WO2020108219A1 - Procédé et système d'analyse de différence et de division de groupe basée sur un risque de sécurité routière - Google Patents

Procédé et système d'analyse de différence et de division de groupe basée sur un risque de sécurité routière Download PDF

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Publication number
WO2020108219A1
WO2020108219A1 PCT/CN2019/114373 CN2019114373W WO2020108219A1 WO 2020108219 A1 WO2020108219 A1 WO 2020108219A1 CN 2019114373 W CN2019114373 W CN 2019114373W WO 2020108219 A1 WO2020108219 A1 WO 2020108219A1
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WIPO (PCT)
Prior art keywords
group
data
attribute
risk
traffic
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PCT/CN2019/114373
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English (en)
Chinese (zh)
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刘林
吕伟韬
陈凝
饶欢
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江苏智通交通科技有限公司
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Publication of WO2020108219A1 publication Critical patent/WO2020108219A1/fr

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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
    • 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
    • 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
    • G08G1/0125—Traffic data processing
    • 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
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • 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
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation

Definitions

  • the invention relates to a method and system for group division and difference analysis based on traffic safety risks.
  • the purpose of the present invention is to provide a method and system for group division and difference analysis based on traffic safety risks, to make up for the defects of the integrated learning algorithm in the description of the risk calibration process through statistical methods, and to dig out the causes of accidents of groups with different risk levels 3.
  • the different characteristics of accident results solve the problem that the existing technology-specific traffic safety management application scenarios for individuals are relatively limited.
  • a group division and difference analysis method based on traffic safety risk which takes drivers and motor vehicles as the object, calibrates the object safety risk through integrated learning algorithm, then divides the group on this basis, and identifies significant differences through statistical methods Indicators; includes the following steps,
  • S1 Determine the objects of traffic participants, including drivers and motor vehicles; obtain the historical records of traffic violations and traffic accidents based on the information of the target objects as sample data;
  • step S3 Determine the secondary attribute dimension of the target object according to the sample data obtained in step S1, and divide it into the secondary attribute set of the cause of the accident and the secondary attribute set of the result of the accident; split the secondary attribute into three levels to determine each secondary level The three-level attribute factor corresponding to the attribute;
  • the construction process of the risk prediction model specifically includes data label definition and data set division, model feature variable screening based on embedding method, data set equalization processing, model training based on cross-validation, and acceptance based on ROC curve
  • the model performance evaluation of the operator's operating curve and the area under the curve AUC selects the model with the best fitting effect; the risk index output by the model is the label classification probability of the data.
  • the fields of the group division data table include object information, time, three-level attribute factors, risk degree, and belonging group; wherein the data of the belonging group field belongs to the threshold range of each group risk degree according to the risk degree of the object information The situation is ok.
  • step S5 Fisher's exact test result is determined according to p_value, and variables with significant differences are used as group safety feature attributes. Specifically, if the fuzzy solution of p-value p_value is less than the set value, then the null hypothesis H0 is accepted; otherwise Reject the original hypothesis H0 and accept the hypothesis H1.
  • a traffic safety risk-based group division and difference analysis system implementing any of the above-mentioned traffic safety risk-based group division and difference analysis methods, including a data docking module, a risk prediction module, and an attribute factor analysis module And group feature recognition module,
  • Data docking module extract traffic accident records and traffic violation records from the database
  • Risk prediction module access to the historical traffic violation data and traffic accident data of the data docking module as samples for model construction; define data labels, divide sample data sets; filter model feature variables; perform balanced processing of data sets; adopt cross-validation Method to train the model, select the model with the best fitting effect according to the ROC curve and AUC value; complete the construction of the risk prediction model, and extract the historical traffic violation records of the specified target object from the data docking module according to user instructions, through the model Process and output the predicted value of the risk degree of the target object; generate a risk degree table;
  • Attribute factor analysis module access the sample data of the data docking module, determine the second-level attribute according to the original sample data field; determine the third-level attribute factor corresponding to the second-level attribute according to the specific value of the sample data field, where the second-level attribute is discrete data , Then the third-level attribute factor is the corresponding data value range. If the second-level attribute is continuous data, the third-level attribute factor is determined through discretization; the second-level attribute table and the third-level attribute table are generated;
  • Group feature recognition module the risk degree prediction module is connected to the risk degree table, and the attribute factor analysis module obtains the second-level attribute table and the third-level attribute table; the group division data table is generated according to the setting of the risk threshold interval; Fisher exact Test and Monte Carlo simulation calculation method to determine the secondary attribute p-value and write it into the secondary attribute table; filter out the secondary attribute with p-value less than the set value, as the differential characteristics of different groups, generate a group characteristic table.
  • a visualization module obtaining a group division data table and a group characteristic table from the group characteristic recognition module, and counting each group sample according to the three-level attributes corresponding to the different characteristics to generate the different characteristics of each group Table; call the visualization engine and use thematic maps to visualize and display the differential characteristics of each group and the statistics of the three-level attribute samples.
  • This grouping and difference analysis method and system based on traffic safety risk mines the characteristics of traffic participants' traffic behavior performance, and calibrates the degree of their safety risk, in order to reduce the application of data for analysis and judgment Granularity, according to the degree of risk, the target groups of different security levels are divided; at the same time, in order to overcome the problem of the lack of interpretation of the integrated learning algorithm in the process of security risk calibration, Fisher Fisher’s exact test is used to identify significant differences between groups, so Accurately describe the characteristics of each risk level group to provide data support for active traffic safety governance.
  • the present invention performs traffic safety risk ratings on traffic participants such as drivers and vehicles, and takes groups of traffic participants of the same level as objects, and explores the differences between groups to solve the traffic safety management of individuals. The problem of relatively limited application scenarios.
