CN112365595A - Analysis method for identifying traffic accident multi-point based on alarm data point element - Google Patents
Analysis method for identifying traffic accident multi-point based on alarm data point element Download PDFInfo
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Abstract
The invention discloses an analysis method for identifying traffic accident multi-occurrence points based on alarm data point elements, which comprises the steps of collecting alarm traffic accident data, generating WGS84 coordinates, importing the alarm traffic accident data into a GIS platform, modeling the accident point elements, fusing the collected temperature data, identifying regional accident multi-occurrence points by adopting Getis-Ord Gi hot spot analysis, carrying out cluster analysis on accidents, carrying out local multi-occurrence point identification through abnormal value analysis and abnormal values around the accident multi-occurrence points, and carrying out cluster analysis through Geographic Information System (GIS) software, Thiessen polygons, nuclear density analysis, hot spot analysis and abnormal value analysis. The invention can provide visual spatial information about accident distribution, and can research the spatial distribution characteristics of the region with higher accident severity degree to identify accident multiple points.
Description
Technical Field
The invention relates to a traffic accident multiple-occurrence-point identification and cause analysis method, in particular to an analysis method for identifying traffic accident multiple-occurrence-point based on alarm data point elements.
Background
At present, 2 approaches are mainly used for researching the spatial distribution characteristics of road traffic accidents, namely 1, the traditional statistical method is used for counting data to determine an accident multi-point area; 2. the traffic accident situation is visually displayed on a map by a Geographic Information System (GIS) technology, and then the spatial distribution characteristics of the traffic accident situation are analyzed by a spatial analysis method. Compared with the method 1, the method for analyzing by utilizing the GIS has the advantages that firstly, the visual characteristics of the GIS can realize more visual and intuitive understanding on the distribution condition of traffic accidents, so that the general grip on the traffic safety condition in the area is quickly formed; secondly, various spatial analysis tools have been developed in the existing GIS technical system, and spatial distribution characteristics of traffic accidents and spatial relations among different traffic accidents can be mined from multiple angles.
Although the accident research based on GIS space analysis is beneficial to research on the analysis idea and method of accident data, the above technology has the following defects: firstly, because the accident records usually have no accurate longitude and latitude coordinates and cannot be positioned in a GIS (geographic information system), the current research based on accident data of traffic management departments mainly focuses on exploring the metering relationship between accident characteristics and influence factors thereof and cannot provide visual spatial information about accident distribution. Secondly, the most intuitive method for measuring the traffic safety level is the occurrence frequency of traffic accidents, and most of the existing documents pay attention to the identification of accident multi-occurrence points based on the thought.
Due to the limitation of point elements, the identification of accident multiple points is carried out by singly identifying the number of accidents of the event points, so that a cheat end exists, and the relation among the event points is split. In actual traffic management, accidents causing serious casualties are objects of major concern of traffic management departments, so that the study on the spatial distribution characteristics of areas with high accident severity degree is also significant. Finally, although both density analysis and cluster analysis have been practiced in traffic accident spatial analysis, there are some limitations on the method, lacking analysis of non-aggregate accident point cluster patterns.
Disclosure of Invention
The invention aims to provide an analysis method for identifying traffic accident multi-occurrence points based on alarm data point elements, which can provide visual spatial information about accident distribution and research the spatial distribution characteristics of an area with higher accident severity to identify the accident multi-occurrence points through Geographic Information System (GIS) software, Thiessen polygons, kernel density analysis and a cluster analysis method of hotspot analysis and abnormal value analysis.
