Methods, apparatus, equipment and storage media for assessing traffic resilience and delay costs under rainfall-induced urban flooding.

Abstract

This invention provides a method, apparatus, equipment, and storage medium for assessing traffic resilience and delay costs under rainfall-induced urban flooding. The method includes: acquiring rainfall data for a target area; performing runoff generation and confluence simulations based on the rainfall data to obtain the water depth; determining a safe driving speed corresponding to the water depth based on the water depth and a speed-depth mapping model; the speed-depth mapping model determining a safe driving speed while ensuring vehicle handling stability, wherein the safe driving speed is obtained based on the road water depth and a scaling factor; obtaining a travel demand matrix based on the spatial distribution of the population in the target area; determining traffic delay time based on the safe driving speed and the travel demand matrix; and obtaining traffic delay costs based on the traffic delay time. This invention can improve the efficiency and accuracy of assessing traffic resilience and delay costs under rainfall-induced urban flooding.

Description

Technical Field

[0001] This invention relates to the field of scenario simulation assessment technology, and in particular to a method, apparatus, equipment and storage medium for assessing traffic resilience and delay costs under rainfall and flooding conditions. Background Technology

[0002] With the intensification of global climate change, the frequency and intensity of extreme precipitation events have increased significantly, and the resulting urban flooding has gradually evolved into one of the key risks threatening urban safety and sustainable development. As a fundamental system supporting socio-economic activities and the daily operation of cities, urban transportation networks are highly sensitive to waterlogging disturbances. In particular, short-duration heavy rainfall events often exceed the storage capacity of urban drainage systems within a very short time, rapidly forming large-scale flooding, which in turn has a sudden and high-intensity impact on road traffic conditions. In this context, the impact of flooding on transportation systems is no longer limited to a gradual degradation process such as reduced driving speed or extended travel time, but may lead to a phased loss of road capacity, and even cause overall deterioration and disorder of traffic function at the network scale.

[0003] Existing methods for assessing the impact of rainfall or flooding on urban traffic operations largely rely on maximum inundation simulations based on rainfall scenarios or changes in road travel time calculated based on geospatial rules. These methods fail to fully integrate dynamic water accumulation process simulations with traffic flow allocation mechanisms that consider driving behavior feedback, resulting in a static and outcome-oriented assessment of road network operation status. Summary of the Invention

[0004] The technical problem this invention aims to solve is to provide a method, apparatus, equipment, and storage medium for assessing traffic resilience and delay costs under rainfall-induced flooding. This can improve the efficiency and accuracy of assessing traffic resilience and delay costs under rainfall-induced flooding.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] A method for assessing traffic resilience and delay costs under rainfall-induced urban flooding includes:

[0007] Obtain precipitation data for the target area;

[0008] Based on the precipitation data, runoff generation and confluence simulations were performed to obtain the water depth;

[0009] Based on the water depth and the speed-water depth mapping model, the safe driving speed corresponding to the water depth is obtained; the speed-water depth mapping model determines the safe driving speed under the premise of ensuring vehicle handling stability, and the safe driving speed is obtained based on the road water depth and scaling factor;

[0010] Based on the spatial distribution of the population in the target area, a travel demand matrix is ​​obtained;

[0011] Based on the aforementioned safe driving speed and travel demand matrix, the traffic delay time is determined;

[0012] Based on the traffic delay time, the traffic delay cost is obtained.

[0013] Optionally, based on the precipitation data, runoff generation and confluence simulations are performed to obtain the water depth, including:

[0014] Based on the precipitation data, the runoff flow rate of each grid cell in the target area is obtained;

[0015] Based on the flow rate, the grid cells are simulated to obtain the water depth.

[0016] Optionally, the formula for calculating the flow rate is:

[0017]

[0018] in, Let t represent the flow rate in the x-direction and t represent time. Indicates the time interval step size. Let g represent the unit width flow rate at time t, and g represent the acceleration due to gravity. denoted by h, which represents the water depth at the cross-section; h represents the water depth at the grid cell; z represents the surface elevation; and n represents the Manning roughness coefficient.

[0019] The formula for calculating the water depth is:

[0020]

[0021] in, This indicates that the grid cell in the i-th row and j-th column is located in... The depth of the water at any moment This represents the water depth of the grid cell in the i-th row and j-th column at time t. Indicates the time interval step size. This indicates the size of the grid cell in the x-direction. This indicates the dimension of the grid cell in the y-direction. This represents the inflow rate of the grid cell in the x-direction. This represents the outflow rate of the grid cell in the x-direction. This represents the inflow rate of the grid cell in the y direction. R represents the outflow rate of the grid cell in the y direction, and R represents the net rainfall rate.

[0022] Optionally, based on the water depth and the speed-water depth mapping model, the safe driving speed corresponding to the water depth is obtained, including:

[0023] Obtain a road network distribution map of the target area;

[0024] The grid travel speed is obtained based on the speed-depth mapping model;

[0025] The safe driving speed is obtained by spatially overlaying the grid driving speed with the road network distribution map.

[0026] The velocity-depth mapping model is as follows:

[0027]

[0028] Where h represents the depth of the road flooding, and v(h) is the maximum safe driving speed while ensuring vehicle handling stability. This represents the scaling factor.

[0029] Optionally, based on the spatial distribution of the population in the target area, a travel demand matrix can be obtained, including:

[0030] Based on land use data of the target area, the origin and destination of the trip are obtained;

[0031] The population size is obtained from the population raster data.

[0032] Based on the population size and the origin and destination, a travel demand matrix is ​​obtained.

[0033] Optionally, traffic delay time is determined based on the safe driving speed and travel demand matrix, including:

[0034] Based on the safe driving speed and travel demand matrix, the actual travel time is obtained according to the driving routes of vehicles in the target area;

[0035] The actual travel time is compared with the corresponding normal travel time to obtain the traffic delay time.

[0036] Optionally, based on the traffic delay time, the traffic delay cost is obtained, including:

[0037] The social time value is derived from the GDP per capita of the target region.

[0038] Based on the social time value and traffic delay time, the traffic delay cost is obtained.

