CN112434260B - Road traffic state detection method, device, storage medium and terminal - Google Patents
Road traffic state detection method, device, storage medium and terminal Download PDFInfo
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Abstract
The application discloses a road traffic state detection method, a device, a storage medium and a terminal, wherein the method comprises the following steps: constructing a sampling time point set required by traffic state detection of an ETC portal to be detected; the sampling time point set comprises a current sampling period and a plurality of continuous historical sampling periods; loading flow data and vehicle average speed corresponding to a current sampling period and a plurality of continuous historical sampling periods; creating a linear regression model based on the sampling time point set, the flow data and the average speed of the vehicle, and calculating the Cooks distance of the current sampling period through the linear regression model; and judging whether the traffic state of the ETC portal to be detected is abnormal or not according to the Cook distance of the current sampling period. Therefore, by adopting the embodiment of the application, traffic accidents can be timely alarmed, and more reasonable routes can be planned for drivers on the expressway, so that the subsequent influence degree of congestion and the like caused by the accidents is reduced, and the operation efficiency of the expressway network is improved.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and apparatus for detecting traffic conditions of a highway, 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 method is based on the information of detection speed, flow, lane occupation rate and the like of sensors such as coils and radars, so that a classification model is trained, but different classification models are needed for different roads due to the difference of system parameters among the roads, a large amount of accident data are needed to be collected for model training, the task amount is large, and the model is easy to be influenced by the change of external conditions, so that the model is invalid; meanwhile, the coverage rate of the sensor required by detection is low in China, and the sensor cannot be put into detection of national high-speed road sections under the current condition. The second is based on advanced cameras and other sensors, but the popularity of such sensors in China is not high at present. Thirdly, based on real-time flow data and static road information, a flow prediction model is built by collecting multidimensional information such as roads, time, weather and the like, and a predicted value and a true value of flow are compared to further judge flow mutation. 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 time in the three prior art, and the traffic accidents can not be guaranteed to be treated in time, so that the running efficiency of the expressway network is reduced.
Disclosure of Invention
The embodiment of the application provides a traffic state detection method and device for a highway, 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 method for detecting a traffic state of a highway, where the method includes:
Constructing a sampling time point set required by traffic state detection of an ETC portal to be detected; the sampling time point set comprises a time period corresponding to the current sampling period of the ETC portal to be detected and time periods corresponding to a plurality of continuous historical sampling periods;
Loading flow data and vehicle average speed corresponding to the current sampling period and a plurality of continuous historical sampling periods;
Creating a linear regression model based on the sampling time point set, the flow data and the average speed of the vehicle, and calculating the Cookie distance of the current sampling period through the linear regression model;
And judging whether the traffic state of the ETC portal to be detected is abnormal or not according to the Cook distance of the current sampling period.
Optionally, the constructing a set of sampling time points required for detecting the traffic state of the ETC portal to be detected includes:
Extracting the current sampling period of the ETC portal to be detected from a real-time database;
Extracting a plurality of continuous historical sampling periods before a time period corresponding to the current sampling period from a historical normal database;
and combining the current sampling period and the plurality of continuous historical sampling periods to generate a sampling time point set.
Optionally, the determining whether the traffic state of the ETC portal to be detected is abnormal according to the kuke distance of the current sampling period includes:
And when the Cookie distance of the current sampling period is greater than or equal to a preset Cookie distance threshold, judging that traffic abnormality occurs. And when the Cookie distance in the current time period is smaller than a preset Cookie distance threshold, determining that no traffic abnormality occurs.
Optionally, updating the historical normal database includes:
If the traffic state of the current sampling period is not abnormal, adding the time period, the flow data and the average speed of the vehicle corresponding to the current sampling period into the historical normal database, and deleting the time period, the flow data and the average speed of the vehicle corresponding to the sampling period which is the farthest from the current sampling period.
Optionally, after the determining, according to the kuke distance of the current sampling period, whether the traffic state of the ETC portal to be detected is abnormal, 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 flow data corresponding to the current sampling period and the average speed of the vehicle to the historical normal database for storage.
