Urban multilayer traffic network modeling and toughness analysis method under rainstorm influence

Abstract

The invention discloses an urban multi-layer traffic network modeling and toughness analysis method under the influence of rainstorm, and relates to the technical field of traffic control, a rainstorm ponding model is built based on historical rainfall information, and a runoff depth and a ponding depth are simulated based on the rainstorm ponding model; building a multi-mode traffic model based on SUMO software; a rainstorm ponding model and a multi-mode traffic model are integrated to construct a coupling model, ponding information is matched to a corresponding road through a road ID, failure mechanisms of different traffic modes under rainstorm ponding are obtained, ponding influences are considered for road traffic and rail traffic respectively, and corresponding influence parameters are set. Adjusting the road speed limit in real time and updating the route in real time; the SUMO simulation output result is utilized, the toughness index change in different scenes is calculated, the comprehensive traffic network toughness in different rainstorm scenes is analyzed from the four dimensions of topology, convenience, service and safety, the comprehensive traffic network toughness is evaluated, the dynamic capture of traffic characteristics is realized, and the analysis depth and breadth are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic control, and specifically to a method for modeling and resilience analysis of urban multi-layer traffic networks under the influence of heavy rain. Background Art

[0002] The urban comprehensive traffic network is composed of various traffic modes such as roads, buses, and subways. These traffic modes not only promote each other but also restrict each other, playing a key role in promoting urban function operation, economic and social development, etc. However, due to the impact of global warming, the incidence and intensity of global rainfall events are increasing continuously, and the risk situation faced by the urban comprehensive transportation system is becoming increasingly severe. The resilience of the urban traffic network refers to the ability of the urban traffic network to maintain a certain traffic efficiency and service level when affected by external factors and gradually recover after the disturbance occurs. In the current research on urban traffic resilience under extreme weather, most studies focus on a single traffic mode, lacking the discussion of the cascading failure and recovery mechanism of multiple traffic modes under extreme weather; in the research on the resilience of urban multi-mode traffic networks, some scholars abstract the multi-mode traffic network into a multi-layer topological network structure and simulate external damage by deleting nodes or edges. These attack strategies focus more on random damage and less on the spatio-temporal impact on urban multi-mode traffic under extreme weather; in addition, in terms of resilience assessment, most scholars use common topological metric indicators to measure the resilience change of the system, such as node degree, station capacity, and the largest connected component, etc. The perspective is relatively single and mostly static indicators, unable to reflect the changes of dynamic elements such as travel time, travel satisfaction rate, and safety of the traffic system.

[0003] It can be seen that the simulation technology with characteristics such as microcosmic and continuous is the key to solving the above problems. Some scholars have combined the flood model with SUMO simulation to explore the impact of heavy rain on traffic, but this method is limited to the application of a single traffic mode. The urban traffic is jointly composed of multiple traffic modes and has a more complex impact propagation mechanism than single-mode traffic. How to extend the rainstorm flood model and micro-simulation technology to multi-layer traffic networks has important research value for exploring urban traffic resilience.

[0004] In view of this, the present invention proposes a method for modeling and resilience analysis of urban multi-layer traffic networks under the influence of heavy rain. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for modeling and resilience analysis of urban multi-layer traffic networks under the influence of heavy rain, breaking through the limitations of current single-mode simulation, and at the same time giving full play to the advantages of micro-simulation to evaluate the resilience of the comprehensive traffic network from an objective and comprehensive perspective.

[0006] In the first aspect, the present invention provides a method for modeling and resilience analysis of urban multi-layer traffic networks under the influence of heavy rain, including the following steps:

[0007] Step S1: Build a rainstorm waterlogging model based on historical rainfall information, and simulate the runoff depth and waterlogging depth based on the rainstorm waterlogging model;

[0008] Step S2: Build a multi-modal traffic model based on the SUMO software;

[0009] Step S3: Integrate the rainstorm waterlogging model and the multi-modal traffic model to construct a coupling model, match the waterlogging information to the corresponding roads through the road ID, consider the impact of waterlogging on different traffic modes respectively, set the corresponding impact parameters, generate a damaged road network file for the identified damaged public transport stations and damaged road sections, and adjust the speed limit of the road section in real time and update the route;

[0010] Step S4: Use the SUMO simulation output results to calculate the change of resilience indicators under different scenarios, analyze the dynamic characteristics of the resilience of the comprehensive transportation network under different rainstorm scenarios, and evaluate the resilience of the comprehensive transportation network.

[0011] As a preferred solution of the present invention, the application logic of the rainstorm waterlogging model is as follows:

[0012] Extract the rainfall data of the research area from the historical rainfall information, input the rainfall data and the corresponding coordinates into the map data, use the "spatial interpolation analysis" function of ArcGIS to obtain the spatial distribution of rainfall density, and use the Chicago rainfall pattern method to calculate the time distribution of rainfall;

[0013] Use the SCS model to calculate the runoff depth; Calculation of waterlogging depth:

[0014] Determine the waterlogging depth by subtracting the urban drainage capacity from the surface runoff depth, and correct the waterlogging depth using the digital elevation model.

