A method for judging and visualizing traffic accident risk of urban road network

Abstract

The application discloses a kind of urban road network traffic accident risk research and visual method, comprising: obtaining road network accident data, pedestrian data, vehicle data and environmental data;The data obtained are preprocessed;Obtain the connection relationship of city road network, establish road network adjacency matrix, obtain the spatial correlation of each accident;Establish the random parameter space joint prediction model of road network accident severity considering road network spatial correlation and data heterogeneity;The coefficients corresponding to each variable in the prediction model are analyzed, and the risk factors affecting the severity of the accident are identified;According to the spatial residual term of each road entity, the relative risk value is calculated, and the relative risk of road network heavy accident is identified;According to the relative risk value of each road entity, a road network risk map is drawn, and the relative risk profile visualization is realized.The application can identify the influencing factors of traffic accident severity from the micro level, explore the potential relationship between risk factors and accident severity, and identify accident black spots.

Description

Technical Field

[0001] This invention relates to the technical field of traffic safety, and in particular to a method for assessing and visualizing traffic accident risks in urban road networks. Background Technology

[0002] With the increase in car ownership, traffic safety has become a focus of public concern. Urban roads, due to their complex traffic composition, road network alignment, and dense surrounding buildings, are challenging areas for traffic safety management. Analyzing the risk factors affecting the severity of traffic accidents and identifying accident hotspots in the road network allows relevant departments to propose targeted improvement measures for roads with high potential accident risks, thereby enhancing road safety and reducing casualties and property damage.

[0003] Current risk factor analyses primarily focus on individual road entities, while network-level risk factor analyses are mostly concentrated on macroscopic factors, neglecting the spatial correlation between adjacent roads. However, traffic safety is a microscopic issue, and the above methods ignore the impact of microscopic factors on the severity of traffic accidents. Furthermore, existing traffic accident risk hotspot identification methods are mainly based on accident frequency, ignoring the severity of accidents. Some accidents causing serious injury or death to pedestrians may go unnoticed, affecting the results of accident black spot identification. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of current methods for identifying road network accident risk factors, which rarely consider multiple road entities and micro-level factors, and for identifying accident black spots, which rarely consider the severity of accidents. This invention proposes a method for assessing and visualizing traffic accident risks in urban road networks. Taking the road network as the research object, this method can simultaneously identify risk factors that affect the severity of accidents occurring at intersections and road sections, and identify accident black spots. This method is more efficient than previous methods that take a single road entity as the research object.

[0005] To achieve the above objectives, the technical solution provided by this invention is: a method for assessing and visualizing traffic accident risks in urban road networks, comprising the following steps:

[0006] 1) Acquire data, including: road network accident data, pedestrian data, vehicle data, and environmental data;

[0007] 2) Preprocess the acquired data, including: classifying the acquired data according to accident type, filling missing values, deleting outliers, performing dummy variable processing for categorical variables and multicollinearity test, and forming an accident severity analysis dataset;

[0008] 3) Obtain the urban road network connection relationship, construct the road network adjacency matrix, and match each accident in the accident severity analysis dataset to the nearest road entity corresponding to the accident type according to spatial location, so as to obtain the spatial correlation of each accident;

[0009] 4) Based on the spatial correlation and severity analysis dataset of each accident, a joint spatial prediction model of random parameters for road network accident severity is established. The model performance is evaluated based on two indicators: good fit and prediction accuracy. The joint spatial prediction model of random parameters for road network accident severity introduces a spatial residual term into the binary logistic model framework and transforms the parameters into random parameters to improve the model's adaptability to spatial accident data and its ability to handle data heterogeneity. For any given observed accident, the probability of pedestrian serious injury or death can be obtained.

[0010] 5) Analyze the coefficients of each independent variable in the joint prediction model of random parameters for the severity of road network accidents, and identify risk factors that have a significant impact on the severity of accidents using Bayesian confidence intervals as the criterion.

[0011] 6) Based on the spatial joint prediction model of random parameters of road network accident severity, extract the spatial residual terms of each intersection and road segment, calculate the relative risk value of each road entity, i.e. RR value, and determine the relative risk of pedestrian serious injury or death accidents at each location in the road network.

[0012] 7) Based on the RR values ​​of each intersection and road segment, draw a spatial risk map of accident severity to visualize the relative risk profile.

[0013] Furthermore, in step 1), the road network accident data includes: accident severity, accident type, and accident location and time; the pedestrian data includes: pedestrian age, gender, head injury status, pedestrian location, special circumstances of the pedestrian, and pedestrian risk factors; the vehicle data includes: driver age, gender, driver behavior, driver risk factors, vehicle type, vehicle age, and point of first collision; and the environmental data includes: traffic congestion, weather conditions, number of points of interest, land use entropy, intersection type, intersection control type, road type, number of lanes, and whether a central divider exists.

[0014] Furthermore, step 2) includes the following steps:

[0015] 2.1) The acquired data is classified according to accident type. Accidents are divided into intersection accidents and road segment accidents according to accident type. Factors that may affect the severity of intersection accidents are selected from the data to establish an initial intersection accident severity analysis dataset. Then, factors that may affect the severity of road segment accidents are selected to establish an initial road segment accident severity analysis dataset.

[0016] 2.2) Preprocess the initial intersection accident severity analysis dataset and the initial road segment accident severity analysis dataset, including filling missing values, deleting outliers, and dummy variable processing for categorical feature variables, to form the intersection accident severity analysis dataset D1 and the road segment accident severity analysis dataset D2.

[0017] 2.3) Perform a multicollinearity test on the processed intersection accident severity analysis dataset D1 and road segment accident severity analysis dataset D2. If the variance inflation factor is greater than 10, it is considered that there is serious multicollinearity among the variables. The calculation is shown in formula (1):

[0018]

[0019] In the formula, P is the number of variables, and R is... i Let X be the i-th independent variable. i The negative correlation coefficients were obtained from regression analysis of the remaining independent variables; the variance inflation factor (VIF) of each variable could also be obtained. i Based on the calculation results, independent variables with a variance inflation factor greater than 10 were removed.

[0020] 2.4) Combine D1 and D2 after multicollinearity test into accident severity analysis dataset D.

[0021] Furthermore, in step 3), the spatial connection relationships between various road entities are constructed using ArcGIS software to obtain the road network adjacency matrix w, and the rules are established as follows:

[0022] If road entities m and n are directly connected, then these two road entities are considered to have an adjacency relationship, and the corresponding spatial weight w is determined. mn =1, otherwise w mn = 0, forming a matrix with the number of road segments as the number of rows and the number of intersections as the number of columns, i.e., the road network adjacency matrix w, where w mn It also represents the adjacency weight value between road entities m and n; according to spatial location, each accident in the accident severity analysis dataset D is matched to the nearest road entity corresponding to the accident type.

[0023] Furthermore, in step 4), a joint prediction model for the stochastic parameter space of road network accident severity is established. For any given observed accident j, its accident severity Y is... j The injuries are categorized into two types: minor injury and serious injury or death, denoted as 0 and 1 respectively. Within a binary logistic model framework, a spatial residual term and a random parameter are introduced. Then, within any road entity m, the probability p of accident j being a serious injury or death accident is... j The following relationship exists:

[0024]

[0025]

[0026]

[0027] In the formula, φ m φ is the spatial residual term of road entity m. n Let n be the spatial residual term for road entity n, where n ≠ m, indicating that m and n are two distinct road entities. Here, ρ is the variance parameter of the spatial term, ρ is the weight parameter reflecting the correlation strength, I represents the intersection, S represents the road segment, and r is the variance parameter of the spatial term. j r is a 0-1 variable representing the type of accident. If accident j occurs at an intersection, then r j =1, otherwise r j =0, x jk Let be the k-th independent variable of accident j, α be a constant term, and β be the independent variable of accident j. k The coefficient corresponding to the k-th independent variable is... For β k The mean, μ jk The terms are random and have a mean of 0 and a standard deviation of σ. k The normal distribution, θ j The random error term follows a normal distribution with a mean of 0. For the constructed joint prediction model of random parameters for road network accident severity that considers road network spatial correlation and data heterogeneity, the performance of the model is measured by the bias information criterion (DIC), recall, specificity, and overall classification accuracy. The calculation methods are as follows:

[0028]

[0029]

[0030]

[0031]

[0032] In the formula, DIC, Recall, Specificity, and Total represent the bias information criterion, recall, specificity, and overall classification accuracy, respectively. and p D TP represents the model's fitting accuracy and the number of effective variables, respectively. TP represents the number of serious accidents that are actually serious, and the predicted result is also serious. FN represents the number of minor accidents that are actually serious, and the predicted result is also minor. TN represents the number of minor accidents that are actually minor, and the predicted result is also minor. FP represents the number of serious accidents that are actually minor.

[0033] Furthermore, in step 5), the coefficients corresponding to the independent variables in the joint prediction model of the random parameter space of road network accident severity are analyzed. If the coefficient is significant within the 90% Bayes confidence interval, then the independent variable is considered to be a risk factor affecting the severity of the accident.

[0034] Furthermore, in step 6), based on the constructed joint spatial prediction model of random parameters for road network accident severity, the spatial residual terms of each intersection and road segment are extracted, and their relative risk RR is defined as the expected excess advantage ratio, to determine whether road entity m has a higher probability of serious injury or death accidents than road entities with similar characteristics. The specific formula is as follows:

[0035] RR=exp(φ m (9)

[0036] Road entities with an RR value greater than 1 are considered to have potential risks and require further improvement.

[0037] Furthermore, in step 7), the RR values ​​of each road entity in the road network are matched using ArcGIS software to draw a spatial risk map of accident severity, thereby visualizing the relative risk profile.

[0038] Compared with the prior art, the beneficial effects of this invention are as follows:

[0039] 1. For dense urban road networks, the model takes into account the spatial correlation between road entities within the network and the heterogeneity of data, thus improving the flexibility and adaptability of the model.

[0040] 2. In accident risk assessment, considering that analysis based on accident frequency may overlook certain serious accidents, the severity of the accident is used as the dependent variable, which improves the accuracy of accident black spot identification.

[0041] 3. Establish a joint prediction model of random parameters for the severity of road network accidents. This model can simultaneously analyze the risk factors affecting the severity of accidents on road sections and at intersections, and is more efficient than models that focus on a single road entity.

[0042] 4. By using historical accident data, potential risk factors within the study area can be identified, providing a reference for traffic accident prevention and control.

[0043] 5. It proposes relative risk indicators, which can identify potential accident hotspots, generate regional relative risk maps, and realize the visualization of accident risks. Attached Figure Description

[0044] Figure 1 This is a flowchart of the method of the present invention.

[0045] Figure 2 A road network accident risk map for specific implementation cases. Detailed Implementation

[0046] The present invention will be further described below with reference to the embodiments and accompanying drawings, but the implementation of the present invention is not limited thereto.

[0047] like Figure 1 As shown in the figure, this embodiment discloses a method for assessing and visualizing traffic accident risks in urban road networks, including the following steps:

[0048] Step 1: Historical Accident Data Collection for the Road Network. Five years of pedestrian accident data were collected for a specific road network, which includes 204 intersections and 366 road segments. The data records accident information, pedestrian information, vehicle information, and environmental information. Accident information includes accident severity, accident type, accident location, and time. Pedestrian information includes: pedestrian age, gender, head injury details, pedestrian location, special circumstances, and pedestrian risk factors. Vehicle information includes: driver age, gender, driver behavior, driver risk factors, vehicle type, vehicle age, and point of first collision. Environmental information includes: traffic congestion, weather conditions, number of points of interest, land use entropy, intersection type, intersection control type, road type, number of lanes, and presence of a median strip.

[0049] Step 2: Data Preprocessing. The acquired data is categorized into intersection accidents and road segment accidents based on accident type. Factors that may influence the severity of intersection accidents are selected from the data to establish an initial intersection accident severity analysis dataset. Simultaneously, an initial road segment accident severity analysis dataset is established following the same approach. Abnormal accident data is deleted, and missing data is filled in. The categorical variables shown in Table 1 are processed using dummy variables to form Table 2, thus establishing the intersection accident severity analysis dataset D1 and the road segment accident severity analysis dataset D2.

[0050] Table 1 Vehicle Type Classification

[0051] Accident Number Vehicle type 1 Car 2 Car 3 bus 4 taxi 5 Car 6 the bus 7 truck 8 Car 9 bus 10 Car

[0052] Table 2. Dumb Variable Transformation Operations

[0053] Accident Number truck bus taxi the bus 1 0 0 0 0 2 0 0 0 0 3 0 1 0 0 4 0 0 1 0 5 0 0 0 0 6 0 0 0 1 7 1 0 0 0 8 0 0 0 0 9 0 1 0 0 10 0 0 0 0

[0054] When performing dummy variable transformation, you need to select one as the reference item. In the example above, select cars as the reference item and create dummy variables for the four categories of trucks, buses, taxis, and public buses respectively.

[0055] Multicollinearity tests were performed on the processed intersection accident severity analysis dataset D1 and road segment accident severity analysis dataset D2. If the variance inflation factor (VIF) is greater than 10, it is considered that there is serious multicollinearity among the variables. The calculation is shown in formula (1):

[0056]

[0057] In the formula, P is the number of variables, and R is... i Let X be the i-th independent variable. i The negative correlation coefficients were obtained from regression analysis of the remaining independent variables. The variance inflation factor (VIF) of each variable was also obtained. i Based on the calculation results, independent variables with a variance inflation factor greater than 10 were removed.

[0058] In this embodiment, R language was used to perform a multicollinearity test on the data processed in step 1. The results showed that in dataset D1, the VIF values ​​of the variables "vehicle type" and "driver hazard factors" were greater than 10; in dataset D2, the VIF value of the variable "driver hazard factors" was greater than 10. These variables were removed, and D1 and D2 were merged into a single dataset D for accident severity analysis, as shown in Table 3.

[0059] Table 3 Dataset for Accident Severity Analysis

[0060]

[0061] When merging D1 and D2, factors that only affect the severity of one type of accident are recorded as null values ​​at the corresponding location for the other type of accident, as shown in the Lane Count column of Table 3.

[0062] Step 3: In ArcGIS software, construct the spatial connection relationships between the 206 intersections and 366 road segments of the road network, and obtain the road network adjacency matrix w, as shown in Table 4. The rules are as follows:

[0063] If road entities m and n are directly connected, then these two road entities are considered to have an adjacency relationship, and the corresponding spatial weight w is determined. mn =1, otherwise w mn = 0, forming a matrix with the number of road segments as the number of rows and the number of intersections as the number of columns, i.e., the road network adjacency matrix w, where w mn It also represents the adjacency weight value between road entities m and n.

[0064] Each accident record in the accident severity analysis dataset D is matched to the nearest road entity corresponding to the accident type according to its spatial location.

[0065] Table 4. Road Network Adjacency Matrix

[0066] Road entity number 1 2 3 4 5 … 202 203 204 1 0 1 0 0 0 … 0 0 0 2 0 0 0 0 0 … 0 0 0 3 0 0 0 0 0 … 0 0 0 4 0 0 0 0 0 … 0 0 0 5 0 0 0 1 0 … 0 0 0 … … … … … … … … … … 364 0 0 0 0 0 … 0 0 0 365 0 0 0 0 0 … 0 1 0 366 0 0 0 0 0 … 0 0 0

[0067] Step 4: Construct a joint spatial prediction model for the stochastic parameters of road network accident severity in WinBUGS software, considering road network spatial correlation and data heterogeneity. For any given observed accident j, its accident severity Y... j The injuries are categorized into two types: minor injury and serious injury or death, denoted as 0 and 1 respectively. Within a binary logistic model framework, a spatial residual term and a random parameter are introduced. Then, within any road entity m, the probability p of accident j being a serious injury or death accident is... j The following relationship exists:

[0068]

[0069]

[0070]

[0071] In the formula, φ m φ is the spatial residual term of road entity m. n Let n be the spatial residual term for road entity n, where n ≠ m, indicating that m and n are two distinct road entities. Here, ρ is the variance parameter of the spatial term, ρ is the weight parameter reflecting the correlation strength, I represents the intersection, S represents the road segment, and r is the variance parameter of the spatial term. j r is a 0-1 variable representing the type of accident. If accident j occurs at an intersection, then r j =1, otherwise r j =0, x jk Let be the k-th independent variable of accident j, α be a constant term, and β be the independent variable of accident j. k The coefficient corresponding to the k-th independent variable is... For β k The mean, μ jk The terms are random and have a mean of 0 and a standard deviation of σ. k The normal distribution, θ j The random error term follows a normal distribution with a mean of 0. For the constructed joint prediction model of random parameters for road network accident severity that considers road network spatial correlation and data heterogeneity, the performance of the model is measured by the Deviation Information Criterion (DIC), recall, specificity, and overall classification accuracy. The calculation methods are as follows:

[0072]

[0073]

[0074]

[0075]

[0076] In the formula, DIC, Recall, Specificity, and Total represent the bias information criterion, recall, specificity, and overall classification accuracy, respectively. and p D TP represents the model's fitting accuracy and the number of effective variables, respectively. TP represents the number of serious accidents that are actually serious, and the predicted result is also serious. FN represents the number of minor accidents that are actually serious, and the predicted result is also minor. TN represents the number of minor accidents that are actually minor, and the predicted result is also minor. FP represents the number of serious accidents that are actually minor.

[0077] Step 5: Analyze the coefficients of each independent variable in the joint prediction model of random parameters for road network accident severity. If the coefficient is significant within the 90% Bayes confidence interval, then the independent variable is considered to be a risk factor affecting the severity of the accident.

[0078] Step 6: Calculate the spatial residuals of each intersection and road segment within the study area, and define the relative risk (RR) of major accidents in the road network as the expected excess advantage ratio to determine whether road entity m has a higher rate of serious accidents causing serious injuries or fatalities than other traffic entities with similar characteristics. The specific formula is as follows:

[0079] RR=exp(φ m (9)

[0080] Among them, intersection nodes with an RR value greater than 1 are considered to have potential risks and need to be improved.

[0081] Step 7: Using ArcGIS software, match the RR values ​​of each road entity in the road network to create a spatial risk map of accident severity, thus visualizing the relative risk overview. Figure 2 As shown.

[0082] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any improvements and modifications made without departing from the spirit and principle of the present invention are included within the protection scope of the present invention.

Claims

1. A method for assessing and visualizing traffic accident risks in urban road networks, characterized in that, Includes the following steps: 1) Acquire data, including: road network accident data, pedestrian data, vehicle data, and environmental data; 2) Preprocess the acquired data, including: classifying the acquired data according to accident type, filling missing values, deleting outliers, performing dummy variable processing for categorical variables and multicollinearity testing, and forming an accident severity analysis dataset; 3) Obtain the urban road network connection relationship, construct the road network adjacency matrix, and match each accident in the accident severity analysis dataset to the nearest road entity corresponding to the accident type according to spatial location, so as to obtain the spatial correlation of each accident; 4) Based on the spatial correlation and severity analysis dataset of each accident, a spatial joint prediction model of random parameters for road network accident severity is established. The model performance is evaluated based on two indicators: good fit and prediction accuracy. The spatial joint prediction model of random parameters for road network accident severity introduces a spatial residual term into the binary logistic model framework and transforms the parameters into random parameters to improve the model's adaptability to spatial accident data and its ability to handle data heterogeneity. For any given observed accident, the probability of pedestrian serious injury or death can be obtained. Establish a joint prediction model for the stochastic parameter space of road network accident severity, for any given observed accident The severity of its accident Injuries are categorized into minor injuries and serious injuries or death, denoted as 0 and 1 respectively. Within a binary logistic model framework, spatial residuals and random parameters are introduced. Then, for any road entity... accident The probability of a serious injury or death. The following relationship exists: （2）； （3）； （4）； In the formula, Represents road entities and Spatial weights between them For road entities Spatial residual term, For road entities Spatial residual term, express and For two different road entities, Let be the variance parameter of the spatial term. The weighted parameters that reflect the strength of the correlation. Represents an intersection. Representative road sections, A 0-1 variable representing the type of accident; if the accident... If it occurs at an intersection, then ,otherwise , For the accident No. One independent variable, For constant terms, For the first The coefficients corresponding to the independent variables for The mean, The random term has a mean of 0 and a standard deviation of . The normal distribution The random error term follows a normal distribution with a mean of 0. For the constructed joint prediction model of random parameters for road network accident severity that considers road network spatial correlation and data heterogeneity, the performance of the model is measured by the bias information criterion (DIC), recall, specificity, and overall classification accuracy. The calculation methods are as follows: （5）； （6）； （7）； （8）； In the formula, , , , These represent the bias information criterion, recall, specificity, and overall classification precision, respectively. and These represent the model's fitting accuracy and the number of effective variables, respectively. This indicates the actual number of serious accidents resulting in death or injury, and the predicted result is also the number of such accidents. This represents the number of accidents that actually resulted in serious injuries or fatalities, while the predicted result represents the number of accidents resulting in minor injuries. This indicates that the actual number of accidents resulting in minor injuries is also the predicted number of such accidents. This indicates the number of accidents that actually resulted in minor injuries, while the predicted result represents the number of serious accidents with injuries or fatalities. 5) Analyze the coefficients of each independent variable in the joint prediction model of random parameters of road network accident severity. Using the Bayes confidence interval as the criterion, if the coefficient is significant within the 90% Bayes confidence interval, then the independent variable is considered to be a risk factor affecting the severity of the accident, and risk factors that have a significant impact on the severity of the accident are identified. 6) Based on the spatial joint prediction model of random parameters of road network accident severity, extract the spatial residual terms of each intersection and road segment, calculate the relative risk value of each road entity, i.e., RR value, and determine the relative risk of pedestrian serious injury or death accidents at various locations in the road network. 7) Based on the RR values ​​of each intersection and road segment, draw a spatial risk map of accident severity to visualize the relative risk profile.

2. The method for assessing and visualizing urban road network traffic accident risks according to claim 1, characterized in that, In step 1), the road network accident data includes: accident severity, accident type, and accident location and time; the pedestrian data includes: pedestrian age, gender, head injury status, pedestrian location, special circumstances of the pedestrian, and pedestrian risk factors; the vehicle data includes: driver age, gender, driver behavior, driver risk factors, vehicle type, vehicle age, and point of first collision; and the environmental data includes: traffic congestion, weather conditions, number of points of interest, land use entropy, intersection type, intersection control type, road type, number of lanes, and whether a median strip exists.

3. The method for assessing and visualizing urban road network traffic accident risks according to claim 2, characterized in that, Step 2) includes the following steps: 2.1) The acquired data is classified according to accident type. Accidents are divided into intersection accidents and road segment accidents according to accident type. Factors that may affect the severity of intersection accidents are selected from the data to establish an initial intersection accident severity analysis dataset. Then, factors that may affect the severity of road segment accidents are selected to establish an initial road segment accident severity analysis dataset. 2.2) Preprocessing the initial intersection accident severity analysis dataset and the initial road segment accident severity analysis dataset includes filling missing values, removing outliers, and performing dummy variable processing on categorical feature variables to form the intersection accident severity analysis dataset. Dataset for analyzing the severity of road accidents ; 2.3) Analysis of the severity dataset of handled intersection accidents Dataset for analyzing the severity of road accidents Perform a multicollinearity test. If the variance inflation factor is greater than 10, it is considered that there is severe multicollinearity among the variables. The calculation is shown in formula (1): （1）； In the formula, For the number of variables, For the first One independent variable The negative correlation coefficients were obtained from regression analysis of the remaining independent variables; the variance inflation factor of each variable can be obtained. Based on the calculation results, independent variables with a variance inflation factor greater than 10 were removed. 2.4) After performing multicollinearity tests and Merged into an accident severity analysis dataset .

4. The method for assessing and visualizing urban road network traffic accident risks according to claim 3, characterized in that, In step 3), ArcGIS software is used to construct the spatial connection relationships between various road entities to obtain the road network adjacency matrix. The rules are as follows: If the road entity , If they are directly connected, then the two road entities are considered to have an adjacency relationship, and the corresponding spatial weight is determined accordingly. ,otherwise This forms a matrix with the number of road segments as the number of rows and the number of intersections as the number of columns, i.e., a road network adjacency matrix. ,in It also refers to the physical road. and Adjacency weight values ​​between them; analyze the severity of accidents based on spatial location in the dataset. Each accident is matched to the nearest road entity corresponding to its accident type.

5. The method for assessing and visualizing urban road network traffic accident risks according to claim 4, characterized in that, In step 6), based on the constructed joint spatial prediction model of random parameters for road network accident severity, the spatial residual terms of each intersection and road segment are extracted, and the relative risk is defined. To determine the expected excess advantage ratio, in order to identify the road entity The formula for determining whether a road entity with similar characteristics has a higher probability of serious injury or death is as follows: （9）； in, Road entities with a value greater than 1 are considered to have potential risks and require further improvement.

6. The method for assessing and visualizing urban road network traffic accident risks according to claim 5, characterized in that, In step 7), ArcGIS software is used to visualize the road entities in the road network. Values ​​are matched to create a spatial risk map of accident severity, enabling visualization of relative risk profiles.

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