Optionally, before the constructing the sampling time point set required for detecting the traffic state of the ETC portal to be detected, the method further includes:
and constructing a historical normal database and a real-time database.
Optionally, the constructing the historical normal database includes:
collecting flow data of the ETC portal to be detected according to a preset period;
Acquiring time, flow data and vehicle average speed of the sampling period marked as the traffic normal state, and sending the time period, the flow data and the vehicle average speed corresponding to each sampling period to a history normal database;
when the number of sampling periods in the normal traffic state reaches a preset number in an accumulated manner, the construction of the historical normal database is completed;
the constructing the real-time database comprises the following steps:
and acquiring the time, the flow data and the vehicle average speed of the sampling period marked as the normal traffic state, and transmitting the time period, the flow data and the vehicle average speed corresponding to the sampling period to a real-time database for storage.
In a second aspect, an embodiment of the present application provides a traffic state detection apparatus for a highway, including:
The time point set generation module is used for constructing a sampling time point set required by traffic state detection of the ETC portal to be detected; the sampling time point set comprises a time period corresponding to the current sampling period of the ETC portal to be detected and time periods corresponding to a plurality of continuous historical sampling periods;
the parameter loading module is used for loading flow data and vehicle average speed corresponding to the current sampling period and a plurality of continuous historical sampling periods;
the Cookie distance calculation module is used for creating a linear regression model based on the sampling time point set, the flow data and the average speed of the vehicle, and calculating the Cookie distance of the current sampling period through the linear regression model;
And the traffic abnormality judging module is used for judging whether the traffic state of the ETC portal to be detected is abnormal or not according to the Cook distance of the current sampling period.
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 state detection device of a highway firstly constructs a sampling time point set required by traffic state detection of an ETC portal to be detected; the method comprises the steps that a sampling time point set comprises a time period corresponding to a current sampling period of an ETC portal to be detected and a time period corresponding to a plurality of continuous historical sampling periods, flow data and vehicle average speed corresponding to the current sampling period and the plurality of continuous historical sampling periods are loaded, then a linear regression model is built based on the sampling time point set, the flow data and the vehicle average speed, the Cook distance of the current sampling period is calculated through the linear regression model, and finally whether the traffic state of the ETC portal to be detected is abnormal is judged according to the Cook distance of the current sampling period. According to the application, through acquiring ETC portal flow and vehicle average speed data to be detected on a highway, judging the idea of abnormal value of the linear model and recent historical flow and speed data of the ETC portal position to be detected based on the Cooks distance, constructing a speed-flow linear regression model, calculating the Cooks distance of the latest time point under the linear model, and judging whether the traffic state is abnormal or not, so that traffic accidents can be timely found out to give an alarm, other vehicles on the highway can be timely reminded to plan more reasonable travel routes, and congestion caused by the traffic accidents is reduced, thereby improving the operation efficiency of the expressway network.
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 method for detecting traffic conditions of a highway according to an embodiment of the present application;
FIG. 2 is a schematic block diagram of a road traffic state detection process according to an embodiment of the present application;
FIG. 3 is a flow chart of another method for detecting traffic conditions of a highway according to an embodiment of the present application;
Fig. 4 is a schematic structural diagram of a road traffic state detection device 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 method is based on the information of detection speed, flow, lane occupation rate and the like of sensors such as coils and radars, so that a classification model is trained, but different classification models are needed for different roads due to the difference of system parameters among the roads, a large amount of accident data are needed to be collected for model training, the task amount is large, and the model is easy to be influenced by the change of external conditions, so that the model is invalid; meanwhile, the coverage rate of the sensor required by detection is low in China, and the sensor cannot be put into detection of national high-speed road sections under the current condition. The second is based on advanced cameras and other sensors, but the popularity of such sensors in China is not high at present. Thirdly, based on real-time flow data and static road information, a flow prediction model is built by collecting multidimensional information such as roads, time, weather and the like, and a predicted value and a true value of flow are compared to further judge flow mutation. 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 road traffic state detection method, a road traffic state detection device, a storage medium and a terminal, so as to solve the problems in the related technical problems. According to the technical scheme provided by the application, the traffic accident can be timely found out to give an alarm, other vehicles on the expressway can be timely reminded to plan more reasonable travel routes, so that congestion caused by the traffic accident is reduced, the expressway network operation efficiency is improved, and the method is further described in detail by adopting an exemplary embodiment.