[0015] As a preferred solution of the present invention, the historical rainfall information includes road network data, land use data, soil classification data, DEM data, historical rainfall data and rainstorm intensity; and the change of rainfall volume over time is obtained by substituting the rainstorm intensity calculation formula into the Chicago rainfall pattern method, and the spatio-temporal distribution result of rainfall volume is obtained in combination with the corresponding information of the road section.

[0016] As a preferred solution of the present invention, the steps for building the multi-modal traffic model are as follows:

[0017] Use the SUMO software to build an integrated transportation network, and the integrated transportation network is a network in which multiple traffic modes corresponding to the roads in the research area work together;

[0018] Based on the integrated transportation network, adjust the maximum allowable speed of different road levels, and determine the operation time, departure interval and vehicle stop time of public transportation;

[0019] Calculate the average daily commuting volume and generate an OD demand matrix. Import the OD demand matrix as traffic volume into the simulation and determine the transfer relationship between bus stops and subway stations.

[0020] With the shortest travel time as the goal, generate an initial travel route in the integrated transportation network. Follow the car-following model, LC2013 lane-changing model, and Webster traffic signal algorithm, and integrate them into a multi-modal traffic model to simulate the vehicle operation status.

[0021] As a preferred solution of the present invention, the road network data includes all urban road categories. The road network data used by the multi-modal traffic model is the same as the road network data used by the rainstorm waterlogging model. Map the waterlogging information to the road network data accurately through the road ID.

[0022] As a preferred solution of the present invention, the integrated transportation network includes a road layer, a bus layer, and a subway layer. Set the operation status of the urban comprehensive transportation system in advance based on the actual situation. The operation status includes OD traffic flow, public transportation operation routes, and public transportation operation schedules.

[0023] As a preferred solution of the present invention, the application logic of the multi-modal traffic system resilience evaluation index is as follows:

[0024] Real-time track and output the operation status of various transportation modes. Statistically calculate the weighted network efficiency, average travel delay time, number of path completions, and number of conflicts of each type of transportation mode in each time period. Construct a multi-dimensional and full-time-space dynamic resilience evaluation system from four aspects: topology, convenience, serviceability, and safety.

[0025] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0026] By introducing multi-modal traffic network resilience modeling, the present invention improves the multi-dimensionality and dynamics of traffic network resilience research, enhances the accuracy and practicality of evaluation, provides a more effective tool for the resilience evaluation and management of traffic networks in the face of disasters such as rainstorms, and realizes the dynamic capture of traffic characteristics by using the ability of SUMO to output the characteristics of the traffic system in real time, improving the depth and breadth of analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0028] Figure 1It is the flowchart of the method of the present invention;

[0029] Figure 2 It is the schematic diagram of the transfer mode of each traffic mode of the present invention under damaged conditions;

[0030] Figure 3 It is the schematic diagram of the multi - mode traffic network of the present invention;

[0031] Figure 4 It is the schematic diagram of the comprehensive resilience calculation per unit time of the present invention;

[0032] Figure 5 It is the diagram of the change of each index of the comprehensive traffic network under different scenarios of the present invention;

[0033] Figure 6 It is the diagram of the change of each index of different network layers under different scenarios of the present invention;

[0034] Figure 7 It is the diagram of the dynamic resilience value of the comprehensive traffic network under different scenarios of the present invention. Detailed implementation manners

[0035] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0036] Embodiment 1

[0037] Please refer to Figure 1 As shown, the method for modeling and resilience analysis of an urban multi - layer traffic network under the influence of heavy rain described in this embodiment includes the following steps:

[0038] Step S1: Build a heavy - rain waterlogging model based on historical rainfall information, and simulate the runoff depth and waterlogging depth based on the heavy - rain waterlogging model;

[0039] Specifically, the building logic of the heavy - rain waterlogging model is as follows:

[0040] Extract the rainfall data of the research area from the historical rainfall information, input the rainfall data and the corresponding coordinates into the map data, use the "spatial interpolation analysis" function of ArcGIS to obtain the spatial distribution of rainfall density, and calculate the time distribution of rainfall based on the Chicago rainfall pattern method;

[0041] Calculate the runoff depth using the SCS model; Calculation of waterlogging depth:

[0042] The ponding depth is determined by subtracting the urban drainage capacity from the surface runoff depth, and the ponding depth is corrected using a digital elevation model.