[0039] Embodiments of the present invention also provide a device for assessing traffic resilience and delay costs under rainfall-induced flooding, comprising:

[0040] The acquisition module is used to acquire precipitation data for the target area;

[0041] The processing module is used to perform runoff generation and confluence simulations based on the precipitation data to obtain the water depth; to obtain the safe driving speed corresponding to the water depth based on the water depth and a speed-depth mapping model; the speed-depth mapping model determines the safe driving speed under the premise of ensuring vehicle handling stability, and the safe driving speed is obtained based on the road water depth and scaling factor; to obtain a travel demand matrix based on the spatial distribution of the population in the target area; to determine the traffic delay time based on the safe driving speed and the travel demand matrix; and to obtain the traffic delay cost based on the traffic delay time.

[0042] Embodiments of the present invention also provide a computing device, comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for assessing traffic resilience and delay costs under rainfall-induced urban flooding as described in the present invention.

[0043] Embodiments of the present invention also provide a computer-readable storage medium storing a program that, when executed by a processor, implements the method for assessing traffic resilience and delay costs under rainfall-induced flooding as described in the present invention.

[0044] The above-described technical solution of the present invention has at least the following technical effects:

[0045] The above-mentioned method for assessing traffic resilience and delay costs under rainfall-induced flooding of the present invention involves: acquiring rainfall data of the target area; performing runoff generation and confluence simulations based on the rainfall data to obtain the water depth; determining the safe driving speed corresponding to the water depth based on the water depth and a speed-depth mapping model; the speed-depth mapping model determining the safe driving speed while ensuring vehicle handling stability, wherein the safe driving speed is obtained based on the road water depth and a scaling factor; obtaining a travel demand matrix based on the spatial distribution of the population in the target area; determining the traffic delay time based on the safe driving speed and the travel demand matrix; and obtaining the traffic delay cost based on the traffic delay time. This method improves the efficiency and accuracy of assessing traffic resilience and delay costs under rainfall-induced flooding. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating the method for assessing traffic resilience and delay costs under rainfall-induced urban flooding according to the present invention.

[0047] Figure 2 This is a schematic diagram of the traffic resilience and delay cost assessment device under rainfall and flooding conditions according to the present invention. Detailed Implementation

[0048] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0049] like Figure 1 As shown, embodiments of the present invention propose a method for assessing traffic resilience and delay costs under rainfall-induced urban flooding, including:

[0050] Step S1: Obtain precipitation data for the target area;

[0051] Step S2: Based on the precipitation data, perform runoff generation and confluence simulation to obtain the water depth;

[0052] Step S3: Based on the water depth and the speed-water depth mapping model, obtain the safe driving speed corresponding to the water depth; the speed-water depth mapping model determines the safe driving speed under the premise of ensuring vehicle handling stability, and the safe driving speed is obtained based on the road water depth and scaling factor;

[0053] Step S4: Obtain the travel demand matrix based on the spatial distribution of the population in the target area;

[0054] Step S5: Determine the traffic delay time based on the safe driving speed and travel demand matrix;

[0055] Step S6: Obtain the traffic delay cost based on the traffic delay time.

[0056] In this embodiment, as Figure 1 As shown, in the method for assessing traffic resilience and delay costs under rainfall-induced flooding, the first step is to acquire precipitation data for the target area. To comprehensively capture the differences in the impact of rainfall intensity and duration on traffic, especially to identify the unique impact of short-duration heavy rainfall, this invention integrates meteorological standards and related research, establishing 20 mm / h as the threshold for distinguishing between heavy and non-heavy rainfall. Based on this, four sets of short-duration heavy rainfall scenarios (20, 30, 40, 50 mm / h) and five sets of non-heavy rainfall comparison scenarios (2, 4, 6, 8, 10 mm / h) were designed, with each intensity further set with a rainfall duration of 1, 2, and 3 hours, resulting in a total of 27 rainfall scenarios. This design can analyze the impact of continuous changes in time and intensity, and also compare the differences in the impact mechanisms of heavy and non-heavy rainfall.

[0057] Then, based on the precipitation data, runoff generation and confluence simulations are performed to obtain the water depth;

[0058] Next, based on the water depth and the speed-water depth mapping model, the safe driving speed corresponding to the water depth is obtained; the speed-water depth mapping model determines the safe driving speed under the premise of ensuring vehicle handling stability, and the safe driving speed is obtained based on the road water depth and scaling factor;

[0059] Next, based on the spatial distribution of the population in the target area, a travel demand matrix is ​​obtained;

[0060] Next, based on the aforementioned safe driving speed and travel demand matrix, the traffic delay time is determined;

[0061] Finally, the traffic delay cost is obtained based on the traffic delay time.

[0062] This invention is based on a coupled analysis framework that integrates short-duration rainfall scenario setting, urban flooding simulation and traffic micro-simulation. By setting up multiple scenario combinations covering short-term non-heavy rainfall and heavy rainfall, it explores the evolution characteristics of urban road network operation under rainfall flooding disturbance.

[0063] In an optional embodiment of the present invention, step S2, based on the precipitation data, involves performing runoff generation and confluence simulation to obtain the water depth, including:

[0064] Step S21: Based on the precipitation data, obtain the runoff generation rate of each grid cell in the target area;

[0065] Step S22: Based on the flow rate, perform a flow simulation on the grid unit to obtain the water accumulation depth.

[0066] In this embodiment, the target area's digital elevation model (DEM) is used to acquire the target area's micro-topography. Then, precipitation data is converted into a rainfall rate input file, and the time resolution is set to 5 minutes. The target area is discretized into regular grid cells. Finally, based on the target area's DEM, the runoff generation between each grid cell and its four adjacent cells is calculated. The runoff generation calculation formula is as follows:

[0067]

[0068] in, Let t represent the flow rate in the x-direction and t represent time. Indicates the time interval step size. Let g represent the unit width flow rate at time t, and g represent the acceleration due to gravity. denoted by h, which represents the water depth at the cross-section; h represents the water depth at the grid cell; z represents the surface elevation; and n represents the Manning roughness coefficient.

[0069] In the above formula, the water depth h of the grid cell is the vertical distance from the free water surface to the land surface, in meters; the land surface elevation z is provided by the digital elevation model; h+z represents the free water surface elevation, which is the driving force of water flow, as water always flows from areas of higher elevation to areas of lower elevation; flow rate... The water flow rate through the boundary between two adjacent cells in the x-direction per unit time is expressed in cubic meters per second; the water depth at the cross-section is also indicated. This refers to the effective depth at which water can flow between two adjacent units, defined as the difference between the higher free water surface and the higher surface elevation in the two units; the Manning roughness coefficient n reflects the resistance of the surface to water flow; the gravitational acceleration g is usually taken as 9.81. ; The water surface gradient, i.e., the slope of the water surface between two adjacent units, is the core driving force for water flow; the flow rate calculation in the y direction is the same as the above formula.

