CN112434075B - ETC portal-based traffic abnormality detection method and device, storage medium and terminal - Google Patents
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Abstract
The application discloses a traffic abnormality detection method, a device, a storage medium and a terminal based on an ETC portal, wherein the method comprises the following steps: acquiring first flow, a first flow change rate and first variation coefficient data of a current sampling period of an ETC portal to be detected in real time according to a preset period; acquiring second flow, second flow change rate and second variation coefficient data of each period in a preset second sampling time point set in a historical database, and calculating according to the generated multiple parameters to generate first flow data, first flow change rate and Z fractions corresponding to the first variation coefficients; weighting and summing the Z scores to generate traffic parameters of the ETC portal to be detected; and determining whether traffic abnormality occurs or not based on the traffic parameters of the ETC portal to be detected. Therefore, by adopting the embodiment of the application, the traffic accidents can be found in time, so that the traffic accidents can be treated in time, and the running efficiency of the expressway network is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a traffic abnormality detection method and device based on an ETC portal, a storage medium and a terminal.
Background
Along with the development of the ETC technology of the expressway portal in China, the availability and the credibility of traffic information on the expressway are gradually improved. Traffic on highways is often more stable than traffic in cities, but traffic accidents at high speeds cause more serious losses due to inter-urban traffic jams caused during the accident occurrence and cleaning phases. Therefore, it is necessary to detect traffic accidents on the expressway in time and plan a more reasonable route for the driver based on the detected accidents, thereby reducing the degree of traffic jam caused by the accidents.
Currently, in traffic accident detection, three technical implementations are generally included. The first is to collect speed data of the floating car based on the floating car information acquisition system, so that accidents are judged through abnormal speed performance. However, since the floating car data cannot cover all the high-speed road sections and the driving path has randomness, there are often many missing values, although the prior art can fill the missing values to some extent by the data of the nearby road sections, the reliability of the filled data is reduced, and some road sections with too many missing values are difficult to fill, so the stability and the reliability are limited. The second is to train classification models based on information such as detection speed, flow and lane occupancy of sensors such as coils and radars through some systems, but different classification models are needed for different roads due to the difference of system parameters among the roads, and the model training needs to collect a large amount of accident data, so that the task amount is large, the model is easily influenced by the change of external conditions, the model is invalid, and meanwhile, the coverage rate of the sensors needed for detection is low in China, and the sensors cannot be put into the detection of national highway sections under the current conditions. Thirdly, the accident detection method based on the abrupt change of traffic flow needs to rely on information such as a road, time, weather and the like in multiple dimensions to construct a flow prediction model, and the predicted value of the flow is compared with the true value so as to judge the abrupt change. However, the construction of the traffic prediction model needs to collect more complex network topology information, when the road segment relation is changed, the model will fail, and the training of the classifier still needs a large amount of historical accident data, which is difficult to popularize in the national traffic network detection.
The traffic accidents can not be found in the three prior art, and the traffic accidents can not be guaranteed to be processed in time, so that the operation efficiency of the expressway network is reduced.
Disclosure of Invention
The embodiment of the application provides a traffic abnormality detection method and device based on an ETC portal, a storage medium and a terminal. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present application provides a traffic anomaly detection method based on an ETC portal, where the method includes:
Acquiring first flow, first flow change rate and first variation coefficient data of a current sampling period of an ETC portal to be detected in real time according to a preset period;
acquiring second flow, second flow change rate and second variation coefficient data of each period in a preset second sampling time point set in a historical database;
Based on the second flow rate, the second flow rate change rate, the second variation coefficient and the sampling time point set in the preset second sampling time point set, performing Z-fraction conversion on the first flow rate data, the first flow rate change rate and the first variation coefficient data of the current sampling period to generate Z fractions corresponding to the first flow rate data, the first flow rate change rate and the first variation coefficient,
Weighting and summing the first flow data, the first flow change rate and the Z fraction corresponding to the first variation coefficient to generate a traffic parameter of the current sampling period of the ETC portal to be detected;
And judging whether traffic abnormality occurs or not based on the traffic parameters of the ETC portal to be detected.
Optionally, the preset second sampling time point set refers to a set of time points of m periods before and after the current sampling period in the past n days;
The sampling time point set refers to a set of time points corresponding to the preset second sampling time point set and the current sampling period.
Optionally, the determining whether the traffic abnormality occurs based on the traffic parameter of the ETC portal to be detected includes:
And when the traffic parameter of the ETC portal to be detected is smaller than the preset traffic parameter, judging that traffic abnormality occurs. And when the traffic parameter of the ETC portal to be detected is greater than or equal to the preset traffic parameter, judging that no traffic abnormality occurs.
Optionally, after determining whether the traffic abnormality occurs based on the traffic parameter of the ETC portal to be detected, the method further includes:
When traffic abnormality occurs, traffic abnormality information is generated and sent to related departments for early warning; and
And when no traffic abnormality occurs, sending the first flow data corresponding to the current sampling period to the historical database for storage.
