A method for mining urban road traffic islands under waterlogging scenarios

Abstract

The present application provides a method for mining urban road traffic islands in waterlogging scenarios, which relates to the field of traffic monitoring technology. The method includes: determining whether waterlogging occurs in the study area based on rainfall data; in the case of waterlogging in the study area, determining the accessibility values ​​of roads in the study area at preset time intervals starting from the time of waterlogging; determining whether roads in the study area are blocked roads for urban waterlogging traffic based on the accessibility values; determining urban waterlogging road traffic islands in the study area based on the various urban waterlogging traffic blocked roads in the study area. The purpose is to mine road traffic islands in urban waterlogging scenarios, provide support for the stable operation of the transportation system, and reduce the losses caused by waterlogging disasters.

Description

Technical Field

[0001] The present application relates to the field of traffic monitoring technology, and in particular to a method for mining urban road traffic islands in waterlogging scenarios. Background Art

[0002] Urban transportation is an important support for urban life. Its smoothness is directly related to residents' daily travel, economic activities and emergency rescue. Traffic congestion and road closures caused by extreme rainfall events seriously affect the operating efficiency and safety of the transportation system. Therefore, in-depth research on the impact of extreme rainfall events on transportation is crucial, which will help improve the resilience of urban transportation, ensure the stable operation of the transportation system and reduce the losses caused by disasters. Summary of the invention

[0003] In view of this, the present application provides a method for mining urban road traffic islands in waterlogging scenarios, aiming to mine road traffic islands in urban waterlogging scenarios, provide support for the stable operation of the transportation system and reduce the losses caused by waterlogging disasters.

[0004] In a first aspect of the present application, a method for mining urban road traffic islands in a waterlogging scenario is provided, the method comprising:

[0005] Based on rainfall data, determine whether waterlogging occurs in the study area;

[0006] In the case of waterlogging in the study area, the accessibility values ​​of the roads in the study area are determined at preset intervals starting from the time of waterlogging.

[0007] According to the accessibility value, determine whether the road in the study area is a road blocked by urban waterlogging traffic;

[0008] According to the urban waterlogging traffic-blocked roads in the study area, the urban waterlogging road traffic islands in the study area are determined.

[0009] Optionally, determine whether waterlogging has occurred in the study area based on rainfall data, including:

[0010] By calculating the rainfall data of the study area, the average rainfall in the study area is obtained;

[0011] According to the average rainfall, determine the target rainfall in the first time period in the future of the study area;

[0012] Comparing the target rainfall with a first threshold value to obtain a comparison result;

[0013] When the comparison result shows that the target rainfall is greater than or equal to the first threshold, determining that waterlogging occurs in the study area;

[0014] When the comparison result is that the target rainfall is less than the first threshold, it is determined that no waterlogging occurs in the study area.

[0015] Optionally, when waterlogging occurs in the study area, the accessibility values ​​of the roads in the study area are determined at preset time intervals starting from the time when the waterlogging occurs, including:

[0016] In the case of waterlogging in the study area, traffic situation data of the study area within a preset time period is collected every preset time period starting from the time of waterlogging.

[0017] By resampling the collected traffic situation data of the study area within the preset time period, the target traffic situation data of the study area within the preset time period is obtained;

[0018] According to the road network topology characteristics of the roads in the study area, determine the initial accessibility values ​​of the roads in the study area;

[0019] Determine accessibility weight factors of roads in the study area within a preset time period according to the target traffic situation data;

[0020] The accessibility values ​​of the roads in the study area within a preset time period are determined according to the initial accessibility value and the accessibility weight factor.

[0021] Optionally, determining whether a road in the study area is a road blocked by urban waterlogging traffic according to the accessibility value includes:

[0022] Comparing the reachability value with a second threshold value to obtain a first comparison result;

[0023] When the first comparison result shows that the accessibility value is less than or equal to the second threshold, determining that the road in the study area is a road blocked by urban waterlogging traffic;

[0024] When the first comparison result is that the accessibility value is greater than the second threshold, it is determined that the road in the study area is a non-urban waterlogging traffic-blocked road.

[0025] Optionally, determining whether a road in the study area is a road blocked by urban waterlogging traffic according to the accessibility value includes:

[0026] Determine the difference between the reachability value and the reachability value within the previous preset time period;

[0027] Comparing the difference with a third threshold to obtain a second comparison result;

[0028] When the second comparison result is that the difference is greater than or equal to the third threshold, determining that the road in the study area is a road blocked by urban waterlogging traffic;

[0029] When the second comparison result is that the difference is less than the third threshold, it is determined that the road in the study area is a non-urban waterlogging traffic-blocked road.

[0030] Optionally, determining whether a road in the study area is a road blocked by urban waterlogging traffic according to the accessibility value includes:

[0031] Comparing the accessibility value with the second threshold, and comparing the difference between the accessibility value and the accessibility value within the previous preset time period with the third threshold;

[0032] When the accessibility value is less than or equal to the second threshold, and the difference between the accessibility value and the accessibility value within the previous preset time period is greater than or equal to the third threshold, it is determined that the road in the study area is an urban waterlogging traffic congestion road.

[0033] Optionally, based on each urban waterlogging traffic blocked road in the study area, determine the urban waterlogging road traffic islands in the study area, including:

[0034] Match the traffic-blocked roads in each city in the study area with the road network data to obtain the target road network data in the study area;

[0035] A closed-loop area formed by roads blocked by urban waterlogging traffic in the target road network data is determined, and the closed-loop area is determined as an urban waterlogging road traffic island in a study area.

[0036] Optionally, based on each urban waterlogging traffic blocked road in the study area, determine the urban waterlogging road traffic islands in the study area, including:

[0037] According to the urban waterlogging traffic blocked roads in the study area, the blocking values ​​of the nodes in the graph network of the study area corresponding to the urban waterlogging traffic blocked roads are assigned as target values, and a target graph network is obtained, wherein the nodes in the graph network are points constructed based on road sections, the edges are roads between nodes, and the nodes in the target graph network whose blocking values ​​are the target values ​​are blocking nodes;

[0038] Searching the nodes in the target graph network by using a search algorithm, determining each blocked node whose number of consecutive neighbors is greater than or equal to a fourth threshold as a target blocked node;

[0039] A closed-loop area formed by edges corresponding to target blocking nodes in the target graph network is determined, and the closed-loop area is determined as an urban waterlogging road traffic island in the study area.

[0040] Optionally, the method further includes:

[0041] Based on historical rainfall data, the time of waterlogging occurrence in the study area was determined;

[0042] Starting from the time when the waterlogging occurs, determine the accessibility values ​​of the roads in the study area at every preset time interval;

[0043] According to the accessibility value, determining whether the roads in the study area are urban waterlogging traffic blocked roads within each preset time period;

[0044] According to the various urban waterlogging traffic blocked roads in the study area within each preset time period, the urban waterlogging road traffic islands in the study area within each preset time period are determined;

[0045] According to the urban waterlogging road traffic islands in the study area within each preset time period, the areas prone to urban waterlogging road traffic islands in the study area are determined.

