Urban road network traffic accident risk studying, judging and visualizing method

Abstract

The invention discloses an urban road network traffic accident risk studying, judging and visualizing method. The method comprises the following steps: acquiring road network accident data, pedestrian data, vehicle data and environment data; the obtained data is preprocessed; acquiring an urban road network connection relation, establishing a road network adjacency matrix, and obtaining a spatial correlation relation of each accident; establishing a road network accident severity random parameter space joint prediction model considering road network space correlation and data heterogeneity; coefficients corresponding to the variables in the prediction model are analyzed, and risk factors influencing the severity of the accident are judged; calculating a relative risk value according to the spatial residual item of each road entity, and judging the relative risk of the road network major accident; and according to the relative risk value of each road entity, a road network risk map is drawn, and relative risk general situation visualization is realized. The traffic accident severity influence factors can be identified from the microscopic level, the potential relation between the risk factors and the accident severity is mined, and the accident black spots are discriminated.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic safety, and in particular to a method for assessing and visualizing the risk of traffic accidents on an urban road network. Background Art

[0002] With the increase in the number of cars, traffic safety issues have become a focus of social attention. Urban roads are difficult areas for traffic safety control due to their complex traffic composition and road network lines, as well as dense surrounding buildings. Analyzing the risk factors that affect the severity of traffic accidents and identifying road network accident black spots will enable relevant departments to propose targeted improvement measures for roads with high potential accident risks, improve road safety, and reduce casualties and property losses.

[0003] The current analysis of risk factors mainly focuses on single road entities, while the risk factor analysis based on the road network level is mostly concentrated on the macro factor level, and ignores the spatial correlation between adjacent roads. However, traffic safety issues are micro issues, and the above methods ignore the impact of micro factors on the severity of traffic accidents. In addition, the existing identification of traffic accident risk hotspots is mainly based on the frequency of accidents, ignoring the severity of the accidents. Some accidents that cause serious injuries or deaths to pedestrians may not be noticed, affecting the results of accident black spot identification. Summary of the invention

[0004] The purpose of the present invention is to address the shortcomings of the current road network accident risk factor identification, which rarely considers multiple road entities and micro-level factors, and the accident severity when identifying accident black spots. A method for risk assessment and visualization of urban road network traffic accidents is proposed. Taking the road network as the research object, it can simultaneously identify the risk factors that affect the severity of accidents occurring at intersections and road sections, and check accident black spots. It is more efficient than the previous method that takes a single road entity as the research object.

[0005] To achieve the above purpose, the technical solution provided by the present invention is: a method for evaluating and visualizing the risk of traffic accidents in urban road networks, comprising the following steps:

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

[0007] 2) Preprocessing the acquired data, including: classifying the acquired data by accident type, filling missing values, deleting outliers, performing dummy variable processing and multicollinearity test on categorical variables, and forming an accident severity analysis data set;

[0008] 3) Obtain the urban road network connection relationship, construct the road network adjacency matrix, match each accident in the accident severity analysis data set to the closest road entity corresponding to the accident type according to the spatial position, and obtain the spatial correlation relationship of each accident;

[0009] 4) Based on the spatial correlation and accident severity analysis data set of each accident, a random parameter spatial joint prediction model for road network accident severity is established, and the model performance is evaluated based on two indicators: goodness of fit and prediction accuracy. The random parameter spatial joint prediction model for road network accident severity introduces spatial residual terms under the framework of a binary logistic model and converts the parameters into random parameters to improve the model's adaptability to spatial accident data and its processing effect on data heterogeneity. For any given observed accident, the probability of serious injury or death of pedestrians can be obtained;

[0010] 5) Analyze the coefficients corresponding to the variables in the joint prediction model of random parameter space of road network accident severity, and identify the risk factors that have a significant impact on the severity of the accident using the Bayesian confidence interval as the judgment criterion;

[0011] 6) According to the random parameter spatial joint prediction model of road network accident severity, the spatial residual items of each intersection and road section are extracted, and the relative risk value of each road entity, i.e., the RR value, is calculated to determine the relative risk of serious injury or death of pedestrians at various locations in the road network;

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

[0013] Further, 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, pedestrian location, pedestrian special circumstances, and pedestrian risk factors; the vehicle data includes: driver age, gender, driver behavior, driver risk factors, vehicle type, vehicle age, and first collision point; 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 there is a central divider.

