A method for evaluating the impact of road networks considering the impact of urban agglomeration emergencies

Abstract

The present invention provides a method for evaluating the impact of urban agglomeration emergencies on road networks, comprising the following steps: obtaining urban road network data of the area to be studied; calculating the travel volume between traffic zones and calculating daily travel spatio-temporal data; establishing a 24-hour urban traffic simulation model for weekdays; simulating the load-bearing and operating speed of sections in the road system of the urban agglomeration before an emergency; establishing an emergency simulation layer; analyzing the changes in the load-bearing and operating speed of sections in the road network of the urban agglomeration when and after an emergency occurs; evaluating the impact on the structure and function of the road system of the urban agglomeration after an emergency. From a microscopic perspective, it simulates the behavior of heterogeneous vehicles reselecting routes in the large urban agglomeration road traffic system under emergencies, quantifies the impact of emergencies, and at the same time uses Baidu Map big data to verify the system operation parameters to ensure the rationality of the evaluation, which is of great significance for the policy-making and emergency management of the transportation department.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency management, and particularly relates to a method for evaluating the impact of road networks considering the influence of urban agglomeration emergencies. Background Art

[0002] In recent years, with the increasing frequency and intensity of extreme weather and public health events, the impact of various types of emergencies has received more and more attention. As a crucial infrastructure in daily life, the urban road traffic system is severely threatened by emergencies, which threatens the normal operation of society and causes serious economic losses. The intensity and frequency of emergencies that have a greater impact on the road system are on the rise. These events are mainly divided into natural disasters, traffic accidents, public health emergencies, and social security events. The nature, scale, impact, duration, and source of emergencies are different. However, any kind of emergency will significantly reduce the performance of the urban road network. In addition, the formation of urban agglomerations around megacities has become an inevitable trend of economic development. Due to the increasing demand for population and vehicle growth, the interruption of road transportation in urban agglomerations is more serious. The traffic connections within urban agglomerations are becoming closer and closer. The present invention considers the traffic between cities to evaluate the impact of emergencies on the entire urban agglomeration.

[0003] Currently, the main methods for evaluating the impact on the road system are structural analysis of the system using graph theory and complex network theory, network traffic modeling analysis driven by big data, and small-scale network simulation based on simulation. These methods have certain limitations in studying the impact assessment of large-scale urban agglomerations under emergencies, and cannot simulate the dynamic traffic flow under emergencies, nor can they evaluate the impact results under different traffic flow scales (peak and off-peak). Summary of the Invention

[0004] In view of this, the problem to be solved by the present invention is to provide a method for evaluating the impact of road networks considering the influence of urban agglomeration emergencies, so as to statistically evaluate the impact of the system with more microscopic perspective by various index data.

[0005] The present invention solves the above technical problems through the following technical means: The present invention provides a method for evaluating the impact of road networks considering the influence of urban agglomeration emergencies, including the following steps:

[0006] Obtain the urban road network data of the area to be studied;

[0007] Based on the obtained urban road network data, use the population-weighted opportunity model to calculate the travel volume between traffic zones, and calculate the daily travel spatio-temporal data accordingly;

[0008] Based on the daily travel spatio-temporal data, establish a 24-hour urban traffic simulation model for weekdays;

[0009] Based on the simulation model, simulate the load-bearing and operating speed of sections in the urban agglomeration road system before emergencies, and calibrate the traffic model;

[0010] Abstract the emergency into a damage mechanism to the system, and establish an emergency simulation layer;

[0011] Overlay the spatial distribution data on the urban agglomeration road network, and overlay the emergency simulation layer on the peak and off-peak hours of the road respectively, and analyze the changes in the load-bearing and operating speed of sections in the urban agglomeration road network when and after the emergency occurs;

[0012] Evaluate the impacts on the structure and function of the urban agglomeration road system after the emergency.

[0013] Furthermore, the obtaining of the urban road network data of the area to be studied further includes:

[0014] Obtain the administrative boundary data of the area to be studied and the vector data set of the road network, where road intersections are abstracted as nodes and sections are abstracted as edges;

[0015] Check the connectivity of the physical network to obtain a fully connected physical network model;

[0016] Fill in the missing attributes in the road network data table;

[0017] Simplify and eliminate nodes. For nodes along the bends of one-way roads, merge the edges between real nodes into one edge, then merge the statistical features, and combine node clusters close to each other into one node to represent the road intersection;

[0018] Conduct visual display in SUMO, and modify incorrect lane connections by visual inspection, simulation tests or comparison with map software that can display map street views.