In order to realize the purpose of the invention, the invention adopts the following technical scheme: an analysis method for identifying traffic accident multiple points based on alarm data point elements comprises the following steps: step 1, screening traffic accident data, which specifically comprises the following operations: modeling by adopting Geographic Information System (GIS) software according to the original data of the alarm center of the traffic police terminal, converting the alarm location into longitude and latitude for geocoding, matching the returned data with the original data after the geocoding is finished, and screening the data;
step 2, visualizing the accident data GIS, which specifically comprises the following operations: x y data are added into the GIS, the x field selects WGS84 longitude, the y field selects WGS84 latitude, the coordinate system selects WGS84 geographic coordinate system, and the point elements are generated and positioned;
step 3, identifying accident multi-occurrence points by a point element-based clustering analysis method, which specifically comprises the following steps: step 3-1, collecting events, specifically comprising the following operations: the GIS graph has n accident point elements in total, but because a plurality of point elements are overlapped, only m misaligned points are actually displayed;
step 3-2, visualizing the event, specifically comprising the following operations: after the collection event is finished, the output graph layer can be visualized, an 'Incount' field is used as a value field by utilizing a symbol system of the graph layer, the size of points is changed through grading symbols, the range is 8-50, the points are classified into 5 types, the classification method is a natural discontinuous point classification method, after the size classification is finished, the colors of point elements of different types are changed, and the minimum value, the maximum value, the average value and the standard deviation are obtained after statistics;
step 3-3, modeling the point elements; step 3-4, fusing temperature attributes with accidents;
step 3-5, hotspot analysis, which specifically comprises the following operations: calculating Getis-Ord Gi statistics for each element in the data set by the hotspot analysis tool, and obtaining the clustering position of the high-value or low-value element on the space through the obtained z value and p value; and 3-6, clustering and abnormal value analysis.
Preferably, in step 1, the original data of the alarm center includes field information of alarm time, alarm location, alarm type, alarm content, alarm time, approval time, address, ambiguity, level and coordinate, the geocode uses a hundred degree map coordinate query and conversion tool developed by python, and after the accident address is input, returns the longitude and latitude of WGS84 coordinate system, and returns the ambiguity and address level thereof, and the address level includes door address, road, medical treatment, village and company enterprise.
Preferably, the data screening firstly eliminates non-traffic accident data, and an invalid null value is generated in the geocoding process, so that the invalid null value needs to be eliminated; and then removing invalid fields, such as alarm time and approval time, only reserving alarm time, alarm category, address, ambiguity, level and coordinate information, and obtaining screened traffic accident data after preliminary screening.
Preferably, in step 2, the WGS84 coordinate system is converted into the WGS84 — mercator projection coordinate system, and the conversion method specifically includes the following operations: the coordinate system of the data frame is transformed into a WGS 84-mercator projection coordinate system from a WGS84 coordinate system, then point element layer data is derived, the same coordinate system as the data frame is selected, the derived point element layer data is added to the current layer, in this case, one point element in the drawing represents one traffic accident, but partial point data coordinates are consistent, so partial point elements are completely overlapped, and one point in the drawing represents a plurality of point elements.
Preferably, after the coordinate system conversion is completed, loading a prepared GIS base map, using a WGS 84-WEB mercator projection coordinate system downloaded for Local Space View (three-dimensional digital earth) of the base map to project the 12-level GIS base map of the city under the coordinate system, removing part of point data beyond the city administrative region range, only retaining data in the city administrative region range, introducing the prepared administrative region vector map into the map layer, performing superposition analysis by using an analysis tool-intersection in an Arc Toolbox tool of the GIS, taking the point data map layer and the administrative region vector map layer as input data, obtaining point elements after the point elements and the surface elements are intersected, and obtaining final accident point data after the intersection is completed.
Preferably, in step 3-3, the point element modeling specifically includes the following operations: when a plurality of point elements coincide in a GIS (geographic information system) image, a traffic accident point is modeled again, one point element is used for replacing a plurality of coincident point elements, namely one point element represents a plurality of traffic accidents, after a new point image layer is output, a new point element is generated, and an 'Incount' field is added to the new point element, wherein the field is the number of accidents occurring at the point.
Preferably, in step 3-4, the temperature condition when the accident occurs is collected, the temperature data downloads the weather data of the analysis city of the analysis year from the network, is made into an Excel format and is matched with the accident, the temperature data only records the lowest and highest temperature of the day, and the following processing is performed when the temperature is matched with the accident: when the accident occurrence time is from 0 point to 6 points and from 18 points to 24 points, the temperature is the lowest temperature; when the accident occurrence time is 10 to 14 points, taking the highest temperature on the day; and taking the average value of the highest air temperature and the lowest air temperature for accidents in other time.
Preferably, the coordinates of the accident point element are stored in 2 fields of 'WGS 84 longitude' and 'WGS 84 latitude', and when 2 fields are fused at the same time, a single field of the same element is fused, if 2 points do not coincide, the longitude coordinates are also fused, and before the element fusion, a field is added, a field calculator is used for assigning the sum of the text contents of the 2 fields, and the two fields are separated by commas, so that only the coincident points are fused, the temperature field is reserved during the fusion, the temperature average value of the fused element is calculated, and the fused element is assigned to the fused multipoint element.
