node set of the undirected road network topology model, a = 30 is the critical water depth at which the vehicle is forced to stop, b = 5 is the decay exponent, tanh represents the hyperbolic tangent function, and N = 100 represents the number of nodes in the node set of the undirected road network topology model;

[0109] Step 3: Based on the design speed set for the waterlogging condition of the edge and the edge length set in the undirected road network topology model, the travel time set in the undirected road network topology model is obtained. Based on the Dijkstra algorithm, the shortest time point path set between each node in the undirected road network topology model and the shortest time edge path set between each node in the undirected road network topology model are generated.

[0110] The travel time set in the undirected road network topology model described in step 3 is specifically:

[0111]

[0112] 1≤i≠j≤N

[0113] Where, t i，jis the travel time of the edge formed by the i-th node and the j-th node in the node set of the road undirected network topology model, l i，j is the length of the edge formed by the i-th node and the j-th node in the node set of the road undirected network topology model, e i，j Is the connection between the i-th node and the j-th node in the node set of the road undirected network topology model, N = 100 represents the number of nodes in the node set of the road undirected network topology model;

[0114] Step 3 generates the shortest time point path set between nodes of the road undirected network topology model based on the Dijkstra algorithm, as follows:

[0115] Step 3.1: Introduce node set M = {m i}0≤i≤N, let the initial node count set The shortest time point path set between nodes in the road undirected network topology model

[0116] Step 3.2: Select any two nodes m from M o、 m d As the path starting node and ending node respectively, and m o 、m d join in In, such as Figure 2 As shown, different from the original betweenness centrality, this method uses time rather than distance as the weight, that is, the travel time set in the road undirected network topology model is used as the weight, and the Dijkstra algorithm is used to obtain the node m o、 m d The shortest path between them, represented as a node sequence

[0117] Add the node sequence to S;

[0118] Step 3.3: Repeat step 3.2 until all nodes in the initial set M are added And when each node is N-1, the iteration ends

[0119] Thus, the shortest time point path set between each node of the road undirected network topology model is obtained, which is as follows:

[0120] S={s1,s2,…,s N*(N-1) / 2}

[0121] Among them, s qrepresents the qth node sequence in the shortest time point path set between nodes in the road undirected network topology model, N*(N-1) / 2 represents the number of node sequences in the shortest time point path set between nodes in the road undirected network topology model, q∈[1, N*(N-1) / 2];

[0122] Each node sequence in the shortest time point path set between nodes of the road undirected network topology model is sequentially converted through the corresponding edges of two adjacent nodes to obtain the edge sequence corresponding to each node sequence in the shortest time point path set between nodes of the road undirected network topology model;

[0123] Construct the shortest time edge path set between each node in the road undirected network topology model through the edge sequence corresponding to each node sequence in the shortest time point path set between each node in the road undirected network topology model;

[0124] The shortest time edge path set between nodes in the road undirected network topology model is as follows:

[0125] P={p1,P2,…,P N*(N-1) / 2}

[0126] Among them, s q Represents the qth time series in the set of the shortest time edge paths between nodes in the road undirected network topology model, q∈[1, N*(N-1) / 2];

[0127] Step 4: Calculate the betweenness centrality of the edges in the undirected road network topology model based on the shortest time edge path set between each node in the undirected road network topology model, the traffic flow set of the edges in the undirected road network topology model, and the edge set; calculate the betweenness centrality of the nodes in the undirected road network topology model based on the shortest time point path set between each node in the undirected road network topology model and the traffic flow set of the nodes in the undirected road network topology model;

[0128] Step 4 calculates the betweenness centrality of the edge in the road undirected network topology model, such as Figure 3 As shown in Figure 2, unlike the original betweenness centrality, this method introduces the traffic flow of the edge as the weight in the calculation, as follows:

[0129]

[0130] Among them, EB i,j is the betweenness centrality of the edge formed by the i-th node and the j-th node in the node set of the road undirected network topology model; r i，j is the traffic flow of the edge formed by the i-th node and the j-th node in the node set of the road undirected network topology model, σ i，jis the number of edges formed by the i-th node and the j-th node in the node set of the road undirected network topology model that appear in the subset of the shortest time edge path set between each node in the road undirected network topology model; N = 100 is the number of nodes in the node set in the road undirected network topology model;

[0131] Step 4 calculates the betweenness centrality of nodes in the road undirected network topology model as follows:

[0132]

[0133] Among them, EB i is the betweenness centrality of the i-th node in the node set of the road undirected network topology model; y i is the traffic flow of the i-th node in the node set of the road undirected network topology model, σ i is the number of nodes in the node set of the road undirected network topology model that appear in the shortest time point path set subset between the nodes of the road undirected network topology model; N = 100 is the number of nodes in the node set of the road undirected network topology model;

[0134] Step 5: The edges between two nodes in the set of edges in the road undirected network topology model are sorted according to the betweenness centrality of the edges using a merge sort algorithm to obtain a set of edges in the road undirected network topology model after descending sorting, and the flooded road sections corresponding to the edges between two nodes in the set of edges in the road undirected network topology model after descending sorting are repaired in sequence; each node in the node set in the road undirected network topology model is sorted according to the betweenness centrality of the node using a merge sort algorithm to obtain a set of nodes in the road undirected network topology model after descending sorting, and the flooded intersections corresponding to each node in the set of nodes in the road undirected network topology model after descending sorting are repaired in sequence.