[0019] The filling in the missing attributes in the road network data table includes filling the speed of sections of non-ramp types with the average value of all sections of the same type; for sections of ramp types, filling with 1 / 3 of the average maximum speed limit of all roads of the same type; for missing lane numbers of roads, selecting and filling with the integer value of the average lane number of all roads of the same type; and correcting the road speed limit according to the road speed limit standard.

[0020] Furthermore, the using of the population-weighted opportunity model to calculate the travel volume between traffic zones further includes:

[0021] Divide the study area into grids with a resolution of 1 km by 1 km in space, and each grid is used as a traffic analysis zone TAZ;

[0022] Place the origin and destination of the trip at the grid centroid, estimate the trip volume between TAZs using the PWO model, and the probability of choosing destination j is proportional to the population m of that place j , and inversely proportional to the total population s between the location of the traveler and the destination ij , and the trip opportunity number at this location is: P ij ∝m j / S ij ;

[0023] Based on the above formula, calculate the trip volume between traffic zones. The probability that a traveler chooses a destination is proportional to the population m of the destination j , and inversely proportional to the total population s between the location of the traveler and the destination ij , then the trip volume T between the two zones ij is: T ij =O i ·P ij =(O i / ∑ j (m j / s ij ))·(m j / s ij );

[0024] Among them, P ij represents the probability of being attracted by point j starting from point i, and O i represents the occurrence volume (times) of zone i;

[0025] Allocate the spatial distribution data of trip volume based on the incremental traffic assignment model according to the daily trip distribution curve. Divide the spatial distribution data of trip volume into several parts, allocate one part of the trip volume each time, and determine the shortest path for the next allocation according to the road network flow condition.

[0026] Furthermore, the establishment of a 24-hour urban traffic simulation model for weekdays includes:

[0027] Calibrate the speed factor parameters of the simulation model, input the road network data and trip schedule data into SUMO, and initialize each parameter in its configuration file for simulation;

[0028] Iterate to obtain the optimal route with the goal of minimizing the travel cost, and through multiple simulations, adjust the overall scale parameter and speed factor parameter of the trip volume;

[0029] Compare the experimental data with the urban daily trip distribution data and urban average commuting time data provided by the third-party data platform, and conduct error analysis until the error is adjusted to the standard range to determine the final model parameters.

[0030] Furthermore, the emergencies include:

[0031] Natural disasters represented by flood levels: The CaMa-Flood hydrological model is used to model the surface runoff distribution under different rainfall intensities, calculate the water depth of the road section, and divide the road section into completely submerged and submerged sections with traffic capacity according to the following function:

[0032] where h represents the submerged water depth (mm), c is an empirical parameter. The roads with a submerged depth greater than 300 mm are directly failed parts, and those less than or equal to 300 mm calculate the road traffic speed according to the above formula;

[0033] Traffic accidents: Traffic accidents are abstracted as a random attack on the network. Randomly deleting a certain proportion of road sections in the entire network represents the traffic accidents that occur. The damage intensity range is set to 5% - 70%, with a step size of 5%. Random failures are independently repeated 5 times at each failure intensity;

[0034] Public health emergencies: Public health emergencies are abstracted as a local failure. Starting from a random root node of the network, all edges connected to it are selected, and a set of edges within the specified range is selected in order of increasing distance from the root node;

[0035] Social security incidents: The malicious nuclear attack that causes the greatest damage to the system is abstracted as a target attack. Road sections are removed proportionally in order of betweenness centrality of the edges in the system. The damage intensity range is set to 5% - 70%, with a step size of 5%;

[0036] Furthermore, abstracting the emergency events as a destruction mechanism for the system and establishing an emergency event simulation layer further includes, according to the above four destruction mechanisms, identifying and removing directly failed road sections and adjacent nodes in the system, and then judging the connectivity of the network again according to the structural characteristics of the network, and removing the parts that are not connected to the largest connected subgraph.

