CN109087505B - Traffic safety regulation effectiveness analysis method for motorcycle - Google Patents
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Abstract
A traffic safety regulation effectiveness analysis method for motorcycles. Analyzing the incidence relation between the motorcycle traffic accident and the traffic safety regulation based on a multivariable dynamic Tobit model; the output content is a correlation coefficient between the motorcycle traffic accident and the traffic safety regulation index; the input information comprises quantifiable traffic safety regulation index data of different cities, including speed limit, drunk driving and overspeed punishment measures; the input data also comprises the data of urban traffic accidents and the social and economic data, including the contents of population, income and education conditions. The method has the advantages of evaluating the effectiveness of the traffic safety regulation, analyzing the incidence relation between the traffic safety regulation and the motorcycle traffic accident, researching the influence of the traffic safety regulation on the traffic safety so as to establish a motorcycle traffic accident prevention method and provide decision basis for making relevant policy measures.
Description
Technical Field
The invention relates to a traffic safety regulation effectiveness analysis method for a motorcycle, and belongs to the technical field of computers.
Background
The traditional traffic accident analysis method only considers human, vehicle, road and environmental factors, but cannot evaluate the influence of traffic safety regulations on traffic safety. The making of traffic safety regulations is to standardize the driving behavior of drivers and further avoid the occurrence of traffic accidents. However, the compliance rate and the implementation effect of the traffic safety regulations are different due to the difference between local implementation rules and law enforcement regulations. How to evaluate the effectiveness of traffic safety regulations and the influence on traffic safety is an urgent problem to be solved.
8693 traffic accidents happen to the expressway in China in 2013, 5843 people die, and the fatality rate of each accident is 0.67. Motorcycle traffic accidents often account for 30% -40% of all traffic accidents, and the traffic accidents causing serious injuries and even death involve 70% -80% of motorcycles. The death rate of motorcycle traffic accidents is far greater than that of cars. At present, most motorcycles in China belong to 'three-free' motorcycles without license, evidence and insurance, and motorcycle drivers generally lack sufficient law-keeping awareness, so that the study on the effectiveness of the traffic safety regulations of the motorcycles has practical significance.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a traffic safety regulation effectiveness analysis method for motorcycles.
A traffic safety regulation effectiveness analysis method for motorcycles is based on a multivariate dynamic Tobit model to analyze the incidence relation between motorcycle traffic accidents and traffic safety regulations; the output content is a correlation coefficient between the motorcycle traffic accident and the traffic safety regulation index; the input information comprises quantifiable traffic safety regulation index data of different cities, including punishment measures such as speed limit, drunk driving, overspeed and the like; the input data also comprises the data of urban traffic accidents and social and economic data, including the contents of population, income, education condition and the like.
A traffic safety regulation effectiveness analysis system for motorcycles comprises a processor, a communication module and a human-computer interaction interface.
The man-machine interface mainly provides an interface for inputting and outputting data.
The communication module can read the traffic accident starting data from the traffic accident data server.
The processor finishes processing and analyzing data, takes factors related to highway safety legal indexes as explanatory variables, takes social, population and traffic factors as control variables, applies a multivariate dynamic Tobit model to analyze and predict the start of a traffic accident, and integrates an observed lag variable into the multivariate dynamic Tobit model to explain the potential time correlation relationship of traffic accident data; and outputting the analysis result to a human-computer interaction interface.
The method has the advantages of evaluating the effectiveness of the traffic safety regulation, analyzing the incidence relation between the traffic safety regulation and the motorcycle traffic accident, researching the influence of the traffic safety regulation on the traffic safety so as to establish a motorcycle traffic accident prevention method and provide decision basis for making relevant policy measures.
The application provides three models and compares the models, wherein the first multivariate dynamic Tobit model comprises a lag observation variable, the second multivariate dynamic Tobit model comprises a random variable, and the third model is a multivariate static Tobit model. Simulation results show that the first of these three models fits best.
Simulation finds that the multivariate dynamic Tobit model integrated with the lag observation variable can better explain the randomness and the interdependency of the traffic accident starting value in the time evolution process, so that the model calculated value is closer to the actual observed value.
Simulation results show that wearing a helmet while driving a motorcycle can save more lives if law enforcement can continue to educate the public. Casualties and property losses from motorcycle traffic accidents may be reduced if governments make more stringent regulations prohibiting the use of handheld communication devices during driving, increasing speeding and drunk driving fines, and increasing license plate issuing ages.
Detailed Description
It will be apparent that those skilled in the art can make many modifications and variations based on the spirit of the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element, component or section is referred to as being "connected" to another element, component or section, it can be directly connected to the other element or section or intervening elements or sections may also be present. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art.
The following examples are further illustrative in order to facilitate the understanding of the embodiments, and the present invention is not limited to the examples.
Example 1:
a method for analyzing the effectiveness of a traffic safety regulation aiming at a motorcycle comprises the following steps;
step 1, acquiring data, extracting the data from quantifiable terms of traffic safety regulations, manually inputting social and economic data, and reading motorcycle traffic accident data from a database of a traffic department;
and 2, calculating a related coefficient set between the motorcycle traffic accident and the traffic safety regulation indexes.
The multivariate dynamic Tobit model is defined as follows:
wherein, yitViewing for traffic accident type i in time period tThe measured number of the traffic accident origin, define yt＝(y1t,y2t,…,ynt) ' is a vector of n traffic accident origins,
the expected value of the start of the traffic accident in the time period t is the traffic accident type i.