[0046] Optionally, based on the urban waterlogging road traffic islands in the study area within each preset time period, the prone areas of urban waterlogging road traffic islands in the study area are determined, including:

[0047] According to the urban waterlogging road traffic islands in the study area within each preset time period, the number of occurrences of urban waterlogging road traffic islands in the same city is determined;

[0048] When the number of occurrences of urban waterlogging road traffic islands exceeds a set threshold, the urban waterlogging road traffic islands are determined to be a high-risk area for urban waterlogging road traffic islands.

[0049] Compared with the prior art, this application has the following advantages:

[0050] The present application provides a method for mining urban road traffic islands in waterlogging scenarios. First, determine whether waterlogging has occurred in the study area based on rainfall data; in the case of waterlogging in the study area, determine the accessibility values ​​of the roads in the study area at preset time intervals starting from the time when waterlogging occurred; determine whether the roads in the study area are urban waterlogging traffic-blocked roads based on the accessibility values; determine urban waterlogging road traffic islands in the study area based on the various urban waterlogging traffic-blocked roads in the study area. Thus, the present application determines whether waterlogging has occurred in the study area for mining urban road traffic islands through rainfall data, and after determining that waterlogging has occurred in the study area, determines the traffic accessibility of various roads in the study area, and determines the various urban waterlogging traffic-blocked roads with traffic blockage based on the traffic accessibility of each road, and then predicts the formation of urban waterlogging road traffic islands in the study area based on the various urban waterlogging traffic-blocked roads in the study area, thereby realizing the mining of road traffic islands in urban waterlogging scenarios, providing support for the stable operation of the transportation system and reducing the losses caused by waterlogging disasters.

[0051] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0053] Figure 1 A flow chart of a method for mining urban road traffic islands in waterlogging scenarios provided in an embodiment of the present application;

[0054] Figure 2 A schematic diagram of a road traffic island in urban waterlogging in a method for mining urban road traffic islands in waterlogging scenarios provided in an embodiment of the present application;

[0055] Figure 3 Another flow chart of a method for mining urban road traffic islands in waterlogging scenarios provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings.

[0057] Figure 1 A flowchart of a method for mining urban road traffic islands in a waterlogging scenario provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes:

[0058] Step S1: Determine whether waterlogging occurs in the study area based on rainfall data.

[0059] In this embodiment, a research area in the city where urban road traffic island mining is required is first determined, such as a city or an administrative district in a city. Since whether waterlogging occurs in an area is directly related to rainfall, this application determines whether waterlogging disasters occur in the study area based on the collected rainfall data in the study area.

[0060] Step S2: When waterlogging occurs in the study area, the accessibility values ​​of the roads in the study area are determined at preset time intervals starting from the time when the waterlogging occurs.

[0061] In this embodiment, when it is determined based on the rainfall data in the study area that waterlogging has occurred in the study area, the accessibility value of each road in the study area within the corresponding preset time period is determined every preset time period starting from the time when the waterlogging occurs, according to the vehicle traffic conditions on each road in the study area. The accessibility value of each road is directly related to the vehicle traffic conditions on each road. The smoother the vehicle traffic on the road, the higher the accessibility value of the road. For example, the preset time is taken as 10 minutes, and the time when the flooding occurs is 9:00 on a certain day, then starting from 9:00 on that day, the accessibility values ​​of each road in the study area within the preset time of 10 minutes from 9:00 to 9:10 are determined once at 9:10; then starting from 9:10, the accessibility values ​​of each road in the study area within the preset time of 10 minutes from 9:10 to 9:20 are determined once; then starting from 9:20, the accessibility values ​​of each road in the study area within the preset time of 10 minutes from 9:20 to 9:30 are determined once, and so on. The cycle is repeated, and the accessibility values ​​of the roads in the study area within the corresponding preset time are determined every 10 minutes.

[0062] Step S3: Determine whether the road in the study area is a road blocked by urban waterlogging traffic according to the accessibility value.

[0063] In this embodiment, based on the accessibility values ​​of each road in the study area determined in step S2 within the corresponding preset time period, the road whose accessibility value within the preset time period exceeds the set threshold is determined as the urban waterlogging traffic blocked road within the preset time period, and the road whose accessibility value within the preset time period does not exceed the set threshold is determined as the non-urban waterlogging traffic blocked road within the preset time period. The set threshold can be set according to the actual application scenario, and is not specifically limited here, such as being set to 0.1, 0.2, etc.

[0064] Step S4: determining the urban waterlogging road traffic islands in the study area according to the urban waterlogging traffic blocked roads in the study area.

[0065] In this embodiment, if Figure 2 As shown, based on the various urban waterlogging traffic-blocked roads in the study area determined in step S3 within each preset time period, a closed-loop area formed by connecting multiple roads that belong to urban waterlogging traffic-blocked roads within the same preset time period in the study area within the same preset time period is determined, and at the same time, when vehicles in the closed-loop area need to leave the closed-loop area through the urban waterlogging traffic-blocked roads that constitute the boundary of the closed-loop area, the closed-loop area is determined to be an urban waterlogging road traffic island, thereby predicting that the area in the study area will form an urban waterlogging road traffic island at the next moment within the same preset time period. After obtaining the corresponding prediction results, emergency treatment measures for the area are immediately carried out to avoid waterlogging disasters causing losses to residents and property in the urban waterlogging road traffic island. For example, within the preset time period from 9:00 to 9:10 on the same day, there is a closed-loop area in the study area formed by four urban waterlogging traffic congestion roads a, b, c, and d connected front and back. At the same time, vehicles in the closed-loop area need to leave the closed-loop area through the urban waterlogging traffic congestion roads that constitute the boundary of the closed-loop area. Therefore, the closed-loop area is determined to be an urban waterlogging road traffic island. It is predicted that at the next moment 9:10:01 within the preset time period from 9:00 to 9:10 on the same day, there will be an urban waterlogging road traffic island in the study area formed by four urban waterlogging traffic congestion roads a, b, c, and d connected front and back.