[0037] Furthermore, the destruction mechanism also includes dynamically closing specific roads or specifying the driving speed of roads during a specific time period in the simulation process, considering the current and recent traffic conditions in the network during the simulation process, calculating the average driving time of the roads in the network, and thus adjusting the route plan according to the changes brought by traffic congestion and emergency events in the road network. Vehicles will select the fastest path to reach the destination according to the average driving speed of the roads.

[0038] Furthermore, the evaluation of the impact of emergency events on the structure and function of the urban agglomeration road system includes:

[0039] Evaluating the structural loss after emergency events: Selecting the largest connected subgraph as the structural performance evaluation index, that is, the ratio C of the number of nodes in the largest connected subgraph of the network after superimposing the emergency events to the number of nodes in the original network gcc :

[0040] C gcc = n' / n, 0 < C gcc ≤ 1,

[0041] where n' represents the number of nodes in the largest connected subgraph after network damage, and n represents the number of nodes in the original network;

[0042] To evaluate the functional loss after an emergency, the functional indicators are evaluated from three aspects: the proportion of interrupted traffic volume, the change in average travel time, and the change in average travel distance;

[0043] Excluding users whose routes are directly interrupted, the system recalculates the routes of users during the simulation process. With the goal of minimizing the travel cost, the change values I d 、I t :

[0044]

[0045] where n and m respectively represent the number of operating vehicles before and after the emergency, and t and d respectively represent the travel time and distance of the vehicles before the emergency, and t a 、d a represent the travel time and distance of the vehicles after the emergency.

[0046] Furthermore, the interrupted traffic volume includes: the interruption directly caused by the damage mechanism to the user's route; users who cannot reach their destinations due to long-term congestion on the road section.

[0047] As can be seen from the above technical solutions, the beneficial effects of the present invention are as follows: The present invention provides a method for evaluating the impact of urban agglomeration emergencies on road networks, including the following steps: obtaining urban road network data of the area to be studied; based on the obtained urban road network data, using a population-weighted opportunity model to calculate the travel volume between traffic zones, and calculating daily travel spatio-temporal data therefrom; based on the daily travel spatio-temporal data, establishing a 24-hour urban traffic simulation model for weekdays; based on the simulation model, simulating the load-bearing and operating speeds of sections in the urban agglomeration road system before an emergency, and calibrating the traffic model; abstracting the emergency into a damage mechanism for the system, and establishing an emergency simulation layer; overlaying the spatial distribution data onto the urban agglomeration road network, and overlaying the emergency simulation layer onto the peak and off-peak periods of the road respectively, analyzing the changes in the load-bearing and operating speeds of sections in the urban agglomeration road network when and after the emergency occurs; evaluating the impact on the structure and function of the urban agglomeration road system after the emergency. From a microscopic perspective, it simulates the behavior of heterogeneous vehicles reselecting routes in the large urban agglomeration road traffic system under emergencies, quantifies the impact of emergencies, and at the same time uses Baidu Map big data to verify the system operation parameters to ensure the rationality of the evaluation, which is of great significance for the policy-making and emergency management of the transportation department. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally denoted by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to actual scale.

[0049] Figure 1 It is a flowchart of a method for evaluating the impact of an urban agglomeration emergency on a road system provided by the present invention;

[0050] Figure 2 It is a schematic diagram of the structural failure modeling result;

[0051] Figure 3 It is a schematic diagram of the functional failure modeling result. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The following will describe in detail the embodiments of the technical solutions of the present invention with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, so they are only examples and cannot be used to limit the protection scope of the present invention.

[0053] Please refer to Figures 1 to 3, the present invention provides a method for evaluating the impact of road systems considering urban agglomeration emergencies, including: a road network model of the study area, dividing the study area into traffic zones based on kilometer grids, and the travel volume between traffic zones estimated by the Population Weighted Opportunity (PWO) model; calculating daily travel spatio-temporal data, establishing a 24-hour traffic simulation model for the urban agglomeration during weekdays, and with the help of the SUMO platform, simulating the load and running speed of sections in the road system of the urban agglomeration before emergencies, and calibrating the traffic model based on real statistical data; abstracting four types of currently frequent emergencies into four damage mechanisms to the system, establishing an emergency simulation layer, overlaying its spatial distribution data on the road network of the urban agglomeration, overlaying the emergency layer during peak hours and off-peak hours respectively, and analyzing the changes in the load and running speed of sections in the road network of the urban agglomeration when and after emergencies occur; evaluating the impact on the structure and function of the road system in the urban agglomeration after emergencies.

