Expressway accident black spot road section identification method based on ETC data

Abstract

The invention discloses a highway accident black spot road section identification method based on ETC data, and belongs to the technical field of traffic accident analysis, and the method comprises the following steps: S1, extracting various indexes in traffic flow, terrain, weather and accident data; s2, according to the historical accident information of the expressway in the step S1, positioning the historical accident information to a specific position of an expressway network based on a Mars coordinate system in QGIS software; s3, calculating an accident risk index; and S4, taking the calculated accident risk index as a buffer radius, and establishing a buffer area for the center points of all divided road sections by using QGIS software to realize the recognition of the highway accident black spot road sections. The highway accident black spot road section identification method based on the ETC data aims to further clarify road accident black spots and accident high-risk road sections, identify the accident black spot road sections and provide a scientific basis for traffic safety construction planning and policies of highways.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic accident analysis, and in particular to a method for identifying accident black spot sections of expressways based on ETC data. Background Art

[0002] With the increasing volume of road traffic, the frequent occurrence of traffic accidents has become a social problem that needs to be solved urgently. The existing traffic accident black spot identification technology mainly relies on the aggregation of accident locations and mileage to identify accident black spots, but this method can only start from the accident information and fails to deeply explore the impact of traffic flow factors on accident black spots. Summary of the invention

[0003] The purpose of the present invention is to provide a method for identifying accident blackspot sections on highways based on ETC data, aiming to further clarify road accident blackspots and high-risk sections for accidents, scientifically and quantitatively analyze highway traffic accidents in time and space, identify accident blackspot sections, and provide a scientific basis for highway traffic safety construction planning and policies.

[0004] To achieve the above object, the present invention provides a method for identifying accident black spot sections of highways based on ETC data, comprising the following steps: Step S1, extracting various indicators in traffic flow, terrain, weather and accident data based on highway ETC toll data, highway historical accident information, accident section road alignment information and meteorological station observation data; Highway ETC toll collection data, including truck traffic volume, passenger car traffic volume, vehicle speed, and gantry unit length; Historical highway accident information, including accident location, type, severity, timestamp, number of deaths, number of injuries, amount of property damage, and number of damaged vehicles; The road alignment information of the accident section was obtained through the Shuijing Micro-Map software; The meteorological station observation data is the weather type information obtained from the meteorological station during the period when the accident occurred; Step S2: Based on QGIS software, the WGS 84 coordinate system of the road network, gantry and base map is converted into the GCJ02 Mars coordinate system, and according to the highway historical accident information in step S1, it is located at the specific position of the highway network based on the Mars coordinate system in QGIS software; Step S3: Calculate the accident risk index according to step S1 ARI ; Step S4: The accident risk index calculated in step S3 ARI As the buffer radius, QGIS software is used to establish a buffer zone for the center points of all divided sections to identify the accident blackspot sections of the highway.

[0005] Preferably, step S3 specifically comprises: Step S301: Calculate the gantry unit vehicle speed variance based on the highway ETC data extracted in step S1 Mixing rate with trucks ; Step S302: Calculate the accident risk index based on step S301 and the historical accident information of high-speed roads collected in step S1. ARI.

[0006] Preferably, in step S301, the gantry unit vehicle speed variance The calculation method is: Calculate the average speed of each vehicle 5 minutes before the accident between the gantry units of each road section, and then calculate the variance of the speed based on the average speed; ; ; ; in, Representative Car; represents the average speed of the vehicle; Represents the number of vehicles; Represents the length between the gantry units of the road section; Represents the moment when the vehicle passes through the downstream gantry; Represents the moment when the gantry passes the upstream gantry.

[0007] Preferably, the truck mixing rate in step S301 The calculation method is: Calculate the ratio of trucks to buses 5 minutes before the accident between the gantry units of each road section, and then calculate the mixing rate of trucks; ; in, It represents the truck traffic volume 5 min before the gantry unit accident; Represents the passenger vehicle traffic volume 5 minutes before the gantry unit accident occurs.

[0008] Preferably, in step S302, the accident risk index is calculated ARI The calculation formula is as follows: ; in, Represents the number of deaths; represents the number of injured; Representing accident property damage; Represents the number of lost vehicles; Represents the road type; Represents the weather type; , , , , , , , Represents the coefficient.

[0009] Preferably, in step S4, the center points of all divided road sections are calculated using QGIS software. Create a buffer zone It is expressed as: ; in, represents any point in space; d Represents any point in space To the center point of the segment The Euclidean distance of .

[0010] Preferably, based on the established buffer zone, the accident buffer zone layer generated by all accident points is overlapped with the highway base map layer, the highway network map layer, and the gantry unit layer to form a comprehensive accident analysis layer, and the road section area where the accident black spot exists is determined according to the degree of overlap of the buffer zone.

[0011] Therefore, the present invention adopts the above-mentioned highway accident black spot section identification method based on ETC data, and the technical effects are as follows: Based on highway ETC data and historical accident data, the present invention constructs a data set covering a variety of traffic flow and accident indicators, comprehensively extracts the key information required for the identification of accident blackspot sections, and ensures the integrity and reliability of the data foundation.