Optionally, the method further comprises:
initializing the history database; wherein the initialization history database comprises:
collecting flow data of the ETC portal to be detected according to a second preset period;
calculating a second variation coefficient of flow data of the ETC portal in each second preset period;
when the variation coefficient of the flow data of the ETC portal to be detected is smaller than a preset variation coefficient threshold, sending the time period corresponding to the second preset period and the flow data to the historical database for storage until the stored flow data reach the preset quantity.
Optionally, the data in the history database is iterated step by step, and the iterating step includes:
When the traffic of the current sampling period is judged not to be abnormal, sending the time period and the flow data corresponding to the current sampling period to the historical database;
and deleting the time period and the flow data corresponding to one sampling period which is the farthest from the current sampling period in the historical database.
Optionally, the weighted sum of the first flow data, the first flow rate change rate and the Z fraction corresponding to the first variation coefficient is 1.
In a second aspect, an embodiment of the present application provides a traffic anomaly detection device based on an ETC portal, where the device includes:
The first parameter acquisition module is used for acquiring first flow data, a first flow change rate and a first variation coefficient of the current sampling period of the ETC portal to be detected in real time according to a preset period;
The second parameter acquisition module is used for acquiring second flow, second flow change rate and second variation coefficient data of each period in a preset second sampling time point set in the historical database;
The Z fraction generation module is configured to perform Z fraction conversion on the first flow data, the first flow rate change rate, and the first coefficient of variation data in the current sampling period based on the second flow rate, the second flow rate change rate, the second coefficient of variation data, and the set of sampling time points in the preset second set of sampling time points, so as to generate first flow rate data, a first flow rate change rate, and a Z fraction corresponding to the first coefficient of variation
The sampling time point set is formed by combining the preset second sampling time point set and a current sampling period; the traffic parameter generation module is used for weighting and summing the first flow data, the first flow change rate and the Z fraction corresponding to the first variation coefficient to generate traffic parameters of the ETC portal to be detected;
And the traffic abnormality determining module is used for determining whether traffic abnormality occurs or not based on the traffic parameters of the ETC portal to be detected.
In a third aspect, embodiments of the present application provide a computer storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor and to perform the above-described method steps.
In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps described above.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
In the embodiment of the application, a traffic abnormality detection device based on an ETC portal obtains first flow data, a first flow change rate and a first variation coefficient of a current sampling period of the ETC portal to be detected in real time according to a preset period, obtains second flow, a second flow change rate and a second variation coefficient data of each period in a preset second sampling time point set in a historical database, calculates according to a plurality of generated parameters, generates Z fractions corresponding to the first flow data, the first flow change rate and the first variation coefficient, sums the weighted Z fractions corresponding to the first flow data, the first flow change rate and the first variation coefficient, generates traffic parameters of the ETC portal to be detected, and finally determines whether traffic abnormality occurs based on the traffic parameters of the ETC portal to be detected. According to the application, by introducing big data and methods of probability and statistics, the flow change on the expressway is detected in real time based on ECT portal data, so that traffic accidents can be timely found out to give an alarm, other vehicles on the expressway can be timely reminded to plan more reasonable travel routes, the congestion caused by the traffic accidents is reduced, and the expressway network operation efficiency is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic flow chart of a traffic abnormality detection method based on an ETC portal provided by an embodiment of the present application;
fig. 2 is a schematic flow chart of initializing a historical database during traffic anomaly detection based on an ETC portal according to an embodiment of the present application;
FIG. 3 is a schematic block diagram of a traffic anomaly detection process based on an ETC portal provided by an embodiment of the application;
Fig. 4 is a schematic structural diagram of a traffic abnormality detection device based on an ETC portal according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
It should be understood that the described embodiments are merely some, but not all, embodiments of the invention. 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.
When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention as detailed in the accompanying claims.
In the description of the present invention, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art. Furthermore, in the description of the present invention, unless otherwise indicated, "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
To date, in traffic accident detection, three technical implementations are generally included. The first is to collect speed data of the floating car based on the floating car information acquisition system, so that accidents are judged through abnormal speed performance. However, since the floating car data cannot cover all the high-speed road sections and the driving path has randomness, there are often many missing values, although the prior art can fill the missing values to some extent by the data of the nearby road sections, the reliability of the filled data is reduced, and some road sections with too many missing values are difficult to fill, so the stability and the reliability are limited. The second is to train classification models based on information such as detection speed, flow and lane occupancy of sensors such as coils and radars through some systems, but different classification models are needed for different roads due to the difference of system parameters among the roads, and the model training needs to collect a large amount of accident data, so that the task amount is large, the model is easily influenced by the change of external conditions, the model is invalid, and meanwhile, the coverage rate of the sensors needed for detection is low in China, and the sensors cannot be put into the detection of national highway sections under the current conditions. Thirdly, the accident detection method based on the abrupt change of traffic flow needs to rely on information such as a road, time, weather and the like in multiple dimensions to construct a flow prediction model, and the predicted value of the flow is compared with the true value so as to judge the abrupt change. However, the construction of the traffic prediction model needs to collect more complex network topology information, when the road segment relation is changed, the model will fail, and the training of the classifier still needs a large amount of historical accident data, which is difficult to popularize in the national traffic network detection. The traffic accidents can not be found in the three prior art, and the traffic accidents can not be guaranteed to be processed in time, so that the operation efficiency of the expressway network is reduced. Therefore, the application provides a traffic abnormality detection method, a traffic abnormality detection device, a traffic abnormality detection storage medium and a traffic abnormality detection terminal based on an ETC portal, so as to solve the problems in the related technical problems. According to the technical scheme provided by the application, because the traffic accident can be timely found out to give an alarm and other vehicles on the expressway can be timely reminded to plan more reasonable travel routes by introducing big data and a method of probability and statistics, the traffic accident caused congestion is reduced, the expressway network operation efficiency is improved, and the method is described in detail by adopting an exemplary embodiment.