[0066] The embodiment of the present application provides a method for mining urban road traffic islands in waterlogging scenarios. First, determine whether waterlogging has occurred in the study area based on rainfall data; in the case of waterlogging in the study area, determine the accessibility values ​​of the roads in the study area at preset time intervals starting from the time when waterlogging occurred; determine whether the roads in the study area are urban waterlogging traffic-blocked roads based on the accessibility values; determine urban waterlogging road traffic islands in the study area based on the various urban waterlogging traffic-blocked roads in the study area. Therefore, the present application determines whether waterlogging has occurred in the study area for mining urban road traffic islands through rainfall data, and after determining that waterlogging has occurred in the study area, determines the traffic accessibility of various roads in the study area, and determines the various urban waterlogging traffic-blocked roads with traffic blockage based on the traffic accessibility of each road, and then predicts the formation of urban waterlogging road traffic islands in the study area based on the various urban waterlogging traffic-blocked roads in the study area, thereby realizing the mining of road traffic islands in urban waterlogging scenarios, providing support for the stable operation of the transportation system and reducing the losses caused by waterlogging disasters. This application proposes the concept of road traffic islands in urban waterlogging, explores road traffic islands in urban waterlogging scenarios, improves the efficiency of identifying urban waterlogging traffic islands, expands the availability and ease of use of urban waterlogging traffic anomaly detection research models, and can discover areas where traffic anomalies often occur in urban waterlogging scenarios, providing important technical support and theoretical guidance for urban disaster prevention and mitigation and urban planning. In this application, urban waterlogging road traffic islands are the area range of closed areas formed by traffic congestion on multiple continuous roads at the same time when urban waterlogging disasters occur or are continuously affected by urban waterlogging disasters.

[0067] In combination with the above embodiments, in one implementation, the present application embodiment further provides a method for mining urban road traffic islands in waterlogging scenarios. In the method for mining urban road traffic islands in waterlogging scenarios, step S1 may include steps S11 to S15:

[0068] Step S11: Obtain the average rainfall in the study area by calculating the rainfall data in the study area.

[0069] In this embodiment, rainfall data of the city is collected, and rainfall data in the study area of ​​the city is determined through spatial filtering. The rainfall data in the study area is calculated by the average rainfall algorithm to obtain the average rainfall of the units in the study area within a unit time period. The expression of the average rainfall algorithm is: ,in, represents the average rainfall in the study area during the period t, and the duration corresponding to the period t is the unit duration, and the value of the unit duration is preferably 1 hour; i and j represent the row and column numbers of the grid rainfall data (i.e., TIF rainfall data) collected by IMERG (Integrated Multi-satellite Retrievals for GPM) in the study area, m represents the total number of rows of TIF rainfall data in the study area, n represents the total number of columns of TIF rainfall data in the study area, and k represents the total number of grids of TIF rainfall data in the study area; Represents the rainfall in time period t for the grid corresponding to the i-th row and j-th column in the TIF rainfall data in the study area.

[0070] In this embodiment, the average rainfall in the study area of ​​the current unit time length can be obtained by bringing the collected TIF rainfall data in the study area of ​​the current unit time length into the above-mentioned average rainfall algorithm for calculation. For example, if it is currently 9 o'clock in the day, the TIF rainfall data in the study area of ​​the current unit time length in the day (that is, the unit time length from 8 o'clock to 9 o'clock in the day) is brought into the above-mentioned average rainfall algorithm for calculation, and the average rainfall in the study area of ​​the current unit time length can be obtained, that is, the average rainfall in the study area in the time period from 8 o'clock to 9 o'clock in the day.

[0071] Step S12: Determine the target rainfall in the study area within a first time period in the future according to the average rainfall.

[0072] In this embodiment, after the average rainfall in the study area of ​​the current unit time length is calculated in step S11, the target rainfall in the first future time length of the study area is predicted by multiplying the average rainfall by a preset multiple, and the duration of the first future time length is the preset multiple of the unit time length. For example, after the average rainfall in the study area of ​​the current unit time length is calculated, the target rainfall in the study area in the next 24 hours starting from the current moment is predicted by multiplying the average rainfall by 24 times.

[0073] Step S13: Compare the target rainfall with the first threshold value to obtain a comparison result.

[0074] In this embodiment, after the target rainfall in the first time period in the future starting from the current moment is predicted in step S12, the target rainfall is compared with the first threshold to obtain a corresponding comparison result, and the comparison result is used to determine whether waterlogging disasters have begun to occur in the study area. The first threshold can be set according to the actual application scenario, and is not specifically limited here, such as taking the first threshold as 50, 60, 70, etc.

[0075] Step S14: when the comparison result is that the target rainfall is greater than or equal to the first threshold, it is determined that waterlogging occurs in the study area.

[0076] In this embodiment, after obtaining a comparison result by comparing the target rainfall in the first future time period of the study area with the first threshold, if the comparison result is that the target rainfall in the study area is greater than or equal to the first threshold, it is determined that waterlogging begins to occur in the study area at this time, and the starting time of calculating the target rainfall is determined as the time when waterlogging occurs. For example, it is currently 9 o'clock in the day, after calculating the average rainfall in the study area from 8 o'clock to 9 o'clock in the day, if the target rainfall calculated by multiplying the average rainfall by 24 is greater than or equal to the first threshold, the time period corresponding to the target rainfall at this time is from 9 o'clock in the day to 9 o'clock on the second day, so 9 o'clock in the day is determined as the time when waterlogging occurs.

[0077] Step S15: When the comparison result is that the target rainfall is less than the first threshold, it is determined that no waterlogging occurs in the study area.

[0078] In this embodiment, after obtaining a comparison result by comparing the target rainfall in the study area with the first threshold, if the comparison result is that the target rainfall in the study area is less than the first threshold, it is determined that waterlogging has not begun to occur in the study area.

[0079] In combination with the above embodiments, in one implementation, the present application embodiment further provides a method for mining urban road traffic islands in waterlogging scenarios. In the method for mining urban road traffic islands in waterlogging scenarios, step S2 may include steps S21 to S25:

[0080] Step S21: When waterlogging occurs in the study area, traffic situation data of the study area within a preset time period is collected every preset time period starting from the time when the waterlogging occurs.

[0081] In this embodiment, when it is determined that waterlogging has begun to occur in the study area, traffic situation data of the city within a preset time period is collected every preset time period starting from the time when waterlogging occurs. The starting time for collecting traffic situation data is the time when waterlogging occurs as determined in step S14, and the preset time period can be set according to the actual application scenario, and is not specifically limited here, such as being set to 10 minutes, 20 minutes, etc. For example, the preset time length is 10 minutes, and the current time is 9:00 in the day. Through calculation, it is determined that 9:00 is the time when urban flooding occurs. At this time, 9:00 is used as the starting time for collecting traffic situation data. After 10 minutes, the traffic situation data of the city within the preset time length of 9:00 to 9:10 is collected once; after collecting the traffic situation data of the city within the preset time length of 9:00 to 9:10, after 10 minutes, the traffic situation data of the city within the preset time length of 9:10 to 9:20 is collected again; after collecting the traffic situation data of the city within the preset time length of 9:10 to 9:20, after 10 minutes, the traffic situation data of the city within the preset time length of 9:20 to 9:30 is collected again, and so on. The traffic situation data of the city within the preset time length is collected once after every preset time length.