The following describes in detail the method for detecting traffic conditions on a highway according to the embodiment of the present application with reference to fig. 1 to fig. 3. The method may be implemented in dependence on a computer program and may be run on a traffic state detection device of a road based on von neumann system. The computer program may be integrated in the application or may run as a stand-alone tool class application. The traffic state detection device of the highway 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 method for detecting traffic conditions of a highway is provided in 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, constructing a sampling time point set required by traffic state detection of an ETC portal to be detected; the sampling time point set comprises a time period corresponding to the current sampling period of the ETC portal to be detected and time periods corresponding to a plurality of continuous historical sampling periods;
the ETC portal is electronic equipment applied to the expressway for vehicle charging and vehicle information acquisition, and the current traffic state of the expressway corresponding to the traffic of the ETC portal to be detected is analyzed by processing data generated by the ETC portal.
In a possible implementation manner, when traffic state analysis is performed on a highway corresponding to an ETC portal to be detected, a user terminal needs to construct a sampling time point set required by traffic state detection of the ETC portal to be detected, and when the time point set is constructed, firstly, a time period corresponding to a current sampling period of the ETC portal to be detected is extracted from a real-time database, then, a plurality of time periods corresponding to continuous historical sampling periods before the time period corresponding to the current sampling period is extracted from a historical normal database, and then, the time period corresponding to the current sampling period and the time periods corresponding to the continuous historical sampling periods are combined to generate a sampling time point set.
Further, in one possible implementation, the time period corresponding to the plurality of consecutive historical sampling periods refers to a time period corresponding to the plurality of consecutive historical sampling periods that are closest to the current sampling period time.
Further, the construction of a historical normal database and a real-time database is further included before the construction of a sampling time point set required by traffic state detection of the ETC portal to be detected, when the historical normal database is constructed, firstly, flow data of the ETC portal to be detected are collected according to a preset period, time, flow data and vehicle average speed of the sampling period marked as the traffic normal state are obtained, and a time period, flow data and vehicle average speed corresponding to each sampling period are sent to the historical normal database;
And when the number of the sampling periods in the normal traffic state reaches a preset number in an accumulated manner, the construction of the historical normal database is completed.
It should be noted that when the history normal database is constructed, that is, when the history normal database is initialized, traffic data marked as a sampling period of a traffic normal state is obtained, and the marked traffic normal state may be manually determined and marked, or a small time variation coefficient or a daily variation coefficient of the traffic may be used as a determination standard, and when the daily variation coefficient is smaller than a certain threshold value, it is determined that the traffic data of the hour or the day is normal, and the traffic data of the hour or the day is sent to the history normal database until the number of sampling periods of the history normal database reaches a preset number.
. The historical normal database is used for storing traffic flow, speed and time data with normal traffic states in a past period of time.
When a real-time database is constructed, flow data of a plurality of ETC (electronic toll collection) portal frames are obtained in real time according to a preset period, and a time period, flow data and average speed of a vehicle corresponding to the sampling period are sent to the real-time database for storage, for example, flow and speed data of each portal frame position are obtained by taking 5 minutes as a statistical interval. The flow and speed of the ith statistical period is denoted as c (i), v (i).
Further, when the historical normal database is detected to be updated, when the historical normal database receives traffic data and vehicle average speed corresponding to a sampling period in which traffic abnormality does not occur, the time period, the traffic data and the vehicle average speed corresponding to the current sampling period are added into the historical normal database, and the time period, the traffic data and the vehicle average speed corresponding to the sampling period which is the longest from the current sampling period are deleted. Therefore, the data in the historical normal database can be guaranteed to be the data with the normal traffic state of the preset quantity closest to the current sampling period time, and the traffic abnormality analysis result is more accurate.
The preset period is set by the user according to the actual application scene, and is not limited herein. For example, the preset period may be 5 minutes or 10 minutes. The kuke distance is a common distance in statistical analysis for diagnosing the presence or absence of abnormal data in various regression analyses, and in the embodiment of the present application, the kuke distance threshold is preferably 1.