[0070] Next, a flow simulation is performed on the grid cell. The flow rate flowing into the cell is subtracted from the flow rate flowing out of the cell, and then the net rainfall received by the cell is added to calculate the water depth of the cell at the next time step. The formula for calculating the water depth is:

[0071]

[0072] in, This indicates that the grid cell in the i-th row and j-th column is located in... The depth of the water at any moment This represents the water depth of the grid cell in the i-th row and j-th column at time t. Indicates the time interval step size. This indicates the size of the grid cell in the x-direction. This indicates the dimension of the grid cell in the y-direction. This represents the inflow rate of the grid cell in the x-direction. This represents the outflow rate of the grid cell in the x-direction. This represents the inflow rate of the grid cell in the y direction. R represents the outflow rate of the grid cell in the y direction, which is the net rainfall rate, i.e., the constant rainfall rate minus the infiltration loss, representing the actual amount of water participating in surface runoff, in meters per second.

[0073] Finally, time iteration is performed, repeating the steps of calculating flow rate and updating water depth, advancing the simulation forward with an internal calculation step size until the simulation of the entire rainfall event process is completed, and finally outputting the water depth raster map at each time point according to the set 5-minute time interval.

[0074] This embodiment generates spatiotemporal dynamic water accumulation data that closely matches actual heavy rainfall and urban flooding scenarios. It not only clarifies the spatial distribution of water accumulation under different rainfall scenarios but also captures the temporal evolution of water accumulation. This provides accurate basic data support for the subsequent mapping of water depth and safe driving speed of vehicles, avoiding traffic assessment deviations caused by distortion in urban flooding simulation.

[0075] In an optional embodiment of the present invention, step S3, obtaining the safe driving speed corresponding to the water depth based on the water depth and the speed-water depth mapping model, includes:

[0076] Step S31: Obtain the road network distribution map of the target area;

[0077] Step S32: Obtain the grid travel speed based on the speed-depth mapping model;

[0078] Step S33: Spatially overlay the grid driving speed with the road network distribution map to obtain the safe driving speed.

[0079] In this embodiment, road network data of the target area is acquired, and all motor vehicle roads within the target area are extracted from the map data, including highways, arterial roads, secondary arterial roads, branch roads, and residential roads. The basic attributes such as the grade, direction, and length of each road are determined to obtain a road network distribution map. The speed-water depth mapping model formula is used to calculate the driving speed of each grid. When the water depth h ≥ 300 mm, the road section is determined to be completely impassable, and the speed limit is 0 km / h. The grid driving speed is spatially superimposed with the road network, and the restricted driving speed is calculated hourly and segment by segment to obtain the safe driving speed.

[0080] In an optional embodiment of the present invention, in step S32, the velocity-depth mapping model is:

[0081]

[0082] Where h represents the depth of the road flooding, and v(h) is the maximum safe driving speed while ensuring vehicle handling stability. This represents the scaling factor. When using this formula, the water depth *h* and the safe driving speed should be considered. The data are dimensionless, all pure numerical values ​​without physical units, and are empirical relationships obtained based on experimental / data fitting. To correct the coefficients, the dimensions on both sides will be automatically matched to make the equation physically valid.

[0083] In this embodiment, the driving safety and speed of vehicles on flooded road sections are strictly constrained by the water depth. The speed-water depth mapping model is scaled proportionally according to the design free-flow velocity of different road grades. For example, highways have high free-flow velocities and large scaling factors, while local roads have low free-flow velocities and small scaling factors, making the model adaptable to the actual traffic characteristics of various road types. Specifically, given the differences in design free-flow velocities for different road grades, the original "speed-water depth" function is scaled proportionally while maintaining its original form. By introducing scaling factors, the function is matched with the free-flow velocities corresponding to various road types, thereby enhancing the model's adaptability to real traffic conditions. Specifically, the design free-flow velocity of highways is 140 km / h, with a corresponding scaling factor of 1.533; the design free-flow velocity of trunk roads, main roads, and secondary roads is 100 km / h, with a scaling factor of 1.150; the design free-flow velocity of local roads is 80 km / h, with a corresponding scaling factor of 0.920; and the design free-flow velocity of residential area roads is 50 km / h, with a scaling factor of 0.575. On a time scale, the water depth grid results every 5 minutes are spatially overlaid with the road network, and the restricted driving speed of each road segment at the corresponding time step is calculated hourly. For road segments that cross multiple water grids, the worst-case principle is adopted, that is, the minimum speed value within the coverage area of ​​the road segment is taken as the speed limit condition for that time step, so as to reflect the constraint effect of waterlogging on road traffic capacity.

[0084] In an optional embodiment of the present invention, step S4, obtaining a travel demand matrix based on the spatial distribution of the population in the target area, includes:

[0085] Step S41: Based on the land use data of the target area, obtain the departure point and destination of the trip;

[0086] Step S42: Obtain the population size based on the population raster data;

[0087] Step S43: Obtain the travel demand matrix based on the population size, origin, and destination.

[0088] In this embodiment, the generation of travel demand depends on the population size and spatial distribution characteristics of the origin and destination. Firstly, based on land use data of the target area, and following the travel mechanism of travel generated by residential land and attracted by non-residential land, the target area is divided into traffic zones. Residential land is defined as the travel generation zone, corresponding to origin i, while non-residential land such as commercial, office, and public service areas is defined as the travel attraction zone, corresponding to destination j, thus spatially defining the model variables. and Functional attributes.

[0089] Based on this, population raster data with a resolution of 100m was used to statistically analyze the population size within each traffic zone, and the values ​​were assigned to the population at the origin. With the destination population Simultaneously, based on the geometric distance between traffic zones, the distance centered on origin i and extending to... The cumulative population within a circular area of ​​radius i (excluding i and j) This allows for a complete depiction of the required spatial competition environment for population. Total travel demand at the origin. Based on its population size The allocation should be proportional to ensure the conservation of total travel volume at the regional scale.

[0090] Finally, based on the above population parameters and spatial relationships, the expected travel volume among all combinations of generating and attracting areas is calculated. This generates a travel demand matrix that corresponds to the model variables and has clear spatial behavioral meaning, providing reasonable demand input for subsequent traffic simulation and multi-scenario analysis.

