Road traffic safety risk digital portrait and risk assessment method and device

Abstract

The invention discloses a road traffic safety risk digital portrait and risk assessment method and device, and relates to the technical field of traffic safety risk assessment, and the method comprises the steps: obtaining the multi-dimensional information of a target road in different scenes; the multi-dimensional information comprises road geometric condition dimension information, road area environment dimension information, road safety facility dimension information, road traffic operation dimension information, road traffic safety awareness dimension information and road traffic sudden change emergency dimension information; performing digital filing on each scene based on the multi-dimensional information of the target road in different scenes; determining target feature indexes of the target road in different scenes based on the multi-dimensional information of the target road in different scenes; and respectively inputting the target characteristic indexes of the target road in different scenes into the risk assessment models in the corresponding scenes to obtain traffic safety risk levels and hidden danger sequences of the target road in different scenes. And comprehensive assessment of the road traffic safety risk is realized.

Description

Technical Field

[0001] The present application relates to the technical field of traffic safety risk assessment, and in particular to a road traffic safety risk digital portrait and risk assessment method and device. Background Art

[0002] Methods for road traffic safety risk analysis include: road traffic accident historical data analysis, road environment analysis, traffic flow analysis, and traffic conflict analysis. Among them, the road traffic accident historical data analysis method can conduct an in-depth investigation of the location where the road safety accident occurred, and then analyze the causes and hidden dangers to improve safety. However, the occurrence of accidents is random and accidental. Therefore, relying solely on historical accident data to analyze road safety hazards and prediction methods has factual bias and cannot be fully applied to the actual situation of the continuous rapid growth and constant changes of roads, vehicles and drivers. The road environment analysis method conducts quantitative evaluation from the perspective of road design and traffic safety facilities. It is based on standards and operating specifications such as GB5768-2022 and JTGD81-2023. It is relatively easy to implement in actual work, but traffic safety is a complex system engineering integrating people, vehicles, roads, and environment, and road conditions are only one dimension. Traffic flow analysis method evaluates road traffic safety from the perspective of traffic flow distribution and changes. It is direct and combines the analysis based on the correlation between accident data and traffic flow characteristic data at the accident point. However, traffic safety is a complex system engineering integrating people, vehicles, roads and environment, and traffic flow data is only one dimension. Traffic conflict analysis method uses traffic conflict as an alternative indicator to analyze road traffic safety hazards and risk descriptions. It can directly quantify the risk level and analyze the distribution of conflict points and conflict types for traffic safety improvement directions and evaluation of improvement effects. Compared with rare traffic accident data, traffic conflicts themselves have a high incidence rate, so data is easier to obtain accurately. However, traffic conflicts are related to accidents but cannot be simply replaced.

[0003] The above methods only consider the problem from the perspective of road environment and analyze from the perspective of traffic flow, and the difficulty in collecting professional data is relatively large, which makes the analysis results of road traffic safety risks one-sided and limited. Summary of the invention

[0004] The purpose of this application is to provide a road traffic safety risk digital portrait and risk assessment method and device, which uses easy-to-collect traffic conflict and traffic six-dimensional portrait data to solve the problem that the analysis results of road traffic safety risks are one-sided and limited, and cannot conduct a comprehensive road traffic safety risk assessment.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In the first aspect, the present application provides a road traffic safety risk digital portrait and risk assessment method, including:

[0007] Acquire multi-dimensional information of the target road in different scenarios; the multi-dimensional information includes: road geometry information, road environment information, road safety facilities information, road traffic operation information, road traffic safety awareness information, and road traffic sudden change emergency information;

[0008] Digitally archive each scene based on the multi-dimensional information of the target road in different scenes;

[0009] Based on the multi-dimensional information of the target road in different scenarios, determine the target characteristic indicators of the target road in different scenarios;

[0010] The target characteristic indicators of the target road in different scenarios are respectively input into the risk assessment model under the corresponding scenario to obtain the traffic safety risk level of the target road in different scenarios; the risk assessment model of any current scenario is obtained by training the XGBOOST model using the training set of the current scenario; the training set includes: multiple groups of historical values ​​of target characteristic indicators and corresponding historical values ​​of traffic safety risk levels.

[0011] Optionally, the process of determining the risk assessment model in the current scenario includes:

[0012] Obtain multiple sets of historical traffic conflict data, historical traffic accident data and historical multi-dimensional information in the current scenario;

[0013] Based on each historical traffic conflict data and historical traffic accident data, determine the historical value of the corresponding traffic safety risk level;

[0014] Using the minimum absolute shrinkage and selection regression algorithm to screen each group of historical multi-dimensional information in the current scenario, the historical values ​​of multiple groups of target characteristic indicators in the current scenario are obtained; the number of characteristic indicators in the target characteristic indicators does not exceed the number of data in the historical multi-dimensional information;

[0015] Build the XGBOOST model;

[0016] Taking each group of target characteristic indicators in the current scenario as input and the corresponding historical values ​​of traffic safety risk levels as output, the XGBOOST model is trained to obtain the risk assessment model in the current scenario.

[0017] Optionally, the road geometric condition dimensional information includes: road flat curve radius, flat curve superelevation, main line sight distance limited, number of one-way lanes on the main road, intersection type, intersection angle, number of access points, intersection sight distance, main road lane width, main road median type, main road vertical curve, main road longitudinal slope, branch road slope, main road non-motorized lane width, lane width where the branch road joins the main road, intersection area right turn radius, intersection opening spacing, motor vehicle and non-motorized vehicle separation type, intersection area left turn radius, intersection access channel density, main road type, branch road type and pedestrian crossing.

[0018] Optionally, the road environment dimension information includes: the type of road section where the road intersection is located, environmental lighting, proximity to water and cliffs, road interference, illegal signs, real-time weather, road surface coverage, road surface defects and distance to roadside hazards.

[0019] Optionally, the road safety facility dimensional information includes: the setting status of main road traffic signs at road intersections, the setting status of main road traffic markings, median vibration strips, shoulder vibration strips, intersection channelization, pedestrian guardrails, signal control, night sign conditions, night marking conditions, the end of the median isolation strip at intersections, main road speed management measures, branch road speed management measures, branch road right-of-way control facilities, branch road traffic sign setting status and branch road traffic marking setting status.

[0020] Optionally, the road traffic operation dimension information includes: the operating speed difference between different types of road vehicles, branch road peak hour traffic flow, main road speed limit, the difference between the main road and the speed limit, the proportion of large vehicles, the ratio of decelerated vehicles at main road intersections, main road peak hour traffic flow, main road peak hour occupancy, traffic accidents, traffic conflicts, the ratio of decelerated vehicles at branch road intersections, the proportion of three-wheeled vehicles, the proportion of two-wheeled vehicles, branch road peak hour occupancy, main road traffic flow in the past hour, branch road traffic flow in the past hour, the highest main road occupancy in the past hour, the highest branch road occupancy in the past hour and the number of conflicts in the past hour.

[0021] Optionally, the road traffic safety awareness dimension information includes: traffic violations on main roads, traffic violations on branch roads, large vehicle overspeed rate, non-motor vehicle pedestrian violation rate, serious traffic conflict distribution, traffic incident detection, proportion of not wearing helmets, proportion of illegal passenger carrying and proportion of illegal detention.

[0022] Optionally, the road traffic sudden change emergency dimension information includes: a sudden increase in traffic flow year-on-year, a surge in the proportion of large vehicles year-on-year, road construction and road accidents, sudden severe weather, emergency measures, a sudden increase in main road speeds, a sudden increase in traffic violation rates, road occupation business operations, changes in road coverage, emergency warning signs, sudden obstacles, main road congestion and illegal parking.

[0023] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the digital portrait and risk assessment method for road traffic safety risks described in any one of the above items.

[0024] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0025] This application discloses a road traffic safety risk digital portrait and risk assessment method and device, based on the vulnerability analysis and sudden factors of safety theory, combined with relevant standards and specifications of road traffic engineering, to achieve a comprehensive index evaluation method of historical accident data, actual conflict data, dynamic and static index factors of traffic engineering, avoiding the one-sidedness of considering the problem from the perspective of road environment alone and the limitation of analyzing from the perspective of traffic flow alone. The XGBOOST model is used to evaluate the traffic safety risk level, which has better generalization ability and higher prediction accuracy than the traditional fixed weight cumulative summation method, and while evaluating the risk, it can quantify and rank the hidden danger factors that cause the risk according to the contribution according to the model, providing a solution direction for subsequent traffic safety improvement measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] 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 will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 A schematic diagram of a road traffic safety risk digital profiling and risk assessment method flow chart provided in one embodiment of the present application;

[0028] Figure 2 A schematic diagram of the training and application flow chart of the risk assessment model;

[0029] Figure 3 This is a schematic diagram of the road traffic safety risk indicator system;

[0030] Figure 4 This is a schematic diagram of the feature importance ranking results;

[0031] Figure 5 This is a schematic diagram of the importance results of characteristic hidden dangers;

[0032] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0034] The purpose of this application is to provide a road traffic safety risk digital portrait and risk assessment method and device, aiming to achieve a comprehensive assessment of road traffic safety risks.

[0035] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0036] In an exemplary embodiment, Figure 1-Figure 3 As shown, the road traffic safety risk digital portrait and risk assessment method in this embodiment includes:

[0037] Step 1: Obtain multi-dimensional information of the target road in different scenarios.

[0038] Among them, multi-dimensional information includes: road geometric condition dimensional information, road environment dimensional information, road safety facility dimensional information, road traffic operation dimensional information, road traffic safety awareness dimensional information and road traffic sudden change emergency dimensional information.

[0039] Specifically, the scenarios include: day, night and extremely severe weather.

[0040] The multi-dimensional information includes 89 characteristic indicators in total, and these 89 characteristic indicators constitute the evaluation index system.

[0041] As an optional implementation, the road geometry dimensional information includes: road flat curve radius, flat curve superelevation, main line sight distance limited, number of one-way lanes on the main road, intersection type, intersection angle, number of access points, intersection sight distance, main road lane width, main road center strip type, main road vertical curve, main road longitudinal slope, branch road slope, main road non-motorized lane width, lane width where the branch road joins the main road, intersection area right turn radius, intersection opening spacing, motor vehicle and non-motorized vehicle separation type, intersection area left turn radius, intersection access channel density, main road type, branch road type and pedestrian crossing.

[0042] As an optional implementation, the road environment dimension information includes: the type of road section where the road intersection is located, ambient lighting, proximity to water and cliffs, road interference, illegal signs, real-time weather, road surface coverage, road surface defects and the distance to roadside hazards.

[0043] As an optional implementation method, the dimensional information of road safety facilities includes: the status of the main road traffic signs at road intersections, the status of the main road traffic markings, the middle vibration strip, the shoulder vibration strip, intersection channelization, pedestrian guardrails, signal control, night sign conditions, night marking conditions, the end of the middle isolation strip at the intersection, main road speed management measures, branch road speed management measures, branch road right-of-way control facilities, branch road traffic sign settings and branch road traffic marking settings.

[0044] As an optional implementation method, the road traffic operation dimension information includes: the operating speed difference between different types of road vehicles, branch road peak hour traffic flow, main road speed limit, the difference between the main road and the speed limit, the proportion of large vehicles, the ratio of slowed vehicles at main road intersections, main road peak hour traffic flow, main road peak hour occupancy, traffic accidents, traffic conflicts, the ratio of slowed vehicles at branch road intersections, the proportion of three-wheeled vehicles, the proportion of two-wheeled vehicles, branch road peak hour occupancy, main road traffic flow in the past hour, branch road traffic flow in the past hour, the highest main road occupancy in the past hour, the highest branch road occupancy in the past hour and the number of conflicts in the past hour.

[0045] As an optional implementation method, the road traffic safety awareness dimension information includes: traffic violations on main roads, traffic violations on branch roads, large vehicle overspeed rate, non-motor vehicle pedestrian violation rate, serious traffic conflict distribution, traffic incident detection, proportion of not wearing helmets, proportion of illegal passenger carrying and proportion of illegal detention.

[0046] As an optional implementation method, the emergency dimension information of sudden changes in road traffic includes: a sudden increase in traffic flow year-on-year, a surge in the proportion of large vehicles year-on-year, road construction and road accidents, sudden severe weather, emergency measures, a sudden increase in main road speeds, a sudden increase in traffic violation rates, road occupation business operations, changes in road coverage, emergency warning signs, sudden obstacles, main road congestion and illegal parking.

[0047] Step 2: Based on the multi-dimensional information of the target road in different scenarios, digitally archive each scene.

[0048] Step 3: Based on the multi-dimensional information of the target road in different scenarios, determine the target characteristic indicators of the target road in different scenarios.

[0049] Step 4: Input the target characteristic indicators of the target road in different scenarios into the risk assessment model under the corresponding scenarios to obtain the traffic safety risk level of the target road in different scenarios.

[0050] Among them, the risk assessment model of any current scene is obtained by training the XGBOOST model using the training set of the current scene; the training set includes: historical values ​​of multiple groups of target feature indicators and historical values ​​of corresponding traffic safety risk levels.

[0051] Specifically, traffic safety risk levels are divided into levels one to four, represented by red, orange, yellow and green respectively.

[0052] As an optional implementation, the process of determining the risk assessment model in the current scenario includes:

[0053] Step 41: Obtain multiple sets of historical traffic conflict data, historical traffic accident data, and historical multi-dimensional information in the current scenario.

[0054] Step 42: Based on each historical traffic conflict data and historical traffic accident data, determine the historical value of the corresponding traffic safety risk level.

[0055] Specifically, historical traffic conflict data is usually used to directly classify the corresponding risk levels. The corresponding relationship between traffic conflict data and traffic safety risk levels is shown in Table 1.

[0056] Table 1 Correspondence between traffic conflict data and traffic safety risk levels

[0057]

[0058] Among them, the severity of the conflict is divided into categories, for example, the severe conflict threshold is 0.8 seconds, the violent conflict threshold is 1.5 seconds, the general conflict threshold is 2.5 seconds, and the minor conflict threshold is 3.5 seconds.

[0059] Standard conflict = the sum of the converted values ​​of all types of conflicts within the statistical period. According to the statistical standards of the traffic management department, the risk level of road intersections and sections is divided using traffic accident statistics within 3 years (samples with unchanged road geometry and dimension information are selected). According to Heinrich's law, there is a direct correlation between standard traffic conflict x and traffic conflict Y, so the linear function is constructed as follows:

[0060] Y = ax + b, which can be easily solved to obtain a method for road risk division using conflicts. a and b are coefficients.

[0061] Step 43: Use the least absolute shrinkage and selection regression algorithm (LASSO) to screen each group of historical multi-dimensional information in the current scenario to obtain the historical values ​​of multiple groups of target feature indicators in the current scenario; the number of feature indicators in the target feature indicators does not exceed the number of data in the historical multi-dimensional information.

[0062] Step 44: Build the XGBOOST model.

[0063] Step 45: Taking each group of target characteristic indicators in the current scenario as input and the corresponding historical values ​​of traffic safety risk levels as output, the XGBOOST model is trained to obtain a risk assessment model for the current scenario.

[0064] Furthermore, after the training of the boosted tree (XGBOOST) model is completed, the structural characteristics of the XGBOOST model are used to calculate the impact of each characteristic indicator on the output of the XGBOOST model when it changes, and then the impact of all characteristic indicators is averaged to calculate the SHAP value of each characteristic indicator to represent the contribution of each characteristic indicator to the prediction of the XGBOOST model. This is used as a quantitative basis for the interpretation of risk hazards and the management of hidden dangers.

[0065] After improving and enhancing traffic safety hazards, the effectiveness of safety improvement measures is evaluated using the risk assessment results of XGBOOST for severe traffic conflicts and corresponding scenarios every hour.

[0066] After improving and enhancing traffic safety hazards, the risk assessment results of XGBOOST for severe traffic conflicts and corresponding scenarios every hour are used to evaluate the effectiveness of safety improvement measures and conduct further hazard analysis.

[0067] Specifically, the characteristic quantization assignment of each characteristic indicator in this application is shown in Table 2.

[0068] Table 2 Feature quantization assignment table

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076]

[0077] According to different scene divisions, the corresponding scene daytime, nighttime, attributes (curves, non-signalized intersections, steep slopes, rainy days, snowy days, foggy days) and their combinations are calculated to form a model set corresponding to the segmented scenes. For each segmented scene, LASSO is used to realize automatic selection of feature indicators, and the feature selection results of the segmented scenes are stored accordingly to complete feature dimensionality reduction.

[0078] After LASSO feature screening for a certain segmentation scenario, the importance of each feature is ranked, and the importance ranking results of some features are as follows: Figure 4 And as shown in Table 3.

[0079] Table 3 Feature importance ranking table

[0080]

[0081]

[0082] The actual values ​​of the target characteristic indicators of the corresponding scenes are input into the XGBOOST model to obtain the corresponding hidden danger ranking when the traffic safety risk level is medium risk. Figure 5 The hidden dangers are ranked as shown in Table 4.

[0083] Table 4 Hidden danger ranking table

[0084]

[0085] In an exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a digital portrait of road traffic safety risks and a risk assessment method.

[0086] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, a digital portrait of road traffic safety risks and a risk assessment method are implemented.

[0087] In an exemplary embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements a road traffic safety risk digital portrait and risk assessment method.

[0088] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a digital portrait and risk assessment method for road traffic safety risks is realized.

[0089] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0090] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0091] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0092] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0093] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0094] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A road traffic safety risk digital portrait and risk assessment method, characterized in that: The road traffic safety risk digital portrait and risk assessment method include: Acquire multi-dimensional information of the target road in different scenarios; the multi-dimensional information includes: road geometry information, road environment information, road safety facilities information, road traffic operation information, road traffic safety awareness information, and road traffic sudden change emergency information; Digitally archive each scene based on the multi-dimensional information of the target road in different scenes; Based on the multi-dimensional information of the target road in different scenarios, determine the target characteristic indicators of the target road in different scenarios; The target characteristic indicators of the target road in different scenarios are respectively input into the risk assessment model under the corresponding scenario to obtain the traffic safety risk level of the target road in different scenarios; the risk assessment model of any current scenario is obtained by training the XGBOOST model using the training set of the current scenario; the training set includes: multiple groups of historical values ​​of target characteristic indicators and corresponding historical values ​​of traffic safety risk levels.

2. The road traffic safety risk digital portrait and risk assessment method according to claim 1 is characterized in that: The process of determining the risk assessment model in the current scenario includes: Obtain multiple sets of historical traffic conflict data, historical traffic accident data and historical multi-dimensional information in the current scenario; Based on each historical traffic conflict data and historical traffic accident data, determine the historical value of the corresponding traffic safety risk level; Using the minimum absolute shrinkage and selection regression algorithm to screen each group of historical multi-dimensional information in the current scenario, the historical values ​​of multiple groups of target characteristic indicators in the current scenario are obtained; the number of characteristic indicators in the target characteristic indicators does not exceed the number of data in the historical multi-dimensional information; Build the XGBOOST model; Taking each group of target characteristic indicators in the current scenario as input and the corresponding historical values ​​of traffic safety risk levels as output, the XGBOOST model is trained to obtain the risk assessment model in the current scenario.

3. The road traffic safety risk digital portrait and risk assessment method according to claim 1 is characterized in that: The road geometric condition dimensional information includes: road flat curve radius, flat curve superelevation, main line sight distance limited, number of one-way lanes on the main road, intersection type, intersection angle, number of access points, intersection sight distance, main road lane width, main road median type, main road vertical curve, main road longitudinal slope, branch road slope, main road non-motorized lane width, lane width where the branch road joins the main road, intersection area right turn radius, intersection opening spacing, motor vehicle and non-motorized vehicle separation type, intersection area left turn radius, intersection access channel density, main road type, branch road type and pedestrian crossing.

4. The road traffic safety risk digital portrait and risk assessment method according to claim 1 is characterized in that: The road environment dimension information includes: the type of road section where the road intersection is located, environmental lighting, proximity to water and cliffs, road interference, illegal signs, real-time weather, road surface coverage, road surface defects and distance to roadside hazards.

5. The road traffic safety risk digital portrait and risk assessment method according to claim 1 is characterized in that: The road safety facility dimensional information includes: the status of the main road traffic signs at the road intersection, the status of the main road traffic markings, the middle vibration strip, the shoulder vibration strip, the intersection channelization, the pedestrian guardrail, the signal control, the night sign status, the night marking status, the end of the middle isolation strip at the intersection, the main road speed management measures, the branch road speed management measures, the branch road right-of-way control facilities, the branch road traffic signs and the branch road traffic markings.

6. The road traffic safety risk digital portrait and risk assessment method according to claim 1 is characterized in that: The road traffic operation dimension information Including: the difference in operating speed between different types of vehicles on the road, branch road peak hour traffic flow, main road speed limit, the difference between the main road and the speed limit, the proportion of large vehicles, the ratio of slowed vehicles at main road intersections, main road peak hour traffic flow, main road peak hour occupancy, traffic accidents, traffic conflicts, the ratio of slowed vehicles at branch road intersections, the proportion of three-wheeled vehicles, the proportion of two-wheeled vehicles, branch road peak hour occupancy, main road traffic flow in the past hour, branch road traffic flow in the past hour, the highest main road occupancy in the past hour, the highest branch road occupancy in the past hour and the number of conflicts in the past hour.

7. The road traffic safety risk digital portrait and risk assessment method according to claim 1 is characterized in that: The road traffic safety awareness dimension information includes: traffic violations on main roads, traffic violations on branch roads, large vehicle overspeed rate, non-motor vehicle pedestrian violation rate, serious traffic conflict distribution, traffic incident detection, proportion of not wearing helmets, proportion of illegal passenger carrying and proportion of illegal detention.

8. The road traffic safety risk digital portrait and risk assessment method according to claim 1 is characterized in that: The road traffic sudden change emergency dimension information includes: a sudden increase in traffic flow year-on-year, a surge in the proportion of large vehicles year-on-year, road construction and road accidents, sudden severe weather, emergency measures, a sudden increase in main road speeds, a sudden increase in traffic violation rates, road occupation business operations, changes in road coverage, emergency warning signs, sudden obstacles, main road congestion and illegal parking.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the road traffic safety risk digital portrait and risk assessment method as described in any one of claims 1-8.

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