The following describes in detail the traffic abnormality detection method based on the ETC portal provided by the embodiment of the application with reference to fig. 1 to fig. 3. The method can be realized by a computer program and can be operated on an ETC portal-based traffic abnormality detection device based on a von Neumann system. The computer program may be integrated in the application or may run as a stand-alone tool class application. The traffic abnormality detection device based on the ETC portal in the embodiment of the present application may be a user terminal, including but not limited to: personal computers, tablet computers, handheld devices, vehicle mounted devices, wearable devices, computing devices, or other processing devices connected to a wireless modem, and the like. User terminals may be called different names in different networks, for example: a user equipment, an access terminal, a subscriber unit, a subscriber station, a mobile station, a remote terminal, a mobile device, a user terminal, a wireless communication device, a user agent or user equipment, a cellular telephone, a cordless telephone, a Personal Digital Assistant (PDA), a terminal device in a 5G network or a future evolution network, etc.
Referring to fig. 1, a flow chart of a traffic anomaly detection method based on an ETC portal is provided for an embodiment of the present application. As shown in fig. 1, the method according to the embodiment of the present application may include the following steps:
S101, acquiring a first flow, a first flow change rate and first variation coefficient data of a current sampling period of an ETC portal to be detected in real time according to a preset period;
The preset period is a period of time set by the user according to the actual application scenario, for example, may be 5 minutes as a period, or may be 10 minutes as a period, and the period length is set by the user according to the actual application scenario, which is not limited herein. ETC portal is a device deployed on a highway for charging vehicles and data acquisition. The current sampling period is a set period corresponding to the current period in a plurality of continuous periods. The first flow data is flow data acquired in the current latest period, and the flow data is acquired based on the passing number and speed information of the vehicle. The flow data is data information (i.e., information on the number of vehicles passing and speed, etc.) generated when the vehicles pass through the ETC door frame of the highway.
In one possible implementation manner, when traffic anomaly detection is performed based on flow data of the ETC portal, the user terminal first needs to continuously acquire the flow data of the ETC portal to be detected in real time according to a preset period from the flow data generated by the ETC portal.
For example, when the flow data of each ETC portal on the highway is acquired with 5 minutes as one period, the flow data of the ith statistical period on the jth day is denoted as c (j, i).
In probability theory and statistics, the coefficient of variation, also called "discrete coefficient" (English: coefficient of variation), is a normalized measure of the degree of dispersion of the probability distribution, which is defined as the ratio of standard deviation to average value. In the present application, the specific calculation of the first coefficient of variation can be referred to step S103, which is not described herein.
S102, acquiring second flow, second flow change rate and second variation coefficient data of each period in a preset second sampling time point set in a historical database;
The historical database is used for storing data of traffic abnormal flow. The first flow rate change and the first variation coefficient are calculated according to the current sampling period and the first flow data in step S101.
In a possible implementation manner, after the first flow data corresponding to the current sampling period is generated according to step S101, first calculation is performed based on the first flow data corresponding to the current sampling period, a first flow change rate and a first variation coefficient corresponding to the current sampling period are generated, then a plurality of second flow data corresponding to a plurality of second time periods before and after the current sampling period in a plurality of days are obtained from a historical flow database, calculation is performed based on the plurality of second flow data corresponding to the plurality of second time periods, a plurality of second flow change rates and a plurality of second variation coefficients corresponding to the plurality of second time periods are generated, and finally, a sampling time point set is generated after the current sampling period and the plurality of second time periods are combined.
Specifically, the flow rate change rate of the jth and ith statistical periods of a certain ETC portal of the highway is calculated and can be recorded as r (j, i). The first flow rate change rate and the plurality of second flow rate change rate calculation formulas are thus:
specifically, the variation coefficient of the flow in the ith statistical period and the past n r statistical periods on the jth day of a portal is calculated and is denoted as s (j, i), so that the calculation formulas of the first variation coefficient and the second variation coefficient are as follows:
wherein, the calculation formula of the parameter mu 0 (j, i) in the denominator is:
wherein, the calculation formula of the parameter sigma 0 (j, i) in the molecule is as follows:
It should be noted that, when calculating the coefficient of variation of the current sampling period according to the formula, the coefficient of variation of the flow in the past 1 hour may be generally calculated, that is, n r =12, that is, when calculating the coefficient of variation of the current sampling period, flow data in the past 1 hour needs to be used; to avoid that the calculation cannot be performed when the molecule is 0, if the molecule is 0, 0.01 is used instead.