[0043] An exemplary description of the construction of the rainstorm ponding model is as follows:

[0044] (1) Runoff simulation

[0045] ① Rainfall has the characteristics of uneven temporal and spatial distribution. To simulate the temporal and spatial distribution of rainfall, first, we extract the rainfall data of the study area from the historical rainfall information from 2000 to 2019, input its rainfall amount and corresponding coordinates into the ArcGIS map, and use the "spatial interpolation analysis" function of ArcGIS to obtain the spatial distribution of rainfall density on the map. Then, we obtain the rainfall intensity formula applicable to the city where the study area is located and use the Chicago rain type method to calculate the temporal distribution of rainfall. The Chicago rain type method is a commonly used short-duration rainfall simulation method. It derives the rainfall pattern using the rainfall intensity formula and the rainfall peak coefficient, has little data dependence, is simple to derive, and has higher accuracy compared to other short-term design rain types.

[0046] ② The SCS model is a runoff model that simulates the hydrological processes of river basins. Compared with models such as MIKE and SWMM, the SCS model does not require a complex operation interface, and the requirements for topographic and hydrological information are also relatively simplified. In addition, it can effectively capture the impact of variables such as soil and land use on runoff. Therefore, we use the SCS model to calculate the runoff depth, as shown in equations (1)-(3);

[0047] L a = B × λ; (1)

[0048]

[0049] Where: R represents the total rainfall amount (mm), Q represents the runoff depth (mm), L a represents the initial rainfall loss amount (mm), B represents the maximum soil water storage capacity (mm); λ represents the correction coefficient, and the appropriate value proposed by the US Soil Conservation Service is λ = 0.2. The CN value is a dimensionless parameter, which is distinguished according to soil type and land use type, and can be determined by referring to the specific CN value table of each city.

[0050] (2) Ponding depth calculation

[0051] When estimating the depth of accumulated water, the depth of surface runoff is usually subtracted by the urban drainage volume to determine the depth of accumulated water. However, considering that certain specific areas are more prone to waterlogging due to their low terrain, in this paper, the Digital Elevation Model (DEM) is imported into ArcGIS, and the "depression filling analysis" function of ArcGIS is used to obtain the DEM after depression filling. Then, the data after depression filling is subtracted from the DEM source data to obtain the low-lying values of each area. Finally, the depth of accumulated water is corrected with this low-lying value, as shown in Equation 4.

[0052] F = Q - F pipe *t + F*u; (4)

[0053] In the formula, F is the depth of accumulated water (mm), F pipe is the urban drainage volume per hour (mm / h), t represents the drainage time, Q represents the runoff depth (mm), and u is the ratio of the low-lying value of a specific area to the maximum low-lying value within the research scope.

[0054] Step S2: Build a multi-modal traffic model based on the SUMO software:

[0055] Specifically, SUMO (Simulation of Urban Mobility) is a microscopic traffic simulation software that can import multiple traffic modes. Compared with other simulation software, SUMO can control the functions of each vehicle or each road in large-scale simulations and output real-time operation parameters, so that the operation characteristics of the multi-modal traffic network can be analyzed in detail during disasters. Therefore, this patent uses SUMO to build a multi-modal traffic model, and the specific building steps and corresponding rules are as follows:

[0056] ① The simulation includes three main traffic modes: private cars, buses, and subways, and walking is used as the default traffic mode to connect different travel modes. In the following, we will call the network where these modes work together the integrated transportation network.

[0057] ② According to the "Urban Road Engineering Design Specification" and the actual situation of the research area, adjust the maximum allowable speed of different road levels. In addition, determine the start time, end time, departure interval, and vehicle stop time of bus and subway operations according to the actual operation situation, and use this data and the station and line files to generate the operation timetables of buses and subways.

[0058] ③Based on the statistical yearbooks of the research area, calculate the daily average commuting volume of the research area, generate the origin-destination (OD) demand matrix of residents, and import it into the simulation as traffic volume. Among them, the calculation method of the OD demand matrix is as follows: First, use web crawler technology to obtain data such as the AOI and building height of public service facilities (including hospitals, financial institutions, governments, schools, companies, etc.) within the research area, use the projection tool of ArcGIS to calculate the total area of each workplace, refer to the "Office Building Design Standard (JGJ / T 67-2019)", calculate the number of workstations at the standard of 10 square meters per person, and use it as the arrival quantity of each area, that is, the D value of each area, and sum to obtain the total number of daily commuters within the research scope. Then, obtain the permanent resident population of each district in the city from the urban statistical yearbook, calculate the ratio of the permanent resident population of each district to the total permanent resident population within the research scope, multiply the total number of commuters in a certain area by the proportion of the permanent resident population of each district to obtain the departure quantity of this area, that is, the O value of this area, and so on, to obtain the O values of all areas. Finally, the origin and destination of the trip are randomly generated according to the roads in the area where the OD points are located, and the "od2trips" command built into SUMO is used to map the OD flow to the traffic network to generate a routing file.

[0059] ④Among all bus stops and subway stations, some can be used as transfer stations, that is, passengers can transfer different transportation modes at these transfer stations. These transfer modes include bus to bus, subway to subway, bus to subway, and subway to bus.