Preferably, in step 3-5, the local sum of a certain element and its neighboring elements is compared proportionally with the sum of all elements, and when the local sum is too different from the expected local sum to be a randomly generated result, a statistically significant z-score is generated, and for the statistically significant positive z-score, the higher the z-score, the more closely the hot spots are clustered; for statistically significant negative z-scores, the lower the z-score, the more tightly the cold spots are clustered;
the formula for Getis-Ord Gi is:
the conceptualization method of the hotspot analysis spatial relationship selects an inverse distance method, namely, the closer elements have greater influence on the target elements, and the distance method selects the Euclidean distance (the straight distance between two points).
Preferably, in step 3-6, the clustering and outlier analysis tool can identify spatial clusters of elements having high or low values, the tool calculates a Local Moran's value, a z-score, a p-value, and a cluster code representing each element, the z-score and the p-value representing a statistical significance of the calculated index value, as calculated by:
wherein statistics of local Moire index for data point i are representedAmount, n is the total number of data points, xi，xjIs the attribute of a data point, is a global average of the attributes, is the spatial weight between data point i and other data points j, typically taken as the inverse of the distance between the two points,
the second-order sample of the attribute of all data points except the data point is calculated by the following formula:
compared with the prior art, the analysis method for identifying traffic accident multiple points based on the alarm data point elements has the following beneficial effects that: by adopting the analysis method for identifying the traffic accident multi-occurrence points based on the alarm data point elements, the alarm traffic accident data is collected and the WGS84 coordinate is generated, the GIS platform is introduced, the accident point elements are modeled, the collected temperature data is fused, Getis-Ord Gi hot spot analysis is adopted to identify the regional accident multi-occurrence points, the accident is subjected to cluster analysis, and the regional accident multi-occurrence points are identified through abnormal value analysis and abnormal value around the accident multi-occurrence points, so that visual spatial information about accident distribution can be provided, the spatial distribution characteristics of the region with higher accident severity are researched, and the accident multi-occurrence points are identified.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of an analysis method for identifying multiple points of a traffic accident based on alarm data point elements according to the present invention;
FIG. 2 is a table of accident statistics for different temperature data in this embodiment;
FIG. 3 is a diagram illustrating the result of collecting events in this embodiment;
FIG. 4 is a diagram illustrating the result of the accident fusion temperature point element in this embodiment;
FIG. 5 is a diagram illustrating the results of hotspot analysis in the present example;
FIG. 6 is a diagram showing the result of the abnormal value analysis of the number of accidents in this embodiment;
FIG. 7 is a diagram showing the results of the abnormal temperature value analysis in this example.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of an analysis method for identifying multiple points of a traffic accident based on alarm data point elements, which includes the following steps: step 1, screening traffic accident data, which specifically comprises the following operations: according to the original data of the alarm center of the traffic police, the original data of the alarm center comprises field information of alarm time, alarm place, alarm type, alarm content, alarm time, examination and approval time, address, ambiguity, level and coordinate. And modeling by adopting Geographic Information System (GIS) software, wherein the software needs longitude and latitude coordinates, and the alarm place is firstly converted into longitude and latitude to carry out geocoding. The geocoding adopts a hundred-degree map coordinate query and conversion tool developed by python, returns the longitude and latitude of a WGS84 coordinate system after an accident address is input, and returns the ambiguity and the address grade of the accident address, wherein the address grade comprises a gate address, a road, medical treatment, a village and a company enterprise.
And matching the returned data with the original data after the geocoding is finished, and screening the data. The data screening firstly eliminates non-traffic accident data, and invalid null values are generated in the geocoding process, so that the non-traffic accident data need to be eliminated; and then removing invalid fields such as alarm output time and approval time, only leaving alarm receiving time, alarm output category, address, ambiguity, level and coordinate information, and obtaining screened traffic accident data after preliminary screening.
Step 2, visualizing the accident data GIS, which specifically comprises the following operations: and x y data are added into the GIS by adopting a GIS visualization technology, the x field selects WGS84 longitude, the y field selects WGS84 latitude, and the coordinate system selects WGS84 geographical coordinate system to generate and position the point elements.
