CN116631186B - Expressway traffic accident risk assessment method and system based on dangerous driving event data - Google Patents
Expressway traffic accident risk assessment method and system based on dangerous driving event data Download PDFInfo
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Abstract
The invention discloses a highway traffic accident risk assessment method and system based on dangerous driving event data, wherein the method comprises the following steps: acquiring expressway traffic accident data and four types of dangerous driving event data; searching and counting the frequency of dangerous driving events on road sections before accidents and when no accidents occur and the average value of the maximum acceleration and the maximum speed of vehicles in all the same type of dangerous driving events respectively; constructing a sample data set by taking the dangerous driving event information as an independent variable and taking whether an accident is a dependent variable; processing the data into an image form, constructing and training a deep learning model, and taking into consideration the internal relation of dangerous driving event data in different event types and upstream and downstream space positions, so as to mine the rule of dangerous driving events on a road section before an accident occurs; and finally, acquiring dangerous driving event data of the whole highway section in real time, and evaluating the traffic accident risk in real time according to the trained model.
Description
Technical Field
The invention provides a highway traffic accident risk assessment method and system based on dangerous driving event data, and belongs to the technical field of road traffic safety.
Background
Traffic safety issues on highways have been a major concern for governments. In recent years, real-time evaluation of highway traffic accident risk becomes a research hotspot, and has extremely important significance for developing active traffic safety management and reducing traffic accident occurrence rate.
The existing expressway traffic accident risk assessment method generally uses traffic flow data collected by fixed detectors (such as ETC (electronic toll collection) portal frames and ground annular coils) to judge accident risk, but the data are limited by the arrangement positions and the arrangement densities of the fixed detectors, so that traffic running states at all positions of a whole road section are difficult to dynamically reflect, and the accident risk assessment accuracy is affected. With the progress of data acquisition technology, dangerous driving behavior data of a vehicle are easier to acquire, and a series of dangerous driving behavior monitoring, risk assessment and early warning methods are presented. However, these methods are all methods for risk assessment by using a single individual vehicle as a study object, and lack a method for traffic accident risk assessment based on dangerous driving event data of vehicles on road segments by using road segment accident risks as study objects from the perspective of road traffic managers.
In the aspect of accident risk assessment models, most of the existing methods adopt statistical analysis or machine learning models to mine characteristic rules of factors such as road traffic flow before accidents occur, and some of the existing methods also adopt a Recurrent Neural Network (RNN) deep learning model to consider the time correlation of traffic flow data for accident risk modeling. However, in all the methods, the one-dimensional vector form data is used for representing factors such as road traffic flow before an accident occurs, and the internal connection of the data in multiple dimensions such as time, space and influence factors is difficult to consider. The Convolutional Neural Network (CNN) deep learning model can efficiently extract local modes in image data, comprehensively consider the correlation of the data in multiple dimensions to perform model training and prediction, and has much smaller calculation cost ratio RNN.
Because dangerous driving event data is acquired through vehicle navigation software, the acquisition range can cover all positions of the whole highway section of the highway and is not influenced by weather conditions. Therefore, dangerous driving event data of vehicles on the road are used and processed into image forms, the image forms are input into the CNN deep learning model, multidimensional correlation contained in the data can be comprehensively considered, and real-time refined assessment of traffic accident risks of all road sections of the expressway can be realized under the condition that new equipment is not required to be installed.
Disclosure of Invention
The invention aims to solve the technical problems that: aiming at the defects of the prior art, the invention uses dangerous driving event data of vehicles on the expressway, processes the dangerous driving event data into an image form according to different dangerous driving event types and spatial position relations, utilizes a deep learning model comprising a Convolutional Neural Network (CNN) and other modules to mine dangerous driving event rules on a road section before traffic accidents happen, and provides an expressway traffic accident risk assessment method and system based on the dangerous driving event data, which aims at solving the problems that the existing expressway traffic accident risk assessment method is difficult to accurately assess accident risks at each position of the expressway due to the limitation of the arrangement positions and the arrangement densities of fixed detectors, and the intrinsic relation of the data in different influence factors and spatial positions is difficult to extract and learn, so that the risk assessment precision is limited.
The invention adopts the following technical scheme for solving the technical problems:
The invention provides a highway traffic accident risk assessment method based on dangerous driving event data, which comprises the following steps:
s1: determining a research road section and a statistical time range, and respectively acquiring the following two types of information:
(1) And (3) counting dangerous driving event data of vehicles on the highway research road section within a time range, wherein the dangerous driving event data comprise the occurrence time of dangerous driving events, the longitude and latitude of the occurrence place and the uplink and downlink directions, the types of dangerous driving events (sudden acceleration, sudden deceleration, sudden left lane change and sudden right lane change), and the maximum acceleration and maximum speed of the vehicles in the dangerous driving events.
(2) And counting traffic accident data occurring on the highway research road section within the time range, wherein the traffic accident data comprise the starting time and the ending time of the accident, the longitude and the latitude of the accident place and the uplink and downlink directions.
S2, aiming at each traffic accident, searching dangerous driving event data of an upstream road section and a downstream road section of an accident site before the accident occurs, taking the dangerous driving event data as independent variables, and taking the accident as the dependent variables to construct an accident case data set;
S3, searching dangerous driving event data of an upstream road section and a downstream road section when no traffic accident occurs, taking the dangerous driving event data as an independent variable, taking the non-occurring accident as a dependent variable, constructing a non-accident case data set as a comparison group, and combining the accident case data set and the non-accident case data set to obtain a sample data set;
s4, carrying out standardized processing on the data: processing dangerous driving event data into an image form according to different dangerous driving event types and spatial position relations, constructing a deep learning model, and training and parameter adjustment until the model effect is optimal;
S5, acquiring dangerous driving event data of vehicles on all road sections of the expressway in real time, calculating to obtain occurrence frequency of various dangerous driving events and average value of maximum acceleration and maximum speed of the vehicles in the dangerous driving events according to the S2, inputting the average value into the deep learning model constructed in the S4, and calculating to obtain traffic accident risk assessment values and risk levels of all road sections.
Specifically, step S2 is specifically:
S2.1: and marking dangerous driving events and traffic accident positions on corresponding road sections of the expressway according to the longitude, latitude and uplink and downlink directions of the dangerous driving events and the traffic accident positions, and establishing an uplink and downlink relation between the road sections.