Further, the representation process for generating the sampling time point set T is as follows: representing the ith statistical time point of the jth day as (j, i), determining the sampling time point set used in the time point (j, i) as
Wherein, For a second set of time periods (i.e. obtained in the history database)/>Representing the number of days of harvesting,/>Representing the statistical total period. { (j, i) } is a first set of time periods.
Parameter description: m, n is determined by the traffic demand. The larger m is, the larger the influence of the daily flow change trend on the abnormality judgment is; the larger n is, the larger the influence of the daytime flow change trend on the abnormality judgment is. Meanwhile, since parameter derivation is based on an assumption that the flow at all time points in the sampling time point set obeys the same distribution, the larger m, n is, the lower the possibility that the assumption is established, the sensitivity to abnormality judgment will be lowered.
S103, performing Z fraction conversion on the first flow data, the first flow change rate and the first variation coefficient data of the current sampling period based on the second flow, the second flow change rate, the second variation coefficient data and the sampling time point set in the preset second sampling time point set, and generating Z fractions corresponding to the first flow data, the first flow change rate and the first variation coefficient.
Wherein the Z fractions corresponding to the first flow data, the first flow change rate and the first variation coefficient are respectively recorded asJ. i refers to the data of the ith statistical period on the jth day, wherein the sampling time point set is formed by combining the preset second sampling time point set and the current sampling period;
Wherein the Z-score is also called standard score (standard score) which is a process of dividing the difference between the number and the average by the standard deviation. In statistics, a standard score is a number of symbols for which the value of an observed or data point is higher than the standard deviation of the average of observed or measured values.
In one possible implementation manner, according to steps S101 and S102, a plurality of parameters including a sampling time point set, first flow data, a first flow rate change rate, a first variation coefficient, a plurality of second flow data, a plurality of second flow rate change rates and a plurality of second variation coefficients are obtained, and according to the obtained plurality of parameters, Z fractions corresponding to the flow data, the flow rate change rate and the variation coefficients of the ETC portal to be detected are calculated, that is, c (j, i), r (j, i), S (j, i) of each portal are converted into values representing the degree of deviation of the traffic state from the normal state
Specifically, the sampling time point set T and the Z fraction corresponding to the data in the sampling time point set T in the process of calculating the flow data of the ETC portal to be detectedIn this case, the parameters c (j, i), r (j, i), s (j, i) at (j, i) are normalized (using the central limit theorem) to be calculated as:
Wherein, The z value obtained after modification of the central limit theorem. The smaller the z value (less than 0), the lower the flow is indicated to be at a normal level.
Wherein, the molecular parameter mu 1 (j, i) has a calculation formula:
Wherein, the calculation formula of the denominator parameter sigma 1 (j, i) is as follows:
N T in the above formula is the number of elements in the sampling time point set T.
Specifically, the Z fraction corresponding to the flow rate change rate of the ETC portal to be detected is calculatedThe calculation formula is as follows:
Assuming that the traffic state is still normal at the moment (j, i), mu X,μY,σX,σY can be estimated by taking the time points contained in the T set as samples and using the sample mean value and the standard deviation, so as to derive a probability density function F (·) and a cumulative distribution function F (·) of the flow rate r (j, i) distribution at the moment
F (r (j, i)) represents the probability that the flow rate change rate is smaller than r (j, i) if the traffic state is normal. The smaller the probability, the more extreme the flow rate change at this time, the lower the likelihood that the assumption that the traffic state is normal is true.
To unify the concepts of F (r (j, i)) and z scores, F (r (j, i)) is transformed, The smaller (less than 0) the more likely the flow will be to collapse unexpectedly.
Wherein the parameters are
Wherein the parameters are
Wherein the parameters are
Wherein the parameters are
Wherein the parameters are
Wherein the parameters are
Wherein the parameters are
Wherein the parameters are
Wherein the parameters are
Wherein the parameters are
In the above formula, the parameter Φ (·) is a cumulative distribution function of the standard normal distribution, and n T is the number of elements in the sampling time point set T.
Specifically, the Z fraction corresponding to the variation coefficient of the ETC portal to be detected is calculatedThe calculation formula is as follows:
If the traffic state at (j, i) time is still normal, the time point (j, i) included in the T set is taken as a sample, mu 3 is estimated by using the average value of the variation coefficients, and the estimated value of the theoretical variation coefficient (s theory) at the time period is taken as the estimated value to derive Probability density function F (·) and cumulative distribution function F (·)
F (s (j, i)) represents the probability that the coefficient of variation is greater than s (j, i) if the traffic state is normal. The smaller the probability, the higher the coefficient of variation (fluctuation level) of the flow at this time is, the lower the possibility that the assumption that the traffic state is normal is established.
