CN115547037A - Method, system, device and medium for predicting duration of highway traffic accident - Google Patents
Method, system, device and medium for predicting duration of highway traffic accident Download PDFInfo
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Abstract
The invention discloses a method, a system, a device and a medium for predicting the duration time of a highway traffic accident, wherein the method comprises the following steps: acquiring historical traffic data of a highway, performing data space-time correlation by taking a traffic accident as a unit, and constructing a data set; carrying out correlation test and collinearity diagnosis on observation factors in the data set to obtain a prediction variable of the duration time of the traffic accident; establishing a time-varying acceleration failure time model facing to the prediction of the duration time of the highway traffic accident according to the prediction variable, and selecting an optimal time-varying acceleration failure time model according to AIC indexes; and acquiring traffic accident data for the highway traffic accident to be predicted, and acquiring a duration survival function curve according to the traffic accident data and the optimal time-varying acceleration failure time model. The invention considers time-varying factors and time-invariant factors, so that the model is closer to the actual situation, and the method has practical application value. The invention can be widely applied to the field of traffic safety.
Description
Technical Field
The invention relates to the field of traffic safety, in particular to a method, a system, a device and a medium for predicting the duration time of a traffic accident on a highway.
Background
While providing a large amount of passenger and goods transportation, the highway traffic accidents are frequent. The traffic accidents on the highway not only cause huge casualties and property loss, but also cause serious traffic jam, and are one of the main causes of occasional traffic jam. The duration of the traffic accident is a key factor for judging the influence range of the traffic jam in time and space. Therefore, the prediction of the duration of the highway traffic accident and the analysis of the influence factors are crucial to the emergency response decision.
In the existing prediction research of the duration of the highway traffic accident, the influence of static factors such as road conditions, accident characteristics and the like on the duration is analyzed by adopting a statistical regression method represented by a survival analysis model according to historical traffic data, but the full consideration of time-varying factors such as real-time traffic conditions, weather conditions and the like is lacked. Traffic conditions and weather conditions are important factors that affect the time of arrival and obstacle clearance of rescuers and can change dramatically from time to time (e.g., short-term thunderstorm weather in summer in coastal areas). With the continuous improvement of the construction of traffic and meteorological information systems, the acquisition of real-time traffic flow and meteorological data becomes easier. In the expressway traffic accident duration prediction model, the time-varying characteristics of factors such as traffic conditions and weather conditions are ignored, so that the influence of the factors on the accident duration is difficult to accurately quantify, and the accuracy of accident duration prediction is further reduced.
Disclosure of Invention
To solve at least some of the problems of the prior art, it is an object of the present invention to provide a method, system, device and medium for predicting the duration of a highway traffic accident.
The technical scheme adopted by the invention is as follows:
a method for predicting the duration of a highway traffic accident comprises the following steps:
acquiring historical traffic data of a highway, performing data space-time association by taking a traffic accident as a unit, and constructing a data set;
carrying out correlation test and collinearity diagnosis on observation factors in the data set to obtain a predictive variable of the duration time of the traffic accident; wherein the observation factors include time-varying factors and time-invariant factors;
establishing a time-varying acceleration failure time model facing the expressway traffic accident duration prediction according to the prediction variables, and selecting an optimal time-varying acceleration failure time model according to AIC indexes;
and acquiring traffic accident data for the highway traffic accident to be predicted, and acquiring a duration survival function curve according to the traffic accident data and the optimal time-varying acceleration failure time model to predict the duration of the highway traffic accident.
Further, the historical traffic data includes highway traffic accident data, road design data, real-time traffic flow data, and real-time weather data;
the time-varying factors include: traffic flow, traffic composition, wind speed, precipitation, visibility, air temperature and humidity in every preset time period after an accident occurs and before the accident is cleared;
the time-invariant factors include: hit-and-run vehicle type and extent of damage, severity of casualty of accident, type of accident, and road conditions at the site of the accident.