[0091] The mathematical expression for travel demand is:

[0092]

[0093] in, , This represents the expected travel volume from origin i to destination j; The total travel demand from origin i; and These represent the population size of the origin and destination, respectively. Represents the straight-line distance between the origin i and the destination i, centered at origin i. The cumulative population within a circular area (excluding i and j) with radius ; Let N be the total travel demand in the target area, and N be the total population size of the area.

[0094] Calculate the expected trip volume among all "generation zone - attraction zone" combinations. This generates a complete travel demand matrix. Based on land use data, this embodiment clarifies the functional attributes of parameters by dividing the area into generation and attraction zones, thus solving the problems of vague parameter assignments and disconnection from actual travel behavior in traditional technologies. Travel demand relies solely on population spatial distribution data, eliminating the need to introduce excessive empirical parameters and addressing the pain point of accurately generating travel demand in scenarios with limited monitoring data.

[0095] In an optional embodiment of the present invention, step S5, determining the traffic delay time based on the safe driving speed and travel demand matrix, includes:

[0096] Step S51: Based on the safe driving speed and travel demand matrix, and according to the driving routes of vehicles in the target area, obtain the actual driving time.

[0097] Step S52: Compare the actual travel time with the corresponding normal travel time to obtain the traffic delay time.

[0098] In this embodiment, in the road network of the target area, the driving path of each vehicle in the target area is simulated according to the safe driving speed and travel demand matrix. The travel route is calculated repeatedly through feedback iteration until all travelers can no longer shorten their own travel time by unilaterally changing their driving path, thereby approximating the dynamic user equilibrium state and obtaining the actual travel time.

[0099] Specifically, under normal, non-rainy, free-flow conditions, the origin and destination nodes are obtained based on the travel demand matrix. At this stage, vehicles are only defined by their origin and destination, and specific travel routes have not yet been assigned. An open list is established to store road network nodes to be evaluated, and a closed list is established to store road network nodes that have been evaluated. Path search is performed using an evaluation function, which is:

[0100]

[0101] in, This represents the cost of node n. This represents the actual cumulative travel cost from the starting point to the current node n, usually measured in travel time. This is a heuristic function used to estimate the minimum residual cost from node n to the endpoint;

[0102] For each adjacent node, skip it if it is already in the closed list; otherwise, proceed to the next calculation. Calculate the minimum distance from the current node to its adjacent nodes. Cost reduction involves repeated calculations, continuously selecting the optimal node, evaluating neighboring nodes, and updating the cost and list. Through heuristic guidance, it can efficiently search for near-global optimal paths in large-scale road networks, significantly improving computational efficiency while ensuring path search accuracy.

[0103] This process effectively simulates a driver's route selection behavior under non-rainy conditions; it can simulate the process of a vehicle traveling along a predetermined path and record the actual operating status of the road network. Simultaneously, dynamic speed limit rules for road sections under rainy conditions are embedded into the simulation process to characterize the time-varying characteristics of road capacity under rainy scenarios. The total simulation duration for all scenarios is uniformly set to 3 hours, which fully covers the entire rainfall duration of the set rainy scenarios; this unified time frame ensures that different scenarios operate on comparable time scales, facilitating the system's comparison of the impact of different combinations of rainfall intensity and duration on the traffic system's operating status.

[0104] Bicycle traffic delays can be broken down into two parts: departure delay and travel time loss. The calculation formula is as follows:

[0105]

[0106] in, Indicates the delay time for bicycle traffic. Departure delay refers to the waiting time that a vehicle cannot enter the road network at the scheduled departure time due to road network congestion, occupied departure positions, or failure to meet safe insertion conditions; The travel time loss refers to the additional time a vehicle spends during actual travel due to factors such as traffic congestion, signal control, or queuing, compared to the theoretical shortest travel time under free-flow conditions. This indicator directly reflects the degree of decline in road network operating efficiency.

[0107] Total traffic delay time per vehicle = Actual travel time of the vehicle - Normal travel time of the vehicle, where normal travel time is the standard travel time without waterlogging constraints; sum up the traffic delay times of all vehicles and take the arithmetic mean, which is the total traffic delay time.

[0108] This embodiment captures phenomena such as road network congestion and road section closure (impassable) caused by waterlogging, and quantifies the operational status of the traffic system under different scenarios (such as traffic delay time and congestion range), providing core data support for subsequent traffic resilience assessment (such as road network anti-interference ability and recovery ability) and traffic delay cost calculation.

[0109] In an optional embodiment of the present invention, step S6, obtaining the traffic delay cost based on the traffic delay time, includes:

[0110] Step S61: Obtain the social time value based on the per capita GDP of the target region;

[0111] Step S62: Obtain the traffic delay cost based on the social time value and traffic delay time.

[0112] In this embodiment, traffic delay costs are calculated based on the unit time value method, comprehensively considering traffic delay time, number of vehicles, and social time value to quantify the social time value under different rainfall scenarios; the social time value is obtained based on the per capita GDP of the target area; social time value. Through per capita GDP Average annual effective working hours The calculation yields the following formula:

[0113]

[0114] in, Represents the social value of time. This represents GDP per capita. This indicates the average effective working time per person per year;

[0115] The cost of traffic delays can be expressed as:

[0116] in, For the cost of traffic delays, For the first Traffic delay time for vehicles Social time value, that is, the value of a unit of time. This represents the total number of vehicles.

[0117] During the simulation, for vehicles that have not reached their destination by the end of the 3-hour simulation, their traffic delay time is counted as the full 3 hours to fully reflect the most adverse traffic impact under extreme rainfall scenarios.

[0118] This invention focuses on short-duration heavy rainfall, achieving high-resolution hydrological-traffic coupling in its methodology. It reveals the nonlinear failure transition of the transportation system, forming a comprehensive analysis scheme with significant innovation. The advantages of this scheme are specifically reflected in the following aspects:

[0119] First, a multi-scenario coupled analysis framework for "short-duration heavy rainfall" was constructed, highlighting the traffic disturbance mechanism of high-frequency, high-impact rainfall events. Unlike previous methods that focused on long-duration or typical rainstorms, this approach takes short-duration heavy rainfall as its core focus, systematically setting up rainfall scenarios covering both non-heavy and heavy rainfall. This achieves continuous and comparative characterization in both rainfall intensity and duration, addressing the shortcomings of existing technologies in addressing the insufficient attention to high-frequency extreme rainfall.

