Method and system for evaluating efficiency of post-disaster repair strategy of urban multi-modal transportation network

Abstract

The application provides a kind of urban multi-mode traffic network post-disaster repair strategy efficiency evaluation method and system, belongs to the technical field of urban traffic operation management, constructs multi-mode traffic network structure model using road network and bus network open source data;Using traffic travel demand data to simulate urban traffic distribution, construct multi-mode traffic network function model;Based on multi-mode traffic network function model, according to the spatial distribution characteristics of disaster and post-disaster network repair strategy, construct multi-mode traffic network structure recovery model, evaluate the efficiency of structure recovery;Based on multi-mode traffic network structure recovery model, reload travel demand, construct multi-mode traffic network function recovery model, evaluate the efficiency of function recovery.The application can comprehensively consider various traffic modes and the complex dynamic game between travelers, provide more accurate and more comprehensive repair strategy evaluation, can enhance the resilience of urban integrated transport system, so that it can better cope with various disasters and emergencies.

Description

Technical Field

[0001] This invention relates to the field of urban traffic operation management technology, specifically to a method and system for evaluating the efficiency of post-disaster recovery strategies for urban multimodal transportation networks. Background Technology

[0002] The safety of critical transportation infrastructure systems is a pillar of economic prosperity. However, global climate change, leading to increased extreme weather events such as rising sea levels, storm surges, and urban flooding, poses a growing risk to these systems. Unprecedented urbanization and population expansion further exacerbate this risk. Furthermore, damage to critical transportation infrastructure systems can trigger cascading failures and chain reactions, impacting other critical infrastructure systems such as energy and communications, and causing significant indirect harm to various sectors of agriculture, the economy, and society.

[0003] Currently, methods for evaluating the efficiency of post-disaster recovery strategies for urban transportation networks primarily focus on single-modal transportation networks, such as car networks. These methods utilize complex network methods or traffic assignment methods, based on network structure or functional indicators, to predict the recovery efficiency of road network structure and function under different recovery strategies. In terms of structure, existing methods often use the maximum connected subgraph to measure the recovery efficiency of network connectivity; while in terms of function, existing methods prefer to apply complex network methods such as cascading failure and seepage, and traffic assignment methods such as continuous averaging, Frank-Wolf, and gradient projection to quantify the recovery efficiency of the transportation system's demand unloading capacity and traffic flow index. However, most of these methods focus on a single mode of transportation, neglecting the inherent multimodal nature of urban transportation systems. Specifically, under different recovery strategies, travelers compete and adjust their travel routes and modes of transportation based on the relative advantages of private and public transportation networks. Ignoring this crucial issue may lead to inaccurate assessments of post-disaster infrastructure recovery strategies, affecting the recovery of the transportation system's structure and function, and even delaying normal production and daily life, causing significant economic losses.

[0004] Therefore, current urban transportation network post-disaster recovery strategy assessment techniques have the problem of being unable to assess the efficiency of post-disaster recovery strategies for multimodal transportation systems that couple private and public transportation. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for evaluating the efficiency of post-disaster repair strategies for urban multimodal transportation networks. This method measures the impact of repair strategies for critical transportation infrastructure after disasters such as floods and earthquakes on the structural and functional recovery of urban integrated transportation systems. It helps to formulate economical and efficient emergency recovery plans, enhance the resilience of urban integrated transportation systems, and solve at least one of the technical problems existing in the background art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for evaluating the efficiency of post-disaster recovery strategies for urban multimodal transportation networks, comprising:

[0008] Construct a multimodal transportation network structure model using open-source data from road and public transport networks;

[0009] Based on the multimodal transportation network structure model, we simulate urban traffic distribution using traffic travel demand data and construct a multimodal transportation network functional model.

[0010] Based on the multimodal transportation network functional model, a multimodal transportation network structural recovery model is constructed according to specific disaster spatial distribution characteristics and post-disaster network repair strategies, and the structural recovery efficiency is evaluated.

[0011] Based on the multimodal transportation network structure recovery model, travel demand is reloaded, a multimodal transportation network function recovery model is constructed, and the function recovery efficiency is evaluated.

[0012] Furthermore, road network data and public transport network data are collected to construct a multimodal transportation network structure model, including:

[0013] Road network data includes road network topology, node attributes, and edge attributes. Road network data for the study area is obtained from an open street map. Intersections are abstracted as nodes, and road segments are abstracted as edges to construct a directed network (N... r A r ), where N r Let A be a set of nodes. r This is the set of edges. To ensure the connectivity of the road network, we identify the maximum connected subgraph of the network and remove nodes and edges that are not on the maximum connected subgraph, thereby constructing the network's topology.

[0014] The public transport network data includes the public transport network topology, node attributes, and edge attributes. Based on the map open platform, station information, route information, and departure timetable information of the transportation system are crawled, and the Space L method is applied to construct the topology of the directed public transport network.

[0015] Based on the walking transfer threshold, construct the transfer connection edges and attributes of the public transportation system to improve the public transportation network.

[0016] Furthermore, based on traffic demand data, a multi-modal transportation network functional model is constructed by simulating traffic distribution, including:

[0017] Based on a multimodal transportation network structure model, segment cost functions for road segments, bus segments, and transfer segments are modeled separately.

[0018] Path cost functions for road networks and public transport networks are constructed based on the segment cost function.

[0019] Dynamic multimodal traffic demand is allocated based on segment cost function and path cost function;

[0020] The system filters completed travel demands and, based on the cost of updating the road and public transport networks for vehicles still stuck in the network, divides the traffic demands with departure time ζ1 into segments according to the same preset ratio. It then loads all traffic demands within this departure time segment using the continuous averaging method and updates the network impedance. This process continues until all traffic demands with departure times have been loaded, and records the number of arrival demands and the average speed of each arrival demand for each time window.

