CN113887904A - Freight vehicle driver risk grade assessment method based on analytic hierarchy process - Google Patents

Freight vehicle driver risk grade assessment method based on analytic hierarchy process Download PDF

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CN113887904A
CN113887904A CN202111113853.2A CN202111113853A CN113887904A CN 113887904 A CN113887904 A CN 113887904A CN 202111113853 A CN202111113853 A CN 202111113853A CN 113887904 A CN113887904 A CN 113887904A
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王雪松
鲍彦莅
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Tongji University
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Abstract

The invention relates to a freight vehicle driver risk grade assessment method based on an analytic hierarchy process, which comprises the following steps: step 1) acquiring accident influence factors and illegal influence factors of a driver of a freight vehicle, and constructing a risk level evaluation model of the driver of the freight vehicle; step 2) adopting an analytic hierarchy process to construct a driver risk grade evaluation module containing accident influence factors and illegal influence factors, and calculating weight coefficients of the accident influence factors and the illegal influence factors; and 3) incorporating the weight coefficient obtained in the step 2) into a risk grade evaluation model of the freight vehicle driver, and calculating the risk grade of the freight vehicle driver, wherein the lower the score is, the higher the risk of the freight vehicle driver is, and the risk grade of the freight vehicle driver is divided into 6 risk grades with no risk to extremely high risk. Compared with the prior art, the method has the advantages that the method is convenient for freight enterprises to find key safety problems of drivers and pertinence driver safety management and the like.

Description

Freight vehicle driver risk grade assessment method based on analytic hierarchy process
Technical Field
The invention relates to a vehicle driver risk level assessment method, in particular to a freight vehicle driver risk level assessment method based on an analytic hierarchy process.
Background
With the development of social economy and the continuous increase of the holding capacity of trucks and the road freight volume, the problems of freight transportation safety and safety management of the freight industry face huge challenges. The bad driving behaviors such as giving way and overspeed are prominent in the driving process of a driver of the freight vehicle due to failure of giving way according to regulations, and the bad driving behaviors are important reasons for traffic accidents of the freight vehicle. The improvement of bad behaviors of freight drivers requires effective management of freight enterprises, and the safety management of the freight drivers requires safety education and assessment of the drivers by departments and personnel specially responsible for safety management.
Through historical accident and illegal analysis of the freight drivers, key safety problems of the freight drivers can be known. At present, a freight enterprise usually records accidents and illegal conditions of a driver, but is only used for safety assessment of the freight driver, main safety problem judgment based on traffic accidents and traffic violation data is lacked, and the management focus of the enterprise is unclear. Considering that the freight drivers have large individual difference and various representation modes of the risk states of the drivers, how to construct automatic evaluation of the risk level of the freight drivers is required, so that the risk division is carried out on the freight drivers, and the driver assessment and safety management of freight enterprises are facilitated, which becomes a technical problem to be solved.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a freight vehicle driver risk grade evaluation method based on an analytic hierarchy process to realize the risk division of a freight driver, so that a freight enterprise can find key safety problems of the driver and aim driver safety management conveniently.
The purpose of the invention can be realized by the following technical scheme:
according to one aspect of the invention, a method for assessing the risk level of a driver of a freight vehicle based on an analytic hierarchy process is provided, characterized in that the method comprises the following steps:
step 1) acquiring accident influence factors and illegal influence factors of a driver of a freight vehicle, and constructing a risk level evaluation model of the driver of the freight vehicle;
step 2) adopting an analytic hierarchy process to construct a driver risk grade evaluation module containing accident influence factors and illegal influence factors, and calculating weight coefficients of the accident influence factors and the illegal influence factors;
and 3) incorporating the weight coefficient obtained in the step 2) into a risk grade evaluation model of the freight vehicle driver, and calculating the risk grade of the freight vehicle driver, wherein the lower the score is, the higher the risk of the freight vehicle driver is, and the risk grade of the freight vehicle driver is divided into 6 risk grades with no risk to extremely high risk.
As a preferable technical scheme, the accident influence factors comprise accident frequency, accident severity, accident reason, accident responsibility and road environment.