[0120] Secondly, this approach achieves deep coupling between high spatiotemporal resolution flooding simulation and microscopic traffic simulation, breaking through the traditional static assessment paradigm. This scheme maps the results of two-dimensional flooding simulation with a 5m resolution and a 5-minute time step onto the road network hourly and embeds them into the microscopic traffic simulation process, dynamically depicting the time-varying evolution of road capacity. Compared to traditional methods based on "static inundation range combined with shortest path recalculation," this scheme can realistically reflect the dynamic feedback mechanism between water accumulation processes, changes in road speed limits, driver route choices, and traffic congestion accumulation, significantly improving the process realism and mechanistic explanatory power of traffic impact assessment.

[0121] Third, a scaling method for the "speed-water depth" function that considers differences in road class is proposed, improving the level of detail in traffic operation characterization. Based on the classic empirical "speed-water depth" relationship function, this scheme introduces a scaling factor for the free-flow velocity corresponding to the road class, enabling different types of roads to exhibit differentiated operational responses under the same water depth. This better reflects real traffic operation characteristics and enhances the model's applicability and reliability in complex urban road networks.

[0122] Fourth, from a system evolution perspective, the scheme identifies the critical transition characteristics of transportation networks from gradual degradation to a low-sensitivity failure state. It not only focuses on the linear or monotonic changes in traffic performance with increasing rainfall, but also reveals through multi-scenario comparisons that after exceeding specific rainfall intensity and duration thresholds, the transportation system enters a low-sensitivity failure state, becoming "insensitive to external disturbances." This phased transition from continuous degradation to systemic functional collapse provides new quantitative evidence for understanding the nonlinear failure mechanism of transportation systems under extreme weather conditions.

[0123] Fifth, the quantification of traffic degradation enhances its significance in supporting decision-making. Based on microscopic traffic simulation, this scheme further combines incomplete trips, accumulated traffic delays, and social time value to quantitatively assess the indirect social time losses caused by short-term heavy rainfall. This achieves a complete chain analysis of "physical water accumulation—traffic operation—social impact," providing a quantitative basis directly applicable to decision-making for urban traffic risk management, emergency response, and resilience enhancement.

[0124] Road speeds are dynamically updated in 5-minute time steps. To visually demonstrate extreme scenarios of road network operation under rainfall, the minimum operating levels under both non-heavy rainfall and heavy rainfall scenarios are visualized. The changes in free-flow velocity under different combinations of rainfall duration and intensity show that, under the same rainfall duration, as rainfall intensity increases from 2 mm / h to 50 mm / h, the average free-flow velocity continuously decreases, while the proportion of roads with traffic interruptions significantly increases. In the 1-hour rainfall scenario, the average free-flow velocity decreases from 74.87 km / h to 32.47 km / h, and the corresponding proportion of interrupted roads increases from 2.03% to 31.32%. When the rainfall duration is 2 hours, the average free-flow velocity decreases from 72.77 km / h to 29.18 km / h, and the proportion of interrupted roads increases from 3.14% to 40.30%. In the 3-hour, 50 mm / h scenario, the average free-flow velocity further decreases to 27.82 km / h, and the proportion of interrupted roads reaches 44.32%. Under the same rainfall intensity, extending the duration of rainfall also exacerbates the degradation of road network operation. Taking 20 mm / h as an example, when the rainfall duration is extended from 1 hour and 2 hours to 3 hours, the average free-flow velocity is 44.80 km / h, 40.30 km / h, and 38.49 km / h, respectively, and the proportion of interrupted roads increases accordingly from 14.35% to 21.28% and 25.27%, showing a trend of continuous deterioration with increasing rainfall duration.

[0125] All road classes exhibited a decrease in free-flow velocity under the influence of rainfall, with an overall reduction of approximately 60%. Specifically, expressways maintained relatively high operating speeds across all scenarios, but their average free-flow velocity continued to decrease with increasing rainfall intensity and duration, dropping from 95.91 km / h in the 1-hour, 2 mm / h scenario to 30.19 km / h in the 3-hour, 50 mm / h scenario, a decrease of approximately 68.5%. The average free-flow velocity of secondary arterial roads decreased from 86.89 km / h in the 1-hour, 2 mm / h scenario to 35.89 km / h in the 3-hour, 50 mm / h scenario, a decrease of approximately 58.7%. The decrease in free-flow velocity for arterial roads, main arterial roads, tertiary roads, and residential roads fell between that of expressways and secondary arterial roads.

[0126] Under all rainfall scenarios, the frequency distribution of road traffic conditions remained consistent overall, mainly concentrated in the ranges of density (0-20 veh / km), speed (0-50 km / h), and flow rate (0-40 veh / h). With increasing rainfall intensity and duration, the density distribution shifted towards higher values, while the speed and flow rate distributions converged towards lower values, indicating that road traffic under rainfall conditions gradually concentrated in low-speed, low-flow, and medium-to-high-density ranges.

[0127] On average, the three key elements of road traffic exhibit consistent changing characteristics under different combinations of rainfall duration and intensity. Under the same rainfall duration, as rainfall intensity increases from 2 mm / h to 50 mm / h, average road density continuously increases, while average speed and average flow rate decrease simultaneously. Under the same rainfall intensity, extending the rainfall duration also exacerbates road network degradation. Taking a 1-hour rainfall scenario as an example, average road density increases from 2.19 veh / km to 6.71 veh / km, average speed decreases from 49.08 km / h to 39.06 km / h, and average flow rate decreases from 17.06 veh / h to 9.69 veh / h. Under 2-hour and 3-hour scenarios, the changing trends of the three elements are basically consistent with the 1-hour scenario, but the overall operational level further declines.

[0128] With increasing rainfall intensity and duration, both travel time and overlapping travel time gradually increased. Under 2 mm / h conditions, travel time increased from 36.79 s for 1 hour to 42.25 s for 3 hours; under 50 mm / h conditions, travel times were 195.62 s, 196.21 s, and 198.09 s, respectively, with the magnitude of change decreasing significantly with increasing duration. In contrast, time loss and waiting time exhibited phased variations across different rainfall intensities. Under non-heavy rainfall conditions (≤10 mm / h), both indicators increased synchronously with increasing rainfall intensity and duration; when rainfall intensity reached or exceeded 20 mm / h, time loss and waiting time no longer increased significantly in the 2-hour and 3-hour scenarios, and in some scenarios, their values ​​tended to stabilize or decrease slightly. Under a rainfall intensity of 20 mm / h, the time loss decreased from 3496.01 s for 1 hour to 3334.55 s for 3 hours; under a rainfall intensity of 50 mm / h, the waiting times were 5235.98 s, 5128.19 s, and 5102.49 s, respectively. The response of road traffic indicators shows that when the rainfall duration reaches 2 hours and the rainfall intensity reaches 20 mm / h, the variation in the time indicators of road operation significantly decreases, and the road segment capacity enters a relatively stable range, exhibiting typical capacity interruption characteristics.