[0021] Furthermore, based on the spatial damage characteristics of disasters and post-disaster recovery strategies, a structural recovery model for multimodal transportation networks is constructed to evaluate structural recovery efficiency, including:

[0022] Based on the spatial characteristics of disasters, identify and eliminate nodes and road segments that are directly or indirectly failing in the multimodal transportation network;

[0023] Based on the post-disaster recovery strategy, identify and add nodes and road segments that are directly restored at each time step of the multimodal transportation network;

[0024] Detect and add indirectly recovered nodes and road segments at each time step; if a directly recovered node reconnects the indirectly failed node and the maximum connected subgraph, then the indirectly failed node is indirectly recovered.

[0025] Assess the recovery efficiency of multimodal transportation network structures.

[0026] Furthermore, traffic demand is reloaded, a functional recovery model for the multimodal transportation network is constructed, and the efficiency of functional recovery is evaluated, including:

[0027] Based on the network structure recovery model, the time-varying cost of road segments in a multimodal traffic network is constructed;

[0028] Based on the dynamic multimodal traffic assignment method, travel demand is reloaded on the time-varying multimodal traffic network.

[0029] A blank control group was constructed to build a multimodal transportation network function recovery model by reloading travel demand in scenarios without any network repair.

[0030] Calculate the function recovery efficiency of the multimodal transportation network and the speed-weighted function recovery efficiency.

[0031] Furthermore, the functional recovery efficiency R of the multimodal transportation network is calculated. d and speed-weighted function recovery efficiency Among them, the functional recovery efficiency R d :

[0032]

[0033] Among them, R d A value between 0 and 1 indicates that the closer a value is to 1, the faster the multimodal transportation system can recover its capacity to offload traffic demand; conversely, a value closer to 1 indicates a slower recovery.

[0034] Calculation speed-weighted function recovery efficiency

[0035]

[0036] in This represents the ratio of the speed of demand k in the repair scenario to the speed in the non-destructive scenario. This represents the ratio of the speed of demand k in the no-repair scenario to the speed in the no-destruction scenario; A value between 0 and 1 indicates that the multimodal transportation system can meet travel demand more efficiently and achieve smoother travel speeds under the repair strategy. Conversely, the repair strategy may lead to severe traffic congestion and fail to effectively alleviate demand pressure.

[0037] Secondly, the present invention provides an efficiency evaluation system for post-disaster recovery strategies of urban multimodal transportation networks, comprising:

[0038] The first building module is used to construct a multimodal transportation network structure model using open-source data from road and public transport networks.

[0039] The second building module is used to simulate urban traffic distribution based on traffic demand data and construct a multimodal transportation network functional model based on the multimodal transportation network structure model.

[0040] The third construction module is used to construct a multimodal transportation network structure recovery model based on the multimodal transportation network functional model, according to specific disaster spatial distribution characteristics and post-disaster network repair strategies, and to evaluate the structure recovery efficiency.

[0041] The reloading module is used to reload travel demand based on the multimodal transportation network structure recovery model, construct a multimodal transportation network function recovery model, and evaluate the function recovery efficiency.

[0042] Thirdly, the present invention provides a non-transitory computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the method for evaluating the efficiency of post-disaster repair strategies for urban multimodal transportation networks as described in the first aspect.

[0043] Fourthly, the present invention provides a computer device including a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the efficiency evaluation method for post-disaster recovery strategy of urban multimodal transportation network as described in the first aspect.

[0044] Fifthly, the present invention provides an electronic device, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the method for evaluating the efficiency of urban multimodal transportation network post-disaster recovery strategies as described in the first aspect.

[0045] The beneficial effects of this invention are as follows: It utilizes four modules—a multimodal transportation network structure model, a functional model, a structural recovery model, and a functional recovery model—to evaluate the efficiency of post-disaster recovery strategies for urban multimodal transportation networks; it comprehensively considers multiple modes of transportation, such as cars, surface public transport, and rail public transport, as well as the complex dynamic game among travelers; it provides a more accurate and comprehensive assessment of recovery strategies, helping urban planners and policymakers to allocate resources more effectively, optimize pre-disaster preparedness planning, emergency response during disasters, and rapid post-disaster recovery; and it enhances the resilience of urban integrated transportation systems, enabling them to better cope with various disasters and emergencies.

[0046] The advantages of additional aspects of the invention will be set forth more clearly in the following description or will be learned by practice of the invention. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating the framework for evaluating the efficiency of post-disaster recovery strategies for urban multimodal transportation networks as described in this embodiment of the invention. Detailed Implementation

[0049] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0050] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0051] It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as here.

[0052] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, and / or groups thereof.

[0053] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0054] To facilitate understanding of the present invention, the present invention will be further explained and described below with reference to the accompanying drawings and specific embodiments. However, the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0055] Those skilled in the art should understand that the accompanying drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily essential for implementing the present invention.

[0056] Example 1

[0057] In this embodiment 1, a system for evaluating the efficiency of post-disaster repair strategies for urban multimodal transportation networks is first provided. This system includes: a first construction module for building a multimodal transportation network structure model using open-source data from road and public transport networks; a second construction module for simulating urban traffic distribution using travel demand data based on the multimodal transportation network structure model to build a multimodal transportation network functional model; a third construction module for building a multimodal transportation network structure recovery model based on the multimodal transportation network functional model, according to specific disaster spatial distribution characteristics and post-disaster network repair strategies, and evaluating the structure recovery efficiency; and a reloading module for reloading travel demand based on the multimodal transportation network structure recovery model to build a multimodal transportation network functional recovery model and evaluate the functional recovery efficiency.