[0054] It includes the following steps:

[0055] Step 1: Obtain the administrative boundary and road network data of the study area to construct a road network model.

[0056] Step 1.1: Use Python to call the osmnx toolkit to obtain the administrative boundary data of the study area and the vector dataset of the road network on the open-source map OpenStreetMap (OSM). Intersections are abstracted as nodes, and sections are abstracted as edges. Check the connectivity of the physical network to obtain a fully connected physical network model. Fill in the missing attributes in the road network vector data table. The speed of non-ramp type sections is filled with the average value of all sections of the same type; for ramp type sections, it is filled with 1 / 3 of the average maximum speed limit of all sections of the same type. For other missing situations, for example, the number of lanes of the road is missing, the study selects the integer value of the average number of lanes of all sections of the same type to fill. Calibrate the road speed limit according to the Chinese road speed limit standard.

[0057] Step 1.2: Process the OSM map file using JOSM to ensure that the attributes and ranges of all roads are up-to-date and correct. The obtained file format is an XML file; then convert the file to a SUMO-specific XML file ( https: / / sumo.dlr.de / docs / netconvert.html#configuration ); Simplify and eliminate unnecessary nodes. For nodes along the bends of one-way roads, merge the edges between real nodes into one edge, and then merge statistical features, and combine clusters of nodes close to each other into one node to represent road intersections.

[0058] Step 1.3: Conduct visual display in SUMO, and modify incorrect lane connections and other issues by visual inspection, simulation tests, or comparison with map software that can display map street views.

[0059] Step 2: Divide the study area into traffic analysis zones based on kilometer grids, and estimate the trips between traffic analysis zones by the Population Weighted Opportunity (PWO) model.

[0060] Step 2.1: Calculate the trip opportunities. Divide the study area spatially into grids with a resolution of 1 km by 1 km, and each grid is used as a Transportation Analysis Zone (TAZ). Use the PWO model to calculate the traffic trips between every two TAZs.

[0061] Assume that the trip origin and destination are located at the grid centroids. Use the PWO model to estimate the trips between TAZs. The location opportunity number is proportional to its population m i , and inversely proportional to the total population s between the location of the traveler and the destination ij . The trip opportunity number at this location is:

[0062] P ij ∝ m j / S ij

[0063] Step 2.2: Obtain the demographic data from WorldPop Hub (https: / / hub.worldpop.org / ). According to the research method, raster data with a resolution of 1 km can be selected to calculate the trip opportunity number P at a certain location. ij .

[0064] Step 2.3: Calculate the trips between traffic analysis zones based on the above formula. The probability that a traveler selects a destination is proportional to the population m of the destination i , and inversely proportional to the total population s between the location of the traveler and the destination ij . Therefore, the expected trips T between two traffic analysis zones ij are as follows:

[0065] T ij = O i P ij = (O i / ∑ j (m j / s ij ))(m j / s ij )

[0066] where P ij represents the probability of being attracted from point i to point j; O i represents the generation volume (trips) of zone i.

[0067] O iIt is calculated by multiplying the population of the area by the per capita travel rate. Finally, the number of car trips is obtained based on the proportion of car trips.

[0068] Step 2.4: The spatial distribution data of trip volume is distributed based on the daily trip distribution curve using the Incremental Assignment Method. The spatial distribution data of trip volume (OD table) is divided into several parts. Each time, one part of the trip volume is distributed. The shortest path for the next distribution is determined according to the traffic flow condition of the road network. Before each OD distribution, the impedance of each section on the road network and the shortest path of each OD pair are recalculated. The impedance is the running time or generalized cost of the section. Each OD table is allocated to the corresponding shortest path according to the all-or-nothing principle. Finally, the timetable for all trips is obtained, including information such as travel time and the sections passed through.

[0069] Step 3: Based on the calculated daily trip spatio-temporal data, establish a 24-hour traffic simulation model for the urban agglomeration during weekdays. With the help of the SUMO platform, simulate the load-bearing and running speed of the sections in the road system of the urban agglomeration before the emergency, and calibrate the traffic model according to the real statistical data.