[0060] ⑤SUMO aims at the shortest travel time and uses the Gawron algorithm to generate initial travel routes for all passengers. Private cars and buses follow traffic rules such as the LC2013 lane-changing model and the Webster traffic signal algorithm to simulate the vehicle operation state.

[0061] Step S3: Integrate the rainstorm waterlogging model and the multi-modal traffic model to construct a coupled model, match the waterlogging information to the corresponding roads through the road ID, obtain the impact of rainstorm waterlogging on different traffic modes, consider the impact of waterlogging on road traffic and rail transit respectively, set the corresponding impact parameters, adjust the road speed limit in real time and update the route, and generate a damaged route file for the identified damaged public transportation stations.

[0062] An exemplary description of the coupled model is as follows:

[0063] Since the rainstorm waterlogging model and the traffic model use the same map, we can match the waterlogging information to the corresponding roads through the road ID. However, the rainstorm waterlogging has different impact methods on different traffic modes, which can be divided into two types:

[0064] (1) The impact of rainstorm waterlogging on road traffic

[0065] In this patent, since private cars and buses both run on the road, the impact of waterlogging on road traffic includes the impact on both private cars and buses. Regarding road traffic, the impact of waterlogging is twofold. First, waterlogging can directly lead to road closures or speed limits reduction. Second, it indirectly affects traffic by causing vehicle delays, detours, or forcing trip cancellations. Therefore, we incorporate the water depth results generated by the rainstorm model into the commonly used driving speed decay model that varies with water depth, and regularly update the maximum allowable speed on waterlogged roads.

[0066] (2) Impact of rainstorm waterlogging on rail transit

[0067] The rail transit in this patent mainly refers to the subway. For the operation of the subway, it is mainly divided into two aspects: station damage and line damage. In terms of station damage, due to the inconsistent step heights of different station entrances and exits, it is difficult to quantify the flood prevention measures of the subway station itself. According to the "Technical Specification for Urban Waterlogging Prevention and Control", if the water depth on the road exceeds 15 cm, it will be difficult for general vehicles to operate. For passengers taking the subway, if the water depth outside the station entrance and exit exceeds 15 cm, it will be very difficult for passengers to enter and exit the station. At this time, even if passengers can leave the station safely, their trips will be interrupted due to the closure of the road outside the station. Therefore, we regard the situation where the water depth on the road where the station is located exceeds 15 cm as station damage, and at this time, the station will stop operating. In terms of line damage, we use the risk matrix method to identify the subway lines with the greatest risk of being affected by rainstorms, and the calculation formula is shown in formula (5).

[0068] H = O × Z; (5)

[0069] Where: H represents the risk value, O represents the possibility of danger occurring, and Z represents the severity of the consequences. In this patent, O is represented by the rainfall density. The higher the rainfall density, the greater the possibility of danger occurring in this area. Z is represented by the number of subway lines contained in this area. The higher its value, the higher the severity after route damage.

[0070] (3) Impact of travel interruption on traffic modes

[0071] Due to the interconnection of each layer of the network, in real life, when a certain link in the travel chain is damaged, travelers may continue to complete their trips by waiting in place or changing their travel modes. Usually, passengers traveling by private car will not transfer to buses or subways. Therefore, in the event of a disaster, if the travel of passengers traveling by private car is interrupted, they can use the "reroute" function in SUMO to detour the vehicle or directly choose to walk; if a certain travel link in the bus or subway layer is interrupted, passengers can choose to transfer to other buses or subways, or directly choose to walk. It should be noted that the above various traffic mode switching methods mainly depend on the current traffic conditions and the shortest path calculation, that is, SUMO uses the Dijkstra algorithm to recalculate the current best travel path with the goal of the shortest travel time. The transfer methods between different modes are as Figure 2 shown. Step S4: Use the SUMO simulation output results to calculate the changes in resilience indicators under different scenarios, analyze the resilience of the comprehensive transportation network under different rainstorm scenarios, and evaluate the resilience of the comprehensive transportation network;

[0072] Specifically, the comprehensive transportation network resilience evaluation system is a multi-dimensional resilience evaluation system based on network efficiency, delay time, number of completed paths, and number of conflicts; the comprehensive transportation network includes a road layer, a bus layer, and a subway layer, calculates the ratio of the affected situation to the situation without disasters, and normalizes each indicator.

[0073] An exemplary description of the evaluation index system for the resilience of the urban comprehensive transportation system is as follows:

[0074] The comprehensive transportation network is divided into three layers: the road layer, the bus layer, and the subway layer. The traffic modes in the road layer include private cars and walking, the traffic mode in the bus layer is buses, and the traffic mode in the subway layer is subways. The transportation network can be represented as G m =(n m , ξ m , φ m (t)), where n m , ξ m and φ m (t) respectively represent the sets of nodes, edges, and edge weights of each layer of the network at time t. In this patent, m = 1, 2, 3 respectively correspond to the road layer, the bus layer, and the subway layer. m = 0 represents the comprehensive transportation network, and the specific structure is as Figure 3 shown.