[0058] In this embodiment 1, the above-described system is used to implement a method for evaluating the efficiency of post-disaster recovery strategies for urban multimodal transportation networks, including:

[0059] Step 1: Collect road network data and public transport network data to construct a multimodal transportation network structure model. Step 1 specifically includes the following steps:

[0060] Step 1.1: Road network data includes road network topology, node attributes, and edge attributes. Road network data for the study area is obtained from an Open Street Map (OSM). Intersections are abstracted as nodes, and road segments are abstracted as edges, constructing a directed network (N... r A r ). Where N r Let A be a set of nodes. r This is a set of connected edges. To ensure the connectivity of the road network, the maximum connected subgraph of the network is identified, and nodes and edges not on the maximum connected subgraph are removed, thus constructing the network's topology. Node attributes are extracted from the OSM, including node code i and node longitude lon. i Node latitude lat i And edge attributes including edge code a, length l a Free circulation costs Capacity a .

[0061] Step 1.2: Public transport network data includes the network topology, node attributes, and edge attributes. Based on map open platforms (such as Amap) and Transitland, station information, route information, and departure timetable information for ground public transport and rail public transport systems are crawled. The Space L method is applied to construct the topology of the directed public transport network, which abstracts bus stops as nodes and the interaction relationships between adjacent stops on bus routes as edges, thereby constructing a directed network (N... p Ap ), where N p Let A be a set of nodes. p This is a set of connected edges. Node codes i and longitude lon are extracted from the map open platform. i and node latitude lat i As node attributes, the edge code 'a' and the line departure interval 'λ' are extracted. u Departure frequency f a Scheduled travel time The bus route u is used as the edge attribute.

[0062] Step 1.3: Based on the walking transfer threshold, construct the transfer edges and their attributes of the public transportation system to improve the public transportation network. Using tools such as ArcGIS, filter and extract station pairs whose geographical distance is less than the walking transfer threshold τ. If station i and station j are on different bus routes, construct a bidirectional virtual edge a to connect the station pair, thus constructing the set of transfer edges A of the public transportation network. w And extract the geographic distance l a As an edge attribute.

[0063] Step 2: Simulate traffic distribution based on travel demand data and construct a multi-modal transportation network functional model. Step 2 specifically includes the following steps:

[0064] Step 2.1: Based on the multimodal transportation network structure model, model the segment cost functions for road segments, bus segments, and transfer segments respectively. For road segments, use the Bureau of Public Roads (BPR) function to calibrate the segment cost (or time) c. a Traffic flow on road segment x a Relationship:

[0065]

[0066] Where α = 0.15 and β = 4. For bus routes, buses operate according to a departure timetable, and vehicle arrival follows a Poisson distribution. The cost of the bus route is c. a Traffic flow on road segment x a Relationship:

[0067]

[0068] cap u η is the maximum passenger capacity of a single operating vehicle on bus route u; u,a This is a binary variable; it is 1 when bus route u passes through segment a, and 0 otherwise. Unlike bus segments, the cost c of transfer segments... a Only affected by the distance of the transfer section l a Influence:

[0069]

[0070] Where ξ is the non-linearity coefficient of walking distance, v w This refers to walking speed.

[0071] Step 2.2: Construct path cost functions for the road network and public transport network based on the segment cost function. The travel cost in the road network is the cost of choosing path p from the origin s to the destination v.

[0072]

[0073] Where, δ a,p Let be a binary variable, equal to 1 when segment 'a' is on path 'p', and 0 otherwise. The cost of choosing path 'p' from origin 's' to destination 'v' in a public transport network.

[0074]

[0075] in, This is the expected waiting cost for public transportation line u. Therefore, Represents the cost of road sections. Represents the cost of waiting for a ride; Walking costs.

[0076] Step 2.3: Allocate dynamic multimodal traffic demand based on segment cost function and path cost function. Traffic demand is aggregated according to departure time, and dynamic multimodal traffic demand is allocated using an improved continuous averaging method. First, departure time is divided into different time periods ζ0, ζ1, ζ2 according to the set time granularity ζ from morning to night. 2... The system aggregates travel demands within each time period. When time t = ζ0, the travel demands departing at ζ0 are randomly allocated according to a preset proportion (e.g., 40%, 30%, 20%, 10%) and arranged in descending order. Dijkstra's shortest path algorithm is used to calculate and compare the shortest path and cost for each OD demand on the road network and public transport network, selecting the optimal transportation network for flow allocation and updating road segment flow and impedance. Then, based on the updated road impedance, the optimal transportation mode and shortest path are allocated to 30% of the travel demands, and the road network and public transport network flow and impedance are updated again. This process is repeated until all travel demands for that time period are loaded.

[0077] Step 2.4: When time t = ζ1, filter completed travel demands and update the costs of the road network and public transport network based on the vehicles still stuck in the network. Then, divide the traffic demands with departure time ζ1 into segments according to the same preset ratio, load all traffic demands within this departure time segment using the continuous averaging method, and update the network impedance.

[0078] Step 2.5: Repeat step 2.4 until all travel demands for departure times are loaded, and record the arrival demand d for each time window. 0 (t) and the average speed at which each demand k is reached.