[0012] The present invention utilizes QGIS software to realize the coordinate system conversion and matching of road network data, gantry data and base map, and effectively improves the geographic information accuracy of accident data by spatially restoring historical accident data to the specific location of the highway network.

[0013] The accident risk index proposed by the present invention ( ARI ) By calculating indicators such as the gantry unit vehicle speed variance and truck mixing rate, and combining multiple factors in the accident data (such as weather and road type), the accident risk is scientifically assessed, which helps to accurately identify accident blackspot sections.

[0014] The present invention adopts QGIS software to perform buffer zone analysis, superimposes the accident point buffer zone with the highway base map, road network layer, and gantry unit layer, and systematically identifies accident black spot sections through the overlay analysis method to form a comprehensive accident analysis layer.

[0015] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of an embodiment of a method for identifying accident black spot sections on expressways based on ETC data of the present invention; Figure 2 It is a schematic diagram of a buffer zone layer of an accident point on Chaohui Expressway according to an embodiment of a method for identifying accident black spot sections on expressways based on ETC data of the present invention; Figure 3 It is a schematic diagram of an overlay layer of an accident point buffer zone and a road base map, a highway, and a gantry data in an embodiment of a method for identifying an accident black spot section of a highway based on ETC data of the present invention; Figure 4 It is an accident black spot section 1 of an embodiment of a method for identifying accident black spot sections on highways based on ETC data of the present invention; Figure 5 It is an accident black spot section 2 of an embodiment of a method for identifying accident black spot sections on highways based on ETC data of the present invention; Figure 6 It is the accident black spot section 3 of an embodiment of a method for identifying accident black spot sections on expressways based on ETC data of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0018] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0019] Embodiment 1 The present invention takes Guangdong Chaohui Expressway as an example. Figure 1 As shown, the present invention provides a method for identifying accident black spot sections on highways based on ETC data, comprising the following steps: Step S1, construct a dataset for identifying accident blackspot sections of Chaozhou-Huizhou Expressway in Guangdong Province, extract various indicators in traffic flow, terrain, weather and accident data based on ETC toll collection data of Chaozhou-Huizhou Expressway in Guangdong Province, historical accident information of the expressway, road alignment information of the accident section and observation data of the meteorological station.

[0020] The highway ETC toll collection data includes traffic flow indicators such as truck traffic volume, passenger car traffic volume, vehicle speed, and gantry unit length; Historical highway accident information, including accident location, type, severity, timestamp, number of deaths, number of injuries, amount of property loss, number of damaged vehicles, etc. The road alignment information of the accident section was obtained through the Shuijing Micro-Map software; The meteorological station observation data is the weather type information obtained by the meteorological station during the period when the accident occurred.

[0021] Step S2: Realize the geographic informatization and digitization of Chaohui Expressway network based on QGIS software.

[0022] Step S201, loading Guangdong Province road network data, gantry data, and Amap base map based on WGS 84 (World Geodetic System 1984) and China Geodetic Coordinate System 2000 coordinate systems; Step S202, installing the GeoHey plug-in, converting the WGS 84 (World Geodetic System 1984) coordinate system of the road network, gantry and base map into the GCJ02 (National Survey Bureau Standard No. 02) Martian coordinate system; Step S203: Convert the historical accident data collected in step S1 into a point layer in QGIS software, and perform spatial restoration on each piece of accident data according to the pile number of the traffic accident, and locate it at a specific location on the highway network.

[0023] Step S3: Calculate the accident risk index according to step S1 ARI ; Step S301: Calculate the gantry unit vehicle speed variance based on the highway ETC data extracted in step S1 Mixing rate with trucks .

[0024] Gantry unit vehicle speed variance Mixing rate with trucks The calculation method is: Gantry unit vehicle speed variance : Calculate the average speed of each vehicle 5 minutes before the accident between the gantry units of each section, and then calculate the variance of the speed based on the average speed; ; ; ; in, Representative Car; represents the average speed of the vehicle; Represents the number of vehicles; Represents the length between the gantry units of the road section; Represents the moment when the vehicle passes through the downstream gantry; Represents the moment when the gantry passes the upstream gantry; Truck mix rate : Calculate the ratio of trucks to buses 5 minutes before the accident between the gantry units of each section, and then calculate the mixing rate of trucks; ; in, It represents the truck traffic volume 5 min before the gantry unit accident; Represents the passenger vehicle traffic volume 5 minutes before the gantry unit accident occurs.

[0025] Step S302: Calculate the accident risk index based on step S301 and the historical accident information of high-speed roads collected in step S1. ARI . Accident risk indicators ARI The calculation formula is as follows: ; in, Represents the number of deaths; represents the number of injured; Representing accident property damage; Represents the number of lost vehicles; Represents the road type; Represents the weather type; , , , , , , , Represents the coefficient.