[0075] To make up for the deficiencies of the current traffic resilience evaluation system, which lacks characteristics such as traffic safety, the comprehensive traffic network resilience evaluation system of this patent consists of four characteristics: the network topology, convenience, serviceability, and safety of the traffic system. Each characteristic is represented by four indicators: network efficiency, delay time, number of completed paths, and number of conflicts. The calculation formulas are shown in formulas (6)-(10) respectively:

[0076]

[0077] Among them, e m (t) represents the network efficiency of layer m at time t, where m = 0, 1, 2, 3. n m is the number of nodes in layer m, and s m (t) represents the shortest travel time from node i to node j in layer m at time t.

[0078]

[0079] Among them, d m (t) is the delay time of layer m at time t. is the time lost due to walking at layer m at time t, represents the time lost when the vehicle is running on layer m at time t. and are the time lost due to waiting and transfer at layer m at time t respectively. p m (t) represents the total number of people at layer m at time t.

[0080]

[0081] Among them, c m (t) is the number of trips completed at layer m at time t, where m = 1, 2, 3, and c 0 (t) = c 1 (t) + c 2 (t) + c 3 (t). Here, represents whether the i th th travel chain has completed its last trip on layer m at time t. If so, otherwise, k is the total number of travel chains at layer m at time t.

[0082]

[0083] Among them, s m (t) represents the number of conflicts, where m = 1, 2, 3,, and s 0 (t) = s 1 (t) + s 2 (t) + s 3(t). Parameter v i , x i and l i represent the speed, position, and length of vehicle i, respectively. Vehicle i - 1 is the vehicle in front of vehicle i.

[0084] Subsequently, we need to calculate the ratio of the disaster situation to the non - disaster situation to normalize each indicator. Among them, and are positive indicators, with a minimum value of 0 and a maximum value of the value in the non - disaster situation (as shown in formulas (11) and (13)). and are negative indicators, with a minimum value of the value in the non - disaster situation and maximum values of 1.5 times and 5 times the value in the non - disaster situation, respectively. This is because according to previous research and relevant data statistics, the maximum value of traffic delay time is generally 1.5 times the value in the non - disaster situation, and the number of traffic accidents is generally 5 times the value in the non - disaster situation. The specific normalization methods are as shown in formulas (12) and (14).

[0085]

[0086] Among them, represents the value of the i - th indicator on the m - th layer at time t, where i = 1, 2, 3, 4. and represent the values of each indicator of the integrated transportation network (G 0 ) in the normal non - disaster situation.

[0087] Finally, the indicators of the four dimensions are formed into a quadrilateral to represent the network comprehensive resilience value. As Figure 4 shown, the network comprehensive resilience value includes topology, convenience, safety, and serviceability. Since resilience can be defined as the ratio of the performance under disaster conditions to the performance under non - disaster conditions (normal conditions), the formula for calculating the resilience of the integrated transportation network is:

[0088]

[0089] Among them represents the exponential value of G 0 under disaster conditions at time t, represents time t, and when i = 1, 2, 3, 4, the exponential value of G 0 under normal conditions. R(t) represents the resilience value of G 0 at time t.

[0090] Example 2

[0091] This embodiment takes the area within the Outer Ring Road of Shanghai as the research object, mainly including Hongkou District, Huangpu District, Jing'an District, Pudong New Area, Putuo District, Xuhui District, Yangpu District, Changning District and parts of Baoshan District and Minhang District. The implementation manner of the present application will be described in detail below. The road traffic control method based on mixed traffic flow and weather environment includes the following steps:

[0092] Establishment of heavy rain waterlogging model

[0093] The historical rainfall information required for establishing the heavy rain model includes road network data, land use data, soil classification data, DEM data, historical rainfall data and heavy rain intensity calculation formula. The heavy rain intensity calculation formula for Shanghai is shown in formula (16):

[0094]

[0095] In the formula: q is the heavy rain intensity (L / S·hm2), P is the recurrence period, and t is the duration (min).

[0096] Considering the non-uniformity of rainfall in time and space, we first substitute the heavy rain intensity calculation formula of Shanghai into the Chicago rain type method to obtain the variation of rainfall with time within a given time period at a given recurrence period. Subsequently, based on the historical rainfall data of Shanghai from 2000 to 2019, spatial interpolation analysis is used in ArcGIS to obtain the spatial distribution of rainfall. Then, the land use data and soil classification data are substituted into the SCS runoff model (formulas (1)-(3)) to obtain the road runoff depth. Finally, the DEM data is substituted into the waterlogging depth calculation formula (formula (4)) to obtain the road waterlogging depth distribution.