[0079] Step 3: Based on the spatial damage characteristics of the disaster and post-disaster recovery strategies, construct a structural recovery model for the multimodal transportation network and evaluate the structural recovery efficiency. Step 3 specifically includes the following steps:

[0080] Step 3.1: Based on the spatial characteristics of the disaster, identify and remove nodes and road segments that are directly or indirectly failed in the multimodal transportation network. If a node is located within a disaster area (e.g., a flood-prone area) and the damage intensity exceeds a damage threshold (e.g., the flood depth of the node exceeds its flood threshold), then the node and its edges are considered directly failed. If a node is not located within a disaster area but is no longer connected to the maximum connected subgraph, then it is considered indirectly failed. The remaining maximum connected subgraph is considered a functional network, and its nodes account for P% of the total number of nodes. ∞ .

[0081] Step 3.2: Based on the post-disaster recovery strategy, identify and add nodes and road segments that are directly recovered at each time step of the multimodal transportation network. According to the recovery strategy, determine the direct failures that are recovered at each time step, and add these directly recovered nodes and road segments to the functional network. If a direct recovery is connected to the maximum connected subgraph, it is considered a valid recovery; otherwise, it is considered a newly added indirect failure.

[0082] Step 3.3: Detect and add indirectly recovered nodes and segments at each time step. If a directly recovered node reconnects the indirectly failed node and the maximum connected subgraph, then the indirectly failed node is considered indirectly recovered.

[0083] Step 3.4: Evaluate the recovery efficiency R of the multimodal transportation network structure c :

[0084]

[0085] Where t1 is the network repair start time, and t2 is the network repair end time. R c The value is between 0 and 1. The closer it is to 1, the faster the connectivity of the multimodal transportation network topology is restored, and vice versa.

[0086] Step 4: Reload traffic demand, construct a functional recovery model for the multimodal transportation network, and evaluate the efficiency of functional recovery. Step 4 specifically includes the following steps:

[0087] Step 4.1: Based on the network structure recovery model, construct the time-varying cost of road segments in the multimodal transportation network. If road segment a fails at time t1, then its travel cost c... a It is infinite; if the road segment recovers at time t2, then its travel cost will recover, as shown in equations (1)-(3).

[0088] Step 4.2: Based on the dynamic multimodal traffic assignment method in Steps 2.3-2.5, reload travel demands on the time-varying multimodal traffic network. Before allocating travel demands with the same departure time, update the impedance functions of roads and bus segments using the network structure repair model (refer to 4.1); update the network stagnation demand and the impedance cost functions of roads and bus segments. If, at a certain departure time, there is no reachable path for a certain travel demand (minimum travel time is infinite), then the departure time of that travel demand is postponed to the next departure time window. Record the number of arrival demands d(t) and the average speed s of each arrival demand k at each time t. k .

[0089] Step 4.3: Reload travel demand in a scenario without any network repair to construct a blank control group for the multimodal transportation network function recovery model. Record the number of arrival demands d'(t) and the average speed s' of each arrival demand k in each time window. k .

[0090] Step 4.4: Calculate the multimodal traffic network function recovery efficiency Rd and the speed-weighted function recovery efficiency. Among them, the functional recovery efficiency R d :

[0091]

[0092] Among them, R d A value between 0 and 1 indicates that the closer the value is to 1, the faster the multimodal transportation system can recover its capacity to unload traffic demand; conversely, a value further away indicates a slower recovery. Besides the number of demands completed per unit time, traffic congestion is also a crucial indicator for evaluating the system's performance recovery. Therefore, a speed-weighted functional recovery efficiency is calculated.

[0093]

[0094] in This represents the ratio of the speed of demand k in the repair scenario to the speed in the non-destructive scenario. This represents the ratio of the speed of demand k in the no-repair scenario to the speed in the no-destruction scenario. A value between 0 and 1 indicates that the multimodal transportation system can more efficiently meet travel demand and achieve smoother travel speeds under this remediation strategy. Conversely, a value closer to 1 may lead to severe traffic congestion and fail to effectively alleviate demand pressure.

[0095] Example 2

[0096] like Figure 1 As shown in Example 2, this embodiment provides a method for evaluating the efficiency of post-disaster recovery strategies for urban multimodal transportation networks. This includes: constructing a multimodal transportation network structural model using open-source data such as road and public transport networks; simulating urban traffic distribution using travel demand data to construct a multimodal transportation network functional model; constructing a multimodal transportation network structural recovery model based on specific disaster spatial distribution characteristics and post-disaster network recovery strategies, and evaluating the structural recovery efficiency; and reloading travel demand to construct a multimodal transportation network functional recovery model and evaluating the functional recovery efficiency.

[0097] Specifically, in this embodiment 2, step 1: collecting road network data and public transport network data to construct a multimodal transportation network structure model, includes the following steps:

[0098] Step 1.1: Road network data includes road network topology, node attributes, and edge attributes. Road network data for the study area is obtained from an Open Street Map (OSM). Intersections are abstracted as nodes, and road segments are abstracted as edges, constructing a directed network (N... r A r ). Where N r Let A be a set of nodes. r This is a set of connected edges. To ensure the connectivity of the road network, the maximum connected subgraph of the network is identified, and nodes and edges not on the maximum connected subgraph are removed, thus constructing the network's topology. Node attributes are extracted from the OSM, including node code i and node longitude lon. i Node latitude lat i And edge attributes including edge code a, length l a Free circulation costs Capacity a .

[0099] Step 1.2: Public transport network data includes the network topology, node attributes, and edge attributes. Based on map open platforms (such as Amap) and Transitland, station information, route information, and departure timetable information for ground public transport and rail public transport systems are crawled. The Space L method is applied to construct the topology of the directed public transport network, which abstracts bus stops as nodes and the interaction relationships between adjacent stops on bus routes as edges, thereby constructing a directed network (N... p A p ), where N p Let A be a set of nodes. p This is a set of connected edges. Node codes i and longitude lon are extracted from the map open platform. i and node latitude lat i As node attributes, the edge code 'a' and the line departure interval 'λ' are extracted. u Departure frequency f a Scheduled travel time The bus route u is used as the edge attribute.