[0129] As the duration and intensity of rainfall increased, the number of vehicles completing trips during the simulation period continuously decreased, with rainfall intensity having a more significant impact. Under the 1-hour rainfall scenario, the number of vehicles completing trips gradually decreased from 23,662 under 2 mm / h rainfall to 11,827 under 50 mm / h rainfall. When the rainfall duration extended to 2 hours, the number of vehicles completing trips under the same rainfall intensity further decreased, from 20,600 to 18,765 under 10 mm / h rainfall and to 10,586 under 50 mm / h rainfall. Under the 3-hour rainfall scenario, this trend continued, with 10,287 vehicles completing trips under 50 mm / h rainfall. Under the same rainfall intensity, the number of vehicles completing trips decreased with increasing rainfall duration, but the magnitude of this change was generally smaller than the difference caused by changes in rainfall intensity.

[0130] Under clear weather conditions, approximately 96.5% of vehicles can reach their destination within 30 minutes; this paper defines 30 minutes as the comfortable travel time. After rainfall occurs, the number of vehicles able to complete their journey within this time threshold decreases significantly. In a 1-hour rainfall scenario, the number of vehicles completing their journey within 30 minutes decreases from 22,959 under a 2mm / h rainfall rate to 10,822 under a 50mm / h rainfall rate. When the rainfall lasts for 2 hours, the corresponding number of vehicles decreases from 22,303 under a 2-50mm / h rainfall rate to 10,189. In a 3-hour rainfall scenario with a 50mm / h rainfall rate, this number further decreases to 9,942 vehicles, indicating that both the extended duration and increased intensity of rainfall reduce the number of vehicles capable of comfortable travel.

[0131] Cumulative statistics on traffic delays for all vehicles show that cumulative traffic delays increase continuously with increasing rainfall intensity and duration. Under a 1-hour rainfall scenario, the cumulative traffic delay time increased from 5081.51 hours under a 2 mm / h rainfall condition to 40927.34 hours under a 50 mm / h rainfall condition; in a 2-hour rainfall scenario, the corresponding value increased from 7177.50 hours under 2–50 mm / h rainfall conditions to 43453.85 hours; and under a 3-hour rainfall scenario with a 50 mm / h rainfall condition, the cumulative traffic delay time further increased to 44232.45 hours.

[0132] This invention constructs a coupled analysis framework integrating short-duration rainfall scenarios, urban flooding simulation, and traffic micro-simulation to systematically evaluate the road network operation characteristics, capacity decay, and travel delays in a target area under different rainfall intensities and durations. The results show that both rainfall intensity and duration negatively impact road network speed, capacity, and vehicle delays, with rainfall intensity having a more pronounced effect. Furthermore, when the rainfall duration reaches 2 hours and the rainfall intensity exceeds 20 mm / h, road traffic indicators tend to stabilize, exhibiting a capacity-limiting characteristic. This finding not only reveals the nonlinear response mechanism of urban traffic systems under extreme rainfall disturbances but also provides a quantitative basis for identifying critical thresholds in traffic systems.

[0133] From the perspective of road network operation mechanisms, the degradation of road capacity caused by rainfall is mainly achieved through three pathways: First, road flooding leads to a significant decrease in free-flow velocity, especially under high-intensity rainfall, where the speed reduction on highways and main roads can exceed 60%, and more than 40% of roads are interrupted, thus forming road bottlenecks and causing traffic congestion to spread; Second, as the duration of rainfall increases, the proportion of dense medium- and high-density road sections increases, while the proportion of low-speed and low-flow sections rises, reflecting the concentration of road network operation in inefficient areas; Third, the cumulative effect of traffic delays and waiting times is significant, constraining the number of vehicles completing their journeys, especially under conditions of heavy rainfall and long duration, where vehicle capacity is systematically limited. This invention further quantifies the traffic interruption threshold under multi-duration rainfall conditions and clarifies the systematic impact of 1–3 hours of cumulative rainfall on the overall operation of the road network.

[0134] This invention quantifies vehicle delays based on the unit time value method, revealing the potential social losses of transportation systems under rainfall disturbance conditions. Results show that under a light rainfall scenario of 1 hour and 2 mm / h, the social loss is approximately the first value; however, under a heavy rainfall scenario of 3 hours and 50 mm / h, the loss rapidly rises to above the second value, which is 8 to 9 times the first value, exhibiting a significant amplification characteristic. This indicates that short-duration heavy rainfall not only significantly weakens road operational efficiency but also has a considerable indirect impact on urban activities through travel delays. This finding is consistent with existing technologies in terms of magnitude and trend. Heavy rainfall can increase the social losses of car travel in the target area by approximately 3.7 times compared to the baseline scenario. Under more extreme rainstorm and flood conditions, the user losses caused by traffic delays and detours due to closures in the target area are even greater, and the cumulative losses from multi-day continuous closures are unbearable. In contrast, this invention, starting from rainfall disturbances at the urban scale, assesses the social losses under the combination of rainfall intensity and duration, providing supplementary evidence for understanding the evolution of the impact of rainstorms from normal operation to a high-loss state.

[0135] This invention provides empirical evidence for the nonlinear degradation and critical threshold identification of urban transportation systems under extreme weather disturbances, enriching the application scenarios of the traffic circuit breaker concept. Methodologically, it integrates high temporal and spatial resolution urban flooding simulation with microscopic traffic simulation, and generates travel demand based on a regional radiation model, providing a transferable tool framework for multi-scenario coupled analysis of complex urban systems. Practically, this invention can be used to optimize urban transportation resilience planning, design tiered emergency response plans, and develop travel guidance strategies under short-term heavy rainfall, achieving a balance between disaster risk management and urban operational efficiency.