Further, the correlation test and collinearity diagnosis of the observed factors in the data set includes:
respectively carrying out correlation test and collinearity diagnosis on the time-varying factors and the time-invariant factors;
in the correlation test, if the correlation coefficient between two factor variables meets a preset range, judging that the correlation exists between the two factor variables;
in the collinearity diagnosis, if the tolerance is smaller than a first preset threshold or the variance expansion factor is larger than a second preset threshold, collinearity exists among the factor variables;
and according to the correlation test and the collinearity diagnosis result, removing the observation factors with the correlation or collinearity, and taking the rest observation factors as the prediction variables of the duration time of the traffic accident.
Further, the establishing of the time-varying acceleration failure time model facing the prediction of the duration of the highway traffic accident according to the prediction variables comprises the following steps:
calculating the duration T of the ith highway traffic accident i Dividing the time into k continuous and non-overlapping time periods: [ t ] of 0 ,t 1 ]、(t 1 ,t 2 ]、…、(t k-1 ,t k ]Where 0= t 0 <t 1 <t 2 <…<t k-1 <t k ＝T i (ii) a The prediction variable is kept constant in each time interval, but is changed in different time intervals;
let ht | X j ) And X j Respectively, a hazard function and a set of predicted variables in the time period j, assuming that the functional relationship between the hazard function and the set of predicted variables is as follows:
h(t|X j )＝h 0 [t·exp(βX j )]exp(-βX j )
where β is a coefficient vector to be estimated corresponding to the set of prediction variables, h 0 () is a baseline risk function;
exceeding t in the event duration j-1 Is calculated for the duration of the accident exceeding t j The probability of (c) is:
the duration of the accident exceeds t k The survival probability of (1) is:
further, the selecting an optimal time-varying acceleration failure time model according to the AIC index includes:
performing parameter estimation on the time-varying acceleration failure time model by using a maximum likelihood method;
for the ith occurrence, the number likelihood function of the time-varying acceleration failure time model is:
because the reference risk function can obey different distributions to obtain different model parameter estimation results, model performance comparison is carried out through AIC indexes, and an optimal time-varying accelerated failure time model is selected; the AIC is defined as follows:
AIC＝2(K-LL(β))
in the formula, LL (β) is a log-likelihood value when the model converges, and K is the number of parameters to be estimated in the model.
Further, the benchmark risk function obeys one of the following distributions: exponential distribution, weibull distribution, loglogistic distribution, gompertz distribution, and generalized Gamma distribution.
Further, the acquiring traffic accident data of the highway traffic accident to be predicted, and acquiring a duration time survival function curve according to the traffic accident data and the optimal time-varying acceleration failure time model to realize the prediction of the duration time of the highway traffic accident comprises the following steps:
for the highway traffic accident to be predicted, obtaining values of time-varying factors and time-invariant factors in each time period in the highway traffic accident;
calculating the survival probability of each time point by using an optimal time-varying acceleration failure time model, and drawing an accident duration survival function curve;
and determining a survival probability critical value, and when the survival probability of the accident is equal to the survival probability critical value, determining the corresponding time as the predicted value of the accident duration.
The invention adopts another technical scheme that:
a highway traffic accident duration prediction system comprising:
the data acquisition module is used for acquiring historical traffic data of the highway, performing data space-time association by taking a traffic accident as a unit and constructing a data set;
the variable acquisition module is used for carrying out correlation test and collinearity diagnosis on the observation factors in the data set and acquiring the predictive variable of the duration time of the traffic accident; wherein the observation factors include time-varying factors and time-invariant factors;
the model construction module is used for establishing a time-varying acceleration failure time model facing the expressway traffic accident duration prediction according to the prediction variables and selecting the optimal time-varying acceleration failure time model according to AIC indexes;
and the time prediction module is used for acquiring traffic accident data for the highway traffic accident to be predicted, and acquiring a duration survival function curve according to the traffic accident data and the optimal time-varying acceleration failure time model to realize the prediction of the duration of the highway traffic accident.
The other technical scheme adopted by the invention is as follows:
a highway traffic accident duration prediction apparatus comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method described above.