[0100] Step 1.3: Based on the walking transfer threshold, construct the transfer edges and their attributes of the public transportation system to improve the public transportation network. Using tools such as ArcGIS, filter and extract station pairs whose geographical distance is less than the walking transfer threshold τ. If station i and station j are on different bus routes, construct a bidirectional virtual edge a to connect the station pair, thus constructing the set of transfer edges A of the public transportation network. w And extract the geographic distance l a As an edge attribute.

[0101] Specifically, in this embodiment 2, step 2: simulating traffic distribution based on travel demand data and constructing a multi-modal transportation network functional model, specifically includes:

[0102] Step 2.1: Based on the multimodal transportation network structure model, model the segment cost functions for road segments, bus segments, and transfer segments respectively. For road segments, use the Bureau of Public Roads (BPR) function to calibrate the segment cost (or time) c. a Traffic flow on road segment x a Relationship:

[0103]

[0104] Where α = 0.15 and β = 4. For bus routes, buses operate according to a departure timetable, and vehicle arrival follows a Poisson distribution. The cost of the bus route is c. a Traffic flow on road segment x a Relationship:

[0105]

[0106] cap u η is the maximum passenger capacity of a single operating vehicle on bus route u; u,a This is a binary variable; it is 1 when bus route u passes through segment a, and 0 otherwise. Unlike bus segments, the cost c of transfer segments... a Only affected by the distance of the transfer section l a Influence:

[0107]

[0108] Where ξ is the non-linearity coefficient of walking distance, v w This refers to walking speed.

[0109] Step 2.2: Construct path cost functions for the road network and public transport network based on the segment cost function. The travel cost in the road network is the cost of choosing path p from the origin s to the destination v.

[0110]

[0111] Where, δ a,p Let be a binary variable, equal to 1 when segment 'a' is on path 'p', and 0 otherwise. The cost of choosing path 'p' from origin 's' to destination 'v' in a public transport network.

[0112]

[0113] in, This is the expected waiting cost for public transportation line u. Therefore, Represents the cost of road sections. Represents the cost of waiting for a ride; Walking costs.

[0114] Step 2.3: Allocate dynamic multimodal traffic demand based on segment cost function and path cost function. Traffic demand is aggregated according to departure time, and dynamic multimodal traffic demand is allocated using an improved continuous averaging method. First, departure time is divided into different time periods ζ0, ζ1, ζ2 according to the set time granularity ζ from morning to night. 2...The system aggregates travel demands within each time period. When time t = ζ0, the travel demands departing at ζ0 are randomly allocated according to a preset proportion (e.g., 40%, 30%, 20%, 10%) and arranged in descending order. Dijkstra's shortest path algorithm is used to calculate and compare the shortest path and cost for each OD demand on the road network and public transport network, selecting the optimal transportation network for flow allocation and updating road segment flow and impedance. Then, based on the updated road impedance, the optimal transportation mode and shortest path are allocated to 30% of the travel demands, and the road network and public transport network flow and impedance are updated again. This process is repeated until all travel demands for that time period are loaded.

[0115] Step 2.4: When time t = ζ1, filter completed travel demands and update the costs of the road network and public transport network based on the vehicles still stuck in the network. Then, divide the traffic demands with departure time ζ1 into segments according to the same preset ratio, load all traffic demands within this departure time segment using the continuous averaging method, and update the network impedance.

[0116] Step 2.5: Repeat step 2.4 until all travel demands for departure times are loaded, and record the arrival demand d for each time window. 0 (t) and the average speed at which each demand k is reached.

[0117] Specifically, in this embodiment 2, step 3: based on the spatial damage characteristics of disasters and post-disaster restoration strategies, a structural restoration model of a multi-modal transportation network is constructed, and the structural restoration efficiency is evaluated, specifically including:

[0118] Step 3.1: Based on the spatial characteristics of the disaster, identify and remove nodes and road segments that are directly or indirectly failed in the multimodal transportation network. If a node is located within a disaster area (e.g., a flood-prone area) and the damage intensity exceeds a damage threshold (e.g., the flood depth of the node exceeds its flood threshold), then the node and its edges are considered directly failed. If a node is not located within a disaster area but is no longer connected to the maximum connected subgraph, then it is considered indirectly failed. The remaining maximum connected subgraph is considered a functional network, and its nodes account for P% of the total number of nodes. ∞ .

[0119] Step 3.2: Based on the post-disaster recovery strategy, identify and add nodes and road segments that are directly recovered at each time step of the multimodal transportation network. According to the recovery strategy, determine the direct failures that are recovered at each time step, and add these directly recovered nodes and road segments to the functional network. If a direct recovery is connected to the maximum connected subgraph, it is considered a valid recovery; otherwise, it is considered a newly added indirect failure.

[0120] Step 3.3: Detect and add indirectly recovered nodes and segments at each time step. If a directly recovered node reconnects the indirectly failed node and the maximum connected subgraph, then the indirectly failed node is considered indirectly recovered.

[0121] Step 3.4: Evaluate the recovery efficiency R of the multimodal transportation network structure c :

[0122]

[0123] Where t1 is the network repair start time, and t2 is the network repair end time. R c The value is between 0 and 1. The closer it is to 1, the faster the connectivity of the multimodal transportation network topology is restored, and vice versa.