[0097] The establishment logic of the multi-mode traffic model is as follows:

[0098] In this example, the road network data includes all urban road categories, namely expressways, urban expressways, main roads, secondary roads, branch roads, railways and sidewalks within the Outer Ring Road of Shanghai, with a total of 158,901 edges and 90,108 nodes. According to the actual situation, we adjust the maximum allowable speeds of expressways, urban expressways, main roads, secondary roads and branch roads in SUMO to 120 km / h, 80 km / h, 60 km / h, 50 km / h and 40 km / h respectively, and the subway speed is 80 km / h. In addition, to reflect the operation specifications of Shanghai, we adjust the timetables of buses and subways. Among them, the service hours of buses and subways are from 05:30 to 22:30. Buses run every 10 minutes and subways run every 5 minutes, and the stop time for both is 3 minutes. It should be noted that the road network data used in the traffic model is the same as the road network data used in the heavy rain waterlogging model, so that the waterlogging information can be accurately mapped to the traffic network through the road ID.

[0099] In terms of traffic flow, this paper mainly considers trips that are inevitable even in extreme weather, namely the commuting flow during the morning rush hour for work or school. Considering the staggered commuting policy in Shanghai, this paper sets the morning rush hour from 06:00 to 10:00. Therefore, in terms of traffic flow, first, by crawling AOI data from Amap and importing it into ArcGIS, the areas of office land and educational land are calculated, including companies, government agencies, schools, and research institutions, etc. The number of workstations within the research scope is estimated based on the standard of 10 square meters per person. The results show that there are approximately 2,197,908 commuters within the research scope every day. Then, based on the traffic hourly flow change diagram in "General Theory of Traffic Engineering", the fluctuation characteristics of the traffic flow in this paper within a day are as follows: the traffic flow from 01:00 to 05:00 accounts for 0.1% of the total commuting flow. From 06:00 to 10:00, the hourly flows account for 1.2%, 3.1%, 8.2%, 5.2%, and 4.3% of the total commuting flow respectively. After the peak period, it is assumed that there is no new traffic flow in the remaining time periods, that is, the new traffic volume from 10:00 to 24:00 is 0. Therefore, there are approximately 494,523 commuters in the simulation. The hourly traffic volume is set according to the flow ratio of each time period above, and the OD distribution is obtained according to the OD matrix calculation method and imported into SUMO.

[0100] Establishment of the Coupling Model between Rainstorm and Multi-Mode Traffic Network and Setting of Simulation Scenarios:

[0101] Spatial Connection between Rainstorm Waterlogging Information and Traffic Network:

[0102] Since the road network data used in the traffic model is the same as the road network data used in the rainstorm waterlogging model, and the road IDs of the same sections in ArcGIS and SUMO are also the same, therefore, we can load the waterlogging depth obtained from the rainstorm waterlogging model into the corresponding waterlogging section attributes through the "Spatial Join" function of ArcGIS, and connect the information of the waterlogging sections in ArcGIS with the SUMO traffic road network according to the road IDs.

[0103] Impact of Rainstorm Waterlogging on Road Traffic

[0104] The chain effect of rainstorm waterlogging on traffic is mainly caused by waterlogging hindering traffic. Severe waterlogging will lead to a decrease in the driving speed of sections or even a complete interruption. A decay model of vehicle driving speed with waterlogging depth is constructed according to the hyperbolic tangent function. The specific calculation method is shown in formula (16):

[0105]

[0106] Where: v is the maximum allowable driving speed of the road (km / h); v0 is the designed speed of this area (km / h); x is the water depth (cm); a is the median value of the water depth that causes the vehicle to be unable to pass (cm); b is the attenuation elastic coefficient, generally taking 3 - 5, and the median value 4 is taken in this example.

[0107] According to the Technical Specification for Urban Waterlogging Prevention and Control, for automobiles (including private cars and buses), to ensure that the vehicle does not stall, the maximum water depth of the road is 15 cm. Therefore, in this study, the water depth that causes the vehicle to be unable to pass is set to 15 cm, and the value of a in formula (16) is taken as 7.5 cm.

[0108] After modifying the maximum allowable speed of the road, the traffic capacity of some sections has decreased significantly. Under rainstorm conditions, there will be varying degrees of congestion or even interruption. At this time, the original routes of some vehicles may have too long waiting times or be impassable. In reality, when encountering road congestion or interruption, most car owners will choose to re - plan their routes and find the optimal path among the remaining feasible roads to replace the original route. To simulate the real situation, the "rerouter" command in SUMO is selected to achieve this function. The "rerouter" in SUMO is called the re - router, which is defined in the attached file. Its function is that once the vehicle arrives on the road equipped with the re - router, the re - router will use the Dijkstra algorithm to automatically calculate the shortest path according to the current driving time in the network and allocate a new route for the vehicle. In this example, it is set to update the current optimal path every 15 minutes.