[0136] This invention constructs a coupled analysis framework integrating short-term rainfall scenario setting, urban flooding simulation, and microscopic traffic simulation. It combines high temporal and spatial resolution flooding simulation, water accumulation-velocity mapping, and travel demand generated based on a radiation model with dynamic microscopic traffic simulation, enabling the evaluation of road network performance under multi-duration and multi-intensity rainfall scenarios. The main conclusions are as follows:

[0137] Free-flow velocity on roads decreased significantly, and traffic disruptions worsened. With increasing rainfall intensity and duration, the average free-flow velocity on the road network continued to decline, from 74.87 km / h to 27.82 km / h, while the proportion of completely disrupted roads increased significantly, from 2.03% to 44.32%. Prolonged rainfall duration further exacerbated road network degradation.

[0138] Road capacity exhibits a circuit breaker characteristic. Under all rainfall scenarios, road traffic conditions are mainly concentrated in low-speed, low-flow, and medium-to-high-density zones, and tend to concentrate in inefficient zones as rainfall intensity and duration increase. When rainfall duration reaches 2 hours and intensity reaches 20 mm / h, the changes in average road speed, density, and flow rate decrease significantly, and the capacity of some road sections tends to stabilize, showing a typical capacity circuit breaker phenomenon.

[0139] Reduced vehicle capacity and increased traffic delays. Rainfall disturbances led to a decrease in the number of vehicles completing their journeys, resulting in accumulated traffic delays. Under extreme scenarios of 3 hours and 50 mm / h rainfall, the cumulative traffic delay reached 44,232 hours, indicating that short-duration heavy rainfall placed significant pressure on urban transportation.

[0140] This invention not only reveals the degradation and capacity disruption mechanism of urban transportation systems under short-term heavy rainfall, but also provides a set of transferable and scalable quantitative tools, providing a scientific basis for the resilience analysis of urban transportation systems and the operational assessment under extreme weather disturbances.

[0141] like Figure 2 As shown, embodiments of the present invention also provide a traffic resilience and delay cost assessment device 20 under rainfall-induced flooding, comprising:

[0142] Module 21 is used to acquire precipitation data for the target area;

[0143] Processing module 22 is used to perform runoff generation and confluence simulation based on the precipitation data to obtain the water depth; to obtain the safe driving speed corresponding to the water depth based on the water depth and the speed-water depth mapping model; the speed-water depth mapping model determines the safe driving speed under the premise of ensuring vehicle handling stability, and the safe driving speed is obtained based on the road water depth and scaling factor; to obtain the travel demand matrix based on the population spatial distribution of the target area; to determine the traffic delay time based on the safe driving speed and the travel demand matrix; and to obtain the traffic delay cost based on the traffic delay time.

[0144] Optionally, based on the precipitation data, runoff generation and confluence simulations are performed to obtain the water depth, including:

[0145] Based on the precipitation data, the runoff flow rate of each grid cell in the target area is obtained;

[0146] Based on the flow rate, the grid cells are simulated to obtain the water depth.

[0147] Optionally, the formula for calculating the flow rate is:

[0148]

[0149] in, Let t represent the flow rate in the x-direction and t represent time. Indicates the time interval step size. Let g represent the unit width flow rate at time t, and g represent the acceleration due to gravity. denoted by h, which represents the water depth at the cross-section; h represents the water depth at the grid cell; z represents the surface elevation; and n represents the Manning roughness coefficient.

[0150] The formula for calculating the water depth is:

[0151]

[0152] in, This indicates that the grid cell in the i-th row and j-th column is located in... The depth of the water at any moment This represents the water depth of the grid cell in the i-th row and j-th column at time t. Indicates the time interval step size. This indicates the size of the grid cell in the x-direction. This indicates the dimension of the grid cell in the y-direction. This represents the inflow rate of the grid cell in the x-direction. This represents the outflow rate of the grid cell in the x-direction. This represents the inflow rate of the grid cell in the y direction. R represents the outflow rate of the grid cell in the y direction, and R represents the net rainfall rate.

[0153] Optionally, based on the water depth and the speed-water depth mapping model, the safe driving speed corresponding to the water depth is obtained, including:

[0154] Obtain a road network distribution map of the target area;

[0155] The grid travel speed is obtained based on the speed-depth mapping model;

[0156] The safe driving speed is obtained by spatially overlaying the grid driving speed with the road network distribution map.

[0157] The velocity-depth mapping model is as follows:

[0158]

[0159] Where h represents the depth of the road flooding, and v(h) is the maximum safe driving speed while ensuring vehicle handling stability. This represents the scaling factor.

[0160] Optionally, based on the spatial distribution of the population in the target area, a travel demand matrix can be obtained, including:

[0161] Based on land use data of the target area, the origin and destination of the trip are obtained;

[0162] The population size is obtained from the population raster data.

[0163] Based on the population size and the origin and destination, a travel demand matrix is ​​obtained.

[0164] Optionally, traffic delay time is determined based on the safe driving speed and travel demand matrix, including:

[0165] Based on the safe driving speed and travel demand matrix, the actual travel time is obtained according to the driving routes of vehicles in the target area;

[0166] The actual travel time is compared with the corresponding normal travel time to obtain the traffic delay time.

[0167] Optionally, based on the traffic delay time, the traffic delay cost is obtained, including:

[0168] The social time value is derived from the GDP per capita of the target region.

[0169] Based on the social time value and traffic delay time, the traffic delay cost is obtained.

[0170] It should be noted that all implementation methods in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effect.

[0171] Embodiments of the present invention also provide a computing device, including: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for assessing traffic resilience and delay costs under rainfall-induced urban flooding as described in the present invention. All implementations in the above method embodiments are applicable to the embodiments of this computing device and can achieve the same technical effects.

[0172] Embodiments of the present invention also provide a computer-readable storage medium storing a program that, when executed by a processor, implements the method for assessing traffic resilience and delay costs under rainfall-induced urban flooding as described in this invention. All implementations in the above method embodiments are applicable to the embodiments of this computer-readable storage medium and can achieve the same technical effects.

[0173] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0174] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0175] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0176] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0177] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0178] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0179] Furthermore, it should be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent solutions of the present invention. Moreover, the steps performing the above series of processes can naturally be executed in the order described, but are not necessarily required to be executed in chronological order; some steps can be executed in parallel or independently of each other. Those skilled in the art will understand that all or any step or component of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or network of computing devices, in hardware, firmware, software, or a combination thereof. This is something that those skilled in the art can achieve by using their basic programming skills after reading the description of the present invention.