[0124] Specifically, in this embodiment 2, step 4: reloading traffic demand, constructing a functional recovery model of the multimodal transportation network, and evaluating the functional recovery efficiency, specifically includes:

[0125] Step 4.1: Based on the network structure recovery model, construct the time-varying cost of road segments in the multimodal transportation network. If road segment a fails at time t1, then its travel cost c... a It is infinite; if the road segment recovers at time t2, then its travel cost will recover, as shown in equations (1)-(3).

[0126] Step 4.2: Based on the dynamic multimodal traffic assignment method in Steps 2.3-2.5, reload travel demands on the time-varying multimodal traffic network. Before allocating travel demands with the same departure time, update the impedance functions of roads and bus segments using the network structure repair model (refer to 4.1); update the network stagnation demand and the impedance cost functions of roads and bus segments. If, at a certain departure time, there is no reachable path for a certain travel demand (minimum travel time is infinite), then the departure time of that travel demand is postponed to the next departure time window. Record the number of arrival demands d(t) and the average speed s of each arrival demand k at each time t. k .

[0127] Step 4.3: Reload travel demand in a scenario without any network repair to construct a blank control group for the multimodal transportation network function recovery model. Record the number of arrival demands d'(t) and the average speed s' of each arrival demand k in each time window. k .

[0128] Step 4.4: Calculate the multimodal traffic network function recovery efficiency Rd and the speed-weighted function recovery efficiency. Among them, functional recovery efficiency R d :

[0129]

[0130] Among them, R d A value between 0 and 1 indicates that the closer the value is to 1, the faster the multimodal transportation system can recover its capacity to unload traffic demand; conversely, a value further away indicates a slower recovery. Besides the number of demands completed per unit time, traffic congestion is also a crucial indicator for evaluating the system's performance recovery. Therefore, a speed-weighted functional recovery efficiency is calculated.

[0131]

[0132] in This represents the ratio of the speed of demand k in the repair scenario to the speed in the non-destructive scenario. This represents the ratio of the speed of demand k in the no-repair scenario to the speed in the no-destruction scenario. A value between 0 and 1 indicates that the multimodal transportation system can more efficiently meet travel demand and achieve smoother travel speeds under this remediation strategy. Conversely, a value closer to 1 may lead to severe traffic congestion and fail to effectively alleviate demand pressure.

[0133] This embodiment uses a multimodal transportation network in a city of a certain country as a case study. Considering the threats posed to the transportation system by sea-level rise and tidal surges in a certain river, as well as the frequent occurrence of floods in the city, a once-in-a-century flood event is selected as the main disaster-causing factor. In this context, it is assumed that the restoration strategy adopts the principle of traffic flow priority restoration, that is, prioritizing the restoration of road sections with high passenger traffic. Through the study of this case, the practicality and effectiveness of the efficiency evaluation method for the post-disaster restoration strategy of urban multimodal transportation networks described in this embodiment are demonstrated.

[0134] (1) Modeling results of the city's multimodal transportation network structure

[0135] Based on OSM, the road network of the city's metropolitan area was extracted and constructed, including 391,475 nodes and 882,276 edges. The public transportation network of the city's metropolitan area was also extracted, including surface buses and rail transit, with a total of 25,827 stations, 9,251 bus routes, and 40,865 departures. The public transportation network was constructed, including 25,827 nodes and 16,225 edges.

[0136] (2) Results of multimodal transportation network functional modeling in the city

[0137] Based on the multimodal transportation network structure modeling results, road segment cost functions, public transport segment cost functions, and transfer segment cost functions are constructed respectively. Let the walking time v wThe speed is 5 km / h, and the non-linearity coefficient ξ for walking distance is set to 1.4. 25% of the traffic demand is extracted from the MATSim open-source simulation scenario of the city, totaling 3,016,262 trips. Simultaneously, the road capacity and the maximum passenger capacity of public transport vehicles are scaled down to 25% of their original values. The time granularity ζ is set to 0.5 hours, and trip demands are aggregated. Based on steps 2.3-2.5, traffic demands at different departure times are loaded sequentially, and the number of arrivals (d) for each event is recorded. 0 (t) and the average speed at which each demand k is reached.

[0138] (3) Results of multimodal transportation network structure restoration modeling and structure restoration efficiency assessment in the city

[0139] Data on a once-in-a-century flood in the city was extracted from the European Centre for Research in Europe (ECRI) data catalog, with a spatial accuracy of 100m. An inundation threshold of 0.3m was set. Direct failures were identified based on the floodplain's extent, and indirect failures were identified based on connectivity. The direct failure rate for the road network was approximately 8.47%, and the indirect failure rate was 51.34%. The direct failure rate for the public transport network was approximately 16.93%, and the indirect failure rate was approximately 24.58%. Based on a traffic-priority recovery strategy, the road segment recovery sequence and the direct and indirect recovery at each time step were calibrated. The structural recovery efficiency R of the multimodal transport network under this strategy was calculated. c It is approximately 0.87.

[0140] (4) Results of multimodal transportation network function restoration modeling and function restoration efficiency assessment in the city

[0141] Based on the multimodal traffic network structure restoration model, the time-varying cost of road segments is constructed, and traffic demand is reallocated. The number of arrivals d(t) at each time point and the average speed s of each arrival demand k are recorded. k Similarly, under the no-repair strategy, the arrival demand quantity d'(t) and the average velocity s' of each arrival demand k are recorded for each time window. k The multimodal transportation network functional recovery efficiency R is calculated based on equation (7-8). d The speed-weighted function recovery efficiency is approximately 0.75. It is approximately 0.68.