To unify the concepts of F (s (j, i)) and z values, F (s (j, i)) is transformed, The smaller (less than 0) the more likely the flow will fluctuate dramatically beyond expectations.
Wherein, the theory is deduced,The probability density function of (2) is given by:
Wherein n=n s (n s data are used for calculating the coefficient of variation)
And S104, weighting and summing the first flow data, the first flow change rate and the Z fraction corresponding to the first variation coefficient to generate a traffic parameter of the current sampling period of the ETC portal.
In one possible implementation, in accordance with step S103Then, a preset weighting parameter is acquired to give/>Weights in different proportions are used for generating passing parameters, z (j, i),/>, of an ETC portal to be detectedWhere z (j, i) refers to the traffic parameter of the ith detection cycle on the jth day.
The traffic parameter is a parameter value for judging traffic abnormality.
In general, z-score conversion is often used to detect outliers in normal distribution, and when a traffic situation occurs, flow data is often negatively deviated from the normal distribution, so if normal time flow data and to-be-judged time flow data are taken as samples, the z-score of the to-be-judged time flow can be used as an evaluation dimension of whether the traffic state is abnormal or not. However, if the judgment is performed through the single dimension of the flow, the system is easy to generate false alarm, and meanwhile, the flow rate change rate and the variation coefficient are monitored to detect the flow dip and the severe fluctuation, so that the system stability can be improved. In order to unify the deviation degree of the flow rate change rate and the variation coefficient to the concept similar to the z score, 3 persons are weighted and comprehensively assessed.
The system estimates the flow change rate and the approximate distribution obeyed by the variation coefficient through probability theory related knowledge, estimates the distribution parameters through data in normal traffic state, calculates the probability of occurrence of the variation coefficient and the flow change rate (or more extreme cases) at a new moment under the distribution, and converts the probability into the 'z fraction' of the non-normal distribution through inverse operation of the normal distribution cumulative distribution function. And finally, giving weights of different proportions of 3 'z scores' according to the service requirements and the state of the traffic system, and comprehensively judging the traffic state of the traffic system.
And S105, judging whether traffic abnormality occurs or not based on the traffic parameters of the ETC portal to be detected.
In the embodiment of the present application, the traffic parameter z (j, i) can be obtained according to step S104, and if z (j, i) < z 0, the traffic state is abnormal. Based on a large amount of data statistics and statistical knowledge, z 0 = -3 is more in line with the usual requirements. So z 0 is preferably a value of-3.
In one possible implementation, when the traffic parameter of the ETC portal to be detected is smaller than the preset traffic parameter, it is determined that traffic abnormality occurs. And when the traffic parameter of the ETC portal to be detected is greater than or equal to the preset traffic parameter, determining that no traffic abnormality occurs.
For example, the smaller z (j, i) is, the lower the portal flow is at the moment i is, the flow is greatly reduced, and the flow is greatly fluctuated, so that the flow characteristics of sudden traffic accidents near the portal are more met. Based on extensive data statistics and statistical knowledge, thresholding z (j, i) to-3 is more consistent with common requirements.
A, b, c are determined by the actual traffic needs. The larger a is, the more the system depends on the difference between the traffic level and the normal level to judge the traffic abnormality caused by the traffic accident, and the whole traffic deviation from the normal level is more easily captured by the system; the larger b is, the more easily the sharp decrease in flow directly affected by the accident is identified; the larger c, the more easily the unexpected severe fluctuations in flow are captured by the system.
The values of a, b and c are also related to the state of the portal at the previous moment. When judging that the blocking point is recovered to pass, due toThe flow is more easily influenced in the past period of time, and the value of c should be relatively low; when the deviation point is judged to be stable, a should be given a smaller value so as to avoid that the stable system deviation is captured by error.
In one possible implementation, the traffic parameter calculation formula is: a+b+c=1. a. b and c are weighting parameters with different proportions, and the weighted sum of the Z fractions corresponding to the first flow data, the first flow change rate and the first variation coefficient is 1.
Further, when traffic abnormality occurs, traffic abnormality information is generated and sent to related departments for early warning, and when traffic abnormality does not occur, first flow data corresponding to the current sampling period is sent to the historical database for storage.
Further, the method also comprises initializing a history database, and when the history database is initialized,
Firstly, acquiring flow data of an ETC portal to be detected according to a second preset sampling period, wherein the second preset sampling period can be one day or half day, when the second preset sampling period is one day, calculating the variation coefficient of the acquired flow data of the ETC portal every day, and when the variation coefficient of the flow data of the ETC portal on the day is smaller than a preset variation coefficient threshold value, transmitting the flow data of the ETC portal on the day and a time period corresponding to the day to the historical database for storage.
Specifically, as shown in fig. 2, for example, a coefficient of variation s (j) of the daily flow rate is calculated, a daily variation coefficient threshold s is set, and if s (j) < s, the daily data is added to the initial database. The process continues from the last date until the number of historical data reaches the system demand (288 n). In this way, the addition of excessive dates of abnormal data to the database is prevented, so that abnormal events cannot be detected when traffic states are detected.
Further, the specific values of a, b, c can be determined through machine learning.
For example, as shown in fig. 3, fig. 3 is a schematic process diagram of a traffic abnormality detection process based on an ETC portal according to an embodiment of the present application. Firstly initializing ETC portal flow data of an expressway to meet the current system requirements, continuously acquiring real-time flow in real time through a preset period to form historical flow, calculating the change rate and the variation coefficient of the historical flow through the formed historical flow, collecting real ETC portal flow data from a historical database, merging the time corresponding to the time for collecting the real ETC portal flow data from the historical database and the time corresponding to the formation of the historical flow to generate a time point set, calculating the change rate and the variation coefficient of the sampling time point set and the historical flow, the change rate and the variation coefficient of the ETC portal flow data, converting the real-time parameters (the change rate and the variation coefficient of the historical flow) to obtain Z scores corresponding to each, finally carrying out weighted summation on the Z scores corresponding to generate final traffic parameters, judging whether the traffic state is abnormal according to the final traffic parameters, carrying out early warning when the traffic state is abnormal, and sending the real-time parameters to the historical database to be stored (namely updating the historical database).
In the embodiment of the application, a traffic abnormality detection device based on an ETC portal obtains first flow, first flow change rate and first variation coefficient data of a current sampling period of the ETC portal to be detected in real time according to a preset period, obtains second flow, second flow change rate and second variation coefficient data of each period in a preset second sampling time point set in a historical database, calculates according to a plurality of generated parameters, generates Z fractions corresponding to the first flow data, the first flow change rate and the first variation coefficient, sums the weighted Z fractions corresponding to the first flow data, the first flow change rate and the first variation coefficient, generates traffic parameters of the ETC portal to be detected, and finally determines whether traffic abnormality occurs based on the traffic parameters of the ETC portal to be detected. According to the application, by introducing big data and methods of probability and statistics, the flow change on the expressway is detected in real time based on ECT portal data, so that traffic accidents can be timely found out to give an alarm, other vehicles on the expressway can be timely reminded to plan more reasonable travel routes, the congestion caused by the traffic accidents is reduced, and the expressway network operation efficiency is improved.
The following are examples of the apparatus of the present invention that may be used to perform the method embodiments of the present invention. For details not disclosed in the embodiments of the apparatus of the present invention, please refer to the embodiments of the method of the present invention.
Referring to fig. 4, a schematic structural diagram of an ETC portal-based traffic abnormality detection device according to an exemplary embodiment of the present invention is shown. The traffic abnormality detection device based on the ETC portal can be realized into all or part of the terminal through software, hardware or a combination of the software and the hardware. The device 1 comprises a flow data generation module 10, a first parameter acquisition module 20, a second parameter acquisition module 30, a Z score generation module 40, a traffic parameter generation module 50 and a traffic abnormality determination module 60.
The flow data generating module 10 is configured to obtain flow data of a current sampling period of an ETC portal to be detected in real time, and calculate a first flow change rate and first coefficient data based on the flow data and the flow data of the past n sampling periods adjacent to the current sampling period;
the first parameter obtaining module 20 is configured to obtain, in real time, first flow data, a first flow change rate, and a first variation coefficient of a current sampling period of the ETC portal to be detected according to a preset period;
A second parameter obtaining module 30, configured to obtain a second flow, a second flow change rate, and second variation coefficient data of each period in a preset second sampling time point set in the history database;
A Z fraction generation module 40 for performing Z fraction conversion on the first flow data, the first flow rate change rate and the first coefficient of variation data in the current sampling period based on the second flow rate, the second flow rate change rate, the second coefficient of variation data and the set of sampling time points in the preset second set of sampling time points to generate Z fraction corresponding to the first flow rate data, the first flow rate change rate and the first coefficient of variation
The sampling time point set is formed by combining the preset second sampling time point set and a current sampling period;
The traffic parameter generating module 50 is configured to weight and sum the first flow data, the first flow change rate, and the Z fraction corresponding to the first variation coefficient, and generate a traffic parameter of the ETC portal to be detected;
The traffic abnormality determination module 60 is configured to determine whether traffic abnormality occurs based on the traffic parameter of the ETC portal to be detected.