The other technical scheme adopted by the invention is as follows:
a computer readable storage medium, in which a program executable by a processor is stored, the program executable by the processor being for performing the method as described above when executed by the processor.
The invention has the beneficial effects that: the invention fully considers time-varying factors and time-invariant factors, so that the model is more accurately established. In addition, by using a time-varying acceleration failure time model instead of a machine learning method, the statistical analysis model can give a clear explanation of the influence of each variable on the accident duration, provides a basis for emergency treatment of the expressway, and has a strong application value.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description is made on the drawings of the embodiments of the present invention or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of the steps of a method for predicting the duration of a highway traffic accident in accordance with an embodiment of the present invention;
FIG. 2 is a graph illustrating a survival function curve of a highway traffic accident as a function of time according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and larger, smaller, larger, etc. are understood as excluding the number, and larger, smaller, inner, etc. are understood as including the number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise specifically limited, terms such as set, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention by combining the specific contents of the technical solutions.
As shown in fig. 1, the embodiment provides a method for predicting the duration of a highway traffic accident, which can effectively overcome the defect that the time-varying characteristics of part of the predictive variables are not considered in the prior art. The method specifically comprises the following steps:
s1, historical traffic data of a highway are obtained, data space-time correlation is carried out by taking a traffic accident as a unit, and a data set is constructed.
The method comprises the steps of obtaining highway traffic accident data, road design data, real-time traffic flow data and real-time meteorological data, preprocessing the data, performing data space-time association by taking a traffic accident as a unit, and constructing a highway traffic accident duration prediction data set.
The highway traffic accident data referred to in this step mainly includes accident duration, road property loss, accident-related vehicle type, casualty degree, collision type, lane occupancy, working day, accident occurrence period, road conditions (e.g., horizontal curvature, gradient, bridge, and ramp). The real-time traffic flow data comprises real-time road section traffic flow and the proportion of vehicles of various types in the real-time traffic flow. The real-time traffic flow is the traffic flow of cars converted into equivalent, and needs to be converted according to the traffic composition ratio given by the detector. The real-time meteorological data includes real-time wind speed, rainfall, visibility, temperature and humidity. The accident influence factor variables can be divided into two types, namely fixed variables and time-varying variables. The fixed variables refer to variables which do not change along with time in the model, such as road yield loss, accident-related vehicle types, road conditions and the like; the time-varying variable refers to a variable that changes with time in the model, and in this patent, refers to an accident influence variable that changes every short time after an accident occurs until the accident is cleared. Weather influence factors and traffic flow data are often analyzed as time-varying variables, and the specific time interval division depends on the characteristics of a data set.
In the data preprocessing, the steps to be completed include data cleaning, classification variable processing and the like. The data cleaning refers to cleaning abnormal data, in this embodiment, data missing or abnormal in accident duration is removed, and the abnormal data includes data with an accident duration of 0 and an accident duration of more than 1000 minutes. In the present embodiment, the categorical variable processing refers to setting one of categorical variables including n items (n.gtoreq.2) as an unreferenced item, and converting the other n-1 items into virtual (dummy) variables represented by a value of "0-1". Taking the classification variable 'accident severity' as an example, the accident severity is divided into three categories of serious injury or death, slight injury and no injury, the no injury is taken as a reference item, and the serious injury or death and the slight injury are processed according to the fact whether the serious injury or death and the slight injury occur or not.
And (3) corresponding the data of the accident occurrence periods of the traffic flow data and the meteorological station data with the accident influence data, wherein the influence variable in the accident data is a fixed variable in the whole accident duration, but the meteorological data and the traffic flow data can change along with time in the whole accident duration. In the event processing, it is necessary to correlate time-varying variables with fixed variables, and the rule of correspondence depends on the minimum time interval among the time-varying variables. Table 1 gives a corresponding example of time-varying variable data, assuming that the time-varying variables include 10-minute average wind speed and 5-minute vehicle flow. The minimum interval in this example is 5 minutes, and the wind speed values are a for both the 0-5 minute interval and the 5-10 minute interval 1 The traffic flow is b in 0-5 min 1 The value of the interval of 5-10 minutes is b 2 . It should be noted that the examples are merely illustrative, and the specific time-varying variable interval depends on the data set.