[0142] Example 3

[0143] This embodiment 3 provides a non-transitory computer-readable storage medium for storing computer instructions. When executed by a processor, the computer instructions implement the efficiency evaluation method for post-disaster recovery strategies of urban multimodal transportation networks as described above. The method includes:

[0144] Construct a multimodal transportation network structure model using open-source data from road and public transport networks;

[0145] Based on the multimodal transportation network structure model, we simulate urban traffic distribution using traffic travel demand data and construct a multimodal transportation network functional model.

[0146] Based on the multimodal transportation network functional model, a multimodal transportation network structural recovery model is constructed according to specific disaster spatial distribution characteristics and post-disaster network repair strategies, and the structural recovery efficiency is evaluated.

[0147] Based on the multimodal transportation network structure recovery model, travel demand is reloaded, a multimodal transportation network function recovery model is constructed, and the function recovery efficiency is evaluated.

[0148] Example 4

[0149] This embodiment 4 provides a computer device, including a memory and a processor, wherein the processor and the memory communicate with each other, and the memory stores program instructions that can be executed by the processor. The processor calls the program instructions to execute the efficiency evaluation method for post-disaster recovery strategies of urban multimodal transportation networks as described above, the method including:

[0150] Construct a multimodal transportation network structure model using open-source data from road and public transport networks;

[0151] Based on the multimodal transportation network structure model, we simulate urban traffic distribution using traffic travel demand data and construct a multimodal transportation network functional model.

[0152] Based on the multimodal transportation network functional model, a multimodal transportation network structural recovery model is constructed according to specific disaster spatial distribution characteristics and post-disaster network repair strategies, and the structural recovery efficiency is evaluated.

[0153] Based on the multimodal transportation network structure recovery model, travel demand is reloaded, a multimodal transportation network function recovery model is constructed, and the function recovery efficiency is evaluated.

[0154] Example 5

[0155] This embodiment 5 provides an electronic device, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes instructions to implement the above-described method for evaluating the efficiency of urban multimodal transportation network post-disaster recovery strategies. The method includes:

[0156] Construct a multimodal transportation network structure model using open-source data from road and public transport networks;

[0157] Based on the multimodal transportation network structure model, we simulate urban traffic distribution using traffic travel demand data and construct a multimodal transportation network functional model.

[0158] Based on the multimodal transportation network functional model, a multimodal transportation network structural recovery model is constructed according to specific disaster spatial distribution characteristics and post-disaster network repair strategies, and the structural recovery efficiency is evaluated.

[0159] Based on the multimodal transportation network structure recovery model, travel demand is reloaded, a multimodal transportation network function recovery model is constructed, and the function recovery efficiency is evaluated.

[0160] In summary, the method for evaluating the efficiency of urban multimodal transportation network post-disaster recovery strategies described in this invention utilizes four modules—a multimodal transportation network structure model, a functional model, a structural recovery model, and a functional recovery model—to assess the efficiency of urban multimodal transportation network post-disaster recovery strategies. Compared to traditional single-modal evaluation methods, this method comprehensively considers multiple transportation modes, such as cars, surface public transport, and rail public transport, as well as the complex dynamic game among travelers. Therefore, this method provides a more accurate and comprehensive assessment of recovery strategies, helping urban planners and policymakers to allocate resources more effectively and optimize pre-disaster preparedness planning, emergency response during disasters, and rapid post-disaster recovery. Ultimately, this method can enhance the resilience of urban integrated transportation systems, enabling them to better cope with various disasters and emergencies.

[0161] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0162] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0163] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0164] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment, whereby a series of operational steps are performed to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0165] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that, based on the technical solutions disclosed in the present invention, various modifications or variations that can be made by those skilled in the art without creative effort should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the efficiency of post-disaster recovery strategies for urban multimodal transportation networks, characterized in that, include: Construct a multimodal transportation network structure model using open-source data from road and public transport networks; Based on a multimodal transportation network structure model, this paper simulates urban traffic distribution using travel demand data and constructs a multimodal transportation network functional model. This model includes: modeling the segment cost functions for road segments, bus segments, and transfer segments based on the multimodal transportation network structure model; constructing path cost functions for the road network and bus network based on the segment cost functions; allocating dynamic multimodal traffic demand based on the segment cost functions and path cost functions; and determining the appropriate time granularity. The departure time is divided into different time periods from morning to night. It aggregates travel demand within each time period; when time At that time, the departure time will be Traffic demand is randomly allocated according to a preset ratio and arranged in descending order. Dijkstra's shortest path algorithm is used to calculate and compare the shortest path and cost for each OD demand on the road network and public transport network. The optimal traffic network allocation is selected, and road segment flow and impedance are updated. Based on the updated road impedance, the optimal traffic mode and shortest path are allocated to 30% of the travel demand, and the road network and public transport network flow and impedance are updated again. This process is repeated until all travel demand for that time period is loaded. At that time, completed travel demands are filtered, and based on the cost of updating the road and public transport networks for vehicles still stuck on the network, departure times are divided according to the same preset ratio. The network impedance is updated by loading all traffic demands within the departure time period using the continuous averaging method until all traffic demands for all departure times are loaded, and the number of arrival demands and the average speed of each arrival demand are recorded for each time window. Based on a multimodal transportation network functional model, and according to specific disaster spatial distribution characteristics and post-disaster network repair strategies, a multimodal transportation network structural recovery model is constructed to evaluate structural recovery efficiency. This includes: identifying and removing nodes and road segments that are directly or indirectly failed in the multimodal transportation network based on disaster spatial distribution characteristics; whereby, if a node is located within a disaster area and its damage intensity exceeds a damage threshold, the node and its edges are considered directly failed; if a node is not located within a disaster area but is no longer connected to the maximum connected subgraph, it is considered indirectly failed; the remaining maximum connected subgraph is considered the functional network, and its nodes account for a certain percentage of the total number of nodes. Based on the post-disaster recovery strategy, identify and add nodes and road segments that are directly recovered at each time step of the multi-modal transportation network. Specifically, based on the recovery strategy, determine the directly recovered failures at each time step and add these directly recovered nodes and road segments to the functional network. If a directly recovered node is connected to the maximum connected subgraph, it is considered a valid recovery; otherwise, it is considered a newly added indirect failure. Detect and add nodes and road segments that are indirectly recovered at each time step. If a directly recovered node reconnects to an indirectly failed node and the maximum connected subgraph, then the indirectly failed node is considered indirectly recovered. Combine the network recovery start time, network recovery end time, and proportion... To assess the recovery efficiency of multimodal transportation network structures; Based on the multimodal transportation network structure recovery model, travel demand is reloaded, a multimodal transportation network function recovery model is constructed, and the function recovery efficiency is evaluated.