S2.2: and according to the space-time position of each traffic accident, four dangerous driving event data of rapid acceleration, rapid deceleration, rapid left lane change and rapid right lane change in the space range of the upstream and downstream of the accident site in the time range of t before the accident occurs are extracted.
S2.3: for each traffic accident, the following are calculated:
(1) Calculating occurrence frequencies of four dangerous driving events in the space-time range in the S2.2, and marking as follows:
Times＝[L_up,L_down,R_up,R_down,A_up,A_down,B_up,B_down]
wherein the first part of each variable represents the dangerous driving event type, namely L, R, A and B respectively represent the frequency of the occurrence of the driving events of sudden left lane change, sudden right lane change, sudden acceleration and sudden deceleration in the time range of t before the accident. The second part of each variable represents the location of the dangerous driving event, i.e. up and down represent road segments in the spatial range upstream and downstream of the accident location, respectively.
(2) Calculating the average value of the maximum acceleration and the maximum speed of the vehicle in the same type of dangerous driving event in the space-time range in S2.2, and marking as:
Acceleration＝[La_up,La_down,Ra_up,Ra_down,Aa_up,Aa_down,Ba_up,Ba_down]
Speed＝[Ls_up,Ls_down,Rs_up,Rs_down,As_up,As_down,Bs_up,Bs_down]
wherein La and Ls in the first portion of each variable represent the average of the maximum acceleration and maximum velocity of the vehicle in all the rapid left-hand lane-change events, respectively, and the remaining Ra, rs, aa, as, ba, bs are synonymous. The second portion of each variable represents a dangerous driving event occurrence location.
S2.4: and combining the time, acception and Speed data corresponding to each traffic accident to obtain dangerous driving event data with 24 characteristic variables. Summarizing dangerous driving event data corresponding to all traffic accidents, taking the dangerous driving event data as independent variables, and marking the independent variables as:
Xi＝[Timesi,Accelerationi,Speedi]
Wherein, times i represents the occurrence frequency of four dangerous driving events corresponding to the ith traffic accident, and the acception i and Speed i have the same meaning.
Each event case data is given a label y=1, and is denoted as y= [ Y i],yi =1, as a dependent variable. And matching the independent variable with the dependent variable to obtain an accident case data set.
Specifically, step S3 is specifically:
s3.1: for each traffic accident, according to the methods in S2.2, S2.3 and S2.4, dangerous driving event data in the space ranges of the upstream and downstream of the accident site in the time range of t 0、t0～2t0、…、(n-1)t0～nt0 before the accident occurs are extracted and calculated, and initial non-accident case data are obtained in a summarizing mode. Wherein n is a positive integer, controls the proportion of the control group selection, and can be selected according to actual conditions.
In order to avoid the influence caused by traffic accidents in the time range, checking the time of all the non-accident case data, and deleting the non-accident case data with the time between t time before any traffic accident and t' time after the accident. Deleting the case with information missing to obtain final non-accident case data, and recording the final non-accident case data as an independent variable:
Xj＝[Timesj,Accelerationj,Speedj]。
wherein, times j represents the occurrence frequency of four dangerous driving events corresponding to the j-th non-accident case, and the acception j and Speed j have the same meaning.
For each non-accident case data, a label y=0 is assigned, and is denoted as y= [ Y j],yj =0, as a dependent variable. And matching the independent variable with the dependent variable to obtain the non-accident case data set.
S3.2: combining the accident case data set and the non-accident case data set to obtain a sample data set, and marking as:
X＝[Timesk,Accelerationk,Speedk]
＝[L_upk,L_downk,R_upk,R_downk,A_upk,A_downk,B_upk,B_downk,La_upk,La_downk,Ra_upk,Ra_downk,Aa_upk,Aa_downk,Ba_upk,Ba_downk,Ls_upk,Ls_downk,Rs_upk,Rs_downk,As_upk,As_downk,Bs_upk,Bs_downk]
Y= [ Y k],yk =0 or 1
Specifically, step S4 is specifically:
s4.1: the mean and standard deviation of each independent variable were calculated and all data were normalized as follows:
xnorm＝(x-μ)/σ
where μ is the mean value of the independent variables and σ is the standard deviation of the independent variables.
S4.2: each dangerous driving event data is processed into a four-way one-dimensional image form as follows. The variables in each channel are as follows:
channels 1:L_up, la_up, ls_up, L_down, la_down, ls_down
Channel 2 R_up, ra_up, rs_up, R_down, ra_down, rs_down
Channels 3: A_up, aa_up, as_up, A_down, aa_down, as_down
Channels 4:B_up, ba_up, bs_up, B_down, ba_down, bs_down
Wherein 6 variables of the same driving behavior type are positioned in the same channel and are arranged according to the relation between the upstream road section and the downstream road section (from up to down) and the sequence of occurrence frequency, average maximum acceleration and average maximum speed of dangerous driving events. The variables for different types of dangerous driving events are located in 4 different lanes. The data form is beneficial to model extraction and learning of internal relations of the data in different dangerous driving event types and upstream and downstream space positions, and hidden modes in the data are mined.
S4.3: and establishing a deep learning model. And constructing a convolution layer, a pooling layer, a flattening layer, a full-connection layer and an output layer in sequence. The four-channel one-dimensional image data obtained in S4.2 is first input into the convolutional layer as input data of the deep learning model.
The convolution layer (Conv 1D layer) has an input dimension of I 1, an output dimension of O 1 and m 1 convolution kernels with a size of n 1. These convolution kernels perform one-dimensional convolution operations in steps s 1 along the direction of the variable alignment within the input data channel (e.g., from l_up to ls_down). The activation function of the convolutional layer is relu. The output of this module serves as the input to the pooling layer.
The input dimension of the pooling layer (AveragePooling D layer) is I 2, the output dimension is O 2, and the one-dimensional average pooling operation with the range of n 2 is performed in the step length of s 2. The output of the module serves as the input to the flattening layer.
The flattened layer (FLATTEN LAYER) has an input dimension I 3 and an output dimension O 3. The multi-channel one-dimensional image data output by the pooling layer is converted into a one-dimensional vector form, and a random discarding method with the ratio of p is adopted to discard the data so as to avoid overfitting. The output of the module serves as the input to the fully connected layer.
The fully connected layer (DENSE LAYER) has an input dimension of I 4, an output dimension of O 4, and m 2 neurons. The activation function of the fully connected layer is relu. And the neurons are discarded by adopting a random discarding method with the ratio of p drop. The output of the module serves as the input to the output layer.
The Output layer (Output layer) has 1 neuron. The activation function of the output layer is sigmoid, which converts the output value into a (0, 1) interval. Therefore, the meaning of the output data of the whole deep learning model is the probability of traffic accidents of the current research road section.
S4.4: training the established deep learning model based on the sample dataset. The Area (AUC, namely Area index), sensitivity (Sensitivity) and Specificity (SPECIFICITY) Under the working characteristic Curve of the test subject are used as evaluation indexes, and a ten-fold cross validation method, a binary cross entropy loss function and a Nadam optimizer are adopted to perform optimization on the established deep learning model based on a sample data set, so that the deep learning model with the optimal effect is obtained.
Specifically, step S5 is specifically:
S5.1: and acquiring four dangerous driving event data of sudden acceleration, sudden deceleration, sudden left lane change and sudden right lane change of the vehicle on the whole highway section in real time. And (2) calculating the occurrence frequency Times ' of four dangerous driving events in the time range of t before each position of the expressway and the average value of the maximum Acceleration Speed ' and the maximum Speed ' of the vehicle in all the dangerous driving events of the same type according to the S2. And summarizing to obtain input data.
S5.2: and inputting the data into the deep learning model constructed in the step S4 to obtain the probability p epsilon (0, 1) of the occurrence of the traffic accident at each position of the current expressway, namely the traffic accident risk assessment value.
When p epsilon (0, threshold), the traffic accident risk level is low risk, and no intervention is needed.
When p is epsilon [ threshold, 1), the traffic accident risk level is high risk, and corresponding measures are needed to be taken for controlling traffic safety risk.
Wherein threshold E (0, 1).
The invention also provides an expressway traffic accident risk assessment system based on dangerous driving event data, which comprises:
The data acquisition module is used for determining a research road section and a statistical time range and acquiring dangerous driving event data on the expressway research road section and traffic accident data on the research road section within the statistical time range;
The sample construction module is used for searching dangerous driving event data of an upstream road section and a downstream road section of an accident site before the accident occurs according to each traffic accident, taking the dangerous driving event data as independent variables and taking the accident as the dependent variables, and constructing an accident case data set; then, searching dangerous driving event data of an upstream road section and a downstream road section when no traffic accident occurs, taking the dangerous driving event data as independent variables, taking the non-occurring accident as dependent variables, constructing a non-accident case data set as a comparison group, and combining the accident case data set and the non-accident case data set to obtain a sample data set;
The model training module is used for carrying out standardized processing on the data, processing the data into an image form according to different dangerous driving event types and upstream and downstream spatial relations, constructing a deep learning model, and carrying out training and parameter adjustment until the model effect is optimal;
The road section accident risk assessment module is used for acquiring dangerous driving event data of vehicles on the whole road section of the expressway in real time, calculating the occurrence frequency of dangerous driving events and the average value of the maximum acceleration and the maximum speed of the vehicles in all the same type of dangerous driving events, inputting the average value into the constructed deep learning model, and calculating to obtain the traffic accident risk assessment value and the risk level of each road section.
Compared with the prior art, the invention has the following beneficial technical effects:
The highway traffic accident risk assessment method based on the dangerous driving event data is not limited by the layout positions and the layout densities of the fixed detectors, can cover the whole road section of the highway and is not influenced by weather conditions, and the traffic accident risk at each road section of the highway can be assessed in real time and in a refined manner under the condition that new equipment is not required to be installed.
Meanwhile, the method comprehensively considers the internal relation of dangerous driving event data in different event types and upstream and downstream space positions, evaluates the road accident risk according to two factors of the occurrence frequency of dangerous driving events on roads and the vehicle motion parameters in the dangerous driving events, can improve the accuracy of risk evaluation, and provides new theory and technical support for the management and control of traffic safety risks by expressway traffic management departments.
Drawings
FIG. 1 is a flow chart of a highway traffic accident risk assessment method in the present invention.
Fig. 2 is a schematic diagram of extraction rules of dangerous driving event data before occurrence of a traffic accident in the present invention.
FIG. 3 is a schematic diagram of a deep learning model framework according to the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be fully described in detail below with reference to the accompanying drawings. Furthermore, the described embodiments are only some, but not all, embodiments of the invention. Based on the embodiments of the present invention, other embodiments that may be obtained by those of ordinary skill in the art without making any inventive effort are within the scope of the present invention.
Fig. 1 is a flow chart of a highway traffic accident risk assessment method based on dangerous driving event data, which can be applied to highway traffic accident risk assessment with traffic accident data and dangerous driving event data including vehicle motion parameters. The method comprises the following specific steps:
s1: determining a research road section and a statistical time range, and respectively acquiring the following two types of information:
(1) And (3) counting dangerous driving event data of vehicles on the highway research road section within a time range, wherein the dangerous driving event data comprise the occurrence time of dangerous driving events, the longitude and latitude of the occurrence place and the uplink and downlink directions, the types of dangerous driving events (sudden acceleration, sudden deceleration, sudden left lane change and sudden right lane change), and the maximum acceleration and maximum speed of the vehicles in the dangerous driving events.
(2) And counting traffic accident data occurring on the highway research road section within the time range, wherein the traffic accident data comprise the starting time and the ending time of the accident, the longitude and the latitude of the accident place and the uplink and downlink directions.
S2: and aiming at each traffic accident, searching dangerous driving event data of the road sections upstream and downstream of the accident site before the accident occurs. And constructing an accident case data set by taking dangerous driving event data as an independent variable and taking an accident as a dependent variable. The method comprises the following specific steps:
S2.1: and marking dangerous driving events and traffic accident positions on corresponding road sections of the expressway according to the longitude, latitude and uplink and downlink directions of the dangerous driving events and the traffic accident positions, and establishing an uplink and downlink relation between the road sections.
S2.2: as shown in fig. 2, according to the space-time position of each traffic accident, four dangerous driving event data, namely, the part circled by the oval dotted line frame in fig. 2, of rapid acceleration, rapid deceleration, rapid left lane change and rapid right lane change in the range of l meters upstream and downstream of the accident site in the range of t minutes before the accident occurs are extracted. Typically t is less than or equal to 30 and l is less than or equal to 1000.
S2.3: for each traffic accident, the following are calculated:
(1) The occurrence frequency of four dangerous driving events in the space-time range shown in fig. 2 is calculated and is recorded as:
Times＝[L_up,L_down,R_up,R_down,A_up,A_down,B_up,B_down]
Wherein the first portion of each variable represents the type of dangerous driving event, i.e., L, R, A and B represent the frequency of occurrence of sudden left-hand lane change, sudden right-hand lane change, sudden acceleration and sudden deceleration driving events, respectively, within the time frame prior to the occurrence of the accident. The second part of each variable represents the location of the dangerous driving event, i.e. up and down represent road segments in the spatial range upstream and downstream of the accident location, respectively.
(2) The average of the maximum acceleration and maximum speed of the vehicle in the same type of dangerous driving event in the space-time range shown in fig. 2 is calculated and is recorded as:
Acceleration＝[La_up,La_down,Ra_up,Ra_down,Aa_up,Aa_down,Ba_up,Ba_down]
Speed＝[Ls_up,Ls_down,Rs_up,Rs_down,As_up,As_down,Bs_up,Bs_down]
wherein La and Ls in the first portion of each variable represent the average of the maximum acceleration and maximum velocity of the vehicle in all the rapid left-hand lane-change events, respectively, and the remaining Ra, rs, aa, as, ba, bs are synonymous. The second portion of each variable represents a dangerous driving event occurrence location.
S2.4: and combining the time, acception and Speed data corresponding to each traffic accident to obtain dangerous driving event data with 24 characteristic variables. Summarizing dangerous driving event data corresponding to all traffic accidents, taking the dangerous driving event data as independent variables, and marking the independent variables as:
Xi＝[Timesi,Accelerationi,Speedi]
Wherein, times i represents the occurrence frequency of four dangerous driving events corresponding to the ith traffic accident, and the acception i and Speed i have the same meaning.
Each event case data is given a label y=1, and is denoted as y= [ Y i],yi =1, as a dependent variable. And matching the independent variable with the dependent variable to obtain an accident case data set.
S3: and searching dangerous driving event data of the upstream road section and the downstream road section when no traffic accident occurs. And constructing a non-accident case data set by taking dangerous driving event data as an independent variable and taking non-accident as a dependent variable as a control group. Combining the accident case data set and the non-accident case data set to obtain a sample data set. The method comprises the following specific steps:
S3.1: for each traffic accident, according to the methods in S2.2, S2.3 and S2.4, dangerous driving event data in the space ranges of the upstream and downstream of the accident site in the time range of t 0、t0～2t0、…、(n-1)t0～nt0 before the accident occurs are extracted and calculated, and initial non-accident case data are obtained in a summarizing mode. Typically t 0 is ≡ 1 and t 0 is a positive integer. n is a positive integer, controls the proportion of the control group selection, and can be selected according to actual conditions.
In order to avoid the influence caused by traffic accidents in the time range, checking the time of all the non-accident case data, and deleting the non-accident case data with the time between t time before any traffic accident and t' time after the accident. Deleting the case with information missing to obtain final non-accident case data, and recording the final non-accident case data as an independent variable:
Xj＝[Timesj,Accelerationj,Speedj]。
wherein, times j represents the occurrence frequency of four dangerous driving events corresponding to the j-th non-accident case, and the acception j and Speed j have the same meaning.
For each non-accident case data, a label y=0 is assigned, and is denoted as y= [ Y j],yj =0, as a dependent variable. And matching the independent variable with the dependent variable to obtain the non-accident case data set.
S3.2: combining the accident case data set and the non-accident case data set to obtain a sample data set, and marking as:
X＝[Timesk,Accelerationk,Speedk]
＝[L_upk,L_downk,R_upk,R_downk,A_upk,A_downk,B_upk,B_downk,La_upk,La_downk,Ra_upk,Ra_downk,Aa_upk,Aa_downk,Ba_upk,Ba_downk,Ls_upk,Ls_downk,Rs_upk,Rs_downk,As_upk,As_downk,Bs_upk,Bs_downk]
Y= [ Y k],yk =0 or 1
S4: and (5) carrying out standardization processing on the data. And processing dangerous driving event data into an image form according to different dangerous driving event types and spatial position relations, constructing a deep learning model, and training and parameter adjustment until the model effect is optimal. The method comprises the following specific steps:
s4.1: the mean and standard deviation of each independent variable were calculated and all data were normalized as follows:
xnorm＝(x-μ)/σ
where μ is the mean value of the independent variables and σ is the standard deviation of the independent variables.
S4.2: each dangerous driving event data is processed into a four-way one-dimensional image form as shown in the input layer of fig. 3 (part of the variable names are not labeled due to space limitations). The variables in each channel are as follows:
channels 1:L_up, la_up, ls_up, L_down, la_down, ls_down
Channel 2 R_up, ra_up, rs_up, R_down, ra_down, rs_down
Channels 3: A_up, aa_up, as_up, A_down, aa_down, as_down
Channels 4:B_up, ba_up, bs_up, B_down, ba_down, bs_down
Wherein 6 variables of the same driving behavior type are positioned in the same channel and are arranged according to the relation between the upstream road section and the downstream road section (from up to down) and the sequence of occurrence frequency, average maximum acceleration and average maximum speed of dangerous driving events. The variables for different types of dangerous driving events are located in 4 different lanes. The data form is beneficial to model extraction and learning of internal relations of the data in different dangerous driving event types and upstream and downstream space positions, and hidden modes in the data are mined.
S4.3: as shown in fig. 3, a convolution layer, a pooling layer, a flattening layer, a full connection layer, and an output layer are sequentially constructed. The four-channel one-dimensional image data obtained in S4.2 is first input into the convolutional layer as input data of the deep learning model.
The convolution layer (Conv 1D layer) has an input dimension of I 1, an output dimension of O 1 and m 1 convolution kernels with a size of n 1. These convolution kernels perform one-dimensional convolution operations in steps of s 1 along the direction of variable alignment within the input data channel (the direction of the input layer arrow of fig. 3). The activation function of the convolutional layer is relu. The output of this module serves as the input to the pooling layer.
The pooling layer (AveragePooling D layer) has an input dimension of I 2 and an output dimension of O 2, and performs one-dimensional average pooling operation with a range of n 2 in a step of s 2 so as to reduce the data dimension and avoid overfitting. The output of the module serves as the input to the flattening layer.