[0070] Step 3.1: The simulation model needs to calibrate the speed factor parameter. At the same time, considering the time cost and calculation efficiency of the experiment, it is necessary to adjust the simulation scale, that is, the proportional scaling of the system.

[0071] Input the road network data and trip timetable data into SUMO, and initialize each parameter in its configuration file for simulation. Through multiple simulations, adjust the overall scale parameter and speed factor parameter of the trip volume. Compare the experimental data with the urban daily trip distribution data and urban average commuting time data provided by a third party, and conduct error analysis until the error is adjusted to a reasonable range to determine the final model parameters.

[0072] Step 4: Abstract the four types of emergencies that occur frequently at present into four damage mechanisms to the system, and establish an emergency simulation layer.

[0073] Step 4.1: Develop four destructive strategies in combination with the nature classification of emergencies to establish an emergency simulation layer:

[0074] (1) Natural disasters represented by flood levels. Select the CaMa-Flood (Catchment-Based Macro-scale Floodplain) hydrological model to model the surface runoff distribution under different rainfall intensities and calculate the water depth of the sections. According to the empirical function, the sections are divided into completely submerged sections and submerged sections with traffic capacity:

[0075]

[0076] Among them, h represents the submerged depth (mm), and c is an empirical parameter. The roads with a submerged depth greater than 300 mm are the directly failed parts, and for those less than or equal to 300 mm, the road passing speed is calculated according to the above formula.

[0077] (2) Traffic accidents. Abstract traffic accidents as a random attack on the network. Randomly deleting a certain proportion of road segments in the whole network represents the occurring traffic accidents. Considering the limitations of computing power and the possibilities of real scenarios, especially large-scale road traffic accidents, the damage intensity range is set to 5% - 70%, with a step size of 5%. Random failures are independently repeated 5 times at each failure intensity.

[0078] (3) Public health emergencies. Abstract public health emergencies such as virus transmission as a kind of local failure. Starting from a random root node in the network, select all the edges connected to it, and select the set of edges within the defined range in the order from far to near to the root node. The damaged road segments in the local attack are correlated with each other.

[0079] (4) Social security incidents. Abstract malicious nuclear attacks such as terrorist attacks that cause the greatest damage to the system as a kind of target attack (deliberate attack). Remove road segments in proportion in the system in the order of betweenness centrality of the edges. The damage intensity range is set to 5% - 70%, with a step size of 5%.

[0080] Step 4.2: According to the above four failure mechanisms, identify and remove the directly failed road segments and the adjacent nodes in the system. Then, based on the structural characteristics of the network, judge the connectivity of the network again, and remove the parts that are not connected to the largest connected subgraph.

[0081] Step 5: Evaluate the impacts on the structure and function of the urban agglomeration road system after the emergency.

[0082] Step 5.1: According to the failure mechanisms in Step 4, during the simulation process, dynamically close specific roads within a specific time period (simulating road interruptions in emergencies) or specify the driving speed of roads (simulating the speed reduction of road segments caused by rainfall). The paths in the original travel schedule are not all optimal. During the road system simulation process, add a dynamic optimization path module to regularly re-plan paths at a certain frequency within the computing power range, so that all or part of the vehicles can update their routes regularly. Consider the current and recent traffic conditions in the network during the simulation process, calculate the average driving time of the roads in the network, and thus adjust the route plan according to the changes brought by traffic congestion and emergencies in the road network. Vehicles will choose the fastest path to reach the destination according to the average driving speed of the roads.

[0083] Step 5.2: Structural index analysis. The largest connected subgraph is used as the structural performance evaluation index, that is, the ratio C of the number of nodes in the largest connected subgraph of the network after superimposing emergencies to the number of nodes in the original network gcc :

[0084] C gcc = n′ / n, 0 < C gcc ≤ 1

[0085] where n′ represents the number of nodes in the largest connected subgraph after network damage, and n represents the number of nodes in the original network.

[0086] Step 5.3: Functional index analysis. The functional index is evaluated from three aspects: the proportion of interrupted traffic volume, the change in average travel time, and the change in average travel distance. The interrupted traffic volume includes two parts. One part is the interruption directly caused by the damage mechanism to the user's route; the other part is the users who cannot reach due to long-term traffic congestion on the road section.

