CN111126868B - Road traffic accident occurrence risk determination method and system - Google Patents
Road traffic accident occurrence risk determination method and system Download PDFInfo
- Publication number
- CN111126868B CN111126868B CN201911389961.5A CN201911389961A CN111126868B CN 111126868 B CN111126868 B CN 111126868B CN 201911389961 A CN201911389961 A CN 201911389961A CN 111126868 B CN111126868 B CN 111126868B
- Authority
- CN
- China
- Prior art keywords
- traffic accident
- determining
- risk
- level
- classification model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 206010039203 Road traffic accident Diseases 0.000 title claims abstract description 185
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000013145 classification model Methods 0.000 claims abstract description 67
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 14
- 238000007477 logistic regression Methods 0.000 claims abstract description 8
- 238000003066 decision tree Methods 0.000 claims description 9
- 238000011478 gradient descent method Methods 0.000 claims description 7
- 238000007637 random forest analysis Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 4
- 230000035622 drinking Effects 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 4
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 description 24
- 239000013598 vector Substances 0.000 description 4
- 208000027418 Wounds and injury Diseases 0.000 description 3
- 230000006378 damage Effects 0.000 description 3
- 208000014674 injury Diseases 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000013179 statistical model Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 235000019441 ethanol Nutrition 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000003064 k means clustering Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000010206 sensitivity analysis Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to a road traffic accident occurrence risk determining method and system. The method comprises the following steps: acquiring data of traffic accidents in a database; acquiring a plurality of classification models which take all risk factors corresponding to each traffic accident as input and take the probability of causing all traffic accident grades as output; according to the data of the traffic accidents, determining the probability of all traffic accident levels by using each classification model; carrying out weighted regression on the probability of all traffic accident levels output by each classification model by utilizing a logistic regression algorithm, and determining an output result after the weighted regression; determining the level of the traffic accident according to the output result after weighted regression; and determining the occurrence risk of the road traffic accident according to the grade of the traffic accident. The method and the system provided by the invention solve the problem that the occurrence risk of road traffic accidents cannot be effectively determined in the prior art.
Description
Technical Field
The invention relates to the field of road evaluation, in particular to a road traffic accident occurrence risk determining method and system.
Background
Road traffic accidents can cause personal injury and serious loss of property. By collecting traffic accident data and traffic environment data during accident, such as vehicle conditions, accident road section positions, accident time, accident road design and the like, the method is beneficial to the management department to reduce accident occurrence probability and loss caused by accidents by improving road infrastructure conditions.
In the past few decades, a great deal of research and investigation have been conducted on the relationship between the accident level and its related risk factors (such as traffic flow, geometric design of roads, age of drivers and external environmental characteristics, etc.), and the risk of occurrence of traffic accidents can be determined according to the related factors, so that corresponding measures are taken to avoid occurrence of serious traffic accidents.
At present, the method for determining the occurrence risk of the road traffic accident mainly comprises two types. The first type is an analysis method based on a statistical model, the method can analyze the influence of each factor on the severity (grade) by constructing mathematical and physical relations between the severity of the accident and each related factor or explanatory variable, and the method has good theoretical explanatory property and clear calculation structure. The second category is accident severity classification methods based on machine learning models, such as artificial neural networks, decision trees, support vector machines, and the like.
In summary, early studies aimed at analyzing the relationship between the accident level and different factors were mainly based on statistical methods. However, the statistical model has a disadvantage that most models assume that factors affect the severity of an accident in a linear manner, and once the assumptions of the statistical model are violated, deducing deviations in the degree of influence of the factors can result. Moreover, the main disadvantages of the machine learning method are that a black box modeling mechanism is adopted, explanation on the accident severity and the related variable correlation is lacking, and each machine learning model brings certain limitation, such as the problems that an artificial neural network is easy to be over-fitted, a decision tree is greatly influenced by sample unbalance, K-means clustering is sensitive to outliers and the like.
Therefore, according to the method, the prior art cannot accurately determine the occurrence risk of the traffic accident according to the related factors.
Disclosure of Invention
The invention aims to provide a method and a system for determining the occurrence risk of a road traffic accident, which solve the problem that the occurrence risk of the road traffic accident cannot be effectively determined in the prior art.