Since the WGS84 coordinate system is a geographical coordinate system, which is in units of decimal, the WGS84 coordinate system is converted into a WGS 84-mercator projection coordinate system, which is in units of m, for subsequent data analysis. The conversion method specifically comprises the following operations: the coordinate system of the data frame is transformed into a WGS 84-mercator projection coordinate system from a WGS84 coordinate system, then point element layer data is derived, the same coordinate system as the data frame is selected, the derived point element layer data is added to the current layer, at the moment, one point element in the map represents one traffic accident, but partial point elements are completely overlapped due to the fact that the partial point data coordinates are consistent, and one point in the map represents a plurality of point elements.
After the coordinate system conversion is completed, loading a prepared GIS base map, using a WGS 84-WEB mercator projection coordinate system downloaded by the base map for Local Space View (three-dimensional digital earth) to remove part of point data beyond the city administrative region range, only retaining data in the city administrative region range, introducing the prepared administrative region vector map into the map layer, performing superposition analysis to intersect through an analysis tool in an Arc Toolbox tool of the GIS, taking the map layer data and the administrative region vector map layer as input data, obtaining point elements after the point elements and the surface elements intersect, and obtaining the final accident point data after the intersection is completed.
Step 3, identifying accident multi-occurrence points by a point element-based clustering analysis method, which specifically comprises the following steps: step 3-1, collecting events, specifically comprising the following operations: the GIS graph has n accident point elements in total, but because a plurality of point elements are overlapped, only m misaligned points are actually displayed;
step 3-2, visualizing the event, specifically comprising the following operations: after the collection event is finished, the output graph layer can be visualized, the symbolic system of the graph layer is utilized, an 'Incount' field is used as a value field, the size of points is changed through grading symbols, the range is 8-50, the points are classified into 5 types, the classification method is a natural discontinuous point classification method, after the size classification is finished, the colors of point elements of different types are changed, and the 'Incount' field is counted in a GIS to obtain the minimum value, the maximum value, the average value and the standard deviation;
step 3-3, modeling the point elements, specifically comprising the following operations: the GIS graph has the condition that a plurality of point elements are overlapped, the concentration degree of accidents cannot be visually seen from the graph due to the condition, and the function failure of the method for identifying the accident multiple points based on the area buffer area algorithm is caused. Therefore, the traffic accident point needs to be modeled again, and one point element is used to replace a plurality of overlapped point elements, namely, one point element represents a plurality of traffic accidents.
The 'event collection' module in the GIS can realize the algorithm, and the principle is that event data is converted into weighted point data, and one traffic accident is an event. And after outputting a new point diagram layer, generating a new point element, wherein the new point element is added with an 'Incount' field, and the field is the number of accidents occurring at the point. (ii) a
Step 3-4, fusing the temperature attribute with the accident, and specifically comprising the following operations: the collection event can only count the number of coincident points, and the field attribute of the accident point element can be ignored, so that when the temperature condition of the accident occurrence is researched, the method for collecting the event cannot be adopted to process the coincident points, and the element fusion function is required to be utilized to reserve and calculate the partial field attribute of the original accident point.
Collecting the temperature condition when the accident happens, downloading the weather data of the analysis city of the analysis year from the network by the temperature data, making the weather data into an Excel format and matching the accident, recording the lowest temperature and the highest temperature of the day by the temperature data, and processing the following steps when the temperature is matched with the accident: when the accident occurrence time is from 0 point to 6 points and from 18 points to 24 points, the temperature is the lowest temperature; taking the highest temperature of the current day when the accident occurrence time is 10-14 points; and taking the mean value of the highest air temperature and the lowest air temperature for accidents in other time.
The coordinates of the accident point elements are stored in 2 fields of 'WGS 84 longitude' and 'WGS 84 latitude', 2 fields are fused at the same time, the elements with the same single field are fused, if 2 points do not coincide, the longitude coordinates are also fused, a field needs to be added before the element fusion, a field calculator is used for assigning the sum of the text contents of the 2 fields, commas are used for separating the fields, only coincident points are fused, the temperature fields are reserved during the fusion, the temperature average values of the fused elements are calculated, and the fused elements are assigned to the fused multipoint elements;
step 3-5, hotspot analysis, which specifically comprises the following operations: and calculating a Getis-Ord Gi statistic for each element in the data set by the hotspot analysis tool, and obtaining the position of clustering of the high-value or low-value elements on the space through the obtained z value and p value.
The principle of hotspot analysis is to look at each element in the context of a nearby element. High value elements are of interest but may not be statistically significant hotspots. To be a statistically significant hotspot, an element should have a high value and be surrounded by other elements that also have a high value.