[0087] Excluding the users whose routes are directly interrupted, the system recalculates the users' routes during the simulation process. With the goal of minimizing the travel cost, the change values I d 、I t :

[0088]

[0089] where n and m respectively represent the number of operating vehicles before and after the emergency, t and d respectively represent the travel time and distance of the vehicle before the emergency, and t a 、d a represent the travel time and distance of the vehicle after the emergency. The larger I d 、I t is, the greater the impact.

[0090] Specifically, taking the road system of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) urban agglomeration as the evaluation object, the highway network in this area is very dense, natural disasters occur frequently, and the network structure characteristics are obvious. The cities in the GBA are evenly distributed on both sides of the delta region. Therefore, bridges play a key role in shortening the travel time between cities and inevitably become the bottleneck of the entire system. Using this example, selecting the failure of the bottleneck bridge in the system as a deliberate attack (target failure) to verify the practicability of a method for evaluating the impact of a road system considering urban agglomeration emergencies of the present invention.

[0091] (1) Modeling results of the GBA road traffic system

[0092] The GBA road system includes road sections, nodes, population, travel trips

[0093] (2) Simulation verification results

[0094] Input the road network and OD data into the SUMO demand calculation module. The travel chain iterates 50 times according to the goal of minimizing the travel cost to obtain the final travel schedule. Put the travel schedule and the road network into the simulation module to simulate the 24-hour travel state, and finally output data such as the average running speed of road sections. Use the urban traffic report provided by Baidu Map traffic big data ( http: / / jiaotong.baidu.com / reports / ) to calibrate and verify the reliability of the traffic simulation in this study. This embodiment mainly uses the commuting congestion index in the report to calculate the average running speed of urban roads through the formula provided in the report to verify the simulation results, as shown in Table 1. The average relative error in the off-peak period is 5%, and the average relative error in the peak period is 17%.

[0095] Table 1 Analysis table of average running speed errors of 11 cities in GBA

[0096]

[0097] (3) Structural failure modeling results

[0098] Establish an emergency simulation layer according to Step 4, and select natural disasters (floods), traffic accidents, and social security incidents for modeling. Random failures (as representatives of traffic accident types) and target failures (as representatives of sudden public security incidents) directly increase the failure intensity in sequence with a step size of 5% of the total number of roads, as Figure 2 shown. The differences in the speed of structural performance degradation brought by different emergencies to the system are very obvious. This embodiment uses the "proportion of direct road section failures - relative size of the largest connected subgraph" curve to describe the relationship between the structural loss of the system and the damage intensity of emergencies.

[0099] In the initial stage when the network is affected by emergencies, that is, when the proportion of direct failures is less than 35%, the relative size of the largest connected subgraph of the network after random failures is larger than that of target failures and flood failures, that is, the network connectivity is higher than that of target failures and flood failures; flood failure, as a local failure, the relative size of the largest connected subgraph of the network after its occurrence is between target failures and random failures. When the network is severely damaged, the relative size of the largest connected subgraph of the network after random failures is larger than that of target failures and smaller than that of flood failures. The structural loss brought by target failures is always greater than that of random failures and flood failures, and the curve of its relative size of the largest connected subgraph is always at the bottom.

