Road network influence assessment method considering urban agglomeration emergency influence

Abstract

The invention provides a road network impact assessment method considering urban agglomeration emergency impact. The method comprises the following steps: acquiring urban road network data of a to-be-researched area; calculating the travel volume between the traffic cells, and calculating daily travel spatio-temporal data; establishing a workday 24-hour urban traffic simulation model; simulating the bearing and running speed of a road section in an urban agglomeration road system before an emergency; establishing an emergency simulation layer; analyzing changes of bearing and running speeds of road sections in the urban agglomeration road network when and after emergencies occur; and the influence on the structure and the function of the urban agglomeration road system after the emergency is evaluated. The behavior of route reselection of heterogeneous vehicles in a large-scale urban agglomeration road traffic system under emergencies is simulated from the microscopic perspective, the influence of emergencies is quantified, meanwhile, system operation parameters are verified by using Baidu map big data, the rationality of evaluation is ensured, and the evaluation accuracy is improved. The method is of great significance to policy making and emergency management of a traffic department.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency management, and in particular to a road network impact assessment method considering the impact of sudden events in urban agglomerations. Background Art

[0002] In recent years, with the increase in the 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 vital infrastructure in daily life, the urban road traffic system is seriously threatened by emergencies, threatening the normal operation of society and causing serious economic losses. The intensity and frequency of emergencies that have a great 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 vary. 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 in economic development. Due to the large population and the increasing demand for more vehicles, the road transportation interruption phenomenon in urban agglomerations is more serious. The traffic links within the urban agglomeration are becoming closer and closer. The present invention considers the passage between cities to evaluate the impact of emergencies on the entire urban agglomeration.

[0003] At present, the main methods for evaluating the impact of road systems include 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 evaluation of large-scale urban agglomerations under emergencies. They cannot simulate the dynamic traffic flow under emergencies, and cannot evaluate the impact results under different traffic scales (peak and non-peak). Summary of the invention

[0004] In view of this, the problem to be solved by the present invention is to provide a road network impact assessment method that takes into account the impact of sudden events in urban agglomerations, and to statistically evaluate the impact of various types of indicator data assessment systems from a more microscopic perspective.

[0005] The present invention solves the above technical problems by the following technical means: The present invention provides a road network impact assessment method considering the impact of urban agglomeration emergencies, comprising the following steps:

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

[0007] 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;

[0008] Based on daily travel time and space data, a 24-hour urban traffic simulation model is established on weekdays;

[0009] 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;

[0010] Abstract emergencies as a mechanism for destroying the system, and establish an emergency simulation layer;

[0011] 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;

[0012] Assess the impact on the structure and function of urban agglomeration road systems after emergencies.

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

[0014] 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;

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

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

[0017] 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;

[0018] 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.

[0019] According to claim 2, a road network impact assessment method considering the impact of sudden events in urban agglomerations is characterized in that the missing attributes are filled in the road network data table, including 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 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.

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

[0021] The study area is spatially divided into grids with a resolution of 1km by 1km, and each grid is used as a traffic analysis zone (TAZ);

[0022] 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 ;

[0023] 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 );

[0024] Among them, 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;

[0025] The spatial distribution data of travel volume is allocated according to the daily travel distribution curve based on the incremental traffic allocation model. The spatial distribution data of travel volume is divided into several parts, and one part of the travel volume is allocated each time. The shortest path for the next allocation is determined based on the flow conditions of the road network.

[0026] Furthermore, the establishment of a 24-hour urban traffic simulation model on working days includes:

[0027] 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;

[0028] 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.

[0029] 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.

[0030] Furthermore, the emergencies include:

[0031] 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:

[0032] 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;

[0033] Traffic accidents: Traffic accidents are abstracted as a random attack on the network. A certain percentage 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.

[0034] 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;

[0035] 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%;

[0036] Furthermore, the emergency is abstracted as a destructive mechanism to the system and an emergency simulation layer is established, which also includes identifying and eliminating directly failed road sections and adjacent nodes in the system according to the above four destructive mechanisms, judging the connectivity of the network again according to the structural characteristics of the network, and eliminating the parts that are not connected to the maximum connected subgraph.

[0037] Furthermore, 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 travel speed of the road.

[0038] Furthermore, the assessment of the impact on the structure and function of the urban agglomeration road system after an emergency event includes:

[0039] 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:

[0040] C gcc =n′ / n,0 <C gcc ≤1,

[0041] 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;

[0042] 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;

[0043] 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 :

[0044]

[0045] 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.

[0046] Furthermore, 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.

[0047] It can be seen from the above technical scheme that the beneficial effects of the present invention are as follows: the present invention provides a road network impact assessment method considering the impact of emergencies in urban agglomerations, comprising 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 using this to calculate the daily travel spatiotemporal data; based on the daily travel spatiotemporal data, establishing a 24-hour urban traffic simulation model on weekdays; based on the simulation model, simulating the load and operating speed of the sections in the urban agglomeration road system before the emergency, and calibrating the traffic model; abstracting the emergency as a destructive mechanism for the system, and establishing an emergency simulation layer; superimposing the spatial distribution data on the urban agglomeration road network, superimposing the emergency simulation layer on the peak hours and non-peak hours of the road respectively, analyzing the changes in the load and operating speed of the sections in the urban agglomeration road network during and after the emergency; and evaluating the impact on the structure and function of the urban agglomeration road system after the emergency. From a micro perspective, this paper simulates the re-routing behavior of heterogeneous vehicles in the road traffic system of a large urban agglomeration under emergency events, quantifies the impact of emergencies, and uses Baidu map big data to verify the system operating parameters to ensure the rationality of the evaluation, which is of great significance to the policy formulation 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 is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0049] Figure 1 A flow chart of a road system impact assessment method considering emergencies in urban agglomerations provided by the present invention;

[0050] Figure 2 Schematic diagram of structural failure modeling results;

[0051] Figure 3 Schematic diagram of the functional failure modeling results. DETAILED DESCRIPTION

[0052] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0053] See also Figures 1 to 3The present invention provides a road system impact assessment method considering urban agglomeration emergencies, including: studying an urban road network model of an area, dividing the study area into traffic zones based on kilometer grids, and estimating the travel volume between traffic zones by a population weighted opportunity model (PWO); calculating daily travel spatiotemporal data, establishing a 24-hour urban agglomeration traffic simulation model on weekdays, simulating the load and operating speed of sections in the urban agglomeration road system before emergencies with the help of a SUMO platform, and calibrating the traffic model based on real statistical data; abstracting four types of emergencies that are currently frequent into four types of damage mechanisms to the system, establishing an emergency simulation layer, superimposing their spatial distribution data on the urban agglomeration road network, superimposing the emergency layer during peak hours and non-peak hours, and analyzing the changes in the load and operating speed of sections in the urban agglomeration road network when and after the emergencies; and assessing the impact on the structure and function of the urban agglomeration road system after the emergencies.

[0054] The steps include:

[0055] Step 1: Obtain administrative boundaries and road network data of the study area to build 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 data set of the road network on the open source map OpenStreetMap (OSM). The intersections are abstracted as nodes and the road sections are abstracted as edges. Check the connectivity of the physical network and obtain a fully connected physical network model. Fill in 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 roads of the same type. For other missing situations, such as the number of lanes on the road is missing, this study selects the average number of lanes of all roads of the same type to fill in. According to the Chinese road speed limit standard, the road speed limit is corrected.

[0057] Step 1.2: The OSM map file is processed using JOSM to ensure that the attributes and extents of all roads are up to date and correct. The resulting file format is an XML file. The file is then converted to a SUMO-specific XML file (https: / / sumo.dlr.de / docs / netconvert.html#configuration). Unnecessary nodes are simplified and eliminated, along the nodes of one-way road bends, the edges between real nodes are merged into one edge, and then statistical features are merged to combine clusters of nodes that are close to each other into one node to represent road intersections.

[0058] Step 1.3: Visualize the display in SUMO and correct problems such as incorrect lane connections through visual inspection, simulation testing, or comparison with mapping software that can display map street views.

[0059] Step 2: Divide the study area into transportation zones based on kilometer grids, and estimate the travel volume between transportation zones using the Population Weighted Opportunity Model (PWO).

[0060] Step 2.1: Calculate the number of travel opportunities. The study area is spatially divided into grids with a resolution of 1km x 1km, and each grid is used as a transportation analysis zone (TAZ). The PWO model is used to calculate the traffic volume between each two TAZs.

[0061] Assume that the origin and destination of a trip are located at the centroid of the grid. Use the PWO model to estimate the travel volume between TAZs. The number of location opportunities is proportional to its population m. i , 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:

[0062] P ij ∝m j / S ij

[0063] Step 2.2: Obtain demographic data from WorldPop Hub (https: / / hub.worldpop.org / ). Depending on the research method, you can choose to use raster data with a resolution of 1km and calculate the number of travel opportunities P at a certain location. ij .

[0064] Step 2.3: Based on the above formula, calculate the travel volume between traffic zones, the probability of travelers choosing a destination and the destination m i The population is proportional to the location and destination of the traveler. ij Therefore, the estimated travel volume between the two communities is T ij As follows:

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

[0066] Among them, P ij represents the probability of being attracted by point j starting from point i; O iIndicates the occurrence amount (times) in cell i.

[0067] O i The population of the area is multiplied 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 travel volume is distributed according to the daily travel distribution curve based on the incremental traffic distribution model (Incremental Assignment Method), and the spatial distribution data of travel volume (OD table) is divided into several parts. Each time a travel volume is distributed, the shortest path for the next distribution is determined according to the flow conditions 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 distributed to the corresponding shortest path according to the all-or-nothing method. Finally, the timetable for all trips is obtained, including travel time, sections passed, and other information.

[0069] Step 3: Based on the calculated daily travel time and space data, a 24-hour urban agglomeration traffic simulation model is established on weekdays. With the help of the SUMO platform, the load and operating speed of the sections in the urban agglomeration road system before the emergency are simulated, and the traffic model is calibrated based on real statistical data.

[0070] Step 3.1: The simulation model needs to calibrate the speed factor parameters. Taking into account the time cost and computational efficiency of the experiment, the simulation scale needs to be adjusted, that is, the system needs to be scaled proportionally.

[0071] The road network data and travel schedule data are input into SUMO, and various parameters are initialized in its configuration file for simulation. Through multiple simulations, the overall scale parameters and speed factor parameters of the travel volume are adjusted. The experimental data are compared with the urban daily travel distribution data and urban average commuting time data provided by a third party, and the error analysis is performed until the error is adjusted to a reasonable range and the final model parameters are determined.

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

[0073] Step 4.1: Formulate four destructive strategies based on the nature of the emergency and establish the emergency simulation layer:

[0074] (1) Natural disasters represented by flood levels. The CaMa-Flood (Catchment-Based Macro-scale Floodplain) hydrological model is used to model the distribution of surface runoff under different rainfall intensities and calculate the water depth of the road section. The road section is divided into completely flooded and flooded with traffic capacity according to the empirical function:

[0075]

[0076] Where h represents the flooding depth (mm), and c is an empirical parameter. Roads with flooding depths greater than 300 mm are directly invalid, and roads with flooding depths less than or equal to 300 mm are calculated according to the above formula for road speed.

[0077] (2) 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. Considering the limitations of computing power and the possibility of realistic scenarios, especially large-scale road traffic accidents, the damage intensity range is set to 5% to 70% with a step size of 5%. Random failures are repeated 5 times independently at each failure intensity.

[0078] (3) Public health emergencies. Public health emergencies such as virus transmission are abstracted as a local failure. Starting from a random root node in the network, all edges connected to it are selected. The set of edges within the defined range is selected from far to near according to the distance from the root node. The damaged sections in the local attack are interrelated.

[0079] (4) Social security events. Malicious nuclear attacks such as terrorist attacks that cause the greatest damage to the system are abstracted into a targeted attack (deliberate attack). Road sections are removed proportionally in the system according to the order of the betweenness centrality of the edges. The damage intensity range is set to 5% to 70%, with a step size of 5%.

[0080] Step 4.2: Based on the above four damage mechanisms, identify and eliminate the directly failed road sections and the nodes adjacent to them in the system, judge the connectivity of the network again based on the structural characteristics of the network, and eliminate the parts that are not connected to the maximum connected subgraph.

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

[0082] Step 5.1: According to the destruction mechanism in step 4, during the simulation process, specific roads are dynamically closed within a specific time period (simulating road interruptions in emergencies) or the road driving speed is specified (simulating road deceleration caused by rain). Not all the paths in the original travel schedule are optimal. During the simulation of the road system, a dynamic optimization path module is added to regularly replan the paths at a certain frequency within the computing power range, so that all or part of the vehicles can update their routes regularly. During the simulation process, the current and recent traffic conditions in the network are considered, and the average travel time of the roads in the network is calculated, so that the route plan can be adjusted according to the 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 speed.

[0083] Step 5.2: Structural index analysis. The maximum connected subgraph is used as the structural performance evaluation index, that is, the ratio of the number of nodes in the maximum connected subgraph of the network after the superposition of the emergency event 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 the network is damaged, and n represents the number of nodes in the original network.

[0086] Step 5.3: Functional indicator analysis. 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. Interrupted traffic includes two parts: one is the interruption of user routes caused directly by the destruction mechanism; the other is the users who cannot reach the route for a long time due to road congestion.