Random variable
Is defined as:
wherein x isijt(1 ≦ j ≦ k-p) is the jth interpretation variable of the traffic safety regulation index x associated with the traffic accident type i during the time t, k is the total number of estimated parameters, βij(1. ltoreq. j. ltoreq.k-p) and lambdaij(1. ltoreq. j. ltoreq.p) is an estimable parameter, yi,t-jLag observation variable for the onset of a traffic accident, p the number of lag observation variables for the onset of a traffic accident, ciIs a random perturbation, uiIs the systematic error; c. CiAnd uitObedience with respect to xj1,xj2,…,xjTIs normally distributed.
yitAnd
may be decomposed into a product of conditional density functions.
Wherein g (■) is a joint probability density function, q (■) is a density function of classical Tobit distribution, h (■) is a sampling density function, and y is a probability density functionitCounting the number of traffic accidents observed in the time period t for the traffic accident type i; y isi,t-1Counting the number of the traffic accidents observed in the time period t-1 for the traffic accident type i;
the expected value of the traffic accident type i in the time period t is the starting value of the traffic accidents;
the expected value of the start number of the traffic accident in the time period t-1 is the traffic accident type i.
Thus, the likelihood function of the multivariate dynamic Tobit model can be written as:
wherein L is the likelihood function of the multivariate dynamic Tobit model, L is the likelihood function of the classical Tobit distribution, g (■) is the joint probability density function, yitCounting the number of traffic accidents observed in the time period t for the traffic accident type i; y isi,t-1Counting the number of the traffic accidents observed in the time period t-1 for the traffic accident type i;
the expected value of the traffic accident type i in the time period t is the starting value of the traffic accidents;
the expected value of the start number of the traffic accident in the time period t-1 is the traffic accident type i.
In specific implementation, the method can be used for
I in (1) is defined as 3, and represents a death accident, a personal injury accident and a property loss accident, respectively. Based on the constructed likelihood function, iterative parameter estimation can be carried out repeatedly by adopting a GHK simulation algorithm (Geweke-Hajivasssilou-Keanesimulator).
And 3, outputting the calculated parameter set theta to a human-computer interface.
As described above, although the embodiments of the present invention have been described in detail, it will be apparent to those skilled in the art that many modifications are possible without substantially departing from the spirit and scope of the present invention. Therefore, such modifications are also all included in the scope of protection of the present invention.
Claims (1)
1. A traffic safety regulation effectiveness analysis method for motorcycles is characterized in that the incidence relation between motorcycle accidents and traffic safety regulations is analyzed based on a multivariable dynamic Tobit model; the output content is a correlation coefficient between the motorcycle traffic accident and the traffic safety regulation index; the input information comprises quantifiable traffic safety regulation index data of different cities, including speed limit, drunk driving and overspeed punishment measures; the input information also comprises urban traffic accident data and social and economic data, including population, income and education condition contents;
comprises the following steps;
step 1, acquiring data, extracting the data from quantifiable terms of traffic safety regulations, manually inputting social and economic data, and reading motorcycle traffic accident data from a database of a traffic department;
step 2, calculating a related coefficient set between motorcycle traffic accidents and traffic safety regulation indexes;
the multivariate dynamic Tobit model is defined as follows:
wherein, yitDefining y for the number of traffic accident origins observed in the time period t for the traffic accident type it＝(y1t,y2t,…,yNt) ' is a vector of N traffic accident origins,
the expected value of the traffic accident type i in the time period t is the starting value of the traffic accidents;
random variable
Is defined as:
wherein x isijt(1 ≦ j ≦ k-p) is the jth interpretation variable of the traffic safety regulation index x associated with the traffic accident type i during the time t, k is the total number of estimated parameters, βij(1. ltoreq. j. ltoreq.k-p) and lambdaij(1. ltoreq. j. ltoreq.p) is an estimable parameter, yi,t-jLag observation variable for the onset of a traffic accident, p the number of lag observation variables for the onset of a traffic accident, ciIs a random perturbation, uitIs the systematic error; c. CiAnd uitObedience with respect to xj1，xj2，…，xjTThe conditional normal distribution of (1);
yitand
the joint probability density function of (a) is decomposed into a product of conditional density functions;
wherein the content of the first and second substances,
as to yit，y* itAnd y* i,t-1A joint probability density function of (a);
as to yitAnd y* i,t-1The density function of the classical Tobit distribution of (1);
as to y* it，yi,t-1And y* i,t-1A sampling density function of; y isitTraffic accidents observed for a traffic accident type i during a time period tCounting; y isi,t-1Counting the number of the traffic accidents observed in the time period t-1 for the traffic accident type i;
the expected value of the traffic accident type i in the time period t is the starting value of the traffic accidents;
the expected value of the traffic accident type i in the time period t-1;
thus, the likelihood function of the multivariate dynamic Tobit model is written as:
wherein
A likelihood function which is a multivariate dynamic Tobit model; l is a likelihood function of classical Tobit distribution;
as to yit，y* itAnd y* i,t-1A joint probability density function of (a); y isitCounting the number of traffic accidents observed in the time period t for the traffic accident type i; y isi,t-1Counting the number of the traffic accidents observed in the time period t-1 for the traffic accident type i;
the expected value of the traffic accident type i in the time period t is the starting value of the traffic accidents;
the expected value of the traffic accident type i in the time period t-1;
will be provided with
I in (1) to (3), then y* 1t，y* 2t，y* 3tRespectively representing expected values of the accident starting numbers of the death accident, the injury accident and the property loss accident in the time t; based on the constructed likelihood function, repeatedly performing iterative estimation on parameters by adopting a GHK simulation algorithm (Geweek-Hajivasssilou-Keane simulator);
and 3, outputting the calculated related coefficient set between the motorcycle traffic accident and the traffic safety regulation indexes.
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