[0100] (4) Functional failure modeling results

[0101] During the entire stage of the emergency, travel delays are caused by the loss of some service functions of the system, and the changes in travel time and distance are caused by system congestion, asFigure 3 As shown. For travel departure delays, that is, the time lag relative to the departure time of the travel schedule, when the damage intensity is less than 30%, the travel departure delays caused by the three types of emergencies will increase with the increase of the damage intensity; but when the damage intensity is greater than 30%, the total delay remains stable. The traffic volume during the peak period (7-9 am) is about 4 times that during the off-peak period (1-3 pm). When the direct loss intensity is greater than 30%, the delay caused during the peak period is about 6 times that during the off-peak period. In the case of 2-mm flood damage occurring on about 6% of the roads, the multiple exceeds 6 times. The impact on the system when it bears high flow will be disproportionately amplified, and even cause the system to saturate so that the flow cannot be relieved. The increases in the average travel time and average travel distance first increase with the intensity, then decrease, and finally remain stable. For the travel distance, when the intensity of the emergency reaches a certain value, the degree of network separation is no longer sufficient to support travelers to find alternative travel routes to bypass, and long-distance travel is forced to be interrupted. From Figure 3 It can also be seen that the rate of increase in the interrupted trips is relatively fast in the 30%-40% stage. At this time, the average travel distance begins to decrease and then stabilizes. For the average travel time, when the intensity of the emergency reaches a certain value, the traffic volume in the network suddenly decreases significantly, some sections become unobstructed, the congestion is relieved, the number of long-distance travelers decreases, and the average travel time decreases instead. The intensity at which the target attack reaches the peak state is the lowest, and the random attack is the second. When the random perturbation intensity is 30%, the travel time during the peak period increases by 400%, and the off-peak period increases by 300%. Under the same emergency intensity, the impact of local damage caused by floods on the travel distance and travel time is less than that of target damage and random damage. However, the travel departure delay caused by flood perturbations is relatively large.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered by the scope of the claims and the specification of the present invention.

Claims

1. A road network impact assessment method considering the impact of emergencies in urban agglomerations, characterized in that: The steps include: Obtain urban road network data for the area to be studied; Based on the urban road network data obtained, the population-weighted opportunity model is used to calculate the travel volume between traffic zones, and the daily travel time and space data are calculated based on this; The study area is spatially divided into grids, and each grid is used as a traffic analysis zone (TAZ); The starting and ending points of the trips are placed at the centroid of the grid, and the PWO model is used to estimate the travel volume between TAZs. The probability of choosing destination j is proportional to the population m of the place. j , inversely proportional to the total population s between the traveler's location and the destination ij , the number of travel opportunities at this location is P ij ∝m j / S ij ; Based on the above formula, the travel volume between traffic zones is calculated, and the probability of a traveler choosing a destination is related to the destination m. j The total population between the traveler's location and destination is proportional to the total population s ij Inversely proportional to, the travel volume between the two communities is T ij =T ij =O i ·P ij =(O i / ∑ j (m j / s ij ))·(m j / s ij ), P ij represents the probability of being attracted to point j starting from point i, O i represents the occurrence amount (times) of cell i; The spatial distribution data of travel volume is distributed according to the daily travel distribution curve based on the incremental traffic distribution model. The spatial distribution data of travel volume is divided into several parts, and one part of travel volume is distributed each time. The shortest path for the next distribution is determined according to the flow conditions of the road network. Establish an urban traffic simulation model based on daily travel time and space data; Based on the simulation model, simulate the load and operating speed of the road sections in the urban agglomeration road system before the emergency, and calibrate the traffic model; Abstract emergencies as a mechanism for destroying the system, and establish an emergency simulation layer; The spatial distribution data is superimposed on the urban agglomeration road network, and the emergency simulation layer is superimposed on the peak and non-peak hours of the road, respectively, to analyze the changes in the load and operating speed of the road sections in the urban agglomeration road network during and after the emergency occurs; Assess the impact on the structure and function of urban agglomeration road systems after emergencies.

2. A road network impact assessment method considering the impact of urban agglomeration emergencies according to claim 1, characterized in that: The step of obtaining the urban road network data of the area to be studied also includes: Obtain administrative boundary data of the area to be studied and a vector dataset of the road network, where road intersections are abstracted as nodes and road sections are abstracted as edges; Check the connectivity of the physical network and obtain a fully connected physical network model; Fill missing attributes in the road network data table; Simplify and eliminate nodes along the bends of one-way roads, merge the edges between real nodes into one edge, and then merge statistical features to combine clusters of nodes that are close to each other into one node to represent road intersections; Visualize it in SUMO and correct incorrect lane connections by visual inspection, simulation testing or comparison with mapping software that can display a map street view.

3. A road network impact assessment method considering the impact of urban agglomeration emergencies according to claim 2, characterized in that: The method of filling missing attributes in the road network data table includes filling the speed of non-ramp type sections with the average value of all sections of the same type; filling the ramp type sections with 1 / 3 of the average maximum speed limit of all roads of the same type; if the number of lanes of a road is missing, the average value of the number of lanes of all roads of the same type is selected to fill it; and correcting the road speed limit according to the road speed limit standard.