Specifically, extracting the sampling time point set T required for judging the portal real-time state from the history normal database and the real-time database includes:
(1) The most recent m time periods (cycles) are obtained from the historical normal database (m.gtoreq.30 in order to meet the large sample requirement). It should be noted that, according to different demands of the service on the sensitivity of the system, there may be different values. The larger m, the more historical data the system contains, the more statistically accurate. But as it increases, the system is more susceptible to overall changes in the linear model that occur over time. Based on a large amount of statistics, the usual requirements are more satisfied, in a preferred embodiment 30.ltoreq.m.ltoreq.60.
(2) The current i-th time period (period) is extracted from the real-time database.
S102, loading flow data and vehicle average speed corresponding to the current sampling period and a plurality of continuous historical sampling periods;
the flow data is the number of vehicles passing through the ETC portal, and the average speed of the vehicles is the average speed of all vehicles passing through the ETC portal.
In one possible implementation, after the set of sampling time points is obtained in step S101, the user terminal needs to load the traffic data and the average speed of the vehicle corresponding to the current i-th time period, and load the traffic data and the average speed of the vehicle corresponding to the continuous m historical time periods.
S103, creating a linear regression model based on the sampling time point set, the flow data and the average speed of the vehicle, and calculating the Cookie distance of the current sampling period through the linear regression model;
The linear regression is a statistical analysis method for determining the quantitative relationship of mutual dependence between two or more variables by using regression analysis in mathematical statistics.
Generally, in the present application, if the ETC portal position is in a normal traffic state, the flow rate and the speed are in a linear relationship, and the parameters of the traffic system will not be suddenly changed in a short time, and the linear model parameters can be approximately regarded as unchanged. Although the speed and flow do not follow a strict linear relationship based on the traffic base map, each segment may be approximated by a linear model after the base map is segmented. The present application assumes that the road conditions will not change abruptly in a short period of time.
Therefore, in the embodiment of the application, if the portal position is blocked or severely blocked due to the influence of traffic accidents, the speed and the flow suddenly deviate from the original linear model, and an abnormal outlier suddenly deviating from a speed-flow fitting line appears in a two-dimensional space of the speed and the flow, and the abnormal outlier has a great influence on the fitting line. Therefore, the kuke distance of the point is calculated, and when the kuke distance is larger than the set threshold value, the influence of the point on the original linear model exceeding the allowable range is indicated under the preset precision, and the traffic state of the point can be judged as abnormal.
In one possible implementation, a linear regression model is first created according to the flow data and the average speed data in each time period in the sampling time point set T and T, and after the linear regression model is generated, the kuke distance D i of (c (i), v (i)) added to the ith statistical period (the time period corresponding to the current sampling period) is calculated through the linear regression model.
Specifically, the calculation formula of the kuke distance D i is:
Wherein the parameters are The calculation formula is as follows:
Wherein { v (i) |i ε T } samples the actual values of the velocity at all points in the set of points in time T,
The velocity estimates for all points in time in the set of points in time T are sampled.
Wherein e i is the i-point residual,The sample standard deviation was estimated, H ii: i point lever value.
Further, the calculation formula of the parameter H ii is:
wherein n T is the number of time points in the sampling time point set T. { c (i) |i ε T } the actual flow values at all time points in the set of time points T are sampled.
Further, parametersThe calculation formula of (2) is as follows:
Wherein the parameters are The calculation formula of (2) is as follows:
Wherein the parameters are The calculation formula of (2) is as follows:
Wherein the parameters are Parameter/>Parameter/>
And S104, judging whether the traffic state of the ETC portal to be detected is abnormal or not according to the Cooky distance of the current sampling period.
In one possible implementation manner, based on step S103, a kurk distance D i may be calculated, where when the kurk distance of the period corresponding to the current sampling period is less than the preset kurk distance threshold, it is determined that no traffic abnormality occurs, and when the kurk distance of the period corresponding to the current sampling period is less than the preset kurk distance threshold, it is determined that no traffic abnormality occurs. When traffic abnormality occurs, traffic abnormality information is generated and sent to related departments for early warning; and when no traffic abnormality occurs, sending the flow data corresponding to the time period corresponding to the current sampling period and the average speed of the vehicle to a historical normal database for storage.