[0135] A specific embodiment of the present invention also provides a computer-readable medium.

[0136] The computer readable medium is a server workstation;

[0137] The server workstation stores a computer program executed by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of the optimized repair method for urban flooded roads according to the embodiment of the present invention.

[0138] It should be understood that parts not elaborated in detail in this specification belong to the prior art.

[0139] It should be understood that the above description of the preferred embodiment is relatively detailed and cannot be regarded as limiting the scope of protection of the patent of the present invention. Under the guidance of the present invention, ordinary technicians in this field can also make substitutions or modifications without departing from the scope of protection of the claims of the present invention, which all fall within the scope of protection of the present invention. The scope of protection requested by the present invention shall be based on the attached claims.

Claims

1. An optimized repair method for urban flooded roads, characterized in that: The following steps are involved: Step 1: Combine road network vector data and ground water surface data to construct a road undirected network topology model; Step 2: Based on the set of waterlogging depths and the set of design vehicle speeds in the undirected road network topology model, the design vehicle speed set for the waterlogging scenario is calculated. This design vehicle speed set for the waterlogging scenario is introduced into the SUMO open-source model for traffic simulation, resulting in the traffic flow set for the nodes and the traffic flow set for the edges in the undirected road network topology model. Step 3: Based on the design speed set for the waterlogging condition of the edge and the edge length set in the undirected road network topology model, the travel time set in the undirected road network topology model is obtained. Based on the Dijkstra algorithm, the shortest time point path set between each node in the undirected road network topology model and the shortest time edge path set between each node in the undirected road network topology model are generated. Step 4: Calculate the betweenness centrality of the edges in the undirected road network topology model based on the shortest time edge path set between each node in the undirected road network topology model, the traffic flow set of the edges in the undirected road network topology model, and the edge set; calculate the betweenness centrality of the nodes in the undirected road network topology model based on the shortest time point path set between each node in the undirected road network topology model and the traffic flow set of the nodes in the undirected road network topology model; Step 4 calculates the betweenness centrality of the edges in the undirected road network topology model as follows: in, It is the betweenness centrality of the edge formed by the i-th node and the j-th node in the node set of the road undirected network topology model; is the traffic flow of the edge formed by the i-th node and the j-th node in the node set of the road undirected network topology model, is the number of edges between the i-th node and the j-th node in the node set of the road undirected network topology model that appear in the subset of the shortest time edge path set between the nodes of the road undirected network topology model; N is the number of nodes in the node set of the road undirected network topology model; Step 4 calculates the betweenness centrality of nodes in the road undirected network topology model as follows: in, is the betweenness centrality of the i-th node in the node set of the road undirected network topology model; is the traffic flow of the i-th node in the node set of the road undirected network topology model, is the number of nodes in the node set of the road undirected network topology model that appear in the shortest time point path set subset between the nodes of the road undirected network topology model; N is the number of nodes in the node set of the road undirected network topology model; Step 5: Repair the flooded road sections one by one based on the betweenness centrality of the edges in the undirected road network topology model; repair the flooded intersections one by one based on the betweenness centrality of the nodes in the undirected road network topology model.

2. The optimized repair method for urban flooded roads according to claim 1 is characterized by: The road undirected network topology model is defined in step 1. The specific definition is as follows: in, Represents the node set in the road undirected network topology model, It represents the i-th node in the node set in the road undirected network topology model, N represents the number of nodes in the node set in the road undirected network topology model, and is obtained through the road network vector data; Represents the set of edges in the road undirected network topology model Indicates whether the i-th node and the j-th node in the set of edges in the undirected road network topology model are connected, obtained through the road network vector data; If the i-th node and the j-th node in the set of edges in the road undirected network topology model are connected, then =1, otherwise =0; L represents the set of edge lengths in the undirected road network topology model Indicates the length of the edge formed by the i-th node and the j-th node in the edge length set in the road undirected network topology model, which is obtained through the road network vector data; If the i-th node and the j-th node in the edge length set in the road undirected network topology model are connected, that is, =1, then is the length of the corresponding road; otherwise =∞; F represents the set of water depths in the undirected road network topology model It represents the water depth of the edge formed by the i-th node and the j-th node in the water depth set in the road undirected network topology model, which is obtained through ground water accumulation data; If the i-th node and the j-th node in the water depth set in the road undirected network topology model are connected, that is, =1, then is the water depth of the corresponding road; otherwise =∞; V represents the set of designed vehicle speeds in the undirected road network topology model Indicates the design speed of the edge formed by the i-th node and the j-th node in the design speed set in the undirected road network topology model, which is obtained through the road network vector data; If the i-th node and the j-th node in the design speed set in the road undirected network topology model are connected, that is, =1, then is the design speed of the corresponding road; otherwise =0; Design speed set representing waterlogging in an undirected road network topology model The design speed for the waterlogging situation on the edge formed by the i-th node and the j-th node in the design speed set for the waterlogging situation in the undirected road network topology model is the variable to be solved; Y represents the traffic flow set of the nodes in the undirected road network topology model. It represents the traffic flow of the ith node in the traffic flow set of nodes in the road undirected network topology model, which is the variable to be solved; R represents the traffic flow set of edges in the undirected road network topology model The traffic flow of the edge formed by the i-th node and the j-th node in the traffic flow set of the edge in the road undirected network topology model is the variable to be solved; If the traffic flow set of the edge in the road undirected network topology model is connected between the i-th node and the j-th node, that is, =1, then is the traffic flow of the corresponding road; otherwise =0; T represents the set of travel times in the undirected road network topology model It represents the travel time of the edge formed by the i-th node and the j-th node in the travel time set in the road undirected network topology model, which is the variable to be solved; If the i-th node and the j-th node in the travel time set in the road undirected network topology model are connected, that is, =1, then is the travel time of the corresponding road; otherwise =∞.