In order to achieve the above object, the present invention provides the following solutions:
a road traffic accident occurrence risk determination method, comprising:
acquiring data of traffic accidents in a database; the data of the traffic accident comprise risk factors of the traffic accident and corresponding traffic accident grades; the risk factors include: expressway ramp length, average daily traffic volume of main road, gradient of road, weather, age of driver, driving age, whether drinking wine or not, and vehicle number;
acquiring a plurality of classification models which take all risk factors corresponding to each traffic accident as input and take the probability of causing all traffic accident grades as output; the classification model is a random forest model, a gradient lifting decision tree and a self-adaptive lifting algorithm;
according to the data of the traffic accidents, determining the probability of all traffic accident grades caused by all risk factors corresponding to each traffic accident by using each classification model;
carrying out weighted regression on the probability of all traffic accident levels output by each classification model by utilizing a logistic regression algorithm, and determining an output result after the weighted regression;
determining the level of the traffic accident according to the output result after the weighted regression;
and determining the occurrence risk of the road traffic accident according to the traffic accident level.
Optionally, the acquiring takes all risk factors as input, takes probabilities of all traffic accident levels as output, and further includes:
determining the weight of each risk factor according to each classification model;
weighted average is carried out on a plurality of weights of each risk factor, and the final weight of each risk factor is determined;
sorting the final weights of all the risk factors, and determining key risk factors; the key risk factors are the risk factors with the highest final weights.
Optionally, the determining the traffic accident level according to the output result after the weighted regression further includes:
constructing a cross entropy loss function according to the traffic accident level and the traffic accident level in the database; the cross entropy loss function is an error function between the traffic accident level and the traffic accident level in the database;
minimizing the cross entropy loss function by adopting a gradient descent method to obtain the weight of each classification model;
and optimizing the output result of the corresponding classification model by adopting the weight.
Optionally, the determining the level of the traffic accident according to the output result after the weighted regression specifically includes:
and according to the output result after weighted regression, determining the grade of the traffic accident by adopting a Sigmoid function.
A road traffic accident occurrence risk determination system, comprising:
the data acquisition module is used for acquiring the data of the traffic accident in the database; the data of the traffic accident comprise risk factors of the traffic accident and corresponding traffic accident grades; the risk factors include: expressway ramp length, average daily traffic volume of main road, gradient of road, weather, age of driver, driving age, whether drinking wine or not, and vehicle number;
the classification model acquisition module is used for acquiring a plurality of classification models which take all risk factors corresponding to each traffic accident as input and take the probability of causing all traffic accident grades as output; the classification model is a random forest model, a gradient lifting decision tree and a self-adaptive lifting algorithm;
the output result module is used for determining the probability of all traffic accident grades caused by all risk factors corresponding to each traffic accident by utilizing each classification model according to the traffic accident data;
the weighted regression determining module is used for carrying out weighted regression on the probability of all traffic accident levels output by each classification model by utilizing a logistic regression algorithm, and determining an output result after the weighted regression;
the generated traffic accident level determining module is used for determining the generated traffic accident level according to the output result after the weighted regression;
and the road traffic accident occurrence risk determining module is used for determining the road traffic accident occurrence risk according to the traffic accident level.
Optionally, the method further comprises:
the risk factor weight determining module is used for determining the weight of each risk factor according to each classification model;
the risk factor final weight determining module is used for carrying out weighted average on a plurality of weights of each risk factor and determining the final weight of each risk factor;
the key risk factor determining module is used for sequencing the final weights of all the risk factors and determining the key risk factors; the key risk factors are the risk factors with the highest final weights.
Optionally, the method further comprises:
the cross entropy loss function construction module is used for constructing a cross entropy loss function according to the generated traffic accident level and the traffic accident level in the database; the cross entropy loss function is an error function between the traffic accident level and the traffic accident level in the database;
the weight determining module of the classification model is used for minimizing the cross entropy loss function by adopting a gradient descent method to obtain the weight of each classification model;
and the optimization module is used for optimizing the output result of the corresponding classification model by adopting the weight.