[0109] Influence of rainstorm waterlogging on rail transit:

[0110] In terms of rail transit, due to the release of early warning information such as weather forecasts, most subway stations will set up flood prevention facilities such as waterproof fences and waterproof sandbags at the entrances and exits of the subway stations in advance before the rainstorm to carry out pre - disaster prevention work. Therefore, the possibility of rainstorm entering the station through the subway entrance is relatively small. According to the Technical Specification for Urban Waterlogging Prevention and Control, if the water depth of the road outside the subway station exceeds 15 cm, the road will be forced to close. At this time, even if passengers can safely leave the subway station, their trips will be interrupted due to the closure of the road outside the subway station. Therefore, it is considered to define the mechanism of subway station damage as: when the water depth of the road where the subway station is located exceeds 15 cm, it is assumed that the station is damaged and the train will no longer stop at this station; otherwise, the train runs normally along the original route.

[0111] In addition to station damage, line damage also needs to be considered. Based on the risk matrix method (Equation 5), we used ArcGIS raster processing to calculate risk values and extracted the risk values for the lines. We found that the line with the highest risk value under heavy rain conditions was Metro Line 4. In the subsequent disaster scenario settings, Metro Line 4 was set to be out of service. Finally, as with road traffic, when a station or line is out of service, the "rerouter" rerouting function of SUMO is used to update the current optimal travel route every 15 minutes.

[0112] Scene setup:

[0113] The scene settings for this example are as follows.

[0114] Scenario 0: Normal scene without heavy rain;

[0115] Scenario 1: A 50-year rainstorm disaster;

[0116] Scenario 2: A 100-year rainstorm disaster;

[0117] Scenario 3: A once-in-a-century rainstorm disaster and the complete interruption of Metro Line 4.

[0118] Due to the characteristics of short-duration rainfall, the rainfall in each scenario lasted for 5 hours. For simplicity, we assumed that the accumulated water completely dissipated 1 hour after the rainfall stopped. The specific values are shown in Table 1.

[0119] Table 1 Parameter settings for rainfall and traffic volume in different scenarios

[0120]

[0121]

[0122] Analysis of multimodal transportation network resilience under heavy rain conditions

[0123] The transportation network resilience of this example considers the network topology, convenience, serviceability and security of the transportation network. Each characteristic is represented by four indicators: network efficiency, delay time, number of completed paths and number of conflicts. Based on the simulation output results, the relationship between the four resilience indicators and time is calculated using formulas (6)-(10): Figure 5 and Figure 6 shown.

[0124] Figure 5 and Figure 6 The changes in various indicators of the comprehensive transportation network under different scenarios and the changes in various indicators of each layer of the network under different scenarios are respectively shown. Figure 6 The changing trends of each network layer are as follows: Figure 5 The trend of the comprehensive network in the provides a more detailed explanation. For example,Figure 5 and Figure 6 From the changes in the corresponding indicators, it can be seen that the decline in the average network efficiency of the comprehensive transportation network is mainly caused by the road layer and the bus layer. The increase in the average delay time and the number of conflicts in the comprehensive transportation network is mainly caused by the road layer. The change in the number of completed paths in the comprehensive transportation network is mainly caused by the road layer and the subway layer. Generally speaking, the road layer and the subway layer bear most of the passenger flow. Among them, the road layer is most vulnerable to traffic flow interruption and rainstorm damage. Due to the fixed timetable and route of the subway system, and most of the subways are located underground, it is least affected by disasters.

[0125] Figure 7 It is the result chart of the dynamic change of the comprehensive network resilience. Among them, Scenario 0 is used as the baseline, and its resilience value is a constant value of 1. The resilience change trends of Scenarios 1 to 3 all show a trend of first decreasing and then increasing. And with the increase in the degree of disaster, the degree of resilience decline also gradually increases. Scenarios 1 to 3 all reach the lowest value about one hour (9:00) after the rainfall peak. The lowest values of the three scenarios are approximately 0.7, 0.6, and 0.5 respectively. That is, the minimum resilience value of Scenario 3 with the most serious disaster is only about 50% of that under normal conditions. It is worth noting that the comprehensive resilience values of Scenarios 1 to 3 do not fully recover to the initial state by the end of the simulation (24:00), which indicates that relying solely on the self-repair process of the transportation network cannot make it return to normal within one day.

[0126] Finally, to further quantify the overall impact of disasters on the transportation network, we calculate the maximum value (R max (t)), minimum value (R min (t)), average value and standard deviation (R σ (t)) of the dynamic resilience of the comprehensive transportation network, and calculate the resilience integral value of each scenario within the time period from 00:00 to 24:00 in one-hour unit time (see Table 2).