Comparing a local sum of an element and its neighboring elements proportionally to the sum of all elements, and when the local sum is too different from the expected local sum to be a randomly generated result, generating a statistically significant z-score, wherein for a statistically significant positive z-score, the higher the z-score, the more closely the hot spots are clustered; for statistically significant negative z-scores, the lower the z-score, the more tightly the cold spots are clustered;
the formula for Getis-Ord Gi is:
the conceptualization method of the hotspot analysis spatial relationship selects an inverse distance method, that is, the closer an element has the greater influence on a target element, the more the distance method selects the euclidean distance (the straight distance between two points).
Step 3-6, clustering and abnormal value analysis, which specifically comprises the following operations: the clustering and outlier analysis tool can identify spatial clusters of elements with high or low values, the tool calculates a Local Moran's value, a z-score, a p-value, and a cluster code representing each element, the z-score and the p-value representing a statistical significance of the calculated index value, as calculated by:
wherein, the statistic of local Moire index of the data point i is represented, n is the total number of the data points, xi，xjIs the attribute of a data point, is a global average of the attributes, is the spatial weight between data point i and other data points j, typically taken as the inverse of the distance between the two points,
the second-order sample of the attribute of all data points except the data point is calculated by the following formula:
in this embodiment, for example, salt city alarm data is taken as an example, a clustering analysis accident multi-point identification method based on point elements is adopted, and the analysis result is as follows:
as shown in fig. 3, which is a schematic diagram showing the results of the collection events, in the GIS diagram in this experiment, 39825 accident point elements are shared, but since there is a case where a plurality of point elements overlap, 4345 actually displayed misaligned points. The data of the whole year is analyzed, and the temperature is classified to obtain the number of accidents at different temperatures, and fig. 2 shows an accident statistical table under different temperature data. After fusing the temperature elements, as shown in FIG. 4, the result of the accident fusion temperature point element is shown schematically,
fig. 5 is a schematic diagram showing the result of hot spot analysis, and as shown in fig. 5, the number of accidents in the areas such as intersections, military road construction, big day roads and the like of the great avenue of the century is dense, and the number of accidents in the areas such as open great avenues, yellow sea roads and the like is the second. The number and the density of accidents in the northern city are far greater than those in the southern city, and the multiple accident points in the southern city are mainly concentrated near the administrative building.
FIG. 6 is a diagram showing the result of abnormal analysis of the number of accidents, and in combination with FIG. 6, the high-high clustering in the north of the city is intensive, and the accidents are concentrated; the south of the city has a few high-high clustering and high-low clustering conditions, and the high-high clustering area in the south of the city is the administrative building area displayed by the hotspot analysis. The high-high clustering indicates that the number of accidents in the area and the surrounding area is high and the accidents are dense, and the traffic safety needs to be improved in a large area for the multiple accident points. The high-low polymer mainly develops a north entrance crossing a major road and an east road of a building army, intersections of a liberation south road and a century major road and 3 areas of a salt city poly-dragon lake hospital, accidents are frequent in the areas, and the number of accidents in the surrounding areas is small, so that the problem that the accidents are frequent obviously exists in the areas, and the traffic safety condition needs to be improved in a targeted manner.
Fig. 7 is a diagram showing the result of temperature outlier analysis, where there are a lot of high-high temperature clusters in the northeast of the city, and a lot of low-low temperature clusters in the middle and south of the city. The high-high temperature clustering shows that the temperature of the accident in the region is high, and the screening statistics shows that the accident in the region is mostly in summer, the temperature is high, and the low-low clustering is opposite, and the accident occurs in winter.
The analysis result is matched with the accident multi-occurrence-point condition provided by the traffic police department, visual spatial information about accident distribution can be provided, the spatial distribution characteristic of an area with higher accident severity is researched, the accident multi-occurrence point is identified, and the urban accident multi-occurrence-point identification is facilitated.
The above is the preferred embodiment of the present invention, and it is obvious to those skilled in the art that several modifications and improvements can be made without departing from the principle of the present invention, and these should be considered as the protection scope of the present invention.