[0082] In this embodiment, the implementation method of spatial filtering the traffic situation data of each city within the preset time length collected is the same, and the implementation method of spatial filtering of the traffic situation data of a city within a preset time length is used as an example to illustrate. In the case where the study area is the administrative area of ​​the city, the traffic situation data of the city within the preset time length collected is spatially filtered through the administrative division data of the city to screen out the traffic situation data in the study area, thereby obtaining the traffic situation data of the study area within the preset time length. Based on the same implementation method, the traffic situation data of the study area within each preset time length can be obtained by spatial filtering. It should be understood that the traffic situation data of a city within a preset time length that can be collected must be a time period that has already occurred. For example, the preset time length is 10 minutes, and the current time is 10:00 in a day, then the traffic situation data of the city within a preset time length collected must be 10 minutes before 10:00 in the day. At the same time, the present application immediately starts to determine the urban waterlogging traffic blocked roads and urban waterlogging road traffic islands within the preset time length after each collection of the traffic situation data of a city within the preset time length.

[0083] Step S22: resampling the collected traffic situation data of the study area within a preset time period to obtain target traffic situation data of the study area within a preset time period.

[0084] In this embodiment, since urban roads include some urban paths and urban alleys, such roads are easily affected by urban flooding disasters and are not conducive to the study and determination of urban road traffic islands. Therefore, this application filters the traffic situation data of such roads, so that the traffic situation data only includes the traffic situation data of the main roads in the city.

[0085] Specifically, the implementation method of resampling the traffic situation data of the study area within each preset time length collected is the same, and the implementation method of resampling the traffic situation data of the study area within a preset time length is used as an example for explanation. The traffic situation data of the study area within the preset time length is matched with the urban road network data in the study area to obtain the first traffic situation data of the study area within the preset time length, and the first traffic situation data will include the roads corresponding to each traffic situation data. Then, based on the road name and other information of the roads corresponding to each traffic situation data in the first traffic situation data, the first traffic situation data is resampled, and the traffic situation data belonging to the urban lanes and urban alleys in the first traffic situation data are filtered out, and then the filtered first traffic situation data is determined as the target traffic situation data of the study area within the preset time length. Thus, based on the same implementation method, the traffic situation data of the study area within each preset time length can be obtained by resampling. Among them, the traffic situation data refers to the data used to measure the traffic situation of the global traffic area, for example, including road congestion data, road speed data, traffic flow data, etc.

[0086] Step S23: Determine the initial accessibility values ​​of the roads in the study area according to the road network topology characteristics of the roads in the study area.

[0087] In this embodiment, the initial accessibility value of the road represents the accessibility of the road itself, and has nothing to do with whether the road is congested or not. Therefore, this application brings the road network topology characteristics of each road involved in the target traffic situation data determined in step S22 into the first algorithm for calculation, thereby obtaining the initial accessibility value of each road. The expression of the first algorithm is: ,in, Representative Road The initial reachability value of Representative Road The proximity of Representative Road The road q in the first algorithm may be any road involved in the target traffic situation data determined in step S22.

[0088] Step S24: Determine accessibility weight factors of roads in the study area within a preset time period according to the target traffic situation data.

[0089] In this embodiment, the present application presets multiple road condition feature levels in advance, and each road condition feature level has a corresponding road condition weighted value, and the road condition weighted value is used to calculate the accessibility of the road. Specifically, after obtaining the target traffic situation data within the preset time length in the study area through step S22, the traffic situation data of each road in the target traffic situation data is subjected to road condition feature extraction, and the road condition feature level corresponding to each road is determined based on the extracted road condition features of each road. Since there is a one-to-one correspondence between the road condition feature level and the road condition weighted value, the road condition weighted values ​​corresponding to the road condition feature level of each road are determined accordingly. Among them, the road condition feature level represents the congestion of the road. For example, the road condition feature level includes 4 levels, the first level represents no congestion, the second level represents slight congestion, the third level represents moderate congestion, and the fourth level represents severe congestion. For example, the traffic situation data of road A in the target traffic situation data of the study area within a preset time period is taken as an example for illustration. By extracting the road condition features of the traffic situation data of road A in the target traffic situation data, the road condition features of road A are obtained. Based on the extracted road condition features, it is determined that the road condition feature level of road A is the second level, representing slight congestion. The road condition weighted value corresponding to the second level of the road condition feature level is 2. Accordingly, the road condition weighted value of road A within the preset time period is determined to be 2.

[0090] In this embodiment, after determining the weighted values ​​of the road conditions of each road involved in the target traffic situation data determined in step S22, the weighted values ​​of the road conditions of each road are inversely calculated to obtain the corresponding accessibility weight factors of each road within the preset duration. For example, when the weighted value of the road condition of road A within the preset duration is 2, the inverse of 2 is calculated to obtain a value of 0.5, and the value 0.5 is determined as the accessibility weight factor of road A within the preset duration.

[0091] Step S25: Determine the accessibility values ​​of the roads in the study area within a preset time period according to the initial accessibility value and the accessibility weight factor.

[0092] In this embodiment, after the initial accessibility values ​​of each road in the study area and the accessibility weight factors of each road within the preset time are determined through the above steps S23 and S24, for any one of the roads, the accessibility value of the road within the preset time is obtained by multiplying the initial accessibility value of the road by the accessibility weight factor of the road within the preset time. Thus, a accessibility value of each road within the preset time can be calculated for each road in the study area.

[0093] In combination with the above embodiments, in one implementation, the present application embodiment further provides a method for mining urban road traffic islands in waterlogging scenarios. In the method for mining urban road traffic islands in waterlogging scenarios, step S3 may include steps S31 to S33:

[0094] Step S31: Compare the reachability value with a second threshold to obtain a first comparison result.

[0095] In this embodiment, after the accessibility values ​​of each road in the study area within the preset time are determined through step S2 or steps S21 to S25, the accessibility values ​​of each road within the preset time are compared with the second threshold value to obtain the first comparison results corresponding to each road. The second threshold value can be set according to the actual application scenario, and is not specifically limited here, such as being set to 0.1, 0.2, etc.

[0096] Step S32: when the first comparison result is that the accessibility value is less than or equal to the second threshold, determining that the road in the study area is an urban waterlogging traffic-blocked road.

[0097] In this embodiment, after determining the first comparison result corresponding to each road through step S31, when it is determined that the first comparison result corresponding to the road itself is an accessibility value less than or equal to the second threshold, it is determined that the road in the study area is an urban waterlogging traffic congestion road within a preset time period.

[0098] Step S33: when the first comparison result is that the accessibility value is greater than the second threshold, determine that the road in the study area is a non-urban waterlogging traffic-blocked road.

[0099] In this embodiment, after determining the first comparison results corresponding to each road through step S31, when it is determined that the first comparison result corresponding to the road itself is that the accessibility value is greater than the second threshold, it is determined that the road in the study area is a non-urban waterlogging traffic congestion road that has not been blocked within the preset time period.

[0100] In combination with the above embodiments, in one implementation, the present application embodiment further provides a method for mining urban road traffic islands in waterlogging scenarios. In the method for mining urban road traffic islands in waterlogging scenarios, step S3 may include steps S301 to S304:

[0101] Step S301: Determine the difference between the reachability value and the reachability value within the previous preset time period.

[0102] In this embodiment, the present application provides another implementation method for step S3: Since the implementation method for determining whether each road is a road blocked by urban waterlogging traffic is the same, one road is used as an example for illustration. After determining the accessibility value of the road in the study area within the current preset time length through step S2 or steps S21 to S25, the accessibility value of the road within the current preset time length is subtracted from the accessibility value of the road within the previous preset time length of the current preset time length to obtain the difference between the two. For example, the preset time length is set to 10 minutes, and the accessibility value of road A within the preset time length from 9:00 to 9:10 in a day is determined to be x1, and the accessibility value of road A within the preset time length from 9:10 to 9:20 in a day is determined to be x2, and the accessibility value x2 of road A within the preset time length from 9:10 to 9:20 in a day is subtracted from the accessibility value x1 of road A within the preset time length from 9:00 to 9:10 in a day to obtain the difference x2-x1 between the two. Therefore, based on the same implementation method, for each road in the study area, the difference between its accessibility value within the current preset time length and the accessibility value within the previous preset time length of the current preset time length can be calculated.

[0103] Step S302: Compare the difference with a third threshold to obtain a second comparison result.

[0104] In this embodiment, after determining the difference between the accessibility value of the road within the current preset duration and the accessibility value within the previous preset duration of the current preset duration in step S301, the difference is compared with the third threshold value to obtain a second comparison result corresponding to the road within the current preset duration. The second threshold value can be set according to the actual application scenario, and is not specifically limited here, such as being set to 0.1, 0.15, etc.

[0105] Step S303: when the second comparison result is that the difference is greater than or equal to the third threshold, determine that the road in the study area is an urban waterlogging traffic congestion road.

[0106] In this embodiment, after determining the second comparison result corresponding to the road within the current preset time length through step S302, when the difference of the second comparison result is greater than or equal to the third threshold, it is determined that the road in the study area is a blocked urban waterlogging traffic congestion road within the current preset time length.

[0107] Step S304: when the second comparison result is that the difference is less than the third threshold, determine that the road in the study area is a non-urban waterlogging traffic-blocked road.

[0108] In this embodiment, after determining the second comparison result corresponding to the road within the current preset time length through step S302, when the difference of the second comparison result is less than the third threshold, it is determined that the road in the study area is an unblocked non-urban waterlogging traffic congestion road within the current preset time length.

[0109] In combination with the above embodiments, in one implementation, the present application embodiment further provides a method for mining urban road traffic islands in waterlogging scenarios. In the method for mining urban road traffic islands in waterlogging scenarios, step S3 may include steps S311 to S312:

[0110] Step S311: Compare the reachability value with the second threshold, and compare the difference between the reachability value and the reachability value within the previous preset time period with the third threshold.

[0111] In this embodiment, the present application provides another implementation method for step S3: Since the implementation method for determining whether each road is a road blocked by urban waterlogging traffic is the same, one road is used as an example for illustration. After the accessibility value of the road in the study area within the current preset time length is determined through step S2 or steps S21 to S25, the accessibility value of the road within the current preset time length is compared with the second threshold value, and at the same time, the accessibility value of the road within the current preset time length is subtracted from the accessibility value of the road within the previous preset time length of the current preset time length, and the difference between the two is compared with the third threshold value.

[0112] Step S312: When the accessibility value is less than or equal to the second threshold, and the difference between the accessibility value and the accessibility value within the previous preset time period is greater than or equal to the third threshold, determine that the road in the study area is an urban waterlogging traffic congestion road.

[0113] In this embodiment, in step S311, the accessibility value of the road within the current preset time length is compared with the second threshold and the comparison result is that the accessibility value is less than the second threshold. At the same time, in step S311, the difference between the accessibility value of the road within the current preset time length and the accessibility value of the road within the previous preset time length of the current preset time length is compared with the third threshold and the comparison result is that the difference is greater than or equal to the third threshold, it is determined that the road is an urban waterlogging traffic congestion road within the current preset time length.

[0114] In combination with the above embodiments, in one implementation, the present application embodiment further provides a method for mining urban road traffic islands in waterlogging scenarios. In the method for mining urban road traffic islands in waterlogging scenarios, step S4 may include steps S41 to S42:

[0115] Step S41: Match each urban waterlogging traffic-blocked road in the study area with the road network data to obtain target road network data in the study area.

[0116] In this embodiment, each urban waterlogging traffic-blocked road in the determined study area within the same preset time period is matched with the urban road network data to represent each urban waterlogging traffic-blocked road in the urban road network data within the study area, thereby obtaining the target road network data of the study area within the same preset time period, such as Figure 2 As shown, the target road network data in the study area will represent the urban road network distribution in the study area, as well as the roads that are blocked by urban waterlogging traffic.

[0117] Step S42: determining a closed-loop area formed by roads blocked by urban waterlogging traffic in the target road network data, and determining the closed-loop area as an urban waterlogging road traffic island in the study area.

[0118] In this embodiment, after determining the target road network data of the study area within the same preset time period in step S41, as shown in FIG. Figure 2 As shown, based on the target road network data, a closed area formed by a plurality of urban waterlogging traffic-blocked roads connected front and back within the same preset time period in the target road network data is determined, such as Figure 2 The closed areas 1 to 6 shown in FIG. Then, when a vehicle in a closed-loop area needs to leave the closed-loop area through the urban waterlogging traffic-blocked road constituting the boundary of the closed-loop area, the closed-loop area is determined to be an urban waterlogging road traffic island, thereby predicting the urban waterlogging road traffic island formed in the study area at the next moment within the same preset time length. After obtaining the corresponding prediction results, emergency treatment measures for the area are immediately carried out to avoid urban waterlogging disasters causing losses to residents and property in the urban waterlogging road traffic island.

[0119] In combination with the above embodiments, in one implementation, the present application embodiment further provides a method for mining urban road traffic islands in waterlogging scenarios. In the method for mining urban road traffic islands in waterlogging scenarios, step S4 may include steps S401 to S403:

[0120] Step S401: According to the urban waterlogging traffic blocked roads in the study area, the blocking values ​​of the nodes corresponding to the urban waterlogging traffic blocked roads in the graph network of the study area are assigned as target values ​​to obtain a target graph network, wherein the nodes in the graph network are points constructed based on road sections, the edges are roads between nodes, and the nodes with blocking values ​​of the target values ​​in the target graph network are blocked nodes.

[0121] In this embodiment, the present application provides another implementation method for step S4: first, a graph network is constructed based on the urban road network data in the study area. Specifically, the endpoints of both ends of each road in the urban road network data in the study area are determined as nodes, and a unique node identifier is assigned to each of all nodes, that is, a node has a uniquely determined node identifier, which is used for subsequent search algorithms to search for nodes. Each road in the urban road network data in the study area is determined as an edge, and a unique edge identifier is assigned to each of all edges, that is, an edge has a uniquely determined edge identifier. The edge identifier is used in the subsequent process of determining urban waterlogging road traffic islands based on historical data, and the attribute information of whether the same road is a road blocked by urban waterlogging traffic in different time periods is recorded. For example, a unique edge identifier A0 is set for a road A, and the record of the attribute information of whether the road A is a road blocked by urban waterlogging traffic in different time periods can be recorded in the data set corresponding to the edge identifier A0, and different time periods need only be set for the attribute information of whether it is a road blocked by urban waterlogging traffic in different time periods. Then, a graph network is constructed with nodes and edges to obtain a graph network in the study area.

[0122] In this embodiment, after determining the blocked roads of urban waterlogging traffic through step S3 or other implementation methods of step S3, the blocking values ​​of the nodes corresponding to the two endpoints of the blocked roads of urban waterlogging traffic within the same preset time in the graph network are assigned to target values, and a target graph network is obtained. The nodes with blocking values ​​of the target values ​​in the target graph network are blocked nodes, while the blocking values ​​of other nodes in the graph network continue to be kept at the initial values. Among them, the preferred value is 1 when assigning values. For the nodes to which the blocking value is assigned, the blocking value of the node is 1, and for the nodes to which the blocking value is not assigned, the blocking value of the node is the initial value 0. After completing the assignment of blocking values ​​to all nodes, the target graph network is obtained. Among them, there is a corresponding target graph network for each preset time, and the blocked nodes in a target graph network are formed by the endpoints of the blocked roads of urban waterlogging traffic within the same preset time.

[0123] Step S402: searching the nodes in the target graph network by using a search algorithm, and determining each blocked node whose number of consecutive neighbors is greater than or equal to a fourth threshold as a target blocked node.

[0124] In this embodiment, since at least three roads consisting of three blocking nodes are required to form a closed area, in order to improve the efficiency of determining urban waterlogging road traffic islands, this application first excludes some blocking nodes that will definitely not form urban waterlogging road traffic islands.

[0125] Specifically: all nodes in the target graph network are searched once through a search algorithm, and no secondary search will be performed after the searched nodes are searched once, regardless of whether they are blocked nodes or not.

[0126] In each search round, a node is randomly selected from all nodes that have not been searched in the target graph network for search. If the first node searched in the current search round is not a blocking node whose blocking value is the target value, the current search round is ended and a new search round is performed.

[0127] In the case where the first node searched in the current search round is a blocking node with a blocking value of the target value, the other nodes directly adjacent to the blocking node continue to be searched, wherein another node (such as node B) directly adjacent to a node (such as node A) means that the one node A only needs to go through one road to reach the other node B, and will not go through other nodes to reach the other node B, then it is determined that the other node B is directly adjacent to the one node A. It should be understood that the other nodes directly adjacent to a node can be one or more. If the other nodes directly adjacent to the blocking node include a blocking node with a blocking value of the target value, then the other nodes directly adjacent to the newly searched blocking node continue to be searched in the current search round until the other nodes directly adjacent to the newly searched blocking node in the current search round no longer include a blocking node, and then the number of all blocked nodes searched in the current search round is determined. If the number of all blocked nodes searched in the current search round is greater than or equal to the fourth threshold, all blocked nodes searched in the current search round are determined as target blocked nodes. If the number of blocked nodes searched in the current search round is less than the fourth threshold, all blocked nodes searched in the current search round are not determined as target blocked nodes. Then continue the search in a new search round, and the search method of each new search round is the same and will not be repeated here. The number of all blocked nodes searched in any one of all search rounds is compared with the fourth threshold. If the number is greater than or equal to the fourth threshold, all blocked nodes searched in any one of the search rounds are determined as target blocked nodes, and if the number is less than the fourth threshold, all blocked nodes searched in any one of the search rounds are not determined as target blocked nodes. The search round is executed in this way until all nodes in the target graph network have been searched once and the search is terminated. Among them, the search algorithm includes but is not limited to depth-first search and breadth-first search.

[0128] Step S403: Determine a closed-loop area formed by edges corresponding to target blocking nodes in the target graph network, and determine the closed-loop area as an urban waterlogging road traffic island in the study area.

[0129] In this embodiment, if Figure 2 As shown, after determining the target blocking node in the target graph network, based on the edges corresponding to the target blocking node, the closed-loop area composed of the edges corresponding to the target blocking node is determined. When vehicles in a closed-loop area need to leave the closed-loop area through the urban waterlogging traffic congestion road that constitutes the boundary of the closed-loop area, the closed-loop area is determined to be an urban waterlogging road traffic island, thereby predicting the area of ​​the urban waterlogging road traffic island formed at the next moment in the study area within the same preset time length.

[0130] In combination with the above embodiments, in one implementation, the present application embodiment further provides a method for mining urban road traffic islands in waterlogging scenarios. In the method for mining urban road traffic islands in waterlogging scenarios, the method further includes:

[0131] Step S5: Determine the time when waterlogging occurs in the study area based on historical rainfall data.

[0132] In this embodiment, the implementation method of determining the time of occurrence of waterlogging in the study area based on historical rainfall data is the same as the implementation method of steps S11 to S15 in the above embodiment. First, the average rainfall in each unit time in the study area in the historical rainfall data is calculated; then, based on the average rainfall in each unit time, the target rainfall within 24 hours after each unit time is calculated. If the target rainfall within 24 hours after a unit time is greater than or equal to the first threshold, the end time of the unit time (such as 9 to 10 o'clock in a day) is determined as the time of waterlogging occurrence (the end time is 10 o'clock in the day), thereby multiple waterlogging occurrence times can be determined based on a large amount of historical rainfall.

[0133] Step S6: starting from the time when the waterlogging occurs, determine the accessibility values ​​of the roads in the study area at preset time intervals.

[0134] In this embodiment, after multiple waterlogging occurrence times are determined from historical rainfall data through step S5, the implementation method for determining the accessibility values ​​of roads in the study area subsequent to each waterlogging occurrence time is the same, and the implementation method for determining the accessibility values ​​of roads in the study area subsequent to a waterlogging occurrence time is taken as an example for explanation. For the roads in the determined study area, starting from the waterlogging occurrence time, the determination of the accessibility values ​​of the roads at every preset time length is the same as the implementation method of steps S21 to S25 in the above embodiment. First, starting from the time of waterlogging determined based on historical rainfall data, traffic situation data within a preset time period is obtained from historical traffic situation data every preset time period, and then the traffic situation data obtained within each preset time period is spatially filtered and resampled to obtain the target traffic situation data of the study area within each preset time period; then the initial accessibility value of the roads in the study area is calculated by the road network topology characteristics of the roads in the study area; then based on the target traffic situation data within the preset time period, the accessibility weight factor of the roads in the study area within the preset time period is determined; finally, the accessibility weight factor of the roads in the study area within each preset time period is multiplied by the initial accessibility value of the road itself to obtain the accessibility value of the road itself within each preset time period. Therefore, for each waterlogging occurrence time, the accessibility values ​​of the roads in the study area within each subsequent multiple preset time periods can be determined. For example, if a flood occurs at 10:00 a.m. on one day, 20 accessibility values ​​for each road in the study area within 20 preset time periods starting from 10:00 a.m. on that day are finally determined; if another flood occurs at 9:00 a.m. on another day, 30 accessibility values ​​for each road in the study area within 30 preset time periods starting from 9:00 a.m. on that other day are finally determined.

[0135] Step S7: Determine, based on the accessibility value, whether the roads in the study area are urban waterlogging traffic-blocked roads within each preset time period.

[0136] In this embodiment, after determining the accessibility values ​​of the roads in the study area within the subsequent multiple preset time periods starting from each waterlogging occurrence time through step S6, the implementation method of determining whether the roads in the study area are urban waterlogging traffic blocked roads within each preset time period based on these accessibility values ​​is the same as the implementation method of the above steps S31 to S33, steps S301 to S304, and steps S311 to S312. It can be that all accessibility values ​​are compared with the first threshold value, and whether the road is an urban waterlogging traffic blocked road is determined based on the comparison result; it can also be that all accessibility values ​​are compared with the second threshold value, and whether the road is an urban waterlogging traffic blocked road is determined based on the comparison result; it can also be that all accessibility values ​​are compared with the first threshold value and the second threshold value, and whether the road is an urban waterlogging traffic blocked road is determined based on the comparison result.

[0137] Step S8: determining the urban waterlogging road traffic islands in the study area within each preset time period according to each urban waterlogging traffic blocked road in the study area within each preset time period.

[0138] In this embodiment, after determining the various urban waterlogging traffic-blocked roads in the study area within each preset time period, it is determined whether there are multiple urban waterlogging traffic-blocked roads within the same preset time period to form a closed area. If there are multiple urban waterlogging traffic-blocked roads within the same preset time period to form a closed area, the closed area is determined as an urban waterlogging road traffic island. Thus, urban waterlogging road traffic islands within each preset time period can be determined for the urban waterlogging traffic-blocked roads in the study area within each preset time period.

[0139] Step S9: determining the areas prone to urban waterlogging road traffic islands in the study area according to the urban waterlogging road traffic islands in the study area within each preset time period.

[0140] In this embodiment, based on the urban waterlogging road traffic islands in the study area within each preset time period, it is determined which places in the study area are prone to form urban waterlogging road traffic islands. If an area often forms urban waterlogging road traffic islands under urban waterlogging scenarios, then the area is determined to be a prone area for urban waterlogging road traffic islands. This result can provide data support for improving the transportation system, thereby better optimizing the urban transportation system. For example, if an area is a prone area for urban waterlogging road traffic islands, it is determined that there are problems with the road traffic in the prone area or there are problems with the urban drainage system in the area, thereby more targeted optimization of the road traffic and urban drainage systems in the area.

[0141] In combination with the above embodiments, in one implementation, the present application embodiment further provides a method for mining urban road traffic islands in waterlogging scenarios. In the method for mining urban road traffic islands in waterlogging scenarios, step S9 may include steps S91 to S92:

[0142] Step S91: determining the number of occurrences of urban waterlogging road traffic islands in the same urban waterlogging road traffic island in the study area within each preset time period.

[0143] In this embodiment, the number of times that urban waterlogging road traffic islands occur in the same urban waterlogging road traffic islands is determined based on the number of urban waterlogging road traffic islands in the study area within a specific historical time period within each preset time period. Figure 2 As shown, the specific historical period is taken as 3 years. Based on the historical data of the past 3 years, it is determined that the location of the closed area 1 in the study area has experienced 3 urban waterlogging road traffic islands in the past 3 years, the location of the closed area 2 has experienced 5 urban waterlogging road traffic islands in the past 3 years, the location of the closed area 3 has experienced 15 urban waterlogging road traffic islands in the past 3 years, the location of the closed area 4 has experienced 6 urban waterlogging road traffic islands in the past 3 years, the location of the closed area 5 has experienced 2 urban waterlogging road traffic islands in the past 3 years, and the location of the closed area 6 has experienced 7 urban waterlogging road traffic islands in the past 3 years.

[0144] Step S92: when the number of occurrences of urban waterlogging road traffic islands exceeds a set threshold, determining the urban waterlogging road traffic islands in the same city as an area prone to urban waterlogging road traffic islands.

[0145] In this embodiment, when it is determined that the number of times that the same urban waterlogging road traffic island occurs in the study area exceeds a set threshold within a specific historical period, the same urban waterlogging road traffic island is determined to be a high-risk area for urban waterlogging road traffic islands, wherein the set threshold can be set according to the actual scenario, and is not specifically limited here, such as 10. For example, continuing to use the example in step S91 above, the set threshold is taken as 10, where only the location of the closed area 3 has experienced 15 urban waterlogging road traffic islands exceeding the set threshold of 10 in the past three years, and therefore the closed area 3 is determined to be a high-risk area for urban waterlogging road traffic islands, thereby providing data support for subsequent optimization of more targeted transportation systems and urban drainage systems.

[0146] In the embodiments of the present application, Figure 3As shown, the present application provides a method for mining urban road traffic islands in waterlogging scenarios. First, the average rainfall in the study area is determined based on rainfall data, and the rainfall in the next 24 hours is determined based on the average rainfall. Then, the rainfall is compared with the first threshold to determine the time when urban waterlogging occurs. Then, starting from the time when the waterlogging occurs, after each preset time period, the accessibility value of the roads within the study range within the preset time period is determined, and then, based on the relationship between the accessibility value of the road and the second threshold and the third threshold, each road that belongs to the urban waterlogging traffic blocked road within the preset time period is determined. Then, the target graph network is constructed, and the nodes corresponding to the urban waterlogging traffic blocked roads in the target graph network are assigned values. Then, by searching the blocked nodes in the target graph network, the target blocked nodes are determined based on the fourth threshold, and then the urban waterlogging road traffic islands are determined based on the target blocked nodes within the preset time period.