Optionally, the traffic accident level determining module specifically includes:
and the generated traffic accident level determining unit is used for determining the generated traffic accident level by adopting a Sigmoid function according to the output result after weighted regression.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the road traffic accident risk determination method and system, the output structures of the classification models are subjected to weighted regression, so that the traffic accident level is determined, the characteristics of the classification models are considered, the problem of limitation of a single classification model is avoided, the accuracy of determining the traffic accident level is improved, and the road traffic accident risk can be effectively determined.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for determining risk of occurrence of road traffic accident;
fig. 2 is a schematic structural diagram of a road traffic accident occurrence risk determining system provided by the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a method and a system for determining the occurrence risk of a road traffic accident, which solve the problem that the occurrence risk of the road traffic accident cannot be effectively determined in the prior art.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Fig. 1 is a schematic flow chart of a road traffic accident occurrence risk determining method provided by the present invention, and as shown in fig. 1, the road traffic accident occurrence risk determining method provided by the present invention includes:
s101, acquiring data of traffic accidents in a database. The data of the traffic accident comprises risk factors of the traffic accident and corresponding traffic accident grades. The risk factors include: expressway ramp length, average daily traffic volume of arterial road, gradient of road, weather, age of driver, driving age, whether drinking alcohol or not, and number of people on vehicle.
S102, acquiring a plurality of classification models which take all risk factors corresponding to each traffic accident as input and take the probability of causing all traffic accident grades as output. The classification model is a random forest model, a gradient lifting decision tree and a self-adaptive lifting algorithm.
S103, determining the probability of all traffic accident grades caused by all risk factors corresponding to each traffic accident by using each classification model according to the traffic accident data;
the multiple classification models can be regarded as highly complex nonlinear feature converters, and the traffic accident data is fed into each classification model respectively, each classification model outputs a vector, and each value in the vector represents the probability of each traffic accident level. The three vectors output by the multiple classification models represent the input features from the perspective of three different approaches. These three vectors are stitched together as a high-dimensional representation of the original feature.
And S104, carrying out weighted regression on the probability of all traffic accident levels output by each classification model by utilizing a logistic regression algorithm, and determining an output result after the weighted regression.
To prevent overfitting, an L2 regularized logistic regression algorithm is used.
S105, determining the level of the traffic accident according to the output result after the weighted regression.
And according to the output result after weighted regression, determining the grade of the traffic accident by adopting a Sigmoid function.
And mapping the weighted regression output result between 0 and 1 by adopting a Sigmoid function.
And determining the traffic accident level with the highest probability.
S106, determining the road traffic accident occurrence risk according to the traffic accident level.
In order to take measures to prevent traffic accident occurrence risk, the method for determining road traffic accident occurrence risk provided by the invention further comprises the following steps:
1) And determining the weight of each risk factor according to each classification model.
2) And carrying out weighted average on the multiple weights of each risk factor, and determining the final weight of each risk factor.
3) Sorting the final weights of all the risk factors, and determining key risk factors; the key risk factors are the risk factors with the highest final weights.
Each classification model is an integrated model based on a decision tree that can determine the importance of the input factors by calculating a normalized value of the amount of information entropy reduction. And extracting the importance of each risk factor from each classification model as a weight, calculating the average value of the weights of the risk factors in each classification model, arranging the risk factors according to the average weight from low to high, and determining the influence level of the risk factors, wherein the higher the value is, the greater the influence degree is. The risk factors belonging to the highest level are key risk factors. Sensitivity analysis is further performed on the obtained key risk factors to quantify the extent to which the key risk factors contribute to the severity of the injury. The specific process is that each time one key risk factor is disturbed, other risk factors are kept unchanged, and the degree of change of classification accuracy on the verification data set is observed. The perturbation of the risk factor ranges from 1 unit increase to 10 units increase, each unit referring to one tenth of the average of all samples of the risk factor. In the disturbance process of the risk factors, the greater the influence on the classification of the traffic accident level, the more sensitive the risk factors are, namely the greater the influence on the severity of the accident. In the process of traffic management and control, the risk factors are improved due to emphasis so as to reduce the probability of traffic accidents and reduce the injury degree of the traffic accidents.
In order to improve the accuracy of determining the level of the traffic accident, the method for determining the risk of the traffic accident on the road provided by the invention further comprises the following steps:
1) Constructing a cross entropy loss function according to the traffic accident level and the traffic accident level in the database; the cross entropy loss function is an error function between the traffic accident level occurring and the traffic accident level in the database.
2) And minimizing the cross entropy loss function by adopting a gradient descent method to obtain the weight of each classification model.
The gradient descent method iterates by continuously calculating the gradient and updating the weight of each classification model until convergence is reached.
3) And optimizing the output result of the corresponding classification model by adopting the weight.
The road traffic accident occurrence risk determining method provided by the invention uses a double-layer Stacking framework for analysis. The acquired multiple classification models are a first layer of a Stacking framework, and the output results of the multiple classification models are logically regressed into a second layer of the Stacking framework.