Table 1 time-varying variable data correspondence example
S2, carrying out correlation test and collinearity diagnosis on observation factors in the data set, and obtaining a prediction variable of the traffic accident duration; wherein the observed factors include time-varying factors and time-invariant factors.
And (3) carrying out correlation test and collinearity diagnosis on the observation factors in the data set, eliminating the factors which have obvious correlation or obvious collinearity with other factors, and taking the rest factors as prediction variables of the duration of the traffic accident.
The observed factors in the data set include both time-varying factors and time-invariant factors. Wherein the time-varying factors include: traffic flow, traffic composition, wind speed, precipitation, visibility, air temperature and humidity every short period of time (e.g., 5 minutes or 10 minutes) after the occurrence of an accident until the end of the removal of the accident; time-invariant factors include: hit-and-run vehicle type and extent of damage, accident casualty severity, accident type, and road conditions at the accident site (e.g., horizontal curvature, grade, bridge, and ramp).
The preprocessed data need to exclude variables with significant correlation or significant collinearity to input a time-varying acceleration failure time model. Correlation tests and collinearity diagnostics are performed on variables in the dataset. An absolute value of the correlation coefficient greater than 0.6 indicates that there is a strong correlation between the variables, and a collinear relationship may exist. In the co-linearity statistic, if the tolerance is less than 0.2 or the variance expansion factor is greater than 10, then multiple co-linearity is considered to exist. According to the results of correlation analysis and collinearity test, the representativeness and importance of each factor are combined, and the observation results with obvious correlation or obvious collinearity with other factors are eliminated.
And S3, establishing a time-varying acceleration failure time model facing the prediction of the duration time of the highway traffic accident according to the prediction variables, and selecting the optimal time-varying acceleration failure time model according to AIC indexes.
The duration of the highway accident in question is the time from the moment of the traffic accident to the moment when the rescue team clears the obstacle interfering with the traffic and the road resumes passing.
The survival time is represented by T, namely the duration of the highway accident, and the distribution function of T is as follows:
F(t)＝P(T≤t)＝∫ 0 t f(u)du
wherein t represents the accident duration; f (T) represents the probability density function of the accident duration T.
The survival function of T is represented by S (T), and represents the probability that an accident has not been processed until time T, also referred to as the cumulative survival probability, and is expressed by the formula:
S(t)＝1-F(t)＝P(T>t)＝∫ t ∞ f(u)du
the survival function corresponds to a risk function, denoted by h (t). h (t) represents the conditional probability that the accident will continue at time t + dt, given that the accident continues until time t (dt is a very small time interval):
the slope of the risk function may reflect the dependency of the duration end probability on the duration length. If it is not
As the duration of the accident extends, the probability of the accident ending soon increases; if it is used
As the duration of the accident extends, the probability of the accident ending soon decreases; if it is not
The probability of the accident duration ending is independent of the duration length.
The accelerated failure time model belongs to a full parameter model in survival analysis, and the key assumption is that the survival time is accelerated or decelerated by a constant factor when different levels of covariates are compared.
The survival function of the accelerated failure time model (the probability that an accident has not been processed until time t) is written as:
S(t|X)＝S 0 [t·exp(βX)]
where X is the covariate vector that affects the duration of the incident and β is the vector of the estimation coefficients. S. the 0 () is the baseline survival function.
The risk function of the accelerated failure time model (conditional probability that an accident continues until time t, and still continues at time t + dt under the condition that the accident continues to time t) is:
h(t|X)＝h 0 [t·exp(βX)]exp(-βX)
wherein h is 0 (.) is a baseline risk function. When using the accelerated time to failure model, it is generally assumed that the baseline risk function may conform to an exponential distribution, a Weibull distribution, a Loglogistic distribution, a Gompertz distribution, and a generalized Gamma distribution. The survival and risk functions for the different distributions are shown in table 2.