It should be noted that, when the traffic abnormality detection device based on the ETC portal provided in the foregoing embodiment executes the traffic abnormality detection method based on the ETC portal, only the division of the foregoing functional modules is used for illustration, in practical application, the foregoing functional allocation may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the traffic abnormality detection device based on the ETC portal provided in the above embodiment belongs to the same concept as the traffic abnormality detection method based on the ETC portal, which embodies the detailed implementation process and is not described herein.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the embodiment of the application, a traffic abnormality detection device based on an ETC portal obtains first flow, first flow change rate and first variation coefficient data of a current sampling period of the ETC portal to be detected in real time according to a preset period, obtains second flow, second flow change rate and second variation coefficient data of each period in a preset second sampling time point set in a historical database, calculates according to a plurality of generated parameters, generates Z fractions corresponding to the first flow data, the first flow change rate and the first variation coefficient, sums the weighted Z fractions corresponding to the first flow data, the first flow change rate and the first variation coefficient, generates traffic parameters of the ETC portal to be detected, and finally determines whether traffic abnormality occurs based on the traffic parameters of the ETC portal to be detected. According to the application, by introducing big data and methods of probability and statistics, the flow change on the expressway is detected in real time based on ECT portal data, so that traffic accidents can be timely found out to give an alarm, other vehicles on the expressway can be timely reminded to plan more reasonable travel routes, the congestion caused by the traffic accidents is reduced, and the expressway network operation efficiency is improved.
The invention also provides a computer readable medium, on which program instructions are stored, which when executed by a processor implement the traffic anomaly detection method based on the ETC portal provided by the above method embodiments.
The invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the traffic anomaly detection method based on the ETC portal of each of the above method embodiments.
Referring to fig. 5, a schematic structural diagram of a terminal is provided in an embodiment of the present application. As shown in fig. 5, terminal 1000 can include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, at least one communication bus 1002.
Wherein the communication bus 1002 is used to enable connected communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may further include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 1001 may include one or more processing cores. The processor 1001 connects various parts within the entire electronic device 1000 using various interfaces and lines, and performs various functions of the electronic device 1000 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and invoking data stored in the memory 1005. Alternatively, the processor 1001 may be implemented in at least one hardware form of digital signal Processing (DIGITAL SIGNAL Processing, DSP), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 1001 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 1001 and may be implemented by a single chip.
The Memory 1005 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). The memory 1005 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described respective method embodiments, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. The memory 1005 may also optionally be at least one storage device located remotely from the processor 1001. As shown in fig. 5, an operating system, a network communication module, a user interface module, and an ETC portal-based traffic anomaly detection application program may be included in a memory 1005 as one type of computer storage medium.
In terminal 1000 shown in fig. 5, user interface 1003 is mainly used for providing an input interface for a user, and acquiring data input by the user; and the processor 1001 may be configured to invoke the ETC portal-based traffic anomaly detection application stored in the memory 1005 and specifically perform the following operations:
Acquiring first flow, first flow change rate and first variation coefficient data of a current sampling period of an ETC portal to be detected in real time according to a preset period;
acquiring second flow, second flow change rate and second variation coefficient data of each period in a preset second sampling time point set in a historical database;
Based on the second flow rate, the second flow rate change rate, the second variation coefficient and the sampling time point set in the preset second sampling time point set, performing Z-score conversion on the first flow rate data, the first flow rate change rate and the first variation coefficient data of the current sampling period, and generating Z scores corresponding to the first flow rate data, the first flow rate change rate and the first variation coefficient;
Weighting and summing the first flow data, the first flow change rate and the Z fraction corresponding to the first variation coefficient to generate a traffic parameter of the current sampling period of the ETC portal to be detected;
And judging whether traffic abnormality occurs or not based on the traffic parameters of the ETC portal to be detected.
In one embodiment, the processor 1001, when executing the determination of whether traffic abnormality occurs based on the traffic parameters of the ETC portal to be detected, specifically executes the following operations:
When the traffic parameter of the ETC portal to be detected is smaller than a preset traffic parameter, judging that traffic abnormality occurs;
And when the traffic parameter of the ETC portal to be detected is greater than or equal to the preset traffic parameter, judging that no traffic abnormality occurs.
In one embodiment, the processor 1001, after performing the determination of whether a traffic abnormality occurs based on the traffic parameters of the ETC portal to be detected, further performs the following operations:
When traffic abnormality occurs, traffic abnormality information is generated and sent to related departments for early warning; and
And when no traffic abnormality occurs, sending the first flow data corresponding to the current sampling period to the historical database for storage.
In one embodiment, the processor 1001, when executing the initializing the history database, specifically performs the following operations:
collecting flow data of the ETC portal to be detected according to a second preset period;
Calculating a second coefficient of variation of the flow data of the ETC portal of each second preset period,
When the variation coefficient of the flow data of the ETC portal to be detected is smaller than a preset variation coefficient threshold, sending the time period and the flow data corresponding to the second preset period to the historical database for storage until the stored flow data reach the preset quantity.
In the embodiment of the application, the traffic abnormality detection device based on the ETC portal obtains first flow data of a current sampling period of the ETC portal to be detected in real time according to a preset period, obtains a first flow change rate and a first variation coefficient based on the first flow data, obtains second flow, a second flow change rate and a second variation coefficient data of each period in a preset second sampling time point set in a historical database, calculates according to a plurality of generated parameters, generates Z scores corresponding to the first flow data, the first flow change rate and the first variation coefficient, sums the weighted Z scores corresponding to the first flow data, the first flow change rate and the first variation coefficient, generates traffic parameters of the ETC portal to be detected, and finally determines whether traffic abnormality occurs based on the traffic parameters of the ETC portal to be detected. According to the application, by introducing big data and methods of probability and statistics, the flow change on the expressway is detected in real time based on ECT portal data, so that traffic accidents can be timely found out to give an alarm, other vehicles on the expressway can be timely reminded to plan more reasonable travel routes, the congestion caused by the traffic accidents is reduced, and the expressway network operation efficiency is improved.