[0087] 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 :

[0088]

[0089] 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. d ,I t The larger the value, the greater the impact.

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

[0091] (1) GBA road traffic system modeling results

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

[0093] (2) Simulation verification results

[0094] The road network and OD data are input into the SUMO demand calculation module, and the travel chain is iterated 50 times according to the goal of minimizing the travel cost to obtain the final travel schedule. The travel schedule and road network are placed in the simulation module to simulate 24 travel states, and finally output data such as the average operating speed of the road section. The urban traffic report provided by Baidu Map Traffic Big Data (http: / / jiaotong.baidu.com / reports / ) is used to calibrate and verify the reliability of the traffic simulation of this study. This embodiment mainly uses the commuting congestion index in the report to calculate the average operating 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 non-peak time periods is 5%, and the average relative error in peak time periods is 17%.

[0095] Table 1 Average running speed error analysis of GBA in 11 cities

[0096]

[0097]

[0098] (3) Structural failure modeling results

[0099] According to step 4, the emergency simulation layer is established, and natural disasters (floods), traffic accidents, and social security events are selected for modeling. Random failure (as a representative of traffic accident types) and target failure (as a representative of sudden public safety events) are directly increased in failure intensity in steps of 5% of the total number of roads, such as Figure 2 As shown. The speed of structural performance degradation caused by different emergencies is very different for the system. This embodiment uses the "direct failure ratio of road sections-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.

[0100] In the early stage of the network being affected by the emergency, that is, when the direct failure ratio is less than 35%, the relative size of the network's maximum connected subgraph after the random failure is greater than the target failure and flood failure, that is, the network connectivity is higher than the target failure and flood failure; as a local failure, the relative size of the network's maximum connected subgraph after the flood failure is between the target failure and the random failure. When the network is highly damaged, the relative size of the network's maximum connected subgraph after the random failure is greater than the target failure but less than the flood failure. The structural loss caused by the target failure is always greater than the random failure and flood failure, and its maximum connected subgraph relative size curve is always at the bottom.

[0101] (4) Functional failure modeling results

[0102] Throughout the emergency phase, travel delays are caused by the loss of service functions of some systems, and changes in travel time and distance are caused by system congestion, such as Figure 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 damage intensity; but when the damage intensity is greater than 30%, the total delay remains stable. The traffic volume during peak hours (7-9 am) is about 4 times that of non-peak hours (13-15 pm). When the direct loss intensity is greater than 30%, the delay caused by peak hours is about 6 times that of non-peak hours. In the case of 2 mm flood damage on about 6% of the roads, the multiple is more than 6 times. The impact on the system when it is subjected to high traffic will be disproportionately magnified, and even the system will be saturated so that it cannot alleviate traffic. The increase in average travel time and average travel distance first increases with the increase in intensity, then decreases, and finally stabilizes. In terms of 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 support their travel, and long-distance travel is forced to be interrupted, from Figure 3 It can also be seen that the interrupted trips increase at a faster rate in the 30%-40% stage, at which 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, congestion is alleviated, the number of long-distance travelers decreases, and the average travel time decreases instead. The intensity of the target attack reaching the peak state is the lowest, followed by the random attack. When the random disturbance intensity is 30%, the travel time during peak hours increases by 400%, and the non-peak hours increase by 300%. Under the same emergency intensity, the local damage caused by floods has less impact on travel distance and travel time than targeted damage and random damage. However, the travel departure delays caused by flood disturbances are relatively large.

[0103] 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 it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and 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; 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 method of using a population weighted opportunity model to calculate the travel volume between traffic zones also includes: The study area is spatially divided into grids with a resolution of 1km by 1km, 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 ); Among them, 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 allocated according to the daily travel distribution curve based on the incremental traffic allocation model. The spatial distribution data of travel volume is divided into several parts, and one part of the travel volume is allocated each time. The shortest path for the next allocation is determined based on the flow conditions of the road network.

5. A road network impact assessment method considering the impact of urban agglomeration emergencies according to claim 1, characterized in that: The establishment of a 24-hour urban traffic simulation model on working days includes: 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.

6. A road network impact assessment method considering the impact of emergencies in urban agglomerations 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. Road sections are removed proportionally in the system 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%.

7. A road network impact assessment method considering the impact of urban agglomeration emergencies according to claim 6, 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.

8. A road network impact assessment method considering the impact of urban agglomeration emergencies according to claim 7, 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.

9. 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.

10. A road network impact assessment method considering the impact of sudden events in urban agglomerations according to claim 9, 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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