3. The optimized repair method for urban flooded roads according to claim 2 is characterized by: The design speed set for calculating the waterlogging situation in step 2 is: in, is the design vehicle speed for the waterlogging condition on the edge formed by the i-th node and the j-th node in the node set in the undirected road network topology model, is the design speed of the edge formed by the i-th node and the j-th node in the node set in the road undirected network topology model, is the water depth of the edge formed by the i-th node and the j-th node in the node set in the undirected road network topology model, a is the critical water depth at which the vehicle is forced to stagnate, b is the attenuation exponent, tanh represents the hyperbolic tangent function, and N represents the number of nodes in the node set in the undirected road network topology model.

4. The optimized repair method for urban flooded roads according to claim 3 is characterized by: The travel time set in the undirected road network topology model described in step 3 is specifically: Where, is the travel time of the edge formed by the i-th node and the j-th node in the node set of the road undirected network topology model, is the length of the edge formed by the i-th node and the j-th node in the node set of the road undirected network topology model, It indicates whether the i-th node and the j-th node in the node set of the road undirected network topology model are connected, and N represents the number of nodes in the node set of the road undirected network topology model.

5. The optimized repair method for urban flooded roads according to claim 4 is characterized in that: Step 3 generates the shortest time point path set between nodes of the road undirected network topology model based on the Dijkstra algorithm, as follows: Step 3.1: Introduce node set M={ } , let the initial node count set , the shortest time point path set S between each node in the road undirected network topology model ; Step 3.2: Select any two nodes from M 、 As the starting node and ending node of the path respectively, 、 join in In the undirected network topology model, the travel time set is used as the weight, and the Dijkstra algorithm is used to obtain the node 、 The shortest path between them, represented as a node sequence { }, add the node sequence to S; Step 3.3: Repeat step 3.2 until all nodes in the initial set M are added , and each node is N-1, the iteration ends Thus, the shortest time point path set between each node of the road undirected network topology model is obtained, which is as follows: S={ , ,…, } in, represents the qth node sequence in the shortest time point path set between nodes in the road undirected network topology model, N*(N-1) / 2 represents the number of node sequences in the shortest time point path set between nodes in the road undirected network topology model, q∈[1,N*(N-1) / 2]; Each node sequence in the shortest time point path set between nodes of the road undirected network topology model is sequentially converted through the corresponding edges of two adjacent nodes to obtain the edge sequence corresponding to each node sequence in the shortest time point path set between nodes of the road undirected network topology model; Construct the shortest time edge path set between each node in the road undirected network topology model through the edge sequence corresponding to each node sequence in the shortest time point path set between each node in the road undirected network topology model; The shortest time edge path set between nodes in the road undirected network topology model is as follows: P={ , ,…, } in, Represents the qth time series in the set of shortest time edge paths between nodes in the road undirected network topology model, q∈[1,N*(N-1) / 2].

6. The optimized repair method for urban flooded roads according to claim 5 is characterized by: Step 5 combines the betweenness centrality of the edges in the undirected road network topology model to repair the flooded road sections one by one, as follows: The edges between two nodes in the set of edges in the road undirected network topology model are sorted using a merge sort algorithm according to the betweenness centrality of the edges to obtain a set of edges in the road undirected network topology model after descending sorting. The flooded road sections corresponding to the edges between two nodes in the set of edges in the road undirected network topology model after descending sorting are repaired in sequence.

7. The optimized repair method for urban flooded roads according to claim 6, characterized in that: In step 5, the flooded intersections are repaired in sequence based on the betweenness centrality of the nodes in the undirected road network topology model, as follows: Each node in the node set in the road undirected network topology model is sorted according to the node's betweenness centrality using a merge sort algorithm to obtain a descending sorted node set in the road undirected network topology model, and the flooded intersection corresponding to each node in the descending sorted node set in the road undirected network topology model is repaired in turn.

8. A computer-readable medium, characterized in that It stores a computer program executed by an electronic device, and when the computer program runs on the electronic device, the electronic device executes the steps of the method according to any one of claims 1 to 7.

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