Claims (10)
1. An analysis method for identifying multiple points of a traffic accident based on alarm data point elements is characterized in that: the method comprises the following steps: step 1, screening traffic accident data, which specifically comprises the following operations: modeling by adopting Geographic Information System (GIS) software according to the original data of the alarm center of the traffic police terminal, converting the alarm location into longitude and latitude for geocoding, matching the returned data with the original data after the geocoding is finished, and screening the data;
step 2, visualizing the accident data GIS, which specifically comprises the following operations: x y data are added into the GIS, the x field selects WGS84 longitude, the y field selects WGS84 latitude, the coordinate system selects WGS84 geographic coordinate system, and the point elements are generated and positioned;
step 3, identifying accident multi-occurrence points by a point element-based clustering analysis method, which specifically comprises the following steps: step 3-1, collecting events, specifically comprising the following operations: the GIS graph has n accident point elements in total, but because a plurality of point elements are overlapped, only m misaligned points are actually displayed;
step 3-2, visualizing the event, specifically comprising the following operations: after the collection event is finished, the output layer can be visualized, an 'Incount' field is used as a value field by utilizing a symbol system of the layer, the size of points is changed through grading symbols, the range is 8-50, the points are classified into 5 types, the classification method is a natural discontinuous point classification method, after the size classification is finished, the colors of point elements of different types are changed, and the minimum value, the maximum value, the average value and the standard deviation are obtained after statistics;
step 3-3, modeling the point elements; step 3-4, fusing temperature attributes with accidents;
step 3-5, hotspot analysis, which specifically comprises the following operations: calculating Getis-Ord Gi statistics for each element in the data set by the hotspot analysis tool, and obtaining the clustering position of the high-value or low-value element on the space through the obtained z value and p value; and 3-6, clustering and abnormal value analysis.
2. The method for analyzing traffic accident multiple points based on alarm data point elements according to claim 1, wherein: in step 1, the original data of the alarm center comprises field information of alarm time, alarm place, alarm type, alarm content, alarm time, approval time, address, ambiguity, level and coordinate, the geocode adopts a hundred-degree map coordinate query and conversion tool developed by python, the accident address is input, the longitude and latitude of a WGS84 coordinate system are returned, the ambiguity and the address level are returned, and the address level comprises door address, road, medical treatment, village and company enterprise.
3. The method for analyzing traffic accident multiple points discrimination based on alarm data point elements according to claim 2, wherein: the data screening firstly eliminates non-traffic accident data, and invalid null values are generated in the geocoding process, so that the non-traffic accident data need to be eliminated; and then removing invalid fields such as alarm output time and approval time, only leaving alarm receiving time, alarm output category, address, ambiguity, level and coordinate information, and obtaining screened traffic accident data after preliminary screening.
4. The method for analyzing traffic accident multiple points based on alarm data point elements according to claim 1, wherein: in step 2, converting the WGS84 coordinate system into a WGS 84-mercator projection coordinate system, the conversion method specifically includes the following operations: the coordinate system of the data frame is transformed into a WGS 84-mercator projection coordinate system from a WGS84 coordinate system, then point element layer data is derived, the same coordinate system as the data frame is selected, the derived point element layer data is added to the current layer, in this case, one point element in the drawing represents one traffic accident, but partial point data coordinates are consistent, so partial point elements are completely overlapped, and one point in the drawing represents a plurality of point elements.
5. The method of claim 4, wherein the method comprises the steps of: after the coordinate system conversion is completed, loading a prepared GIS base map, using a WGS 84-WEB mercator projection coordinate system downloaded by the base map for Local Space View (three-dimensional digital earth) to remove part of point data beyond the city administrative region range, only retaining data in the city administrative region range, introducing the prepared administrative region vector map into the map layer, performing superposition analysis to intersect through an analysis tool in an Arc Toolbox tool of the GIS, taking the point data map layer and the administrative region vector map layer as input data, obtaining point elements after the point elements and the surface elements intersect, and obtaining final accident point data after the intersection is completed.
6. The method for analyzing traffic accident multiple points based on alarm data point elements according to claim 1, wherein: in step 3-3, the point element modeling specifically includes the following operations: the GIS map has the condition that a plurality of point elements coincide, traffic accident points are modeled again, one point element is used for replacing the plurality of coincident point elements, namely one point element represents a plurality of traffic accidents, after a new point map layer is output, a new point element is generated, an 'Incount' field is added to the new point element, and the field is the number of accidents occurring at the point.