[0180] Therefore, the object of the present invention can also be achieved by running a program or a set of programs on any computing device. The computing device can be a known general-purpose device. Therefore, the object of the present invention can also be achieved simply by providing a program product containing program code for implementing the method or apparatus. That is, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent to the present invention. Furthermore, the steps for performing the above series of processes can naturally be performed in the order described, but are not necessarily required to be performed in chronological order. Some steps can be performed in parallel or independently of each other.

[0181] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for assessing traffic resilience and delay costs under rainfall-induced urban flooding, characterized in that, include: Obtain precipitation data for the target area; Based on the precipitation data, runoff generation and confluence simulations were performed to obtain the water depth; Based on the water depth and the speed-water depth mapping model, the safe driving speed corresponding to the water depth is obtained; The speed-water depth mapping model determines the safe driving speed under the premise of ensuring vehicle handling stability. The safe driving speed is obtained based on the road water depth and scaling factor. Based on the spatial distribution of the population in the target area, a travel demand matrix is ​​obtained; Based on the aforementioned safe driving speed and travel demand matrix, the traffic delay time is determined; Based on the traffic delay time, the traffic delay cost is obtained; Based on the precipitation data, runoff generation and confluence simulations are performed to obtain the water depth, including: Based on the precipitation data, the runoff flow rate of each grid cell in the target area is obtained; Based on the flow rate, a flow simulation is performed on the grid cell to obtain the water accumulation depth; The formula for calculating the flow rate is as follows: ， in, Let t represent the flow rate in the x-direction and t represent time. Indicates the time interval step size. Let g represent the unit width flow rate at time t, and g represent the acceleration due to gravity. denoted by h, which represents the water depth at the cross-section; h represents the water depth at the grid cell; z represents the surface elevation; and n represents the Manning roughness coefficient. The formula for calculating the water depth is: ， in, This indicates that the grid cell in the i-th row and j-th column is located in... The depth of the water at any moment This represents the water depth of the grid cell in the i-th row and j-th column at time t. Indicates the time interval step size. This indicates the size of the grid cell in the x-direction. This indicates the dimension of the grid cell in the y-direction. This represents the inflow rate of the grid cell in the x-direction. This represents the outflow rate of the grid cell in the x-direction. This represents the inflow rate of the grid cell in the y direction. R represents the outflow rate of the grid cell in the y direction, and R represents the net rainfall rate. The safe driving speed corresponding to the water depth is obtained based on the water depth and the speed-water depth mapping model, including: Obtain a road network distribution map of the target area; The grid travel speed is obtained based on the speed-depth mapping model; The safe driving speed is obtained by spatially overlaying the grid driving speed with the road network distribution map. The velocity-depth mapping model is as follows: ， Where h represents the water depth, and v(h) is the maximum safe driving speed while ensuring vehicle handling stability. Indicates the scaling factor; Based on the spatial distribution of the population in the target area, a travel demand matrix is ​​obtained, including: Based on land use data of the target area, the origin and destination of the trip are obtained; The population size is obtained from the population raster data. Based on the population size and the origin and destination, a travel demand matrix is ​​obtained.

2. The method for assessing traffic resilience and delay costs under rainfall-induced flooding as described in claim 1, characterized in that, Based on the aforementioned safe driving speed and travel demand matrix, traffic delay times are determined, including: Based on the safe driving speed and travel demand matrix, the actual travel time is obtained according to the driving routes of vehicles in the target area; The actual travel time is compared with the corresponding normal travel time to obtain the traffic delay time.

3. The method for assessing traffic resilience and delay costs under rainfall-induced flooding as described in claim 1, characterized in that, Based on the traffic delay time, the traffic delay cost is calculated, including: The social time value is derived from the GDP per capita of the target region. Based on the social time value and traffic delay time, the traffic delay cost is obtained.

4. A device for assessing traffic resilience and delay costs under rainfall-induced flooding, characterized in that, include: The acquisition module is used to acquire precipitation data for the target area; The processing module is used to perform runoff generation and confluence simulation based on the precipitation data to obtain the water depth; and to obtain the safe driving speed corresponding to the water depth based on the water depth and the speed-water depth mapping model. The speed-depth mapping model determines the safe driving speed while ensuring vehicle handling stability. The safe driving speed is obtained based on the road water depth and scaling factor. The travel demand matrix is ​​obtained based on the spatial distribution of the population in the target area. The traffic delay time is determined based on the safe driving speed and the travel demand matrix. Based on the traffic delay time, the traffic delay cost is obtained; Based on the precipitation data, runoff generation and confluence simulations are performed to obtain the water depth, including: Based on the precipitation data, the runoff flow rate of each grid cell in the target area is obtained; Based on the flow rate, a flow simulation is performed on the grid cell to obtain the water accumulation depth; The formula for calculating the flow rate is as follows: ， in, Let t represent the flow rate in the x-direction and t represent time. Indicates the time interval step size. Let g represent the unit width flow rate at time t, and g represent the acceleration due to gravity. denoted by h, which represents the water depth at the cross-section; h represents the water depth at the grid cell; z represents the surface elevation; and n represents the Manning roughness coefficient. The formula for calculating the water depth is: ， in, This indicates that the grid cell in the i-th row and j-th column is located in... The depth of the water at any moment This represents the water depth of the grid cell in the i-th row and j-th column at time t. Indicates the time interval step size. This indicates the size of the grid cell in the x-direction. This indicates the dimension of the grid cell in the y-direction. This represents the inflow rate of the grid cell in the x-direction. This represents the outflow rate of the grid cell in the x-direction. This represents the inflow rate of the grid cell in the y direction. R represents the outflow rate of the grid cell in the y direction, and R represents the net rainfall rate. The safe driving speed corresponding to the water depth is obtained based on the water depth and the speed-water depth mapping model, including: Obtain a road network distribution map of the target area; The grid travel speed is obtained based on the speed-depth mapping model; The safe driving speed is obtained by spatially overlaying the grid driving speed with the road network distribution map. The velocity-depth mapping model is as follows: ， Where h represents the water depth, and v(h) is the maximum safe driving speed while ensuring vehicle handling stability. Indicates the scaling factor; Based on the spatial distribution of the population in the target area, a travel demand matrix is ​​obtained, including: Based on land use data of the target area, the origin and destination of the trip are obtained; The population size is obtained from the population raster data. Based on the population size and the origin and destination, a travel demand matrix is ​​obtained.

5. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 3.

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