[0147] The present application provides a method for mining urban road traffic islands under waterlogging scenarios, which has the following beneficial effects: the present application proposes urban waterlogging road traffic islands for the first time, and defines urban waterlogging road traffic islands as the regional scope of closed areas formed by traffic congestion on multiple continuous roads at the same time when urban waterlogging disasters occur or under the continuous impact of urban waterlogging disasters; the traffic situation data used in the present application can reflect the real-time status of road traffic, and can reflect traffic anomalies caused by road waterlogging (structural abnormalities) or congestion (functional abnormalities), without deliberately distinguishing the type of anomaly, which can improve the recognition efficiency of urban waterlogging road traffic islands; the urban traffic system under the urban waterlogging scenario is a dynamically evolving complex system. In addition to considering the static characteristics of the road, the present application also considers dynamic influencing factors such as rainfall intensity, road conditions, and road speed, which is helpful to explore urban waterlogging scenarios The spatiotemporal heterogeneity of traffic accessibility is caused by the rainfall threshold, and the rainfall data is relatively simple to obtain. Therefore, from the perspective of rainfall, this application proposes to determine the occurrence time of urban waterlogging based on the rainfall threshold, and takes rainstorm as the triggering factor of urban waterlogging, explores the continuous impact of waterlogging, expands the theory of research on the impact of urban waterlogging on traffic, and improves the availability and ease of use of urban waterlogging road traffic island identification. At the same time, this application can also determine the areas in the city that are prone to frequent formation of urban waterlogging road traffic islands based on historical data, so as to provide data support for the improvement of the transportation system, thereby better optimizing the urban transportation system. When an area is determined to be a prone area for urban waterlogging road traffic islands, it is determined that there are problems with the road traffic in the prone area or the urban drainage system in the area, so as to optimize the road traffic and urban drainage system in the area in a more targeted manner.

[0148] It should be noted that, for the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present application are not limited by the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present application.

[0149] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0150] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application may adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0151] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0152] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0153] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0154] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

[0155] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0156] The above is a detailed introduction to a method for excavating urban road traffic islands under waterlogging scenarios provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for mining urban road traffic islands in waterlogging scenarios, characterized in that: The method comprises: Based on rainfall data, determine whether waterlogging occurs in the study area; In the event of waterlogging in the study area, traffic situation data of the study area within a preset time period is collected every preset time period starting from the time when the waterlogging occurs; target traffic situation data of the study area within a preset time period is obtained by resampling the collected traffic situation data of the study area within a preset time period; initial accessibility values ​​of roads in the study area are determined according to the road network topology characteristics of the roads in the study area; accessibility weight factors of roads in the study area within a preset time period are determined according to the target traffic situation data; accessibility values ​​of roads in the study area within a preset time period are determined according to the initial accessibility values ​​and the accessibility weight factors; According to the accessibility value, determine whether the road in the study area is a road blocked by urban waterlogging traffic; According to each urban waterlogging traffic-blocked road in the study area, the blocking value of each node in the graph network of the study area corresponding to each urban waterlogging traffic-blocked road is assigned a target value to obtain a target graph network, wherein the nodes in the graph network are points constructed based on road sections, the edges are roads between nodes, and the nodes in the target graph network whose blocking values ​​are the target values ​​are blocking nodes; the nodes in the target graph network are searched by a search algorithm, and each blocking node whose number of consecutive neighbors is greater than or equal to a fourth threshold is determined as a target blocking node; a closed-loop area formed by the edges corresponding to the target blocking node in the target graph network is determined, and the closed-loop area is determined as an urban waterlogging road traffic island in the study area; According to the historical rainfall data, the time when urban waterlogging occurs in the study area is determined; starting from the time when the waterlogging occurs, the accessibility values ​​of the roads in the study area at every preset time period are determined; according to the accessibility values, whether the roads in the study area are blocked roads for urban waterlogging traffic within each preset time period is determined; according to the blocked roads for urban waterlogging traffic within each preset time period, the urban waterlogging road traffic islands in the study area within each preset time period are determined; according to the urban waterlogging road traffic islands in the study area within each preset time period, the prone areas of urban waterlogging road traffic islands in the study area are determined.

2. The method for mining urban road traffic islands in waterlogging scenarios according to claim 1, characterized in that: Based on rainfall data, determine whether waterlogging occurs in the study area, including: By calculating the rainfall data of the study area, the average rainfall in the study area is obtained; According to the average rainfall, determine the target rainfall in the first time period in the future of the study area; Comparing the target rainfall with a first threshold value to obtain a comparison result; When the comparison result shows that the target rainfall is greater than or equal to the first threshold, determining that waterlogging occurs in the study area; When the comparison result is that the target rainfall is less than the first threshold, it is determined that no waterlogging occurs in the study area.

3. The method for mining urban road traffic islands in waterlogging scenarios according to claim 1, characterized in that: According to the accessibility value, determine whether the road in the study area is a road blocked by urban waterlogging traffic, including: Comparing the reachability value with a second threshold value to obtain a first comparison result; When the first comparison result shows that the accessibility value is less than or equal to the second threshold, determining that the road in the study area is a road blocked by urban waterlogging traffic; When the first comparison result is that the accessibility value is greater than the second threshold, it is determined that the road in the study area is a non-urban waterlogging traffic-blocked road.

4. The method for mining urban road traffic islands in waterlogging scenarios according to claim 1, characterized in that: According to the accessibility value, determine whether the road in the study area is a road blocked by urban waterlogging traffic, including: Determine the difference between the reachability value and the reachability value within the previous preset time period; Comparing the difference with a third threshold to obtain a second comparison result; When the second comparison result is that the difference is greater than or equal to the third threshold, determining that the road in the study area is a road blocked by urban waterlogging traffic; When the second comparison result is that the difference is less than the third threshold, it is determined that the road in the study area is a non-urban waterlogging traffic-blocked road.

5. The method for mining urban road traffic islands in waterlogging scenarios according to claim 1, characterized in that: According to the accessibility value, determine whether the road in the study area is a road blocked by urban waterlogging traffic, including: Comparing the accessibility value with the second threshold, and comparing the difference between the accessibility value and the accessibility value within the previous preset time period with the third threshold; When the accessibility value is less than or equal to the second threshold, and the difference between the accessibility value and the accessibility value within the previous preset time period is greater than or equal to the third threshold, it is determined that the road in the study area is an urban waterlogging traffic congestion road.

6. The method for mining urban road traffic islands in waterlogging scenarios according to claim 1, characterized in that: According to the various urban waterlogging traffic blocked roads in the study area, the urban waterlogging road traffic islands in the study area are determined, including: Match the traffic-blocked roads in each city in the study area with the road network data to obtain the target road network data in the study area; A closed-loop area formed by roads blocked by urban waterlogging traffic in the target road network data is determined, and the closed-loop area is determined as an urban waterlogging road traffic island in a study area.

7. The method for mining isolated traffic islands on urban roads in waterlogging scenarios according to claim 1, characterized in that: According to the urban waterlogging road traffic islands in the study area within each preset time period, the prone areas of urban waterlogging road traffic islands in the study area are determined, including: According to the urban waterlogging road traffic islands in the study area within each preset time period, the number of times the urban waterlogging road traffic islands occur in the same city is determined; When the number of occurrences of urban waterlogging road traffic islands in the same city exceeds a set threshold, the urban waterlogging road traffic islands are determined to be a high-risk area for urban waterlogging road traffic islands.

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