7. The method for analyzing traffic accident multiple points based on alarm data point elements according to claim 1, wherein: in step 3-4, collecting the temperature condition when the accident occurs, downloading the weather data of the analysis year analysis city from the network by the temperature data, making the weather data into an Excel format and matching with the accident, recording the lowest temperature and the highest temperature of the day by the temperature data, and performing the following processing when the temperature is matched with the accident: when the accident occurrence time is from 0 point to 6 points and from 18 points to 24 points, the temperature is the lowest temperature; when the accident occurrence time is 10 to 14 points, taking the highest temperature on the day; and taking the mean value of the highest air temperature and the lowest air temperature for accidents in other time.
8. The method for analyzing traffic accident multiple points based on alarm data point elements according to claim 7, wherein: the coordinates of the accident point elements are stored in 2 fields of 'WGS 84 longitude' and 'WGS 84 latitude', 2 fields are fused at the same time, elements with the same single field are fused, if 2 points do not coincide, the longitude coordinates are also fused, a field needs to be added before element fusion, a field calculator is used for assigning the sum of the text contents of the 2 fields, commas are used for separating the fields, only coincident points are fused, the temperature fields are reserved during fusion, the temperature average values of the fused elements are calculated, and the fused elements are assigned to the fused multipoint elements.
9. The method for analyzing traffic accident multiple points based on alarm data point elements according to claim 1, wherein: in step 3-5, comparing the local sum of a certain element and its neighboring elements proportionally to the sum of all elements, and when the local sum is too different from the expected local sum to be a randomly generated result, generating a statistically significant z-score, wherein for a statistically significant positive z-score, the higher the z-score, the more tightly the hot spots are clustered; for statistically significant negative z-scores, the lower the z-score, the more tightly the cold spots are clustered;
the formula for Getis-Ord Gi is:
the conceptualization method of the hotspot analysis spatial relationship selects an inverse distance method, that is, the closer an element has the greater influence on a target element, the more the distance method selects the euclidean distance (the straight distance between two points).
10. The method for analyzing traffic accident multiple points based on alarm data point elements according to claim 1, wherein: in step 3-6, the clustering and outlier analysis tool may identify spatial clusters of elements with high or low values, the tool calculates a Local Moran's value, a z-score, a p-value, and a cluster code representing each element, the z-score and the p-value representing a statistical significance of the calculated index value, as calculated by:
wherein, the statistic of local Moire index of the data point i is represented, n is the total number of the data points, xiXj is the attribute of a data point, is the global average of the attribute, is the spatial weight between data point i and other data point j, typically taken as the inverse of the distance between the two points,
the second-order sample of the attribute of all data points except the data point is calculated by the following formula:
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113361854A (en) * | 2021-04-28 | 2021-09-07 | 上海工程技术大学 | Accident multi-point identification method based on Thiessen polygon and application thereof |
| CN115050181A (en) * | 2022-06-06 | 2022-09-13 | 合肥工业大学 | Method for identifying spatial hot spots and influence factors of traffic accidents and traffic violations at intersections |
| CN115223371A (en) * | 2022-09-20 | 2022-10-21 | 深圳市城市交通规划设计研究中心股份有限公司 | Big data analysis system of electric bicycle and working method thereof |
| CN115424430A (en) * | 2022-06-09 | 2022-12-02 | 长沙理工大学 | Highway traffic accident black point section identification method and computer device |
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2020
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113361854A (en) * | 2021-04-28 | 2021-09-07 | 上海工程技术大学 | Accident multi-point identification method based on Thiessen polygon and application thereof |
| CN115050181A (en) * | 2022-06-06 | 2022-09-13 | 合肥工业大学 | Method for identifying spatial hot spots and influence factors of traffic accidents and traffic violations at intersections |
| CN115050181B (en) * | 2022-06-06 | 2023-05-02 | 合肥工业大学 | Spatial hot spot of intersection traffic accident and traffic violation and influence factor identification method thereof |
| CN115424430A (en) * | 2022-06-09 | 2022-12-02 | 长沙理工大学 | Highway traffic accident black point section identification method and computer device |
| CN115424430B (en) * | 2022-06-09 | 2024-01-23 | 长沙理工大学 | Highway traffic accident black point road section identification method and computer device |
| CN115223371A (en) * | 2022-09-20 | 2022-10-21 | 深圳市城市交通规划设计研究中心股份有限公司 | Big data analysis system of electric bicycle and working method thereof |
| CN115223371B (en) * | 2022-09-20 | 2023-02-14 | 深圳市城市交通规划设计研究中心股份有限公司 | Big data analysis system of electric bicycle and working method thereof |
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