[0026] Step S4: Use the accident risk index ARI calculated in step S3 as the buffer radius, and use QGIS software to establish a buffer zone for the center points of all divided sections to identify the accident blackspot sections of the expressway. Taking the Chaohui Expressway in Guangdong Province as an example, establish an accident point buffer zone layer as follows: Figure 2 As shown, the green circle is the buffer zone range of the accident point.

[0027] In step S4, QGIS software is used to locate the center points of all divided road sections. Create a buffer zone It is expressed as: ; in, represents any point in space; d Represents any point in space To the center point of the segment The Euclidean distance of .

[0028] According to the established buffer zone, the accident buffer zone layer generated by all accident points is overlapped with the highway base map layer, highway network map layer, and gantry unit layer to form a comprehensive accident analysis layer. The overlay layer is established as follows: Figure 2 and Figure 3 As shown in the figure, the light yellow line is the expressway, the red dots are the accident points of the expressway (this embodiment only marks the accidents on Chaohui Expressway), the gray dots are the gantry points of the expressway, and the green circles are the buffer areas of the accident points. The road section where the accident black spots exist can be determined based on the degree of overlap of the buffer areas. Figure 4 , Figure 5 and Figure 6 As shown, the red lines correspond to section 1, section 2, and section 3 respectively, and the corresponding accident black spot section 1, accident black spot section 2, and accident black spot section 3 can be identified respectively.

[0029] Therefore, the present invention adopts the above-mentioned method for identifying accident blackspot sections of highways based on ETC data, aiming to further clarify road accident blackspots and high-risk accident sections, identify accident blackspot sections, and provide a scientific basis for highway traffic safety construction planning and policies.

[0030] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0031] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for identifying highway accident black spot sections based on ETC data, characterized in that: The following steps are involved: Step S1, extracting various indicators in traffic flow, terrain, weather and accident data based on highway ETC toll data, highway historical accident information, accident section road alignment information and meteorological station observation data; Highway ETC toll collection data, including truck traffic volume, passenger car traffic volume, vehicle speed, and gantry unit length; Historical highway accident information, including accident location, type, severity, timestamp, number of deaths, number of injuries, amount of property damage, and number of damaged vehicles; The road alignment information of the accident section was obtained through the Shuijing Micro-Map software; The meteorological station observation data is the weather type information obtained from the meteorological station during the period when the accident occurred; Step S2: Based on QGIS software, the WGS 84 coordinate system of the road network, gantry and base map is converted into the GCJ02 Mars coordinate system, and according to the highway historical accident information in step S1, it is located at the specific position of the highway network based on the Mars coordinate system in QGIS software; Step S3: Calculate accident risk index ARI ; Step S4: The accident risk index calculated in step S3 ARI As the buffer radius, QGIS software is used to establish a buffer zone for the center points of all divided sections to identify the accident blackspot sections of the highway.

2. The method for identifying highway accident black spot sections based on ETC data according to claim 1 is characterized in that: Step S3 is specifically as follows: Step S301: Calculate the gantry unit vehicle speed variance based on the highway ETC data extracted in step S1 Mixing rate with trucks ; Step S302: Calculate the accident risk index based on step S301 and the historical accident information of high-speed roads collected in step S1. ARI.

3. The method for identifying highway accident black spot sections based on ETC data according to claim 2 is characterized in that: Step S301: Gantry unit vehicle speed variance The calculation method is: Calculate the average speed of each vehicle 5 minutes before the accident between the gantry units of each road section, and then calculate the variance of the speed based on the average speed; ； ； ； in, Representative Car; represents the average speed of the vehicle; Represents the number of vehicles; Represents the length between the gantry units of the road section; Represents the moment when the vehicle passes through the downstream gantry; Represents the moment when the gantry passes the upstream gantry.

4. The method for identifying highway accident black spot sections based on ETC data according to claim 2 is characterized in that: Truck mixing rate in step S301 The calculation method is: Calculate the ratio of trucks to buses 5 minutes before the accident between the gantry units of each road section, and then calculate the mixing rate of trucks; ； in, It represents the truck traffic volume 5 min before the gantry unit accident; Represents the passenger vehicle traffic volume 5 minutes before the gantry unit accident occurs.

5. The method for identifying highway accident black spot sections based on ETC data according to claim 3 is characterized in that: Step S302: Calculate the accident risk index ARI The calculation formula is as follows: ； in, Represents the number of deaths; represents the number of injured; Representing accident property damage; Represents the number of lost vehicles; Represents the road type; Represents the weather type; , , , , , , , Represents the coefficient.

6. The method for identifying highway accident blackspot sections based on ETC data according to claim 1 is characterized in that: In step S4, QGIS software is used to locate the center points of all divided road sections. Create a buffer zone It is expressed as: ； in, represents any point in space; d Represents any point in space To the center point of the segment The Euclidean distance of .

7. The method for identifying highway accident black spot sections based on ETC data according to claim 6 is characterized in that: According to the established buffer zone, the accident buffer zone layers generated by all accident points are overlapped with the highway base map layer, highway network map layer and gantry unit layer, and the road section areas where accident black spots exist are determined based on the degree of overlap of the buffer zones.

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