[0014] Further, the step 2) comprises the following steps:

[0015] 2.1) The acquired data are classified by accident type, and the accidents are divided into intersection accidents and road section accidents according to the accident type. Factors that may affect the severity of intersection accidents are selected from the data to establish an initial intersection accident severity analysis data set. Factors that may affect the severity of road section accidents are then selected to establish an initial road section accident severity analysis data set;

[0016] 2.2) Preprocessing the initial intersection accident severity analysis dataset and the initial road section accident severity analysis dataset, including filling missing values, deleting outliers, and treating categorical feature variables as dummy variables, to form intersection accident severity analysis dataset D1 and road section accident severity analysis dataset D2;

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

[0018]

[0019] In the formula, P is the number of variables, R i is the ith independent variable X i The negative correlation coefficient of the regression analysis of the other independent variables; each variable can obtain its variance inflation factor VIF i , according to the calculation results, the independent variables with variance inflation factors greater than 10 are eliminated;

[0020] 2.4) After the multicollinearity test, D1 and D2 are merged into the accident severity analysis data set D.

[0021] Furthermore, in step 3), ArcGIS software is used to construct the spatial connection relationship between each road entity, and the road network adjacency matrix w is obtained. The establishment rules are as follows:

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

[0023] Furthermore, in step 4), a random parameter space joint prediction model for the road network accident severity is established. For any given observed accident j, its accident severity Y j are divided into two categories: minor injury and serious injury or death, which are set to 0 and 1 respectively. In the framework of the binary logistic model, the spatial residual term and random parameters are introduced. Then, in any road entity m, the probability p that accident j is a serious injury or death accident is j There are the following relationships:

[0024]

[0025]

[0026]

[0027] In the formula, φ m is the spatial residual term of the road entity m, φ n is the spatial residual term of road entity n, n≠m means m and n are two different road entities, is the variance parameter of the spatial term, ρ is the weight parameter reflecting the strength of correlation, I represents the intersection, S represents the road section, r j 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 is the kth independent variable of accident j, α is a constant term, β k is the coefficient corresponding to the kth independent variable, β k The mean value, μ jk is a random term with a mean of 0 and a standard deviation of σ k The normal distribution of j is a random error term, which obeys a normal distribution with a mean of 0. For the constructed random parameter spatial joint prediction model of road network accident severity considering road network spatial association and data heterogeneity, the deviation information criterion DIC, recall rate, specificity and overall classification accuracy indicators are used to measure the model performance, and the calculation method is as follows:

[0028]

[0029]

[0030]

[0031]

[0032] In the formula, DIC, Recall, Specificity, and Total represent the deviation information criterion, recall rate, specificity, and overall classification accuracy, respectively. and p D They respectively represent the fitting accuracy and the number of valid variables of the model. TP means that the actual accident is serious and the predicted result is also the number of accidents with serious casualties. FN means that the actual accident is serious and the predicted result is the number of accidents with minor injuries. TN means that the actual accident is minor and the predicted result is also the number of accidents with minor injuries. FP means that the actual accident is minor and the predicted result is the number of accidents with serious casualties.

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

[0034] Further, in step 6), according to the constructed road network accident severity random parameter spatial joint prediction model, the spatial residual term of each intersection and road section is extracted, and its relative risk RR is defined as the expected excess odds ratio to determine whether the road entity m has a higher probability of serious injury or death than similar characteristic road entities. The specific formula is as follows:

[0035] RR=exp(φ m ) (9)

[0036] Among them, road entities with RR values ​​greater than 1 are considered to have potential risks and need further improvement.

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

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. Aiming at the dense urban road network, the spatial correlation between road entities in the road network and the heterogeneity of data are considered to improve the flexibility and adaptability of the model.

[0040] 2. In the identification of accident risks, considering that analysis based on accident frequency will ignore certain serious casualties, the severity of the accident is used as the dependent variable, which improves the accuracy of accident black spot identification.

[0041] 3. Establishing a random parameter spatial joint prediction model for the severity of road network accidents can simultaneously analyze the risk factors that affect the severity of road section accidents and intersection accidents, which is more efficient than the model that takes a single road entity as the research object.

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

[0043] 5. Propose relative risk indicators to identify potential accident black spots, generate regional relative risk maps, and visualize accident risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 Flow chart of the method of the present invention.