The invention also provides a road traffic accident occurrence risk determining system corresponding to the road traffic accident occurrence risk determining method provided by the invention, as shown in fig. 2, the road traffic accident occurrence risk determining method provided by the invention comprises the following steps: a data acquisition module 201, a classification model acquisition module 202, an output result module 203, a weighted regression determination module 204, an occurred traffic accident level determination module 205 and a road traffic accident occurrence risk determination module 206.
The data acquisition module 201 is used for acquiring data of traffic accidents in the database; the data of the traffic accident comprise risk factors of the traffic accident and corresponding traffic accident grades; the risk factors include: expressway ramp length, average daily traffic volume of arterial road, gradient of road, weather, age of driver, driving age, whether drinking alcohol or not, and number of people on vehicle.
The classification model obtaining module 202 is configured to obtain a plurality of classification models with all risk factors corresponding to each traffic accident as input and probabilities of causing all traffic accident levels as output; the classification model is a random forest model, a gradient lifting decision tree and a self-adaptive lifting algorithm.
The output result module 203 is configured to determine, according to the data of the traffic accident, probabilities of all traffic accident levels caused by all risk factors corresponding to each traffic accident by using each classification model.
The weighted regression determining module 204 is configured to perform weighted regression on probabilities of all traffic accident levels output by each of the classification models by using a logistic regression algorithm, and determine an output result after the weighted regression.
The traffic accident level determining module 205 is configured to determine the traffic accident level according to the weighted regression output result.
The road traffic accident occurrence risk determining module 206 is configured to determine a road traffic accident occurrence risk according to the traffic accident level.
The system for determining the occurrence risk of the road traffic accident provided by the invention further comprises the following components: the system comprises a risk factor weight determining module, a risk factor final weight determining module and a key risk factor determining module.
The risk factor weight determining module is used for determining the weight of each risk factor according to each classification model.
The risk factor final weight determining module is used for carrying out weighted average on a plurality of weights of each risk factor and determining the final weight of each risk factor.
The key risk factor determining module is used for sequencing the final weights of all the risk factors and determining key risk factors; the key risk factors are the risk factors with the highest final weights.
The system for determining the occurrence risk of the road traffic accident provided by the invention further comprises the following components: the system comprises a cross entropy loss function construction module, a weight determination module of a classification model and an optimization module.
The cross entropy loss function construction module is used for constructing a cross entropy loss function according to the traffic accident level and the traffic accident level in the database; the cross entropy loss function is an error function between the traffic accident level occurring and the traffic accident level in the database.
And the weight determining module of the classification model is used for minimizing the cross entropy loss function by adopting a gradient descent method to obtain the weight of each classification model.
And the optimization module is used for optimizing the output result of the corresponding classification model by adopting the weight.
The traffic accident level determining module 205 specifically includes: and a traffic accident level determining unit.
And the generated traffic accident level determining unit is used for determining the generated traffic accident level by adopting a Sigmoid function according to the output result after weighted regression.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (6)
1. A method for determining the risk of occurrence of a road traffic accident, comprising:
acquiring data of traffic accidents in a database; the data of the traffic accident comprise risk factors of the traffic accident and corresponding traffic accident grades; the risk factors include: expressway ramp length, average daily traffic volume of main road, gradient of road, weather, age of driver, driving age, whether drinking wine or not, and vehicle number;
acquiring a plurality of classification models which take all risk factors corresponding to each traffic accident as input and take the probability of causing all traffic accident grades as output; the classification model is a random forest model, a gradient lifting decision tree and a self-adaptive lifting algorithm;
according to the data of the traffic accidents, determining the probability of all traffic accident grades caused by all risk factors corresponding to each traffic accident by using each classification model;
carrying out weighted regression on the probability of all traffic accident levels output by each classification model by utilizing a logistic regression algorithm, and determining an output result after the weighted regression;
determining the level of the traffic accident according to the output result after the weighted regression;
determining the occurrence risk of the road traffic accident according to the traffic accident level;
the obtaining takes all risk factors as input, takes probability of causing all traffic accident grades as output multiple classification models, and then further comprises:
determining the weight of each risk factor according to each classification model;
weighted average is carried out on a plurality of weights of each risk factor, and the final weight of each risk factor is determined;
sorting the final weights of all the risk factors, and determining key risk factors; the key risk factors are the risk factors with the highest final weights.