2. The method for evaluating the efficiency of urban multimodal transportation network post-disaster recovery strategies according to claim 1, characterized in that, Based on a multimodal transportation network structure recovery model, travel demand is reloaded, a multimodal transportation network function recovery model is constructed, and the efficiency of function recovery is evaluated. This includes: constructing the time-varying cost of road segments in the multimodal transportation network based on the network structure recovery model; wherein, if the road segment... In time If it fails, the travel cost of that route will increase. Then it is infinity; if the road segment is in time If the network structure is restored, the travel cost of the route segment will also be restored. Based on the dynamic multimodal traffic assignment method, travel demand is reloaded on the time-varying multimodal traffic network. Before allocating travel demand at the same departure time, the impedance functions of roads and bus segments are updated by the network structure repair model. The network stagnation demand and the impedance cost functions of roads and bus segments are also updated. If there is no reachable path for a certain travel demand at a certain departure time, the departure time of that travel demand is postponed to the next departure time window. Each time is recorded. Arrival demand quantity and each arrival requirement average speed A blank control group was constructed to reload travel demand in scenarios without any network repair, creating a multimodal transportation network function recovery model; the number of arrival demands was recorded for each time window. and each arrival requirement average speed Combine each time Arrival demand quantity and each arrival requirement average speed The number of arrivals required for each time window and each arrival requirement average speed The function recovery efficiency of the multimodal transportation network and the speed-weighted function recovery efficiency are calculated.

3. The method for evaluating the efficiency of post-disaster recovery strategies for urban multimodal transportation networks according to claim 1, characterized in that, Collect road network data and public transport network data to construct a multimodal transportation network structure model, including: Road network data includes road network topology, node attributes, and edge attributes. Road network data for the study area is obtained from open street maps. Intersections are abstracted as nodes, and road segments are abstracted as edges to construct a directed network. ,in For a set of nodes, The set of edges is used to ensure the connectivity of the road network. The maximum connected subgraph of the network is identified, and nodes and edges that are not on the maximum connected subgraph are deleted, thereby constructing the topological relationship of the network. The public transport network data includes the network topology, node attributes, and edge attributes. Based on the map open platform, station information, route information, and departure timetable information of the transportation system are crawled. The Space L method is applied to construct the topology of the directed public transport network. Based on the walking transfer threshold, the transfer edges and attributes of the public transport system are constructed to improve the public transport network.

4. The method for evaluating the efficiency of post-disaster recovery strategies for urban multimodal transportation networks according to claim 2, characterized in that, Calculate the functional recovery efficiency of multimodal transportation networks and speed-weighted function recovery efficiency Among them, functional recovery efficiency : ； in, A value between 0 and 1 indicates that the closer a value is to 1, the faster the multimodal transportation system can recover its capacity to offload traffic demand; conversely, a value closer to 1 indicates a slower recovery. Calculation speed-weighted function recovery efficiency : ； in Indicate demand The ratio of the speed in the repair scenario to the speed in the non-destructive scenario. Indicate demand The ratio of speed in a non-repair scenario to speed in a non-destructive scenario; A value between 0 and 1 indicates that the multimodal transportation system can meet travel demand more efficiently and achieve smoother travel speeds under the repair strategy. Conversely, the repair strategy may lead to severe traffic congestion and fail to effectively alleviate demand pressure.

5. A system for evaluating the efficiency of urban multimodal transportation network post-disaster recovery strategies based on the method for evaluating the efficiency of urban multimodal transportation network post-disaster recovery strategies as described in any one of claims 1-4, characterized in that, include: The first building module is used to construct a multimodal transportation network structure model using open-source data from road and public transport networks. The second building module is used to simulate urban traffic distribution based on traffic demand data and construct a multimodal transportation network functional model based on the multimodal transportation network structure model. The third construction module is used to construct a multimodal transportation network structure recovery model based on the multimodal transportation network functional model, according to specific disaster spatial distribution characteristics and post-disaster network repair strategies, and to evaluate the structure recovery efficiency. The reloading module is used to reload travel demand based on the multimodal transportation network structure recovery model, construct a multimodal transportation network function recovery model, and evaluate the function recovery efficiency.

6. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the method for evaluating the efficiency of post-disaster recovery strategies for urban multimodal transportation networks as described in any one of claims 1-4.

7. A computer device, characterized in that, The system includes a memory and a processor, which communicate with each other. The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the method for evaluating the efficiency of urban multimodal transportation network post-disaster recovery strategies as described in any one of claims 1-4.

8. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the method for evaluating the efficiency of urban multimodal transportation network post-disaster recovery strategies as described in any one of claims 1-4.

Images