4. A road network impact assessment method considering the impact of urban agglomeration emergencies according to claim 1, characterized in that: The urban traffic simulation model is established, comprising: Calibrate the speed factor parameters of the simulation model, input the road network data and travel schedule data into SUMO, and initialize various parameters in its configuration file for simulation; The optimal route is iterated with the goal of minimizing travel costs. Through multiple simulations, the overall scale parameters and speed factor parameters of travel volume are adjusted. The experimental data are compared and error analyzed with the urban daily travel distribution data and urban average commuting time data provided by a third-party data platform until the error is adjusted to within the standard range and the final model parameters are determined.

5. A road network impact assessment method considering the impact of urban agglomeration emergencies according to claim 1, characterized in that: The emergencies include: Natural disasters represented by flood levels: The CaMa-Flood hydrological model is used to model the distribution of surface runoff under different rainfall intensities, calculate the water depth of the road section, and divide the road section into completely flooded and flooded with traffic capacity according to the following function: Among them, h represents the flooding depth (mm), c is an empirical parameter, and the road with a flooding depth greater than 300mm is directly invalid, and the road speed is calculated according to the above formula when it is less than or equal to 300mm; Traffic accidents: Traffic accidents are abstracted as a random attack on the network. A certain proportion of road sections in the entire network are randomly deleted to represent traffic accidents. The damage intensity range is set to 5% to 70% with a step size of 5%. Random failure is repeated 5 times independently at each failure intensity. Public health emergencies: Abstract public health emergencies as a kind of local failure. Starting from a random root node of the network, all edges connected to it are selected, and the set of edges within the defined range is selected from far to near according to the distance from the root node; Social security events: The malicious nuclear attack that causes the greatest damage to the system is abstracted into a targeted attack. In the system, road sections are removed in proportion according to the order of the betweenness centrality of the edges, and the damage intensity range is set to 5% to 70% with a step size of 5%; 6. A road network impact assessment method considering the impact of sudden events in urban agglomerations according to claim 5, characterized in that: The method of abstracting emergencies into a destructive mechanism for the system and establishing an emergency simulation layer also includes identifying and eliminating directly failed road sections and adjacent nodes in the system based on the above four destructive mechanisms, judging the connectivity of the network again based on the structural characteristics of the network, and eliminating the parts that are not connected to the maximum connected subgraph.

7. A road network impact assessment method considering the impact of urban agglomeration emergencies according to claim 6, characterized in that: The disruption mechanism also includes dynamically closing specific roads or specifying road travel speeds within a specific time period during the simulation process, taking into account the current and recent traffic conditions in the network during the simulation process, and calculating the average travel time of roads in the network, thereby adjusting the route plan based on changes caused by traffic congestion and emergencies in the road network. The vehicle will choose the fastest path to the destination based on the average road travel speed.

8. A road network impact assessment method considering the impact of urban agglomeration emergencies according to claim 1, characterized in that: The assessment of the impact on the structure and function of the urban agglomeration road system after an emergency includes: Evaluate the structural loss after the sudden event: Select the largest connected subgraph as the structural performance evaluation indicator, that is, the ratio of the number of nodes in the largest connected subgraph of the network after the sudden event to the number of nodes in the original network C gcc : C gcc =n′ / n,0<C gcc ≤1, Where n′ represents the number of nodes in the largest connected subgraph after the network is damaged, and n represents the number of nodes in the original network; To evaluate the functional loss after the emergency, the functional indicators are evaluated from three aspects: the proportion of interrupted traffic, the change in average travel time, and the change in average travel distance; Users whose routes are directly interrupted are excluded. The system recalculates the user's route during the simulation process, with the goal of minimizing travel costs. The change in average driving distance and average driving time is calculated through the SUMO platform. d ,I t : Where n and m represent the number of vehicles running before and after the emergency, respectively; t and d represent the driving time and distance of the vehicles before the emergency, respectively; a ,d a Indicates the driving time and distance of the vehicle after the emergency.

9. A road network impact assessment method considering the impact of urban agglomeration emergencies according to claim 8, characterized in that: The interrupted traffic volume includes: interruptions to user routes caused directly by the destruction mechanism; and users who cannot be reached for a long time due to road congestion.

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