  • FIG. 1 is a schematic flowchart of a method for group division and difference analysis based on traffic safety risks according to an embodiment of the present invention.
  • FIG. 2 is an explanatory diagram of a group division and difference analysis system based on traffic safety risks in an embodiment.
  • a group division and difference analysis method based on traffic safety risk which takes drivers and motor vehicles as the object, calibrates the object safety risk through integrated learning algorithm, then divides the group on this basis, and identifies significant differences through statistical methods Index; as shown in figure 1, the specific steps are:
  • the target object information of the driver is the ID number
  • the target object information of the motor vehicle is the combination of the number plate type and the number plate number
  • the time range of historical records usually exceeds one year to ensure a sufficient sample size.
  • the risk degree is the label classification probability of the sample data after the model processing.
  • the risk prediction model construction process includes data label definition and data set division, model feature variable selection based on embedding method, data set equalization processing, model training based on cross-validation, based on ROC curve (receiver operation curve) and under curve
  • the model performance evaluation of the area AUC selects the model with the best fitting effect; the risk index output by the model is the label classification probability of the data.
  • a method for combining the improved sampling method and the RF random forest algorithm is used to construct a risk prediction model, with a model coverage rate of 0.06 and an accuracy of 0.889.
  • step S3 Determine the secondary attribute dimension of the target object according to the sample data obtained in step S1, and divide it into the secondary attribute set of the cause of the accident and the secondary attribute set of the result of the accident; split the secondary attribute into three levels to determine each secondary level The three-level attribute factor corresponding to the attribute.
  • the corresponding elements in the secondary attribute set of the cause of the accident include gender, age, nationality, hukou nature, type of person, driving age, accident identification reason, blood alcohol content, seat belt helmet usage, etc. ;
  • Target the vehicle the secondary attributes of the cause of the accident include the type of vehicle, mode of transportation, nature of use of the vehicle, mileage, legal status, insurance, whether it is overloaded, the status of the light, the amount of load, etc.;
  • the secondary attributes of the accident result include the form of the accident , Accident level, direct property loss, accident liability, etc.
  • step S4 synthesizing the processing results of steps S2 and S3, establishing a group division data table to determine the sample group attribution;
  • the field of the group division data table includes object information, time, three-level attribute factor, risk degree, and belonging group; among which the group field data It is determined according to the attribution of the risk degree of the object information within the risk degree threshold range of each group.
  • the risk threshold interval of the general group is [0,0.15]
  • the risk group interval is (0.15,0.8)
  • the risk group interval is [0.8,1.0].
  • the script for checking the difference of attribute variable values between groups is edited by R language, the fisher.test function in the stats statistical method package is called, the parameter simulate.p.value is set to TRUE, and the number of Monte Carlo simulations B is set to 105; p value is less than 0.05, accept the null hypothesis H0, otherwise reject the null hypothesis H0.
  • the person is the target object
  • Fisher's exact test is performed on the secondary attributes of the accident result.
  • the resulting R*C contingency table for the accident level, accident form, accident liability, and direct property loss is as follows:
  • This method of group division and difference analysis based on traffic safety risk is used to rate traffic safety risks for traffic participants such as drivers and vehicles, and to target groups composed of traffic participants of the same level to explore the differences between groups. , Solve the problem that the application scenarios of traffic safety management for individuals are relatively limited.
  • a traffic participant group division and feature research and judgment system includes a data docking module, a risk prediction module, an attribute factor analysis module, a group feature recognition module, and a visualization module.
  • the data docking module extracts traffic accident records and traffic violation records from the database.
  • Risk prediction module access to the historical traffic violation data and traffic accident data of the data docking module, as a sample of the model construction; define data labels, divide the sample data set; filter model feature variables; perform balanced processing of the data set; use cross-validation Method to train the model, select the best fitting model according to the ROC curve and AUC value; this module completes the construction of the risk prediction model, and extracts the historical traffic violation records of the specified target object from the data docking module according to user instructions, Through the model processing, the predicted risk value of the target object is output; a risk degree table is generated.
  • Attribute factor analysis module access to the sample data of the data docking module, determine the secondary attribute according to the original sample data field; determine the tertiary attribute factor corresponding to the secondary attribute according to the specific value of the sample data field, where the secondary attribute is discrete data , Then the third-level attribute factor is the corresponding data range. If the second-level attribute is continuous data, the third-level attribute factor is determined through discretization; the second-level attribute table and the third-level attribute table are generated.
  • the risk degree prediction module accesses the risk degree table
  • the attribute factor analysis module obtains the second-level attribute table and the third-level attribute table; generates the group division data table according to the setting of the risk threshold interval; adopts Fisher's exact Test and Monte Carlo simulation calculation method to determine the secondary attribute p-value and write it into the secondary attribute table; filter out the secondary attribute with p-value less than the set value, as the differential characteristics of different groups, generate a group characteristic table.
  • the set value is preferably 0.05.
  • the visualization module obtains the group division data table and the group characteristic table from the group characteristic recognition module, counts the samples of each group according to the three-level attributes corresponding to the different characteristics, and generates the different characteristic table of each group; calls the visualization engine and uses the topic
  • the graph visualizes and displays the difference characteristics of each group and the statistics of the three-level attribute samples.
  • the thematic maps include word cloud, histogram, pie chart, doughnut chart, number chart and other proportional and comparative graphic forms.
  • This grouping and difference analysis method and system based on traffic safety risk is based on integrated learning algorithm to mine the characteristics of traffic participants' traffic behavior performance and calibrate their safety risk degree, in order to reduce the data granularity of the analysis and judgment application , Divide several target groups with different security levels according to the degree of risk; at the same time, in order to overcome the problem of the lack of explanatory degree of the integrated learning algorithm in the process of security risk calibration, the Fisher Fisher's exact test is used to identify the significant differences between the groups to accurately describe The characteristics of each risk level group provide data support for active traffic safety governance.
  • the groups of different safety levels are divided, and the R*C contingency table Fisher exact test method is used to identify the difference attribute factors.
  • the method and system of the embodiment use Monte Carlo simulation calculation to obtain the fuzzy solution of p value , Effectively saving the time cost of the algorithm.