[0127] Table 2 Statistical values of the comprehensive network resilience characteristics under different scenarios

[0128]

[0129] The results show that the minimum value, average value, and integral value of the comprehensive resilience all decrease with the increase in the severity of the disaster. The average value and integral value in Scenario 3 are both approximately 25% less than these two values in Scenario 0. Although the maximum value of resilience is 1 in all scenarios, the minimum value of resilience in Scenario 3 is only 48% of that in Scenario 0, and the time for Scenario 1 and Scenario 3 to reach the lowest resilience value is almost the same, which indicates that the decline rate in Scenario 3 is higher, suggesting that the decline rate of resilience increases with the increase in the intensity of the disaster. This conclusion is consistent with the results of the standard deviation of resilience, that is, the greater the degree of the disaster, the greater the standard deviation of resilience, which indicates that more severe disasters will lead to greater fluctuations in the resilience curve.

[0130] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0131] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only one way, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0132] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0134] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or replacements, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for modeling and resilience analysis of urban multi-layer transportation networks under the influence of heavy rain, characterized in that, It includes the following steps: Step S1: Build a rainstorm waterlogging model based on historical rainfall information, and simulate the runoff depth and waterlogging depth based on the rainstorm waterlogging model; Step S2: Build a multi-mode traffic model based on the SUMO software; Step S3: Integrate the rainstorm waterlogging model and the multi-mode traffic model to construct a coupling model, match the waterlogging information to the corresponding roads through the road ID, consider the influence of waterlogging on different traffic modes respectively, set the corresponding influence parameters, generate a damaged road network file for the identified damaged public transport stations and damaged road sections, and adjust the section speed limit and update the route in real time; Step S4: Use the SUMO simulation output results to calculate the change of resilience indicators under different scenarios, analyze the dynamic characteristics of the resilience of the comprehensive traffic network under different rainstorm scenarios, and evaluate the resilience of the comprehensive traffic network.

2. The method for modeling and resilience analysis of urban multi-layer traffic network under the influence of heavy rain according to claim 1, wherein: The application logic of the rainstorm waterlogging model is as follows: Extract the rainfall data of the research area from the historical rainfall information, input the rainfall data and the corresponding coordinates into the map data, use the "spatial interpolation analysis" function of ArcGIS to obtain the spatial distribution of rainfall density, and use the Chicago rain type method to calculate the time distribution of rainfall; Use the SCS model to calculate the runoff depth; Calculation of waterlogging depth: Determine the waterlogging depth by subtracting the urban drainage capacity from the surface runoff depth, and correct the waterlogging depth using the digital elevation model.

3. The method for modeling and resilience analysis of urban multi-layer traffic network under the influence of heavy rain according to claim 2, wherein: The historical rainfall information includes road network data, land use data, soil classification data, DEM data, historical rainfall data and rainfall intensity; The change of rainfall amount over time within a given period is obtained by substituting the rainfall intensity calculation formula into the Chicago rain type method, and the spatio-temporal distribution result of rainfall amount is obtained by combining the corresponding information of the road section.

4. The method for modeling and resilience analysis of urban multi-layer transportation network under the influence of heavy rain according to claim 3, characterized in that: The steps for building the multi-mode traffic model are as follows: Use the SUMO software to construct an integrated transportation network, and the integrated transportation network is a network in which multiple traffic modes corresponding to the roads in the research area city work together; Based on the integrated transportation network, adjust the maximum allowable speed of different road levels, and determine the operation time, departure interval and vehicle stop time of public transportation; Calculate the average daily commuting volume and generate an OD demand matrix, import the OD demand matrix as traffic volume into the simulation, and determine the transfer relationship between bus stops and subway stations; With the shortest travel time as the goal, generate an initial travel route in the integrated transportation network, follow the traffic rules, and simulate the vehicle operation state.

5. The method for modeling and resilience analysis of urban multi-layer transportation network under the influence of heavy rain according to claim 4, characterized in that: The road network data includes all urban road categories, and the road network data used in the multi-mode traffic model is the same as the road network data used in the rainstorm waterlogging model. The waterlogging information is accurately mapped to the road network data through the road ID.

6. The method for modeling and resilience analysis of urban multi-level transportation network under the influence of heavy rain according to claim 5, wherein: The integrated transportation network includes a road layer, a bus layer and a subway layer. The operation status of the urban integrated transportation system is set in advance based on the actual situation, and the operation status includes OD traffic flow, the maximum speed limit value of each type of road, the operation route of public transportation and the public transportation operation schedule.

7. The method for modeling and resilience analysis of urban multi-layer traffic network under the influence of heavy rain according to claim 6, wherein: The application logic of the resilience evaluation index of the multi-mode traffic system is: Track and output the operating status of various transportation modes in real time, and count the weighted network efficiency, average travel delay time, number of path completions, and number of conflicts of various transportation modes in each time period. Construct a multi-dimensional and full-time-and-space dynamic resilience evaluation system from four aspects: topology, convenience, serviceability, and security.

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