TABLE 2 survival and risk function tables with different distributions
The accelerated failure time model allows the effects of time-varying variables to be discussed by extending the model, assuming t i The duration of the incident from the ith may be divided into k non-overlapping but consecutive intervals: [ t ] of 0 ,t 1 ]，(t 1 ,t 2 ]8230; "and t k-1 ,t k ]Wherein 0= t 0 <t 1 <t 2 <…<t k-1 <t k ＝t i . Wherein the length of the k time intervals is not required to be the same. These covariates were held constant during each hour interval, at different timesChanges within the interval.
The jth time ((t) j-1 ,t j ],if 1<j<k；or[t j-1 ,t j ]The risk function for if j =1,k) is:
ht|X j )＝h 0 [t·exp(βX j )]exp(-βX j )
where β is an estimable coefficient vector (comprising a constant, β) corresponding to the covariate vector 0 )，h 0 () is a baseline risk function.
Under this assumption, the duration of the highway accident reaches t j-1 Is continued until t j The probability of (c) is:
t k the survival function of time is expressed as:
the model is solved by using a maximum likelihood method, and for the ith accident, the log-likelihood function can be written as:
the matching effect of the model is reflected by using the AIC index. In general, the smaller the AIC value, the better the fit. And calculating AIC index values of different models corresponding to different distributions, wherein the minimum AIC value is the optimal model.
The AIC index is defined as:
AIC＝2(K-LL(β))
where LL (β) is a log-likelihood value at the time of model convergence, and K is the number of estimation parameters.
As an alternative embodiment, the risk function is estimated using Weibull distribution and Loglogistic distribution:
weibull distribution:
h(t)＝λp(λt) p-1
loglogistic distribution:
where λ and p are the location parameter and the scale parameter, respectively.
Under this assumption, the duration of the highway accident reaches t j-1 Under the condition of (1) and continuing to t j Has a probability of
t k The survival function of time is expressed as:
the model is solved by using a maximum likelihood method, and for the ith accident, the log-likelihood function of the model can be written
The construction of the second term of the log-likelihood function requires the indefinite integration of the functions corresponding to different distributions, the form of the indefinite integration corresponding to the Weibull distribution being (λ t) p The Log logistic distribution corresponds to an indefinite integral of the form Log [1+ (λ t) p ]。
And (4) performing model comparison and selection through the AIC value to select the optimal model.
And S4, acquiring traffic accident data of the highway traffic accident to be predicted, and acquiring a duration survival function curve according to the traffic accident data and the optimal time-varying acceleration failure time model to predict the duration of the highway traffic accident.
For any highway traffic accident, according to the value of relevant factors, the optimal time-varying accelerated failure time model is utilized to draw a survival function curve of the duration time, and the accident duration time is predicted according to the survival function curve.
The result obtained by solving the model in step S3 includes the shape parameters of the parameter distribution and the mean and standard deviation of the influencing factors, the survival function of the calibrated parameters is obtained according to the model parameter estimation result, and the values of the relevant factors are substituted into the optimal time-varying acceleration failure model to obtain a survival function curve, which is specifically shown in fig. 2. It is generally considered that the accident has ended when the probability of continuation of the accident is less than 10%. The time during which the survival probability of the accident duration changes from 100% to 10% is recorded as the accident duration.
In summary, compared with the prior art, the method of the embodiment has the following advantages and beneficial effects:
(1) According to the method, the time-varying characteristics of the accident influence factors are considered, the accident influence factors are not regarded as fixed variables, the model can be established more accurately, and the prediction accuracy is improved. The method not only can obtain more accurate prediction duration, but also considers the influence of time-varying factors on the accident duration, so that the model is closer to the actual situation, and has practical application value.
(2) According to the invention, a time-varying acceleration failure time model rather than a machine learning method is used, and a statistical analysis model can give a clear explanation of the influence of each variable on the accident duration, so that a basis is provided for emergency treatment of the expressway, and the method has a strong application value.
The present embodiment also provides a system for predicting a duration of a highway traffic accident, including:
the data acquisition module is used for acquiring historical traffic data of the highway, performing data space-time correlation by taking a traffic accident as a unit and constructing a data set;
the variable acquisition module is used for carrying out correlation test and collinearity diagnosis on the observation factors in the data set and acquiring a prediction variable of the duration time of the traffic accident; wherein the observation factors include time-varying factors and time-invariant factors;
the model construction module is used for establishing a time-varying acceleration failure time model facing the expressway traffic accident duration prediction according to the prediction variables and selecting the optimal time-varying acceleration failure time model according to AIC indexes;
and the time prediction module is used for acquiring traffic accident data for the highway traffic accident to be predicted, and acquiring a duration survival function curve according to the traffic accident data and the optimal time-varying acceleration failure time model to realize the prediction of the duration of the highway traffic accident.
The system for predicting the duration of the highway traffic accident can execute the method for predicting the duration of the highway traffic accident provided by the embodiment of the method, can execute any combination of the implementation steps of the embodiment of the method, and has corresponding functions and beneficial effects of the method.
The present embodiment also provides a device for predicting a duration of a highway traffic accident, including:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of fig. 1.
The device for predicting the duration of the highway traffic accident can execute the method for predicting the duration of the highway traffic accident provided by the embodiment of the method, can execute any combination of the implementation steps of the embodiment of the method, and has corresponding functions and beneficial effects of the method.
The embodiment of the application also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and executed by the processor, causing the computer device to perform the method illustrated in fig. 1.
The embodiment also provides a storage medium, which stores instructions or programs capable of executing the method for predicting the duration of the highway traffic accident provided by the embodiment of the method of the invention, and when the instructions or the programs are run, the steps can be implemented by any combination of the embodiment of the method, and the corresponding functions and beneficial effects of the method are achieved.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed, and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in a separate physical device or software module. It will also be understood that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer given the nature, function, and interrelationships of the modules. Accordingly, those of ordinary skill in the art will be able to practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The functions may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the foregoing description of the specification, reference to the description of "one embodiment/example," "another embodiment/example," or "certain embodiments/examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method for predicting the duration of a highway traffic accident is characterized by comprising the following steps:
acquiring historical traffic data of a highway, performing data space-time association by taking a traffic accident as a unit, and constructing a data set;
carrying out correlation test and collinearity diagnosis on observation factors in the data set to obtain a prediction variable of the duration time of the traffic accident; wherein the observation factors include time-varying factors and time-invariant factors;
establishing a time-varying acceleration failure time model facing to the prediction of the duration time of the highway traffic accident according to the prediction variable, and selecting an optimal time-varying acceleration failure time model according to AIC indexes;
and acquiring traffic accident data for the highway traffic accident to be predicted, and acquiring a duration survival function curve according to the traffic accident data and the optimal time-varying acceleration failure time model to predict the duration of the highway traffic accident.
2. The method according to claim 1, wherein the historical traffic data includes highway traffic accident data, real-time traffic flow data and real-time weather data;
the time-varying factors include: traffic flow, traffic composition, wind speed, precipitation, visibility, air temperature and humidity in every preset time period after an accident occurs and before the accident is cleared;
the time-invariant factors include: hit-and-run vehicle type and extent of damage, severity of accident casualty, accident type and road conditions at the accident site.
3. The method of claim 2, wherein the correlation test and collinearity diagnosis of the observation factors in the data set comprises:
respectively carrying out correlation test and collinearity diagnosis on the time-varying factors and the time-invariant factors;
in the correlation test, if the correlation coefficient between two factor variables meets a preset range, judging that the correlation exists between the two factor variables;
in the collinearity diagnosis, if the tolerance is smaller than a first preset threshold or the variance expansion factor is larger than a second preset threshold, the collinearity exists among the factor variables;
and according to the correlation test and the collinearity diagnosis result, removing the observation factors with the correlation or collinearity, and taking the rest observation factors as the prediction variables of the duration time of the traffic accident.
4. The method for predicting the duration of the highway traffic accident according to claim 1, wherein the establishing of the time-varying acceleration failure time model facing the prediction of the duration of the highway traffic accident according to the prediction variables comprises the following steps:
calculating the duration T of the ith highway traffic accident i Dividing the time into k continuous and non-overlapping time periods: [ t ] of 0 ,t 1 ]、(t 1 ,t 2 ]、…、(t k-1 ,t k ]Wherein 0= t 0 <t 1 <t 2 <…<t k-1 <t k ＝T i (ii) a The predictor variable remains constant during each time interval, but changes during different time intervals;
let h (t | X) j ) And X j Respectively, a hazard function and a set of predicted variables in the time period j, assuming that the functional relationship between the hazard function and the set of predicted variables is as follows:
h(t|X j )＝h 0 [t·exp(βX j )]exp(-βX j )
where β is a coefficient vector to be estimated corresponding to the set of predictor variables, h 0 () is a benchmark risk function;
exceeding t during the duration of the accident j-1 Under the condition that the calculated accident duration exceeds t j The probability of (c) is:
duration of accident exceeding t k The survival probability of (1) is:
5. the method for predicting the duration of a highway traffic accident according to claim 4, wherein the selecting the optimal time-varying acceleration failure time model according to the AIC index comprises:
performing parameter estimation on the time-varying accelerated failure time model by using a maximum likelihood method;
for the ith occurrence, the number likelihood function of the time-varying acceleration failure time model is:
because the reference risk function can obey different distributions to obtain different model parameter estimation results, model performance comparison is carried out through AIC indexes, and an optimal time-varying accelerated failure time model is selected; the AIC is defined as follows:
AIC＝2(K-LL(β))
in the formula, LL (β) is a log-likelihood value when the model converges, and K is the number of parameters to be estimated in the model.
6. A method for predicting the duration of a highway traffic accident according to claim 5, wherein the benchmark risk function is subject to one of the following distributions: exponential distribution, weibull distribution, loglogistic distribution, gompertz distribution, and generalized Gamma distribution.
7. The method for predicting the duration of the highway traffic accident according to claim 1, wherein the steps of acquiring traffic accident data and acquiring a duration survival function curve according to the traffic accident data and an optimal time-varying acceleration failure time model for the highway traffic accident to be predicted to realize the prediction of the duration of the highway traffic accident comprise:
for the highway traffic accident to be predicted, obtaining values of time-varying factors and time-invariant factors in each time period in the highway traffic accident;
calculating the survival probability of each time point by using an optimal time-varying acceleration failure time model, and drawing an accident duration survival function curve;
and determining a survival probability critical value, and when the survival probability of the accident is equal to the survival probability critical value, determining the corresponding time as the predicted value of the accident duration.
8. A system for predicting the duration of a highway traffic accident, comprising:
the data acquisition module is used for acquiring historical traffic data of the highway, performing data space-time association by taking a traffic accident as a unit and constructing a data set;
the variable acquisition module is used for carrying out correlation test and collinearity diagnosis on the observation factors in the data set and acquiring a prediction variable of the duration time of the traffic accident; wherein the observation factors include time-varying factors and time-invariant factors;
the model construction module is used for establishing a time-varying acceleration failure time model facing the expressway traffic accident duration prediction according to the prediction variables and selecting the optimal time-varying acceleration failure time model according to AIC indexes;
and the time prediction module is used for acquiring traffic accident data for the highway traffic accident to be predicted, acquiring a duration survival function curve according to the traffic accident data and the optimal time-varying acceleration failure time model, and realizing the prediction of the duration of the highway traffic accident.
9. An apparatus for predicting a duration of a highway traffic accident, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of any one of claims 1-7.
10. A computer readable storage medium in which a program executable by a processor is stored, wherein the program executable by the processor is adapted to perform the method according to any one of claims 1 to 7 when executed by the processor.
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