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Abstract

La présente invention concerne un système et un procédé d'analyse de différence et de division de groupe basée sur un risque de sécurité routière consistant : à prendre des conducteurs et des véhicules à moteur comme objets, à délimiter des risques de sécurité des objets à l'aide d'un algorithme d'apprentissage d'ensemble et à effectuer une division de groupe sur la base des risques de sécurité, et à identifier des indices de différence marquante par l'intermédiaire d'une approche statistique ; à explorer des caractéristiques d'usagers de la route à partir de leurs comportements de conduite sur la base de l'algorithme d'apprentissage d'ensemble et à délimiter des degrés de risque de sécurité desdits usagers de la route, et afin de réduire la granularité de données d'un programme d'analyse et de détermination, à diviser, selon les degrés de risque, en une pluralité de groupes cibles ayant différents niveaux de sécurité ; afin de surmonter le problème selon lequel l'algorithme d'apprentissage d'ensemble manque d'explication lors d'une démarcation de risque de sécurité, tester et identifier de manière précise des indices de différence marquante parmi des groupes en utilisant le mélange de Fisher permet de décrire de manière précise des caractéristiques de groupes ayant divers niveaux de risque et de fournir un support de données destiné à une gestion de sécurité routière active.
PCT/CN2019/114373 2018-11-30 2019-10-30 Procédé et système d'analyse de différence et de division de groupe basée sur un risque de sécurité routière WO2020108219A1 (fr)

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CN109598931B (zh) * 2018-11-30 2021-06-11 江苏智通交通科技有限公司 基于交通安全风险的群体划分与差异性分析方法及系统
CN110458425A (zh) * 2019-07-25 2019-11-15 腾讯科技（深圳）有限公司 风险主体的风险分析方法、装置、可读介质及电子设备
CN110555277A (zh) * 2019-09-09 2019-12-10 山东科技大学 一种松散含水层下开采水砂突危险性评价方法
CN110570655B (zh) * 2019-09-19 2021-03-05 安徽百诚慧通科技有限公司 基于层次聚类和决策树的车辆特征评估方法
CN117274762B (zh) * 2023-11-20 2024-02-06 西南交通大学 基于视觉的地铁隧道低照度场景下实时轨道提取方法

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