As a preferred technical scheme, the illegal influencing factors comprise illegal frequency and illegal behaviors.
As a preferable technical solution, the calculation formula of the freight vehicle driver risk level evaluation model in step 1) is as follows:
Figure BDA0003274775610000021
Riskd＝100-(wcrash×Lcrash+wviolation×Lviolation)
wherein RiskdRepresenting the risk level of a driver of the freight vehicle, and the score is 0-100; w is acrashWeight coefficient representing driver accident risk, LcrashRepresenting a driver accident risk level calculated from the accident data; w is aviolationWeight coefficient, L, representing the driver's illicit riskviolationRepresenting the level of driver's violation risk calculated by the violation data.
As a preferable technical scheme, the step 2) of constructing a driver risk level evaluation model including accident influence factors and illegal influence factors specifically comprises the following steps:
step 21) accident risk grades of a driver of the freight vehicle comprise the sum of 6 macroscopic factor grades of accident frequency, accident severity, accident identification reason, accident form, accident responsibility and accident site, and the formula is as follows:
Figure BDA0003274775610000022
in the formula, LcrashTo accident risk class, ciFor macroscopic accident influencing factor risk grade, including accident frequency c1Severity of the accident c2Cause of accident identification c3Accident pattern c4Accident responsibility c5And accident site c6；
Step 22) dividing accident identification reasons into three conditions of freight vehicles and driver reasons, non-motor-driven or pedestrian reasons and road environment reasons, scoring 0-100 aiming at each specific accident identification reason, wherein the higher the score is, the higher the risk level is, and meanwhile, correcting the accident risk level of the driver by combining the accident risk coefficient of the driver on the basis of the accident identification reasons, and comprehensively evaluating the risk level of the driver;
and step 23) taking the risk grade of the driver as a target, taking the macroscopic factors related to the accident risk as a standard detection layer, and taking the microscopic factors contained in the macroscopic factors as an index layer.
Preferably, in step 22), when the accident is determined to be the freight vehicle and the driver, the calculation formula is:
Figure BDA0003274775610000031
in the formula, LcrashFor the driver's accident risk level, wcrashAs a driver's accident risk weight coefficient, rjFor the reason of driver's accident, w is given a score for the freight vehicle and the driver's risk leveliThe accident risk macroscopic influence factor weight coefficient is represented by i, i represents the ith macroscopic accident risk factor, j represents the jth microscopic accident risk factor, and the accident risk grade is subjected to accumulation calculation according to the accident frequency;
when the accident is determined to be caused by non-maneuver or pedestrians, the calculation formula is as follows:
Figure BDA0003274775610000032
in the formula, LcrashFor the driver's accident risk level, wcrashAs a driver's accident risk weight coefficient, rj ′Rating the risk level for non-motorised or pedestrian reasons for driver accident identification, wiIs a macroscopic accident risk influencing factor weight coefficient, wijRepresenting the weight coefficient of the microscopic accident risk influence factors, and performing accumulation calculation on the accident risk level according to the accident frequency;
when the reason for the accident is determined to be the road environment, the calculation formula is as follows:
Figure BDA0003274775610000033
in the formula, LcrashFor the driver's accident risk level, wcrashWeight coefficient of driver accident risk, r ″)jRating the risk level of the road environment for the reason of driver accident identification, wiIs a weight coefficient of macroscopic influence factors, w, of accident riskijAnd expressing the weight coefficient of the microscopic accident risk influence factors, and performing accumulation calculation on the accident risk level according to the accident frequency.
As a preferred technical scheme, the driver risk level evaluation model specifically comprises:
Figure BDA0003274775610000041
as a preferable technical solution, in the step 2), the factor risk grades of the freight vehicle and the driver are given as follows:
Figure BDA0003274775610000042
Figure BDA0003274775610000051
the risk ratings for non-motor vehicles and pedestrian causes are given in the following table:
Figure BDA0003274775610000052
the risk rating for each road environmental cause is given in the following table:
Figure BDA0003274775610000053
as a preferable technical solution, in the step 2), the accident frequency score is accumulated, the accident frequency risk level increases with the increase of the accident frequency, the unit score of each accident is 20, the minimum value is 0, and the maximum value is 100, and the calculation formula is as follows:
c1＝w1×20×n＝0.23×20×n＝4.6n
wherein, c1Indicating the risk level of the accident frequency, w1Representing the accident frequency weight coefficient, and n represents the accident frequency;
according to the accident severity data, the accident severity is calculated by combining the number of the accident and the number of the death and injury persons, and the risk calculation mode is as follows:
Figure BDA0003274775610000061
Figure BDA0003274775610000062
in the formula, c2Indicating the accident type risk level, w2A weight coefficient representing the severity of the accident, R represents the risk level of the accident identified cause, wherein { R ∈ (R ∈ [)i,r′i,r″i)|0≤ri≤100,0≤r′i≤100,0≤r″i≤100}；
The accident reason is classified into 3 types, namely freight vehicle and driver reasons, non-motor vehicle and pedestrian reasons and road environment and facility reasons, and the calculation formula is as follows:
Figure BDA0003274775610000063
wherein, w31Influence coefficients for reasons of freight vehicles and drivers; w is a32Influence coefficients for non-motor vehicles and behavior reasons; w is a33The road environment and facility influence coefficient;
the accident form risk level is calculated according to the following formula:
Figure BDA0003274775610000064
wherein, c4Representing the risk level of the accident pattern, w4Represents the macroscopic accident shape weight coefficient, w4iRepresenting the accident form micro factor weight coefficient;
the calculation formula of the accident liability risk level of the driver is as follows:
Figure BDA0003274775610000071
wherein, c5Indicates the accident liability risk level, w5Represents the accident responsibility macroscopic weight coefficient, w5iRepresenting accident liability microfactor weight coefficients;
the freight vehicle driver accident site risk level has the following calculation formula:
Figure BDA0003274775610000072
wherein, c6Indicating the accident site risk level, w6Represents a macroscopic weight coefficient, w, of the accident site6iRepresenting the incident site micro-factor weight coefficient.
As a preferred technical solution, in step 2), the level of the risk of the frequency of violation increases with the increase of the number of times of violation, the unit score of each violation is 10, the minimum value is 0, and the maximum value is 100, and the calculation formula is as follows:
v′1＝w′1×10×n＝0.68×10×n＝6.8n
in formula (II), v'1Representing a violation frequency risk level, w'1Representing the weight coefficient of the frequency of the violation, wherein n represents the frequency of the violation;
the method comprises the following steps of scoring a sharp driving behavior, a bad driving behavior, a vehicle safety problem, a non-rule and other 5 kinds of illegal behaviors by 0-100 grades, wherein the larger the score is, the higher the risk of the driver is, and the illegal behavior risk calculation mode of the driver is as follows:
Figure BDA0003274775610000073
wherein, v'2A risk level for illegal activity of a driver of the freight vehicle; w'2Weight coefficient, b 'representing illegal behavior of driver'iIs the score of the illegal activity.
Compared with the prior art, the risk level evaluation method for the driver of the freight vehicle is based on accident and illegal data of the driver of the freight vehicle, a risk level evaluation model for the driver of the freight vehicle is constructed, influence factors of the accident of the freight vehicle (accident frequency, accident severity, accident identification reason, accident form, accident responsibility and road environment) and illegal influence factors (illegal frequency and illegal behaviors) are evaluated by adopting a hierarchical analysis method, and reference basis is provided for risk division of the driver of the freight vehicle and safety management of the driver.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2(a) is a pie chart of a driver risk level distribution according to the present invention;
FIG. 2(b) is a bar graph of a driver risk level distribution according to the present invention;
fig. 3 is a schematic view of a hierarchical structure of accident risk levels of a driver.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
As shown in FIG. 1, a method for assessing risk level of a driver of a freight vehicle based on an analytic hierarchy process specifically comprises the following steps
1) According to accident and illegal data of the freight vehicle, accident influence factors (accident frequency, accident severity, accident cause, accident form, accident responsibility and road environment) and illegal influence factors (illegal frequency and illegal behaviors) are extracted, a risk level evaluation model of a driver of the freight vehicle is constructed, and a calculation formula is as follows:
Figure BDA0003274775610000081
Riskd＝100-(wcrash·Lcrash+wviolation·Lviolation)
wherein RiskdRepresenting the risk level of a driver of the freight vehicle, and the score is 0-100; w is acrashWeight coefficient representing driver accident risk, LcrashRepresenting a driver accident risk level calculated from the accident data; w is aviolationWeight coefficient, L, representing the driver's illicit riskviolationRepresenting the level of driver's violation risk calculated by the violation data.
2) An Analytic Hierarchy Process (AHP) is utilized to construct a driver risk level hierarchical structure containing accident influence factors and illegal influence factors, and a method of expert questionnaire scoring is adopted to carry out weight scoring calculation on the accident and illegal influence factors.
3) And (3) bringing the weight coefficient in the step 2) into a risk grade evaluation model of the driver of the freight vehicle, and calculating the risk grade of the driver of the freight vehicle, wherein the lower the score is, the higher the risk of the driver of the freight vehicle is, and the risk grade of the driver of the freight vehicle is divided into 6 risk grades with no risk and extremely high risk.
The step 2) utilizes an Analytic Hierarchy Process (AHP) to construct an accident risk level in a specific calculation mode as follows:
the accident risk grade of a driver of a freight vehicle is the sum of 6 macroscopic factor grades of accident frequency, accident severity, accident identification reason, accident form, accident responsibility and accident site, and the formula is as follows:
Figure BDA0003274775610000091
in the formula, LcrashTo accident risk class, ciAnd the risk grade of the macroscopic accident influencing factor.
Secondly, accident identification reasons are divided into three conditions of freight vehicles, driver reasons, non-motor or pedestrian reasons and road environment reasons, each specific accident identification reason is scored from 0 to 100, and the higher the score is, the higher the risk level is. In addition, the driver accident risk coefficient is combined and corrected on the basis of the accident identification reason, and the risk level of the driver is comprehensively evaluated.
And thirdly, taking the risk grade of the driver as a target, taking the macroscopic factors related to the accident risk as a standard measuring layer, and taking the microscopic factors contained in the macroscopic factors as an index layer. The hierarchy of risk levels for a driver is shown in fig. 3.
In the second step, when the accident is determined as a freight vehicle and a driver, the calculation formula is as follows:
Figure BDA0003274775610000092
in the formula, LcrashDriver accident risk rating, wcrashAs a driver's accident risk weight coefficient, rjFor the reason of driver's accident, w is given a score for the freight vehicle and the driver's risk leveliIs the accident risk macroscopic influence factor weight coefficient, i represents the ith macroscopic accident risk factor, j represents the jth microscopic accident windAnd (4) carrying out accumulated calculation on risk factors and accident risk grades according to the accident frequency.
In the second step, when the cause of the accident is determined to be non-motor or pedestrian, the calculation formula is as follows:
Figure BDA0003274775610000093
in the formula, LcrashDriver accident risk rating, wcrashAs a driver's accident risk weight coefficient, rjRating the risk level for non-motorised or pedestrian reasons for driver accident identification, wiIs a macroscopic accident risk influencing factor weight coefficient, wijAnd expressing the weight coefficient of the microscopic accident risk influence factors, and performing accumulation calculation on the accident risk level according to the accident frequency.
In the second step, when the reason for the accident is determined to be the road environment, the calculation formula is as follows:
Figure BDA0003274775610000101
in the formula, LcrashDriver accident risk rating, wcrashAs a driver's accident risk weight coefficient, rjRating the risk level for non-motorised or pedestrian reasons for driver accident identification, wiIs a weight coefficient of macroscopic influence factors, w, of accident riskijAnd expressing the weight coefficient of the microscopic accident risk influence factors, and performing accumulation calculation on the accident risk level according to the accident frequency.
In the second step, the calculation formula of the driver accident risk level evaluation model is as follows:
Figure BDA0003274775610000102
in the second step, the accident caused by the freight vehicle and the driver is classified into 7 types including no illegal behaviors, aggressive driving behaviors, bad driving behaviors, improper operation of the driver, vehicle safety problems, non-regulations of the vehicle and the driver and other types according to the contents of the accident cause, and the risk levels of the reason of the freight vehicle and the driver are marked in an attached table 1.
TABLE 1
Figure BDA0003274775610000103
Figure BDA0003274775610000111
In the second step, 19 accident identification reasons are included in the accident identification reasons caused by the non-motor vehicles and the pedestrians, and the risk grades of the reasons of the non-motor vehicles and the pedestrians are shown in the table 2:
TABLE 2
Figure BDA0003274775610000112
Figure BDA0003274775610000121
Among the causes of accident identification caused by road environment, there are 3 types of causes of accident identification, and each cause of road environment is given a score as shown in table 3:
TABLE 3
Figure BDA0003274775610000122
In the second step, the accident frequency score is accumulated, the accident frequency risk level increases with the increase of the accident frequency, the unit score of each accident is 20, the minimum value is 0, and the maximum value is 100, and the calculation formula is as follows:
c1＝w1×20×n＝0.23×20×n＝4.6n
in the second step, according to the accident severity data, the accident severity is calculated by combining the accident type and the number of the dead and injured people, and the risk calculation mode is as follows:
Figure BDA0003274775610000123
Figure BDA0003274775610000124
in the formula, c2Indicating the accident type risk level, w2A weight coefficient representing the severity of the accident, R represents the risk level of the accident identified cause, wherein { R ∈ (R ∈ [)i,r′i,r″i)|0≤ri≤100,0≤r′i≤100,0≤r″i≤100}。w2iRepresenting the weight coefficient of the microscopic factors of the accident type, which is shown in Table 4:
TABLE 4
Figure BDA0003274775610000125
Figure BDA0003274775610000131
Note: w is a2jHas a value of 0 to 1, and
Figure BDA0003274775610000132
in the second step, the accident determination reasons are classified into 3 types, namely, freight vehicles and drivers, non-motor vehicles and pedestrians, and road environment and facility reasons, and the calculation formula is as follows:
Figure BDA0003274775610000133
wherein, w31Influence coefficients for reasons of freight vehicles and drivers; w is a32Is not machineInfluence coefficients of bullet trains and behavior reasons; w is a33The road environment and facility influence coefficient. Accident cause of identification is given in table 5:
TABLE 5
Figure BDA0003274775610000134
Note: the weight coefficient value of the accident reason is between 0 and 1, and the sum is 1.
In the second step, the accident form risk level is calculated according to the following formula:
Figure BDA0003274775610000135
wherein, c4Representing the risk level of the accident pattern, w4Represents the macroscopic accident shape weight coefficient, w4iThe weight coefficient of the accident morphology microscopic factor is represented, and the weight value is shown in table 6:
TABLE 6
Figure BDA0003274775610000136
Figure BDA0003274775610000141
In the second step, the calculation formula of the accident liability risk level of the driver is as follows:
Figure BDA0003274775610000142
wherein, c5Indicates the accident liability risk level, w5Represents the accident responsibility macroscopic weight coefficient, w5iThe weight coefficient of the microscopic factor representing the accident responsibility is shown in the table 7:
TABLE 7
Figure BDA0003274775610000143
In the second step, the freight vehicle driver accident site risk level has the following calculation formula:
Figure BDA0003274775610000144
wherein, c6Indicating the accident site risk level, w6Represents a macroscopic weight coefficient, w, of the accident site6iThe weight coefficient of the microscopic factors representing the accident site is shown in table 8:
TABLE 8
Figure BDA0003274775610000145
In the second step, the risk level of the frequency of violation increases with the increase of the number of times of violation, the unit score of each violation is 10, the minimum value is 0, and the maximum value is 100, and the calculation formula is as follows:
v′1＝w′1×10×n＝0.68×10×n＝6.8n
in formula (II), v'1Representing a violation frequency risk level, w'1Representing the weight coefficient of the frequency of the violation, and n represents the frequency of the violation.
In the second step, 0-100 grades are given to aggressive driving behaviors, bad driving behaviors, vehicle safety problems, unconventional behaviors and other 5 types of illegal behaviors, the larger the grade is, the higher the risk of the driver is, and the illegal behavior risk calculation mode of the driver is as follows:
Figure BDA0003274775610000151
wherein, v'2A risk level for illegal activity of a driver of the freight vehicle; w'2Weight coefficient, b 'representing illegal behavior of driver'iScoring an unlawful actThe risk of illegal activity is shown in table 9:
TABLE 9
Figure BDA0003274775610000152
Figure BDA0003274775610000161
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
Counting the freight vehicle accidents and illegal drivers in Shanghai city in 2018, wherein the proportion of the drivers recorded in illegal is 80.98%; the proportion of drivers who have accidents and illegal records is 13.41 percent; the driver with only illegal records is 5.6% in duty.
Only illegal drivers, accident illegal drivers, only accident drivers appear close 13: 2: 1, in a ratio of 1. 2,022 accident violation data are randomly selected for verification, and the data distribution is shown in table 10:
watch 10
Type (B) Frequency of accidents Frequency of law violation (play) Number of people (human)
Accident-only driving 22 0 20
Accident illegal driving 23 38 22
Driving illegally only 0 1963 1219
Total of 46 2001 1261
The risk grade evaluation method of the freight vehicle driver based on the analytic hierarchy process is adopted to evaluate the risk of the truck driver, and the obtained result distribution of the risk grade of the truck driver is shown in fig. 2(a) and fig. 2(b), wherein 86 drivers with higher than moderate risk account for 7.1 percent.
The statistical results of the risk level scores of the drivers are shown in the following table, the risk levels of the drivers are increased along with the increase of the illegal frequency and the accident frequency, the risk levels of the drivers are gradually increased along with the increase of the illegal risk levels and the accident frequency, and the results of the risk levels of the drivers are shown in the table 11:
TABLE 11
Figure BDA0003274775610000162
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A freight vehicle driver risk level assessment method based on an analytic hierarchy process is characterized by comprising the following steps:
step 1) acquiring accident influence factors and illegal influence factors of a driver of a freight vehicle, and constructing a risk level evaluation model of the driver of the freight vehicle;
step 2) adopting an analytic hierarchy process to construct a driver risk grade evaluation module containing accident influence factors and illegal influence factors, and calculating weight coefficients of the accident influence factors and the illegal influence factors;
and 3) incorporating the weight coefficient obtained in the step 2) into a risk grade evaluation model of the freight vehicle driver, and calculating the risk grade of the freight vehicle driver, wherein the lower the score is, the higher the risk of the freight vehicle driver is, and the risk grade of the freight vehicle driver is divided into 6 risk grades with no risk to extremely high risk.
2. The analytic hierarchy process-based freight vehicle driver risk rating assessment method as claimed in claim 1, wherein the accident influence factors include accident frequency, accident severity, accident cause, accident liability, road environment.
3. The analytic hierarchy process-based freight vehicle driver risk rating assessment method as claimed in claim 1, wherein the illegal influencing factors include illegal frequency and illegal activities.
4. The analytic hierarchy process-based freight vehicle driver risk level assessment method according to claim 1, characterized in that the freight vehicle driver risk level assessment model in step 1) is calculated according to the following formula:
Figure FDA0003274775600000011
Riskd＝100-(wcrash×Lcrash+wviolation×Lviolation)
wherein RiskdRepresenting the risk level of a driver of the freight vehicle, and the score is 0-100; w is acrashWeight coefficient representing driver accident risk, LcrashRepresenting a driver accident risk level calculated from the accident data; w is aviolationWeight coefficient, L, representing the driver's illicit riskviolationRepresenting the level of driver's violation risk calculated by the violation data.
5. The method for assessing the risk level of the driver of the freight vehicle based on the analytic hierarchy process as claimed in claim 1, wherein the step 2) of constructing the driver risk level assessment model including the accident influence factor and the illegal influence factor comprises the following steps:
step 21) accident risk grades of a driver of the freight vehicle comprise the sum of 6 macroscopic factor grades of accident frequency, accident severity, accident identification reason, accident form, accident responsibility and accident site, and the formula is as follows:
Figure FDA0003274775600000021
in the formula, LcrashTo accident risk class, ciFor macroscopic accident influencing factor risk grade, including accident frequency c1Severity of the accident c2Cause of accident identification c3Accident pattern c4Accident responsibility c5And accident site c6；
Step 22) dividing accident identification reasons into three conditions of freight vehicles and driver reasons, non-motor-driven or pedestrian reasons and road environment reasons, scoring 0-100 aiming at each specific accident identification reason, wherein the higher the score is, the higher the risk level is, and meanwhile, correcting the accident risk level of the driver by combining the accident risk coefficient of the driver on the basis of the accident identification reasons, and comprehensively evaluating the risk level of the driver;
and step 23) taking the risk grade of the driver as a target, taking the macroscopic factors related to the accident risk as a standard detection layer, and taking the microscopic factors contained in the macroscopic factors as an index layer.
6. The analytic hierarchy process-based risk rating assessment method for driver of freight vehicle according to claim 5, wherein in step 22), when the accident is identified as freight vehicle and driver, the calculation formula is:
Figure FDA0003274775600000022
in the formula, LcrashFor the driver's accident risk level, wcrashAs a driver's accident risk weight coefficient, rjFor the reason of driver's accident, w is given a score for the freight vehicle and the driver's risk leveliThe accident risk macroscopic influence factor weight coefficient is represented by i, i represents the ith macroscopic accident risk factor, j represents the jth microscopic accident risk factor, and the accident risk grade is subjected to accumulation calculation according to the accident frequency;
when the accident is determined to be caused by non-maneuver or pedestrians, the calculation formula is as follows:
Figure FDA0003274775600000023
in the formula, LcrashFor the driver's accident risk level, wcrashAs a driver's accident risk weight coefficient, rj' rating the risk level for non-motorised or pedestrian for driver accident reasons, wiIs a macroscopic accident risk influencing factor weight coefficient, wijRepresenting the weight coefficient of the microscopic accident risk influence factors, and performing accumulation calculation on the accident risk level according to the accident frequency;
when the reason for the accident is determined to be the road environment, the calculation formula is as follows:
Figure FDA0003274775600000031
in the formula, LcrashFor the driver's accident risk level, wcrashAs a driver's accident risk weight coefficient, rj"rating the risk level for the driver for reasons of accident identification due to road environment, wiIs a weight coefficient of macroscopic influence factors, w, of accident riskijAnd expressing the weight coefficient of the microscopic accident risk influence factors, and performing accumulation calculation on the accident risk level according to the accident frequency.
7. The analytical hierarchy method-based freight vehicle driver risk level assessment method according to claim 6, wherein the driver risk level assessment model is specifically:
Figure FDA0003274775600000032
8. the analytic hierarchy process-based freight vehicle driver risk rating assessment method according to claim 7, wherein in the step 2), freight vehicle and driver cause risk ratings are given as follows:
Figure FDA0003274775600000033
Figure FDA0003274775600000041
the risk ratings for non-motor vehicles and pedestrian causes are given in the following table:
Figure FDA0003274775600000042
the risk rating for each road environmental cause is given in the following table:
Figure FDA0003274775600000051
9. the analytic hierarchy process-based freight vehicle driver risk level assessment method according to claim 7, wherein in the step 2), accident frequency scores are cumulatively calculated, the accident frequency risk level increases with the increase of the number of accidents, the unit score of each accident is 20, the minimum value is 0, the maximum value is 100, and the calculation formula is as follows:
c1＝w1×20×n＝0.23×20×n＝4.6n
wherein, c1Indicating the risk level of the accident frequency, w1Representing the accident frequency weight coefficient, and n represents the accident frequency;
according to the accident severity data, the accident severity is calculated by combining the number of the accident and the number of the death and injury persons, and the risk calculation mode is as follows:
Figure FDA0003274775600000052
Figure FDA0003274775600000053
in the formula, c2Indicating the accident type risk level, w2A weight coefficient representing the severity of the accident, R represents the risk level of the accident identified cause, wherein { R ∈ (R ∈ [)i，ri′，ri″)|0≤ri≤100，0≤ri′≤100，0≤ri″≤100}；
The accident reason is classified into 3 types, namely freight vehicle and driver reasons, non-motor vehicle and pedestrian reasons and road environment and facility reasons, and the calculation formula is as follows:
Figure FDA0003274775600000061
wherein, w31Influence coefficients for reasons of freight vehicles and drivers; w is a32Influence coefficients for non-motor vehicles and behavior reasons; w is a33The road environment and facility influence coefficient;
the accident form risk level is calculated according to the following formula:
Figure FDA0003274775600000062
wherein, c4Representing the risk level of the accident pattern, w4Represents the macroscopic accident shape weight coefficient, w4iRepresenting the accident form micro factor weight coefficient;
the calculation formula of the accident liability risk level of the driver is as follows:
Figure FDA0003274775600000063
wherein, c5Indicates the accident liability risk level, w5Represents the accident responsibility macroscopic weight coefficient, w5iRepresenting accident liability microfactor weight coefficients;
the freight vehicle driver accident site risk level has the following calculation formula:
Figure FDA0003274775600000064
wherein, c6Indicating the accident site risk level, w6Represents a macroscopic weight coefficient, w, of the accident site6iRepresenting the incident site micro-factor weight coefficient.
10. The analytic hierarchy process-based freight vehicle driver risk level assessment method according to claim 7, wherein in the step 2), the risk level of the frequency of violation increases with the increase of the number of violations, the unit score of each violation is 10, the minimum value is 0, and the maximum value is 100, and the calculation formula is as follows:
v′1＝w′1×10×n＝0.68×10×n＝6.8n
in formula (II), v'1Representing a violation frequency risk level, w'1Representing the weight coefficient of the frequency of the violation, wherein n represents the frequency of the violation;
the method comprises the following steps of scoring a sharp driving behavior, a bad driving behavior, a vehicle safety problem, a non-rule and other 5 kinds of illegal behaviors by 0-100 grades, wherein the larger the score is, the higher the risk of the driver is, and the illegal behavior risk calculation mode of the driver is as follows:
Figure FDA0003274775600000071
wherein, v'2A risk level for illegal activity of a driver of the freight vehicle; w'2Weight coefficient, b 'representing illegal behavior of driver'iIs the score of the illegal activity.
CN202111113853.2A 2021-09-23 2021-09-23 Freight vehicle driver risk grade assessment method based on analytic hierarchy process Pending CN113887904A (en)

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103218522A (en) * 2013-04-01 2013-07-24 民政部国家减灾中心 Method and device for grading flood risk
CN106228499A (en) * 2016-07-06 2016-12-14 东南大学 A kind of cargo security evaluation model based on people's bus or train route goods multi-risk System source
CN109243178A (en) * 2018-11-14 2019-01-18 上海应用技术大学 Town way Traffic Safety Analysis and evaluation method under the conditions of a kind of bad climate
CN109242227A (en) * 2017-07-10 2019-01-18 卢照敢 The driving risk and assessment models of car steering behavior
CN111667204A (en) * 2020-07-22 2020-09-15 同济大学 Method and system for determining and grading environmental risk degree of automatic driving open test road
US20210110480A1 (en) * 2019-10-13 2021-04-15 TrueLite Trace, Inc. Intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103218522A (en) * 2013-04-01 2013-07-24 民政部国家减灾中心 Method and device for grading flood risk
CN106228499A (en) * 2016-07-06 2016-12-14 东南大学 A kind of cargo security evaluation model based on people's bus or train route goods multi-risk System source
CN109242227A (en) * 2017-07-10 2019-01-18 卢照敢 The driving risk and assessment models of car steering behavior
CN109243178A (en) * 2018-11-14 2019-01-18 上海应用技术大学 Town way Traffic Safety Analysis and evaluation method under the conditions of a kind of bad climate
US20210110480A1 (en) * 2019-10-13 2021-04-15 TrueLite Trace, Inc. Intelligent machine sensing and machine learning-based commercial vehicle insurance risk scoring system
CN111667204A (en) * 2020-07-22 2020-09-15 同济大学 Method and system for determining and grading environmental risk degree of automatic driving open test road

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
田静静 等: "基于模糊集-证据理论-层次分析法的车辆运行风险评估", 《科学技术与工程》 *

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