The flattened layer (FLATTEN LAYER) has an input dimension I 3 and an output dimension O 3. The multi-channel one-dimensional image data output by the pooling layer is converted into a one-dimensional vector form, and the data is discarded by adopting a random discarding method with the ratio of p so as to avoid overfitting. The output of the module serves as the input to the fully connected layer.
The fully connected layer (DENSE LAYER) has an input dimension of I 4, an output dimension of O 4, and m 2 neurons. The activation function of the fully connected layer is relu. Meanwhile, the neurons are discarded by adopting a random discarding method with the ratio of p drop. The output of the module serves as the input to the output layer.
The Output layer (Output layer) has 1 neuron. The activation function of the output layer is sigmoid, which converts the output value into a (0, 1) interval. Therefore, the meaning of the output data of the whole deep learning model is the probability of traffic accidents of the current research road section.
S4.4: training the established deep learning model based on the sample dataset. The effect of the deep learning model was evaluated using the Area Under the test subject's operating characteristics (AUC, i.e., area index), sensitivity (Sensitivity), and Specificity (SPECIFICITY) as evaluation indices. Specifically, the about sign index under each classification threshold is calculated by using the about sign index method, and the calculation formula is as follows:
Youden＝Sensitivity+Specificity-1
Where TP represents the number of incidents in practice and in prediction, FN represents the number of incidents in practice but in prediction, TN represents the number of non-incidents in practice and in prediction, and FP represents the number of incidents in practice but in prediction. The threshold corresponding to the maximum value of the about index (Youden) is selected as the classification threshold (threshold) of the model, and the sensitivity and specificity of the model under the threshold are used as model evaluation indexes. The closer the AUC, sensitivity and specificity values of the model are to 1, the better the model predictive performance.
Based on the three evaluation indexes, a ten-fold cross validation method, a binary cross entropy loss function and a Nadam optimizer are adopted to perform optimization on the established deep learning model based on the sample data set, so that the deep learning model with the optimal effect is obtained.
S5: dangerous driving event data of vehicles on all road sections of the expressway are acquired in real time, input into a deep learning model constructed in the step S4, and the risk assessment value and the risk level of traffic accidents on each road section are calculated. The method comprises the following specific steps:
S5.1: and acquiring four dangerous driving event data of rapid acceleration, rapid deceleration, rapid left lane change and rapid right lane change of vehicles on the whole road section of the expressway in real time, and establishing an upstream-downstream relationship between road sections. And (2) obtaining the occurrence frequency Times ' of four dangerous driving events in the time range of t before each position of the expressway and the average value of the maximum Acceleration Speed ' and the maximum Speed ' of the vehicle in all the dangerous driving events of the same type according to the calculation method in the S2, and collecting the input data X of the model.
S5.2: and inputting X into the deep learning model constructed in the S4 to obtain the probability p epsilon (0, 1) of the occurrence of the traffic accident at each position of the current expressway, namely the traffic accident risk assessment value.
When p epsilon (0, threshold), the traffic accident risk level is low risk, and no intervention is needed.
When p is epsilon [ threshold, 1), the traffic accident risk level is high risk, and corresponding measures are needed to be taken for controlling the safety risk.
Wherein threshold E (0, 1).
Specific cases
In order to show the applicability of the highway traffic accident risk assessment method based on the dangerous driving event data provided by the embodiment of the invention in a real scene, the following specific cases are given for further explanation:
Taking the Liyang-Changxing section of the expressway of China as an example, the data collection time range is from 26 days of 9 months in 2020 to 2 days of 10 months in 2020. Dangerous driving event data of vehicles on the road section are collected, and the time interval for data collection is 1 second. The data fields include the time of occurrence of the dangerous driving event, the longitude, latitude and ascending and descending directions of the place where the event is located, the type of the dangerous driving event (sudden acceleration, sudden deceleration, sudden left lane change and sudden right lane change), and the motion parameters (maximum acceleration and maximum speed) of the vehicle in the dangerous driving event. Traffic accident data on the road section in the time range is collected, and the data fields comprise the starting time and the ending time of the accident, the longitude and the latitude of the accident place and the uplink and downlink directions. After arrangement, 371 traffic accidents are recorded on the research road section in the statistical range.
Because the recorded traffic accident occurrence time is slightly later than the actual occurrence time, the shorter time interval for summarizing the dangerous driving event data can influence the prediction accuracy. In addition, the summary time interval is shorter, the number of dangerous driving events in each interval is smaller, and the distribution difference in different intervals is larger, so that modeling is not facilitated. Thus, the present case selects 30 minutes as the time interval for data summarization. In addition, 250 meters are selected as the space interval selected by the dangerous driving event data, so that dangerous driving events with great influence on traffic accidents can be extracted finely. I.e. t=30, l=250.
According to the step of the specific embodiment S2, dangerous driving event data of the upstream and downstream road sections before the occurrence of the accident are extracted and data fusion is carried out, so that 265 accident cases are obtained. For each traffic accident, the step according to the specific embodiment S3 adopts the dangerous driving event data in 250 meters upstream and downstream of the accident site within the range of 0-1, 1-2 and 2-3 hours (i.e. t 0 =1) before the accident occurs as the corresponding non-accident case of the traffic accident, and the initial non-accident case data is obtained by summarizing.
To avoid the impact of accident cases, the time of all non-accident cases is checked, and the non-accident case data is deleted within 30 minutes before any traffic accident to 180 minutes after the accident. And deleting the cases with the information missing to obtain final 722 pieces of non-accident case data, and forming a sample data set with the accident-to-non-accident ratio of about 1:3.
According to the step of the specific implementation mode S4, data are standardized, each dangerous driving event data are processed into four-channel one-dimensional image forms, and a deep learning model comprising a one-dimensional convolution module is established. The specific parameters of the model are as follows:
The input dimension of the convolution layer is (N, 6, 4) with 512 convolution kernels of size 3. These convolution kernels perform one-dimensional convolution operations in steps of 1. Thus, the output dimension is (N, 4,512). Wherein N is the number of training set samples.
The pooling layer performs a one-dimensional average pooling operation in a range of 3 in steps of 1. Thus, the output dimension is (N, 2,512).
The flattening layer converts the multi-channel one-dimensional image data output by the pooling layer into a one-dimensional vector form, and discards the data by adopting a random discarding method with the ratio of 0.5 so as to avoid overfitting. Thus, the output dimension is (N, 1024).
The fully connected layer has 32 neurons with an activation function relu. Neurons were discarded using a random discard method with a ratio of 0.5. Thus, the output dimension is (N, 32).
The output layer has 1 neuron, and the output dimension is (N, 1), i.e., a one-dimensional vector. The activation function of the output layer is sigmoid. The meaning of the output data is the probability of traffic accidents of the current research road section.
And training a deep learning model by adopting a ten-fold cross validation method, a binary cross entropy loss function and Nadam optimizer, and performing super-parameter tuning to obtain the deep learning model with optimal effect.
And taking the verification set data as dangerous driving event data of the expressway full road section vehicle obtained in real time, and according to the step of the specific implementation mode S5, inputting the dangerous driving event data into a trained deep learning model to calculate a traffic accident risk assessment value. Experiments show that compared with three reference models (input data only supports one-dimensional vector form) of logistic regression, a support vector machine and an artificial neural network model, the AUC value of the deep learning model (input data is in an image form) provided by the invention is highest, 71.3% of accident conditions and 75.1% of non-accident conditions can be identified under an optimal threshold, and the model with sensitivity and specificity being more than 70% is the only model. Meanwhile, the deep learning model has the highest sensitivity under the condition of smaller false alarm rate (less than 30%). Experimental results of specific cases prove that the highway accident risk assessment method based on the dangerous driving event data has the best comprehensive performance, can extract and learn the internal connection of the dangerous driving event data in different event types and upstream and downstream space positions, effectively improves the accuracy of risk assessment, and has higher application value.
The foregoing description of the embodiments of the invention is provided to facilitate the understanding of the principles of the invention and its core ideas, and is not intended to limit the invention. Modifications, equivalents, and alternatives to the described embodiments of the invention will occur to those skilled in the art and are intended to be included within the spirit and principles of the invention.
Claims (3)
1. The highway traffic accident risk assessment method based on the dangerous driving event data is characterized by comprising the following steps of:
S1, determining a research road section and a statistical time range, and acquiring vehicle dangerous driving event related data on the expressway research road section and traffic accident data on the research road section in the statistical time range;
S2, aiming at each traffic accident, searching dangerous driving event data of an upstream road section and a downstream road section of an accident site before the accident occurs, taking the dangerous driving event data as independent variables, and taking the accident as the dependent variables to construct an accident case data set;
S3, searching dangerous driving event data of an upstream road section and a downstream road section when no traffic accident occurs, taking the dangerous driving event data as an independent variable, taking the non-occurring accident as a dependent variable, constructing a non-accident case data set as a comparison group, and combining the accident case data set and the non-accident case data set to obtain a sample data set;
s4, carrying out standardized processing on the data: processing dangerous driving event data into an image form according to different dangerous driving event types and spatial position relations, constructing a deep learning model, and training and parameter adjustment until the model effect is optimal;
s5, acquiring dangerous driving event data of vehicles on all road sections of the expressway in real time, calculating to obtain occurrence frequency of various dangerous driving events and average value of maximum acceleration and maximum speed of the vehicles in the dangerous driving events according to the S2, inputting the average value into a deep learning model constructed in the S4, and calculating to obtain traffic accident risk assessment values and risk levels of all road sections;
The data acquired in step S1 includes:
(1) Dangerous driving event related data of vehicles on an expressway, comprising: the occurrence time of dangerous driving events, the longitude, latitude and uplink and downlink directions of the occurrence place, the type of the dangerous driving events, and the maximum acceleration and maximum speed of the vehicle in the dangerous driving events; dangerous driving event types include: rapid acceleration, rapid deceleration, rapid left lane change and rapid right lane change;
(2) Highway traffic accident data comprising: the starting time and the ending time of the accident, the longitude and the latitude of the accident place and the uplink and downlink directions;
The step S2 specifically comprises the following sub-steps:
S201, marking dangerous driving events and traffic accident positions on corresponding road sections of the expressway according to longitude, latitude and uplink and downlink directions, and establishing an uplink and downlink relation between the road sections;
S202, according to the space-time position of each traffic accident, four dangerous driving event data of rapid acceleration, rapid deceleration, rapid left lane change and rapid right lane change in the space range of the upstream and downstream of the accident site in the time range of t before the accident occurs are extracted;
S203, for each traffic accident, respectively calculating the following contents:
(1) Calculating occurrence frequencies of four dangerous driving events in the space-time range in the step S202, and marking as:
Times＝[L_up,L_down,R_up,R_down,A_up,A_down,B_up,B_down]
Wherein a first part of each variable represents a dangerous driving event type, namely L, R, A and B respectively represent the frequency of occurrence of sudden left lane change, sudden right lane change, sudden acceleration and sudden deceleration driving events in a time range t before an accident occurs, and a second part of each variable represents a dangerous driving event occurrence place, namely up and down respectively represent road sections in a space range of an upstream and a downstream of the accident place;
(2) Calculating the average value of the maximum acceleration and the maximum speed of the vehicle in the same type of dangerous driving event in the space-time range in the step S202, and marking as:
Acceleration＝[La_up,La_down,Ra_up,Ra_down,Aa_up,Aa_down,Ba_up,Ba_down]
Speed＝[Ls_up,Ls_down,Rs_up,Rs_down,As_up,As_down,Bs_up,Bs_down]
Wherein La, ls in the first portion of each variable represent average values of maximum acceleration and maximum velocity of the vehicle in all rapid left lane change events, ra, rs represent average values of maximum acceleration and maximum velocity of the vehicle in all rapid right lane change events, aa, as represent average values of maximum acceleration and maximum velocity of the vehicle in all rapid acceleration events, ba, bs represent average values of maximum acceleration and maximum velocity of the vehicle in all rapid deceleration events, respectively;
S204: combining the time, acception and Speed data corresponding to each traffic accident to obtain dangerous driving event data with 24 characteristic variables, summarizing the dangerous driving event data corresponding to all the traffic accidents, and recording the dangerous driving event data as independent variables as: x i＝[Timesi,Accelerationi,Speedi ], wherein time i represents the occurrence frequency of four dangerous driving events corresponding to the ith traffic accident, and acception i and Speed i represent the average value of the maximum Acceleration and the maximum Speed of the vehicle in the four dangerous driving events corresponding to the ith traffic accident respectively;
Each accident case data is given a label y=1, the label y=1 is taken as a dependent variable, the dependent variable is marked as Y= [ Y i],yi =1, and the independent variable and the dependent variable are matched to obtain an accident case data set;
the step S3 specifically comprises the following sub-steps:
s301: for each traffic accident, extracting and calculating dangerous driving event data in the space range of the upstream and downstream of the accident site in the time range of t 0、t0～2t0、…、(n-1)t0～nt0 before the accident occurs, and summarizing to obtain initial non-accident case data, wherein n is a positive integer for controlling the proportion selected by a control group;
Checking the time of all initial non-accident cases, and deleting non-accident case data with the time between t time before any traffic accident and t' time after the accident; deleting the case with information missing to obtain final non-accident case data, and recording the final non-accident case data as an independent variable: x j＝[Timesj,Accelerationj,Speedj ], wherein time j represents the occurrence frequency of four dangerous driving events corresponding to the jth non-accident case, and acceletion j and Speed j represent the average value of the maximum Acceleration and the maximum Speed of the vehicle in the four dangerous driving events corresponding to the jth non-accident case, respectively;
Each piece of non-accident case data is given a label y=0, the label y=0 is taken as a dependent variable, the dependent variable is marked as Y= [ Y j],yj =0, and the independent variable and the dependent variable are matched to obtain a non-accident case data set;
S302, combining the accident case data set and the non-accident case data set to obtain a sample data set, and marking as:
X＝[Timesk,Accelerationk,Speedk]
＝[L_upk,L_downk,R_upk,R_downk,A_upk,A_downk,B_upk,B_downk,La_upk,La_downk,Ra_upk,Ra_downk,Aa_upk,Aa_downk,Ba_upk,Ba_downk,Ls_upk,Ls_downk,Rs_upk,Rs_downk,As_upk,As_downk,Bs_upk,Bs_downk]
Y= [ Y k],yk =0 or 1;
The step S4 specifically comprises the following sub-steps:
s401, carrying out standardization treatment on data according to x norm = (x-mu)/sigma, wherein mu is an average value, and sigma is a standard deviation;
S402, processing each dangerous driving event data into a four-channel one-dimensional image form, wherein variables in each channel are as follows:
channels 1:L_up, la_up, ls_up, L_down, la_down, ls_down
Channel 2 R_up, ra_up, rs_up, R_down, ra_down, rs_down
Channels 3: A_up, aa_up, as_up, A_down, aa_down, as_down
Channels 4:B_up, ba_up, bs_up, B_down, ba_down, bs_down
The method comprises the steps that 6 variables of the same driving behavior type are located in the same channel, and are arranged according to the relation between an upstream road section and a downstream road section and the sequence of occurrence frequency, average maximum acceleration and average maximum speed of dangerous driving events, and the variables of different types of dangerous driving events are located in 4 different channels;
S403, establishing a deep learning model: sequentially constructing a convolution layer, a pooling layer, a flattening layer, a full-connection layer and an output layer, and inputting the four-channel one-dimensional image data obtained in the step S402 into the convolution layer as input data of a deep learning model; the convolution layer is provided with m 1 convolution kernels with the size of n 1, the convolution kernels carry out one-dimensional convolution operation along the variable arrangement direction in an input data channel in the step length of s 1, the activation function is relu, and the output of the convolution layer is used as the input of the pooling layer;
The pooling layer performs one-dimensional average pooling operation with the range of n 2 by the step length of s 2, and the output of the pooling layer is used as the input of the flattening layer;
The flattening layer converts the multi-channel one-dimensional image data output by the pooling layer into a one-dimensional vector form, a random discarding method with the ratio of p is adopted to avoid overfitting, and the output of the flattening layer is used as the input of the full-connection layer;
The full-connection layer is provided with m 2 neurons, the activation function is relu, the random discarding method with the ratio of p is adopted to avoid overfitting, the random discarding method with the ratio of p drop is adopted, and the output of the full-connection layer is used as the input of the output layer;
The output of the full-connection layer is provided with 1 neuron, the activation function of the output layer is sigmoid, and the meaning of the output data is the probability of traffic accidents in the current research road section;
S404, training and optimizing the established deep learning model based on the sample data set: taking the area, sensitivity and specificity of the test subject under the working characteristic curve as evaluation indexes, and adopting a ten-fold cross validation method, a binary cross entropy loss function and Nadam optimizer for optimization to obtain a deep learning model with optimal effect;
The step S5 specifically comprises the following steps:
s501, obtaining data of four dangerous driving events of rapid Acceleration, rapid deceleration, rapid left lane change and rapid right lane change of a vehicle on a whole highway section in real time, calculating the occurrence frequency Times ' of the four dangerous driving events in a time range of t before each position of the highway according to the step S2, and collecting average values of the maximum Acceleration accelization ' and the maximum Speed ' of the vehicle in all the same type of dangerous driving events to obtain input data of a model;
S502: inputting data into the deep learning model constructed in the step S4, and calculating the probability p E (0, 1) of the occurrence of the traffic accident at each position of the current expressway, namely, the traffic accident risk assessment value;
When p is E (0, threshold), the traffic accident risk level is low risk, and no intervention is needed;
When p is epsilon (threshold, 1), the traffic accident risk level is high risk, and corresponding measures are needed to be adopted to manage and control the safety risk;
Wherein threshold E (0, 1).
2. A highway traffic accident risk assessment system based on the method of claim 1, comprising:
The data acquisition module is used for determining a research road section and a statistical time range and acquiring dangerous driving event data on the expressway research road section and traffic accident data on the research road section within the statistical time range;
The sample construction module is used for searching dangerous driving event data of an upstream road section and a downstream road section of an accident site before the accident occurs according to each traffic accident, taking the dangerous driving event data as independent variables and taking the accident as the dependent variables, and constructing an accident case data set; searching dangerous driving event data of an upstream road section and a downstream road section when no traffic accident occurs, taking the dangerous driving event data as an independent variable, taking the non-occurring accident as a dependent variable, constructing a non-accident case data set as a comparison group, and combining the accident case data set and the non-accident case data set to obtain a sample data set;
The model training module is used for carrying out standardized processing on the data, processing the data into an image form according to different dangerous driving event types and upstream and downstream spatial relations, constructing a deep learning model, and carrying out training and parameter adjustment until the model effect is optimal;
The road section accident risk assessment module is used for acquiring dangerous driving event data of vehicles on the whole road section of the expressway in real time, calculating the occurrence frequency of dangerous driving events and the average value of the maximum acceleration and the maximum speed of the vehicles in all the same type of dangerous driving events, inputting the average value into the constructed deep learning model, and calculating to obtain the traffic accident risk assessment value and the risk level of each road section.
3. The system of claim 2, wherein the model training module builds a deep learning model by sequentially building a convolutional layer, a pooling layer, a flattening layer, a fully-connected layer, and an output layer, wherein
The convolution layer is provided with m 1 convolution kernels with the size of n 1, the convolution kernels carry out one-dimensional convolution operation along the variable arrangement direction in the input data channel in the step length of s 1, the activation function is relu, and the output of the convolution layer is used as the input of the pooling layer;
The pooling layer performs one-dimensional average pooling operation with the range of n 2 by the step length of s 2, and the output of the pooling layer is used as the input of the flattening layer;
The flattening layer converts the multi-channel one-dimensional image data output by the pooling layer into a one-dimensional vector form, a random discarding method with the ratio of p is adopted to avoid overfitting, and the output of the flattening layer is used as the input of the full-connection layer;
The full-connection layer is provided with m 2 neurons, the activation function is relu, the random discarding method with the ratio of p is adopted to avoid overfitting, the random discarding method with the ratio of p drop is adopted, and the output of the flattening layer is used as the input of the output layer;
The output of the full-connection layer is provided with 1 neuron, the activation function of the output layer is sigmoid, and the meaning of the output data is the probability of traffic accidents in the current research road section.
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| CN117351708A (en) * | 2023-10-08 | 2024-01-05 | 北京迈道科技有限公司 | Expressway safety operation management early warning method, system and storage medium |
| CN117273964B (en) * | 2023-11-21 | 2024-02-02 | 国任财产保险股份有限公司 | Intelligent vehicle insurance policy generation system and method for self-adaptive driving data |
| CN118135802B (en) * | 2024-05-10 | 2024-06-28 | 四川高路文化旅游发展有限责任公司 | Bridge road management and control system and method based on deep learning network |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106355877A (en) * | 2016-08-23 | 2017-01-25 | 重庆大学 | Method of simulated estimate of expressway-traffic-accident-affected areas based on particle filter algorithm |
| CN108198415A (en) * | 2017-12-28 | 2018-06-22 | 同济大学 | A kind of city expressway accident forecast method based on deep learning |
| CN110210090A (en) * | 2019-05-22 | 2019-09-06 | 淮阴工学院 | Motor vehicle accident analysis method based on uncertainty theory and genetic algorithm |
| CN110866677A (en) * | 2019-10-25 | 2020-03-06 | 东南大学 | Driver relative risk evaluation method based on benchmark analysis |
| CN112037513A (en) * | 2020-09-01 | 2020-12-04 | 清华大学 | Real-time traffic safety index dynamic comprehensive evaluation system and construction method thereof |
| CN112201038A (en) * | 2020-09-28 | 2021-01-08 | 同济大学 | Road network risk assessment method based on risk of bad driving behavior of single vehicle |
| CN112837533A (en) * | 2021-01-08 | 2021-05-25 | 合肥工业大学 | Highway accident frequency prediction method considering risk factor time-varying characteristics |
| CN112990545A (en) * | 2021-02-08 | 2021-06-18 | 东南大学 | Traffic safety state prediction method for expressway intersection area |
| WO2021133789A1 (en) * | 2019-12-27 | 2021-07-01 | Lyft, Inc. | Systems and methods for incident detection using inference models |
| CN113689071A (en) * | 2021-07-15 | 2021-11-23 | 东南大学 | Multi-driver risk evaluation-based active intervention method for bad driving behaviors |
| CN113762734A (en) * | 2021-08-17 | 2021-12-07 | 淮阴工学院 | Dangerous chemical vehicle highway driving risk assessment method and system |
| KR102416711B1 (en) * | 2022-02-21 | 2022-07-05 | 주식회사 카비 | Accident risk prediction device for each driver using artificial intelligence and accident risk prediction method using the same |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020046662A2 (en) * | 2018-08-22 | 2020-03-05 | Cubic Corporation | Connected and autonomous vehicle (cav) behavioral adaptive driving |
-
2023
- 2023-05-19 CN CN202310569137.8A patent/CN116631186B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106355877A (en) * | 2016-08-23 | 2017-01-25 | 重庆大学 | Method of simulated estimate of expressway-traffic-accident-affected areas based on particle filter algorithm |
| CN108198415A (en) * | 2017-12-28 | 2018-06-22 | 同济大学 | A kind of city expressway accident forecast method based on deep learning |
| CN110210090A (en) * | 2019-05-22 | 2019-09-06 | 淮阴工学院 | Motor vehicle accident analysis method based on uncertainty theory and genetic algorithm |
| CN110866677A (en) * | 2019-10-25 | 2020-03-06 | 东南大学 | Driver relative risk evaluation method based on benchmark analysis |
| WO2021133789A1 (en) * | 2019-12-27 | 2021-07-01 | Lyft, Inc. | Systems and methods for incident detection using inference models |
| CN112037513A (en) * | 2020-09-01 | 2020-12-04 | 清华大学 | Real-time traffic safety index dynamic comprehensive evaluation system and construction method thereof |
| CN112201038A (en) * | 2020-09-28 | 2021-01-08 | 同济大学 | Road network risk assessment method based on risk of bad driving behavior of single vehicle |
| CN112837533A (en) * | 2021-01-08 | 2021-05-25 | 合肥工业大学 | Highway accident frequency prediction method considering risk factor time-varying characteristics |
| CN112990545A (en) * | 2021-02-08 | 2021-06-18 | 东南大学 | Traffic safety state prediction method for expressway intersection area |
| CN113689071A (en) * | 2021-07-15 | 2021-11-23 | 东南大学 | Multi-driver risk evaluation-based active intervention method for bad driving behaviors |
| CN113762734A (en) * | 2021-08-17 | 2021-12-07 | 淮阴工学院 | Dangerous chemical vehicle highway driving risk assessment method and system |
| KR102416711B1 (en) * | 2022-02-21 | 2022-07-05 | 주식회사 카비 | Accident risk prediction device for each driver using artificial intelligence and accident risk prediction method using the same |
Non-Patent Citations (1)
| Title |
|---|
| 信号交叉口左转待行区综合效益评估;李静;王占永;蔡铭;;中山大学学报(自然科学版);20190515(03);全文 * |
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