2. The method for determining risk of occurrence of a road traffic accident according to claim 1, wherein the determining the level of occurrence of the traffic accident according to the weighted regression output result further comprises:
constructing a cross entropy loss function according to the traffic accident level and the traffic accident level in the database; the cross entropy loss function is an error function between the traffic accident level and the traffic accident level in the database;
minimizing the cross entropy loss function by adopting a gradient descent method to obtain the weight of each classification model;
and optimizing the output result of the corresponding classification model by adopting the weight.
3. The method for determining the risk of occurrence of a road traffic accident according to claim 1, wherein the determining the level of the traffic accident according to the weighted regression output result specifically comprises:
and according to the output result after weighted regression, determining the grade of the traffic accident by adopting a Sigmoid function.
4. A road traffic accident occurrence risk determination system, comprising:
the data acquisition module is used for acquiring the data of the traffic accident in the database; the data of the traffic accident comprise risk factors of the traffic accident and corresponding traffic accident grades; the risk factors include: expressway ramp length, average daily traffic volume of main road, gradient of road, weather, age of driver, driving age, whether drinking wine or not, and vehicle number;
the classification model acquisition module is used for acquiring a plurality of classification models which take all risk factors corresponding to each traffic accident as input and take the probability of causing all traffic accident grades as output; the classification model is a random forest model, a gradient lifting decision tree and a self-adaptive lifting algorithm;
the output result module is used for determining the probability of all traffic accident grades caused by all risk factors corresponding to each traffic accident by utilizing each classification model according to the traffic accident data;
the weighted regression determining module is used for carrying out weighted regression on the probability of all traffic accident levels output by each classification model by utilizing a logistic regression algorithm, and determining an output result after the weighted regression;
the generated traffic accident level determining module is used for determining the generated traffic accident level according to the output result after the weighted regression;
the road traffic accident occurrence risk determining module is used for determining the road traffic accident occurrence risk according to the traffic accident level;
the risk factor weight determining module is used for determining the weight of each risk factor according to each classification model;
the risk factor final weight determining module is used for carrying out weighted average on a plurality of weights of each risk factor and determining the final weight of each risk factor;
the key risk factor determining module is used for sequencing the final weights of all the risk factors and determining the key risk factors; the key risk factors are the risk factors with the highest final weights.
5. The road traffic accident occurrence risk determination system according to claim 4, further comprising:
the cross entropy loss function construction module is used for constructing a cross entropy loss function according to the generated traffic accident level and the traffic accident level in the database; the cross entropy loss function is an error function between the traffic accident level and the traffic accident level in the database;
the weight determining module of the classification model is used for minimizing the cross entropy loss function by adopting a gradient descent method to obtain the weight of each classification model;
and the optimization module is used for optimizing the output result of the corresponding classification model by adopting the weight.
6. The system for determining risk of occurrence of a road traffic accident according to claim 4, wherein the traffic accident level determining module comprises:
and the generated traffic accident level determining unit is used for determining the generated traffic accident level by adopting a Sigmoid function according to the output result after weighted regression.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911389961.5A CN111126868B (en) | 2019-12-30 | 2019-12-30 | Road traffic accident occurrence risk determination method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911389961.5A CN111126868B (en) | 2019-12-30 | 2019-12-30 | Road traffic accident occurrence risk determination method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111126868A CN111126868A (en) | 2020-05-08 |
| CN111126868B true CN111126868B (en) | 2023-07-04 |
Family
ID=70504654
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911389961.5A Active CN111126868B (en) | 2019-12-30 | 2019-12-30 | Road traffic accident occurrence risk determination method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111126868B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111784873A (en) * | 2020-07-01 | 2020-10-16 | 上海城市交通设计院有限公司 | Machine learning principle-based expressway safety vehicle-mounted intelligent system and working process |