[0045] Figure 2 Road network accident risk map for specific implementation cases. DETAILED DESCRIPTION

[0046] The present invention will be further described below in conjunction with embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0047] like Figure 1 As shown, this embodiment discloses a method for evaluating and visualizing the risk of traffic accidents in urban road networks, comprising the following steps:

[0048] Step 1: Collect historical accident data of the road network. Collect pedestrian accident data for five years on a road network that includes 204 intersections and 366 road sections. The data records road network 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, pedestrian location, pedestrian special conditions, and pedestrian risk factors. Vehicle information includes: driver age, gender, driver behavior, driver risk factors, vehicle type, vehicle age, and first collision point. 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 whether there is a central divider.

[0049] Step 2: Data preprocessing. The acquired data are divided into intersection accidents and road section accidents according to the accident type, and the factors that may affect the severity of intersection accidents are selected from the data to establish the initial intersection accident severity analysis data set. At the same time, the initial road section accident severity analysis data set is established according to the above ideas, abnormal accident data is deleted, missing data is filled, and the categorical variables in Table 1 are processed into Table 2 through dummy variables to establish the intersection accident severity analysis data set D1 and the road section accident severity analysis data set D2.

[0050] Table 1 Classification of vehicle types

[0051] Incident 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 Dummy variable conversion operations

[0053] Incident 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 a dummy variable conversion operation, you need to select one item as a reference item. For example, in the above example, select car as the reference item and create dummy variables for the four categories of truck, bus, taxi, and bus.

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

[0056]

[0057] In the formula, P is the number of variables, R i is the ith independent variable X i The negative correlation coefficient of the regression analysis of the other independent variables. Each variable can get its variance inflation factor VIF i , according to the calculation results, the independent variables with variance inflation factors greater than 10 are eliminated.

[0058] In this embodiment, R language is used to perform a multicollinearity test on the data processed in step 1. The results show that in data set D1, the VIF values ​​of the variables "vehicle type" and "driver risk factor" are greater than 10; in data set D2, the VIF value of the variable "driver risk factor" is greater than 10. The above variables are eliminated, and D1 and D2 are merged into the accident severity analysis data set D, as shown in Table 3.

[0059] Table 3 Accident severity analysis dataset

[0060]

[0061] When D1 and D2 are combined, factors that only affect the severity of one type of accident are recorded as null values ​​in the corresponding positions of the other type of accident, as shown in the lane number column of Table 3.

[0062] Step 3: Construct the spatial connection relationship between 206 intersections and 366 road sections in the road network in ArcGIS software, and obtain the road network adjacency matrix w, as shown in Table 4. The establishment rules are as follows:

[0063] If road entities m and n are directly connected, then the two road entities are considered to have an adjacency relationship, and the corresponding spatial weight w mn =1, otherwise w mn = 0, forming a matrix with the number of road sections as the number of rows and the number of intersections as the number of columns, namely 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 position.

[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: In WinBUGS software, a random parameter spatial joint prediction model for the severity of road network accidents is constructed, which takes into account the spatial correlation of the road network and the heterogeneity of data. For any given observed accident j, its accident severity Y j are divided into two categories: minor injury and serious injury or death, which are set to 0 and 1 respectively. In the framework of the binary logistic model, the spatial residual term and random parameters are introduced. Then, in any road entity m, the probability p that accident j is a serious injury or death accident is j There are the following relationships:

[0068]

[0069]

[0070]

[0071] In the formula, φ m is the spatial residual term of the road entity m, φ n is the spatial residual term of road entity n, n≠m means m and n are two different road entities, is the variance parameter of the spatial term, ρ is the weight parameter reflecting the strength of correlation, I represents the intersection, S represents the road section, r j 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 is the kth independent variable of accident j, α is a constant term, β k is the coefficient corresponding to the kth independent variable, β k The mean value, μ jk is a random term with a mean of 0 and a standard deviation of σ k The normal distribution of j is a random error term, which obeys a normal distribution with a mean of 0. For the constructed random parameter spatial joint prediction model of road network accident severity considering road network spatial association and data heterogeneity, the Deviation Information Criterion (DIC), recall rate, specificity and overall classification accuracy indicators are used to measure the model performance, which is calculated as follows:

[0072]

[0073]

[0074]

[0075]

[0076] In the formula, DIC, Recall, Specificity, and Total represent the deviation information criterion, recall rate, specificity, and overall classification accuracy, respectively. and p D They respectively represent the fitting accuracy and the number of valid variables of the model. TP means that the actual accident is serious and the predicted result is also the number of accidents with serious casualties. FN means that the actual accident is serious and the predicted result is the number of accidents with minor injuries. TN means that the actual accident is minor and the predicted result is also the number of accidents with minor injuries. FP means that the actual accident is minor and the predicted result is the number of accidents with serious casualties.

[0077] Step 5: Analyze the coefficients corresponding to the independent variables in the random parameter space joint prediction model of road network accident severity. If the coefficient is significant within the 90% Bayesian confidence interval, the independent variable is considered to be a risk factor affecting the severity of the accident.

[0078] Step 6: Calculate the spatial residual items of each intersection and road section in the study area in turn, and define the relative risk RR (Relative Risk) of serious and major accidents in the road network as the expected excess odds ratio to determine whether the road entity m has a higher accident-caused serious casualty rate 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 RR values ​​greater than 1 are considered to have potential risks and need to be improved.

[0081] Step 7: Use ArcGIS software to match the RR values ​​of each road entity in the road network, draw a spatial risk map of accident severity, and visualize the relative risk profile, such as Figure 2 shown.

[0082] The above embodiments are preferred implementations of the present invention, but the implementations of the present invention are not limited to the above embodiments. Any other improvements and modifications that do not deviate from the spirit and principles of the present invention are included in the protection scope of the present invention.

Claims

1. A method for evaluating and visualizing the risk of traffic accidents in urban road networks, characterized in that: The following steps are involved: 1) Obtain data, including: road network accident data, pedestrian data, vehicle data and environmental data; 2) Preprocessing the acquired data, including: classifying the acquired data by accident type, filling missing values, deleting outliers, performing dummy variable processing and multicollinearity test on categorical variables, and forming an accident severity analysis data set; 3) Obtain the urban road network connection relationship, construct the road network adjacency matrix, match each accident in the accident severity analysis data set to the closest road entity corresponding to the accident type according to the spatial position, and obtain the spatial correlation relationship of each accident; 4) Based on the spatial correlation and accident severity analysis data set of each accident, a random parameter spatial joint prediction model for road network accident severity is established, and the model performance is evaluated based on two indicators: goodness of fit and prediction accuracy. The random parameter spatial joint prediction model for road network accident severity introduces spatial residual terms under the framework of a binary logistic model and converts the parameters into random parameters to improve the model's adaptability to spatial accident data and its processing effect on data heterogeneity. For any given observed accident, the probability of serious injury or death of pedestrians can be obtained; 5) Analyze the coefficients corresponding to the variables in the joint prediction model of random parameter space of road network accident severity, and identify the risk factors that have a significant impact on the severity of the accident using the Bayesian confidence interval as the judgment criterion; 6) According to the random parameter spatial joint prediction model of road network accident severity, the spatial residual items of each intersection and road section are extracted, and the relative risk value of each road entity, i.e., the RR value, is calculated to determine the relative risk of serious injury or death of pedestrians at various locations in the road network; 7) Based on the RR values ​​of each intersection and road section, a spatial risk map of accident severity is drawn to visualize the relative risk profile.

2. The method for evaluating and visualizing the risk of traffic accidents in urban road networks according to claim 1 is characterized in that: In step 1), the road network accident data includes: accident severity, accident type, accident location and time; the pedestrian data includes: pedestrian age, gender, head injury, pedestrian location, pedestrian special conditions and pedestrian risk factors; the vehicle data includes: driver age, gender, driver behavior, driver risk factors, vehicle type, vehicle age and first collision point; 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 there is a central divider.

3. The method for evaluating and visualizing the risk of traffic accidents in urban road networks according to claim 2 is characterized in that: The step 2) comprises the following steps: 2.1) The acquired data are classified by accident type, and the accidents are divided into intersection accidents and road section accidents according to the accident type. Factors that may affect the severity of intersection accidents are selected from the data to establish an initial intersection accident severity analysis data set. Factors that may affect the severity of road section accidents are then selected to establish an initial road section accident severity analysis data set; 2.2) Preprocessing the initial intersection accident severity analysis dataset and the initial road section accident severity analysis dataset, including filling missing values, deleting outliers, and treating categorical feature variables as dummy variables, to form intersection accident severity analysis dataset D1 and road section accident severity analysis dataset D2; 2.3) Perform multicollinearity test on the processed intersection accident severity analysis data set D1 and the road section accident severity analysis data set D2. If the variance inflation factor is greater than 10, it is considered that there is serious collinearity between the variables. The calculation is shown in formula (1): In the formula, P is the number of variables, R i is the ith independent variable X i The negative correlation coefficient of the regression analysis of the other independent variables; each variable can obtain its variance inflation factor VIF i , according to the calculation results, the independent variables with variance inflation factors greater than 10 are eliminated; 2.4) After the multicollinearity test, D1 and D2 are merged into the accident severity analysis data set D.

4. The method for evaluating and visualizing the risk of traffic accidents in urban road networks according to claim 3 is characterized in that: In step 3), ArcGIS software is used to construct the spatial connection relationship between each road entity, and the road network adjacency matrix w is obtained. The establishment rules are as follows: If road entities m and n are directly connected, then the two road entities are considered to have an adjacency relationship, and the corresponding spatial weight w mn =1, otherwise w mn = 0, forming a matrix with the number of road sections as the number of rows and the number of intersections as the number of columns, namely the road network adjacency matrix w, where w mn It also represents the adjacency weight value between road entities m and n; According to the spatial position, each accident in the accident severity analysis data set D is matched to the road entity with the closest distance corresponding to the accident type.

5. The method for evaluating and visualizing the risk of traffic accidents in urban road networks according to claim 4 is characterized in that: In step 4), a random parameter space joint prediction model for the road network accident severity is established. For any given observed accident j, its accident severity Y j are divided into two categories: minor injury and serious injury or death, which are set to 0 and 1 respectively. In the framework of the binary logistic model, the spatial residual term and random parameters are introduced. Then, in any road entity m, the probability p that accident j is a serious injury or death accident is j There are the following relationships: In the formula, φ m is the spatial residual term of the road entity m, φ n is the spatial residual term of road entity n, n≠m means m and n are two different road entities, is the variance parameter of the spatial term, ρ is the weight parameter reflecting the strength of correlation, I represents the intersection, S represents the road section, r j 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 is the kth independent variable of accident j, α is a constant term, β k is the coefficient corresponding to the kth independent variable, β k The mean value, μ jk is a random term with a mean of 0 and a standard deviation of σ k The normal distribution of j is a random error term, which obeys a normal distribution with a mean of 0. For the constructed random parameter spatial joint prediction model of road network accident severity considering road network spatial association and data heterogeneity, the deviation information criterion DIC, recall rate, specificity and overall classification accuracy indicators are used to measure the model performance, and the calculation method is as follows: In the formula, DIC, Recall, Specificity, and Total represent the deviation information criterion, recall rate, specificity, and overall classification accuracy, respectively. and p D They respectively represent the fitting accuracy and the number of valid variables of the model. TP means that the actual accident is serious and the predicted result is also the number of accidents with serious casualties. FN means that the actual accident is serious and the predicted result is the number of accidents with minor injuries. TN means that the actual accident is minor and the predicted result is also the number of accidents with minor injuries. FP means that the actual accident is minor and the predicted result is the number of accidents with serious casualties.

6. The method for evaluating and visualizing the risk of traffic accidents in urban road networks according to claim 5 is characterized in that: In step 5), the coefficients corresponding to the independent variables in the random parameter space joint prediction model of road network accident severity are analyzed. If the coefficient is significant within the 90% Bayesian confidence interval, the independent variable is considered to be a risk factor affecting the severity of the accident.

7. The method for evaluating and visualizing the risk of traffic accidents in urban road networks according to claim 6 is characterized in that: In step 6), according to the constructed road network accident severity random parameter spatial joint prediction model, the spatial residual term of each intersection and road section is extracted, and its relative risk RR is defined as the expected excess odds ratio to determine whether the road entity m has a higher probability of serious injury or death than similar characteristic road entities. The specific formula is as follows: RR=exp(φ m ) (9) Among them, road entities with RR values ​​greater than 1 are considered to have potential risks and need further improvement.

8. The method for evaluating and visualizing the risk of traffic accidents in urban road networks according to claim 7 is characterized in that: In step 7), the RR values ​​of each road entity in the road network are matched with the help of ArcGIS software to draw a spatial risk map of accident severity and realize the visualization of relative risk profile.

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