Those skilled in the art will appreciate that implementing all or part of the above-described embodiment methods may be accomplished by computer programs to instruct related hardware, and the programs may be stored in a computer readable storage medium, which when executed may include the embodiment methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, or the like.
The foregoing disclosure is illustrative of the present application and is not to be construed as limiting the scope of the application, which is defined by the appended claims.
Claims (10)
1. The traffic abnormality detection method based on the ETC portal is characterized by comprising the following steps of:
Acquiring first flow, first flow change rate and first variation coefficient data of a current sampling period of an ETC portal to be detected in real time according to a preset period;
acquiring second flow, second flow change rate and second variation coefficient data of each period in a preset second sampling time point set in a historical database;
Based on the second flow rate, the second flow rate change rate, the second variation coefficient and the sampling time point set in the preset second sampling time point set, performing Z-score conversion on the first flow rate data, the first flow rate change rate and the first variation coefficient data of the current sampling period, and generating Z scores corresponding to the first flow rate data, the first flow rate change rate and the first variation coefficient;
Weighting and summing the first flow data, the first flow change rate and the Z fraction corresponding to the first variation coefficient to generate a traffic parameter of the current sampling period of the ETC portal to be detected;
And judging whether traffic abnormality occurs or not based on the traffic parameters of the ETC portal to be detected.
2. The method according to claim 1, wherein the preset second set of sampling time points refers to a set of time points of m cycles before and after the current sampling cycle of the past n days;
The sampling time point set refers to a set of time points corresponding to the preset second sampling time point set and the current sampling period.
3. The method according to claim 1, wherein said determining whether traffic anomalies occur based on traffic parameters of the ETC portal to be detected comprises:
When the traffic parameter of the ETC portal to be detected is smaller than a preset traffic parameter, judging that traffic abnormality occurs;
And when the traffic parameter of the ETC portal to be detected is greater than or equal to the preset traffic parameter, judging that no traffic abnormality occurs.
4. The method according to claim 1, wherein after determining whether traffic abnormality occurs based on the traffic parameters of the ETC portal to be detected, further comprising:
When traffic abnormality occurs, traffic abnormality information is generated and sent to related departments for early warning; and
And when no traffic abnormality occurs, sending the first flow data corresponding to the current sampling period to the historical database for storage.
5. The method according to claim 1, wherein the method further comprises:
initializing the history database; wherein the initialization history database comprises:
collecting flow data of the ETC portal to be detected according to a second preset period;
Calculating a second coefficient of variation of the flow data of the ETC portal of each second preset period,
When the variation coefficient of the flow data of the ETC portal to be detected is smaller than a preset variation coefficient threshold, sending the time period and the flow data corresponding to the second preset period to the historical database for storage until the stored flow data reach the preset quantity.
6. A method according to claim 3, wherein the data in the history database is iterated stepwise, the iterating step comprising:
When the traffic of the current sampling period is judged not to be abnormal, sending the time period and the flow data corresponding to the current sampling period to the historical database;
and deleting the time period and the flow data corresponding to one sampling period which is the farthest from the current sampling period in the historical database.
7. The method of claim 1, wherein the weighted sum of the first flow data, the first flow rate change rate, and the Z fraction corresponding to the first coefficient of variation is 1.
8. Traffic anomaly detection device based on ETC portal, characterized in that, the device includes:
The first parameter acquisition module is used for acquiring first flow data, a first flow change rate and a first variation coefficient of the current sampling period of the ETC portal to be detected in real time according to a preset period;
The second parameter acquisition module is used for acquiring second flow, second flow change rate and second variation coefficient data of each period in a preset second sampling time point set in the historical database;
The Z fraction generation module is configured to perform Z fraction conversion on the first flow data, the first flow rate change rate, and the first coefficient of variation data in the current sampling period based on the second flow rate, the second flow rate change rate, the second coefficient of variation data, and the set of sampling time points in the preset second set of sampling time points, so as to generate first flow rate data, a first flow rate change rate, and a Z fraction corresponding to the first coefficient of variation
The sampling time point set is formed by combining the preset second sampling time point set and a current sampling period;
the traffic parameter generation module is used for weighting and summing the first flow data, the first flow change rate and the Z fraction corresponding to the first variation coefficient to generate traffic parameters of the ETC portal to be detected;
And the traffic abnormality determining module is used for determining whether traffic abnormality occurs or not based on the traffic parameters of the ETC portal to be detected.
9. A computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the method steps of any of claims 1-7.
10. A terminal, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1-7.
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