| CN112561175A (en) * | 2020-12-18 | 2021-03-26 | 深圳赛安特技术服务有限公司 | Traffic accident influence factor prediction method, device, equipment and storage medium |
| CN113946943B (en) * | 2021-09-26 | 2022-06-21 | 清华大学 | Human-vehicle-road micro traffic system modeling and risk identification method and device |
| CN115565373B (en) * | 2022-09-22 | 2024-04-05 | 中南大学 | Expressway tunnel accident real-time risk prediction method, device, equipment and medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106897826A (en) * | 2017-02-23 | 2017-06-27 | 吉林大学 | A kind of street accidents risks appraisal procedure and system |
| CN109558969A (en) * | 2018-11-07 | 2019-04-02 | 南京邮电大学 | A kind of VANETs car accident risk forecast model based on AdaBoost-SO |
| CN109871439A (en) * | 2019-02-18 | 2019-06-11 | 华南理工大学 | A kind of Ask-Answer Community problem method for routing based on deep learning |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102708244B (en) * | 2012-05-08 | 2016-01-20 | 清华大学 | A kind of concept map automatic graph generation method based on importance degree tolerance |
| US10621670B2 (en) * | 2014-08-15 | 2020-04-14 | Scope Technologies Holdings Limited | Determination and display of driving risk |
| CN105045975A (en) * | 2015-06-30 | 2015-11-11 | 北京师范大学 | Bayesian network model based risk evaluation method for road transportation accident |
| CN107273340A (en) * | 2017-06-01 | 2017-10-20 | 南京邮电大学 | A kind of road traffic accident factor-analysis approach based on Logistic models |
| CN109191828B (en) * | 2018-07-16 | 2021-05-28 | 江苏智通交通科技有限公司 | Traffic participant accident risk prediction method based on ensemble learning |
-
2019
- 2019-12-30 CN CN201911389961.5A patent/CN111126868B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106897826A (en) * | 2017-02-23 | 2017-06-27 | 吉林大学 | A kind of street accidents risks appraisal procedure and system |
| CN109558969A (en) * | 2018-11-07 | 2019-04-02 | 南京邮电大学 | A kind of VANETs car accident risk forecast model based on AdaBoost-SO |
| CN109871439A (en) * | 2019-02-18 | 2019-06-11 | 华南理工大学 | A kind of Ask-Answer Community problem method for routing based on deep learning |
Non-Patent Citations (1)
| Title |
|---|
| Deep Learning:A Generic Approach for Extreme Condition Traffic Forecasting;Rose Yu等;《Proceedings of the 2017 SIAM International Conference on Data Mining》;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111126868A (en) | 2020-05-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111126868B (en) | Road traffic accident occurrence risk determination method and system | |
| CN110287180B (en) | Wind control modeling method based on deep learning | |
| CN106095963B (en) | Vehicle driving behavior analysis big data public service platform under internet + era | |
| CN108665093B (en) | Deep learning-based expressway traffic accident severity prediction method | |
| CN111311400A (en) | Modeling method and system of grading card model based on GBDT algorithm | |
| CN104298881A (en) | Bayesian network model based public transit environment dynamic change forecasting method | |
| CN113487066A (en) | Long-time-sequence freight volume prediction method based on multi-attribute enhanced graph convolution-Informer model | |
| CN113392931B (en) | Hyperspectral open set classification method based on self-supervision learning and multitask learning | |
| CN113188807B (en) | Automatic abs result judging algorithm | |
| CN112966871A (en) | Traffic jam prediction method and system based on convolution long-short term memory neural network | |
| CN107025468A (en) | Highway congestion recognition methods based on PCA GA SVM algorithms | |
| CN109887279B (en) | Traffic jam prediction method and system | |
| CN110555565A (en) | Decision tree model-based expressway exit ramp accident severity prediction method | |
| CN111915022B (en) | Gaussian process method and device for rapidly identifying stability coefficient of sliding karst dangerous rock | |
| CN105678406A (en) | Short-term load prediction method based on cloud model | |
| CN113779260A (en) | Domain map entity and relationship combined extraction method and system based on pre-training model | |
| CN112149922A (en) | Method for predicting severity of accident in exit and entrance area of down-link of highway tunnel | |
| CN116933946A (en) | Rail transit OD passenger flow prediction method and system based on passenger flow destination structure | |
| CN111260490A (en) | Rapid claims settlement method and system based on tree model for car insurance | |
| CN113222208A (en) | Ada-XGboost-based traffic accident prediction system | |
| CN110796301B (en) | Passenger flow prediction method and device based on IC card data | |
| CN110717623B (en) | Photovoltaic power generation power prediction method, device and equipment integrating multiple weather conditions | |
| CN110119891B (en) | Traffic safety influence factor identification method suitable for big data | |
| CN116541786A (en) | Network appointment vehicle identification method, device and system based on driving behaviors | |
| CN111708865A (en) | Technology forecasting and patent early warning analysis method based on improved XGboost algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |