CN106448158A - Corresponding analysis based traffic accident cause analyzing method - Google Patents
Corresponding analysis based traffic accident cause analyzing method Download PDFInfo
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- CN106448158A CN106448158A CN201610807054.8A CN201610807054A CN106448158A CN 106448158 A CN106448158 A CN 106448158A CN 201610807054 A CN201610807054 A CN 201610807054A CN 106448158 A CN106448158 A CN 106448158A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- G06F18/00—Pattern recognition
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- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
Abstract
The invention provides a corresponding analysis based traffic accident cause analyzing method, comprising the following steps: inputting traffic accident data and traffic violation data wherein both are correlated through the identity information of a driver; conducting corresponding analysis on the type of traffic accident and the type of traffic violation by the driver; and conducting multiple corresponding analysis on the type of the traffic accident by the driver, the driver's age and gender as well as the car family. The corresponding analysis based traffic accident cause analyzing method of the invention is capable of analyzing the driver's driving actions and the influencing factors on the driving actions through the recorded violations, driving age, gender, car brand of the driver as well as the type of traffic accident so as to make targeted precautions to traffic accident and increase the safety on the road.
Description
Technical field
The present invention relates to a kind of traffic accidents reason analysis method based on correspondence analysis model.
Background technology
With gradually rising for domestic automobile recoverable amount, vehicle accident problem is also more projected；Various types of traffic things
Therefore emerge in an endless stream；Various cause of accident are certainly existed in the behind of these accident patterns, based on driver, be based on
Road environment etc.；It is thus desirable to by substantial amounts of data be analyzed explore, find out cause various accident pattern factors it
Between difference and contact；So as to targetedly prevent the generation of vehicle accident, the safety of road is lifted.
Method for traffic accidents reason analysis lacks careful classification at present, has focused largely on vehicle accident in research
Number of times and its impact relation；And the accident pattern analysis shortage of the driving behavior analysis for driver, driver is ground accordingly
Study carefully；It is the fuse cord for causing finally to occur vehicle accident to break rules and regulations in reality, how to break rules and regulations record exploring from the history of driver
Driver's which kind of act of violating regulations in driving procedure can cause what type of vehicle accident；Secondly the driving behavior of driver again and
The vehicle that driver's driving age, sex drive has some relations；The solution of the problems referred to above has positive to the analysis of Traffic Accidents Reasons Analyzed
Effect.
Content of the invention
It is an object of the invention to provide a kind of traffic accidents reason analysis method based on correspondence analysis model, for difference
Vehicle accident type, using correspondence analysis and the method for cluster, the impact origin cause of formation that accident pattern occurs is carried out various dimensions and divides
Analysis is explored, and solves the above-mentioned problems in the prior art.
The technical solution of the present invention is：
A kind of traffic accidents reason analysis method based on correspondence analysis model, comprises the following steps,
S1, input break in traffic rules and regulations data and vehicle accident data, and break in traffic rules and regulations data and vehicle accident data are passed through
Driver identification information is associated；
S2, vehicle accident type and break in traffic rules and regulations type correspondence analysis, carry out exploratory analysis, first using pivot
Figure statistics violation data, and the sample data violating the regulations of subsequent analysis is filtered out by threshold method；It is right under every kind of type of violation to find out
The accident crowd for answering and its accident pattern of generation, count accounting of every kind of accident pattern in all accident patterns；Secondly, choosing
Taking representational type of fault and its accident pattern carries out simple correspondence analysis；
The Multiple Correspondence Analysis of S3, vehicle accident type and driver's driving age, sex and driving vehicle；Carry from casualty data
Take accident responsibility side's identity information, and its corresponding accident pattern, vehicle brand styles, driver's driving age and four class of sex
Information；T class accident pattern, y class are driven type of vehicle, j class driving age classification, sex one-to-one corresponding, produces altogether x=(2*t*
Y*j) class combination；The corresponding number of each class combination is counted, many using drawing to the method for Multiple Correspondence Analysis in statistics
Weight corresponding relation.
Further, in step S1, the characteristic vector of vehicle accident data is that CS=[C, K, V], wherein C occur for accident
Type；K is personal information, including driver's sex, driving age, accident responsibility ownership；V is vehicle brand information；Break in traffic rules and regulations data
Characteristic vector be WS=[A, H], wherein A for break rules and regulations personnel identity information, H be break rules and regulations personnel content violating the regulations.
Further, in step S2, exploratory analysis are carried out and is specially：
S21, according to vehicle accident type, accident driver is classified；
S22, according to accident driver information, the record violating the regulations of screening accidents happened driver, statistics from break in traffic rules and regulations tables of data
All kinds of number of times violating the regulations of each accident pattern driver；
S23, according to the quantity all kinds of violating the regulations after statistics, retain type of violation of the number of times more than or equal to p time of breaking rules and regulations, wherein p
For screening sample threshold value of breaking rules and regulations；
S24, the corresponding various break in traffic rules and regulations number of times of each type vehicle accident of calculating are total with each type break in traffic rules and regulations
The ratio of number of times；
S25, with the ratio of driver's break in traffic rules and regulations as vertical coordinate, the type of driver's break in traffic rules and regulations is abscissa, will be all types of
The ratio of all kinds of breaks in traffic rules and regulations that the driver of vehicle accident occurs is drawn in this coordinate system, observes all kinds of accident pattern drivers
Ratio all kinds of violating the regulations, desk study accident and relation violating the regulations.
Further, step S24 is specifically, computing formula is as follows：
Vehicle accident A={ a1, a2... ai... am, wherein aiFor vehicle accident type, m is accident pattern order；
Accident number K={ k1, k2... k3..., km, wherein kiFor occurring vehicle accident type to be aiNumber；
Type of violation B={ b1, b2... bi..., bn, biFor break in traffic rules and regulations type, n is type of violation order；
Number violating the regulationskmbnIt is a for driver's vehicle accident typem, which occurs violating the regulations
Type is bnNumber of times；
Type of violation is the total number of persons of the driver of m
Driver breaks rules and regulations total degree
Driver's type of violation scaling matrices
Further, in step S2, choose representational type of fault and its accident pattern is simply corresponded to
Analyze, the standard of selection is：
Wherein, δnDistribution proportion and total accident pattern of the driver in each class type of violation for various accident patterns
The difference that 2 times of number distribution proportion；λ is type of fault outlier threshold corresponding with classification violating the regulations；μ is each class type of violation number
Threshold value.
The practical significance of selection standard is the distribution ratio for finding out the driver of various accident patterns in each class type of violation
Example and the difference of total accident pattern number distribution proportion, draw type of violation with accident pattern number distribution proportion exception with this
Value；
Further, driver's driving age in step S3, sex, drive type of vehicle and vehicle accident type occur more
First correspondence analysis is comprised the following steps：
S31, according to occur vehicle accident classification of type accident responsibility side driver；
S32, the driving age to these drivers carry out simple statistics, obtain driver and change of the accident quantity on driver's driving age occurs
Change trend；
S33, the variation tendency for being obtained according to step S32, the driving age of accident responsibility side driver is carried out segmentation classification；
S34, according to vehicle brand type by vehicle classification；
Under the every class accident pattern of S35, statistics, the driving age of each driver, sex, the type of vehicle for driving, merge similar
Statistics is produced corresponding number under the combination of x=(2*t*y*j) class and the combination of each class altogether；
S36, the result for obtaining step S35 draw multiple corresponding relation using the method for correspondence analysis, in two-dimensional space
With the relation between the reflection variable of the formal intuition of distance between points in coordinate.
The invention has the beneficial effects as follows：Traffic accidents reason analysis method of this kind based on correspondence analysis model, using right
Analysis method is answered to causing the different types of accident origin cause of formation that various dimensions exploration is carried out, by driver's history record, driving age, property violating the regulations
Not, vehicle brand and the type that vehicle accident occurs, are analyzed to driver driving behavior and its influence factor, and it is multiple right to draw
Should be related to；Specific aim so as to realize vehicle accident is prevented, and lifts the safety of road.
Description of the drawings
Fig. 1 is schematic flow sheet of the embodiment of the present invention based on the traffic accidents reason analysis method of correspondence analysis model.
Fig. 2 is the schematic diagram of the accident pattern for obtaining in embodiment and its corresponding type of violation.
Fig. 3 is accident pattern and its type of violation simply corresponding scatterplot in embodiment.
Fig. 4 is the classification point joint figure of Multiple Correspondence Analysis in embodiment.
Specific embodiment
The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.
Embodiment
Corresponding analysis method in embodiment Statistics Application carries out various dimensions spy to causing the different types of accident origin cause of formation
Rope.By driver's history record violating the regulations, embodiment explores driver's which kind of act of violating regulations in driving procedure can cause which kind of type
Vehicle accident；Secondly analysis draws the relation of the vehicle that the driving behavior of driver and driver's driving age, sex drive.
The traffic accidents reason analysis method based on correspondence analysis model of embodiment, comprises the following steps：Input traffic
Casualty data and break in traffic rules and regulations data, the two passes through driver identification information association；Driver's vehicle accident type and its traffic are disobeyed
Chapter type carries out correspondence analysis；To driver's vehicle accident type and driver's driving age, sex, car system Multiple correspondence analysis.
The traffic accidents reason analysis method based on correspondence analysis model of embodiment, by driver's history record violating the regulations,
Driving age, sex, vehicle brand and the type that vehicle accident occurs, are analyzed to driver driving behavior and its influence factor；From
And the specific aim for realizing vehicle accident is prevented, the safety of road is lifted.
Break in traffic rules and regulations data include：Personnel identity information violating the regulations, the content violating the regulations of the personnel that break rules and regulations.
Vehicle accident data include：Accident occurrence type, driver's sex, attribution of liability, driving age, vehicle brand information.
Traffic accidents reason analysis method of this kind based on correspondence analysis model, such as Fig. 1, comprise the following steps,
S1, input break in traffic rules and regulations data and vehicle accident data, and break in traffic rules and regulations data and vehicle accident data are passed through
Driver identification information is associated；It is accident occurrence type that the characteristic vector of vehicle accident data is CS=[C, K, V], wherein C；
K is personal information, including driver's sex, driving age, accident responsibility ownership；V is vehicle brand information；The feature of break in traffic rules and regulations data
Vector is personnel identity information of breaking rules and regulations for WS=[A, H], wherein A, and H is the content violating the regulations of the personnel that break rules and regulations.
S2, vehicle accident type and break in traffic rules and regulations type correspondence analysis, carry out exploratory analysis, first using pivot
Figure statistics violation data, and the sample data violating the regulations of subsequent analysis is filtered out by threshold method；It is right under every kind of type of violation to find out
The accident crowd for answering and its accident pattern of generation, count accounting of every kind of accident pattern in all accident patterns；Secondly, choosing
Taking representational type of fault and its accident pattern carries out simple correspondence analysis.
In step S2, carry out exploratory analysis and be specially：
S21, according to vehicle accident type, accident driver is classified；
S22, according to accident driver information, the record violating the regulations of screening accidents happened driver, statistics from break in traffic rules and regulations tables of data
All kinds of number of times violating the regulations of each accident pattern driver；
S23, according to the quantity all kinds of violating the regulations after statistics, retain type of violation of the number of times more than or equal to p time of breaking rules and regulations, wherein p
For screening sample threshold value of breaking rules and regulations；
S24, the corresponding various break in traffic rules and regulations number of times of each type vehicle accident of calculating are total with each type break in traffic rules and regulations
The ratio of number of times, computing formula is as follows：
Vehicle accident A={ a1, a2... ai... am, wherein aiFor vehicle accident type, m is accident pattern order；
Accident number K={ k1, k2... k3..., km, wherein kiFor occurring vehicle accident type to be aiNumber；
Type of violation B={ b1, b2... bi..., bn, biFor break in traffic rules and regulations type, n is type of violation order；
Number violating the regulationskmbnIt is a for driver's vehicle accident typem, which occurs violating the regulations
Type is bnNumber of times；
Type of violation is the total number of persons of the driver of m
Driver breaks rules and regulations total degree
Driver's type of violation scaling matrices
S25, with the ratio of driver's break in traffic rules and regulations as vertical coordinate, the type of driver's break in traffic rules and regulations is abscissa, will be all types of
The ratio of all kinds of breaks in traffic rules and regulations that the driver of vehicle accident occurs is drawn in this coordinate system, observes all kinds of accident pattern drivers
Ratio all kinds of violating the regulations, desk study accident and relation violating the regulations.
S26, the representational type of fault of selection and its accident pattern carry out simple correspondence analysis, and the standard of selection is：
Wherein, δnDistribution proportion and total accident pattern of the driver in each class type of violation for various accident patterns
The difference that 2 times of number distribution proportion；λ is type of fault outlier threshold corresponding with classification violating the regulations；μ is each class type of violation number
Threshold value.
The practical significance of selection standard is the distribution ratio for finding out the driver of various accident patterns in each class type of violation
Example and the difference of total accident pattern number distribution proportion, draw type of violation with accident pattern number distribution proportion exception with this
Value；
The Multiple Correspondence Analysis of S3, vehicle accident type and driver's driving age, sex and driving vehicle；Carry from casualty data
Take accident responsibility side's identity information, and its corresponding accident pattern, vehicle brand styles, driver's driving age and four class of sex
Information；T class accident pattern, y class are driven type of vehicle, j class driving age classification, sex one-to-one corresponding, produces altogether x=(2*t*
Y*j) class combination；The corresponding number of each class combination is counted, many using drawing to the method for Multiple Correspondence Analysis in statistics
Weight corresponding relation.
Driver's driving age, sex, driving type of vehicle and the Multiple Correspondence Analysis bag that vehicle accident type occurs in step S3
Include following steps：
S31, according to occur vehicle accident classification of type accident responsibility side driver；
S32, the driving age to these drivers carry out simple statistics, obtain driver and change of the accident quantity on driver's driving age occurs
Change trend；
S33, the variation tendency for being obtained according to step S32, the driving age of accident responsibility side driver is carried out segmentation classification；
S34, according to vehicle brand type by vehicle classification；
Under the every class accident pattern of S35, statistics, the driving age of each driver, sex, the type of vehicle for driving, merge similar
Statistics is produced corresponding number under the combination of x=(2*t*y*j) class and the combination of each class altogether；
S36, the result for obtaining step S35 draw multiple corresponding relation using the method for correspondence analysis, in two-dimensional space
With the relation between the reflection variable of the formal intuition of distance between points in coordinate.
Experimental verification
Data Analysis Services done in embodiment method, mainly use EXCEL and IBM SPSS Statistics22.0
Statistical analysis software；It is related to the based process means such as the screening of EXCEL, comparison, PivotTables and the correspondence in SPSS
Analysis method.
As Fig. 1, a kind of traffic accidents reason analysis method based on correspondence analysis model, comprise the following steps：
Input driver's violation data and casualty data, the two is mutually corresponded to by driver identification information.
Case data contains 56651 annual vehicle accident data of certain city 2015 and 2014-2016's 2 years
61473 break in traffic rules and regulations data, are related to more than 113302 people of personnel；Wherein, casualty data includes driver identification card number, accident class
Not, accountability information, driver's driving age, sex, vehicle brand；Violation data include driver identification card number, break rules and regulations code name,
Content violating the regulations.
Processed by Microsoft Excel, accident occurrence type is that of a sort driver and its merging of type of violation data are carried
Take, obtain its normal property type of violation situation of driver that certain class accident occurs；According to certain type of violation generation quantity by 200
Plant violation data to screen to 20 kinds, as a result as shown in Figure 2.
Responsible driver and its situation discovery of breaking rules and regulations in two years in contrast accident, its type of violation concentrates on a few
Class；And find people's warp that different accident patterns correspond to different types violating the regulations occurred frequently, that is, a certain class vehicle accident occurs
The violating the regulations a certain type of perseverance；Fig. 2 shows that the driver of 9 class vehicle accident types of generation is corresponding and the traffic occurred frequently of 20 classes occurs
Ratio shared by type of violation；Its generation of people that rear-end collision for example occurs " enters guided vehicle road, does not travel by prescribed direction
" this type of violation just will be high (accounting 22.29%) than other type of violation；Accident occurrence type is traffic signal violation
Driver, there is " on road of the operating motor vehicles beyond highway, city expressway not by specify lanes " in which
This accident pattern accounting is up to 60.66%；Therefore guess, can be prejudged by driver's history act of violating regulations may generation
Accident pattern, targetedly take precautions against the generation of vehicle accident so as to officials' machine；
According to above-mentioned exploration thinking, choosing representational type of fault and its accident pattern carries out correspondence analysis；Choose
δn>0, cnThe type of violation as experiment of ＞ 50, according to the matrix for obtaining, chooses accident pattern serial number 1-7 as experiment
Accident pattern；The practical significance of selection standard is the distribution ratio for finding out the driver of various accident patterns in each class type of violation
Example and the difference of total accident pattern number distribution proportion, draw type of violation with accident pattern number distribution proportion exception with this
Value；Experimental subject and result table 1
Type of fault and classification violating the regulations that table 1 is tested
Above-mentioned 7 class accident pattern and its respectively corresponding 13 class are broken rules and regulations the quantity of type occurred frequently, carries out correspondence analysis.
The statistical abstract table of 2 correspondence analysis of table
A. degree of freedom 72
Table 2 is the statistical abstract table of correspondence analysis, in this experiment, subtracts 1 according to type of fault (7 class), i.e., 6 dimensions
Degree；" inertia ratio " represents eigenvalue, is to weigh the index for explaining data variation ability；In table, the first dimension illustrates 42.8%
Variation, the second dimension illustrates remaining 28.4% variation；Standard deviation 0.22-0.30 of two dimension, illustrates point estimate ratio
Relatively accurate, correlation coefficient -0.024 of the factor, then explanation factorisation is highly stable；Therefore the reliability that this correspondence analysis is tested
Property is higher.
Shown in Fig. 3 is the scatterplot of correspondence analysis, and obvious in Fig. 3 is type of fault occur for " 1 " and classification
The driver of " 7 " is higher with the dependency of type of violation " g ", " b ", " d "；There is driver and type of violation of the type of fault for " 3 "
The dependency of " a " and " f " is higher；The driver for type of fault occur for " 2 " is higher with the dependency of type of violation " e "；There is thing
Therefore classification is higher with the dependency of type of violation " c " for the driver of " 4 "；Occur type of fault for " 5 " and " 6 " driver its separated
Without obvious difference in chapter classification.
Secondly, from the point of view of according to type of violation and corresponding quantity, there is accident pattern for " 1 ", " 3 ", " 6 ", the driver of " 7 ",
Can be approximate be divided into a class；Thereby it is assumed that out, the driving behavior of this class accident driver exists larger similar.
From accident extracting data accident responsibility side identity information, and its corresponding accident pattern, vehicle brand class
Type, driver's driving age and four category information of sex, such as table 3；Accident pattern is divided into nine classes according to initial data, concrete classification situation and
In weight frequency such as table 3 shown in point table 1；Vehicle brand styles are divided into six classes according to car system, concrete situation and the weight of classifying
In frequency such as table 3 shown in point table 2；Driving age classification is divided into 4 classes according to responsible party driver's driving age distribution situation；Concrete classification situation and
In weight frequency such as table 3 shown in point table 3；In accident responsibility side's Gender Classification and its weight frequency such as table 3 shown in point table 4.
By the accident pattern of accident responsibility side's generation, driving type of vehicle, driving age classification, sex one in 29098 accidents
One corresponds to, and amounts to and produces 356 kinds of efficient combination types, using the method in statistics to Multiple Correspondence Analysis, in dots
More intuitively by the relation between distance two classified variables of reaction in two-dimensional space.
3 descriptive statistic summary sheet of table
Variable main body normal state；
The model abstract of result shows：Cronbach's Alpha meansigma methodss are equal to 0.572, in exploratory study
The upper certainty value is in tolerance interval；Dimension 1 and dimension 2 amount up to 87.6% to the variable explanation degree of model, are considered as anti-
Most of feature of model is reflected；Therefore the statistical tool from Multiple correspondence analysis in the case is suitable.
4 model of table is made a summary
A.Cronbach's Alpha meansigma methodss be based on mean eigenvalue；
Shown in Fig. 4 is the scatterplot of Multiple Correspondence Analysis, i.e., represent the potential of classification and sample by way of figure
Relation, row point is closer with the nearlyer expression relation of the distance of row point；The results contrast for finally drawing is intuitively：
Driving age is more easy to, for the driver of 0-2, the accident pattern for knocking into the back；Driving age is susceptible to violate for the driver of 3-7
The accident pattern of traffic signal；Driving age for 8-12 driver be more easy to occur because stop when non-change into lower gear, do not draw parking braking,
Cause the accident pattern of vehicle sliding；The old driver of more than 12 years does not have particular incident occurrence type.
Male driver be more easy to occur because stop when non-change into lower gear, do not draw parking braking, cause the accident class of vehicle sliding
Type；Women driver is more easy to the accident pattern for occurring not give way by regulation；Men and women driver is susceptible to retrograde accident pattern.
Data analysiss show：The car owner of domestic car be more easy to occur because stop when non-change into lower gear, do not draw parking braking, cause
The accident pattern of vehicle sliding；U.S.A is the accident pattern that the car owner of car is more easy to reversing；The car owner of Korea Spro system car is more easy to chase after
The accident pattern of tail；The accident pattern that the car owner of Japanese car occurs is mostly the other types that should bear all the responsibilities in accordance with the law；Domestic car phase
To being easier to vehicle accident and accident pattern is more complicated；And it is safer for German car other kinds of car relatively.
In general：Domestic car being driven, the driving age is the male driver of 8-12, generation is more easy to for classification because stopping
Che Shiwei change into lower gear, parking braking is not drawn, cause the accident pattern of vehicle sliding.
Claims (6)
1. a kind of traffic accidents reason analysis method based on correspondence analysis model, it is characterised in that：Comprise the following steps,
S1, input break in traffic rules and regulations data and vehicle accident data, and break in traffic rules and regulations data and vehicle accident data are passed through driver
Identity information is associated；
S2, vehicle accident type and break in traffic rules and regulations type correspondence analysis, are carried out exploratory analysis first, are united using pivot chart
Meter violation data, and the sample data violating the regulations of subsequent analysis is filtered out by threshold method；Find out corresponding under every kind of type of violation
Accident crowd and its accident pattern of generation, count accounting of every kind of accident pattern in all accident patterns；Secondly, selection has
Representational type of fault and its accident pattern carry out simple correspondence analysis；
The Multiple Correspondence Analysis of S3, vehicle accident type and driver's driving age, sex and driving vehicle；From accident extracting data thing
Therefore responsible party's identity information, and its corresponding accident pattern, vehicle brand styles, driver's driving age and four class of sex letter
Breath；T class accident pattern, y class are driven type of vehicle, j class driving age classification, sex one-to-one corresponding, produces altogether x=(2*t*y*
J) class combination；The corresponding number of each class combination is counted, multiple using drawing to the method for Multiple Correspondence Analysis in statistics
Corresponding relation.
2. the traffic accidents reason analysis method based on correspondence analysis model as claimed in claim 1, it is characterised in that：Step
In S1, the characteristic vector of vehicle accident data is accident occurrence type for CS=[C, K, V], wherein C；K is personal information, including
Driver's sex, driving age, accident responsibility ownership；V is vehicle brand information；The characteristic vector of break in traffic rules and regulations data is WS=[A, H],
Wherein A is personnel identity information of breaking rules and regulations, and H is the content violating the regulations of the personnel that break rules and regulations.
3. the traffic accidents reason analysis method based on correspondence analysis model as claimed in claim 1, it is characterised in that：Described
In step S2, carry out exploratory analysis and be specially：
S21, according to vehicle accident type, accident driver is classified；
S22, according to accident driver information, from break in traffic rules and regulations tables of data, the record violating the regulations of screening accidents happened driver, counts each
All kinds of number of times violating the regulations of accident pattern driver；
S23, according to the quantity all kinds of violating the regulations after statistics, retain type of violation of the number of times more than or equal to p time of breaking rules and regulations, wherein p is separated
Chapter screening sample threshold value；
S24, the corresponding various break in traffic rules and regulations number of times of each type vehicle accident are calculated with the total degree of each type break in traffic rules and regulations
Ratio；
S25, with the ratio of driver's break in traffic rules and regulations as vertical coordinate, the type of driver's break in traffic rules and regulations be abscissa, by all types of traffic
The ratio of all kinds of breaks in traffic rules and regulations that the driver of accident occurs is drawn in this coordinate system, observes each of all kinds of accident pattern drivers
Class ratio violating the regulations, desk study accident and relation violating the regulations.
4. the traffic accidents reason analysis method based on correspondence analysis model as claimed in claim 3, it is characterised in that：Described
Step S24 is specifically, computing formula is as follows：
Vehicle accident A={ a1, a2... ai... am, wherein aiFor vehicle accident type, m is accident pattern order；
Accident number K={ k1, k2... k3..., km, wherein kiFor occurring vehicle accident type to be aiNumber；
Type of violation B={ b1, b2... bi..., bn, biFor break in traffic rules and regulations type, n is type of violation order；
Number violating the regulationskmbnIt is a for driver's vehicle accident typem, there is type of violation in which
For bnNumber of times；
Type of violation is the total number of persons of the driver of m
Driver breaks rules and regulations total degree
Driver's type of violation scaling matrices
5. the traffic accidents reason analysis method based on correspondence analysis model as claimed in claim 3, it is characterised in that：Described
In step S2, choosing representational type of fault and its accident pattern carries out simple correspondence analysis, and the standard of selection is：
Wherein, δnDistribution proportion and total accident pattern number of the driver in each class type of violation for various accident patterns
The difference that 2 times of distribution proportion；λ is type of fault outlier threshold corresponding with classification violating the regulations；μ is the threshold of each class type of violation number
Value.
6. the traffic accidents reason analysis method based on correspondence analysis model as described in any one of claim 1-5, its feature
It is：Driver's driving age, sex, driving type of vehicle and the Multiple Correspondence Analysis bag that vehicle accident type occurs in step S3
Include following steps：
S31, according to occur vehicle accident classification of type accident responsibility side driver；
S32, the driving age to these drivers carry out simple statistics, obtain driver and occur change of the accident quantity on driver's driving age to become
Gesture；
S33, the variation tendency for being obtained according to step S32, the driving age of accident responsibility side driver is carried out segmentation classification；
S34, according to vehicle brand type by vehicle classification；
Under the every class accident pattern of S35, statistics, the driving age of each driver, sex, the type of vehicle for driving, merge similar statistics
X=(2*t*y*j) class combination and each class combination under corresponding number is produced altogether；
S36, the result for obtaining step S35 draw multiple corresponding relation using the method for correspondence analysis, in two-dimensional space coordinate
In with the formal intuition of distance between points reflection variable between relation.
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| CN108922170A (en) * | 2018-06-13 | 2018-11-30 | 同济大学 | A kind of Intersection Safety Evaluation Method based on electric police grasp shoot data |
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| CN110717035A (en) * | 2018-07-11 | 2020-01-21 | 北京嘀嘀无限科技发展有限公司 | Accident rapid processing method, system and computer readable medium |
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