Specifically, according to the calculated D i and a preset Cookry distance threshold D, the traffic state of the ith statistics period portal position is judged, and a flow and speed database under the historical normal condition is updated.
If D i is more than or equal to D, the ith statistics period portal traffic state is abnormal.
If D i < D, the ith statistics period portal traffic state is normal.
Preferably, the kuke distance threshold is selected such that d=1, and the linear regression outliers are determined.
For example, as shown in fig. 2, fig. 2 is a schematic process diagram of a traffic state detection process of a highway provided by an embodiment of the present application, firstly, when traffic state detection is started on a highway where an ETC portal is located, firstly, speed and flow data of each portal are obtained, then, normal flow data is determined from the speed and flow data of each portal, and the normal flow data is stored in a history normal database, and a real-time database is constructed. And extracting a time point (time period) set T from the historical normal database and the real-time database, constructing a linear regression model of the traffic flow and the speed according to the data information in the set T, calculating a Cookry distance Di according to the linear regression model, and determining that the traffic state at the current moment i is abnormal when Di is greater than or equal to a preset Cookry distance d. When Di is smaller than a preset Cooks distance d, determining that the traffic state at the current moment i is normal, and after the traffic state is normal, sending traffic flow data at the current moment i to a history normal database for storage.
In the embodiment of the application, a traffic state detection device of a highway firstly constructs a sampling time point set required by traffic state detection of an ETC portal to be detected; the method comprises the steps that a sampling time point set comprises a time period corresponding to a current sampling period of an ETC portal to be detected and a time period corresponding to a plurality of continuous historical sampling periods, flow data and vehicle average speed corresponding to the current sampling period and the plurality of continuous historical sampling periods are loaded, then a linear regression model is built based on the sampling time point set, the flow data and the vehicle average speed, the Cook distance of the current sampling period is calculated through the linear regression model, and finally whether the traffic state of the ETC portal to be detected is abnormal is judged according to the Cook distance of the current sampling period. According to the application, through acquiring ETC portal flow and vehicle average speed data to be detected on a highway, judging the idea of abnormal value of the linear model and recent historical flow and speed data of the portal position to be detected based on the Cooks distance, constructing a speed-flow linear regression model, calculating the Cooks distance of the latest time point under the linear model, and judging whether the traffic state is abnormal or not, thereby timely finding out traffic accidents to give an alarm, timely reminding other vehicles on the highway to plan more reasonable travel routes, reducing congestion caused by the traffic accidents, and further improving the operation efficiency of the expressway network.
Fig. 3 is a schematic flow chart of a road traffic state detection method according to an embodiment of the present application. The present embodiment is exemplified by the application of the road traffic state detection method to the user terminal.
The traffic state detection method of the highway may include the steps of:
S201, constructing a history normal database and a real-time database;
s202, extracting the current sampling period of an ETC portal to be detected from a real-time database;
S203, extracting a plurality of continuous historical sampling periods before a time period corresponding to the current sampling period from a historical normal database;
s204, combining the current sampling period and the plurality of continuous historical sampling periods to generate a sampling time point set;
S205, loading flow data and vehicle average speed corresponding to the current sampling period and a plurality of continuous historical sampling periods;
s206, creating a linear regression model based on the sampling time point set, the flow data and the average speed of the vehicle, and calculating the Cookie distance of the current sampling period through the linear regression model;
S207, judging that traffic abnormality occurs when the Cooks distance of the current sampling period is larger than or equal to a preset Cooks distance threshold, and judging that traffic abnormality does not occur when the Cooks distance of the current sampling period is smaller than the preset Cooks distance threshold;
S208, when traffic abnormality occurs, traffic abnormality information is generated and sent to related departments for early warning; and when no traffic abnormality occurs, sending the flow data corresponding to the time period corresponding to the current sampling period and the average speed of the vehicle to a historical normal database for storage.
In the embodiment of the application, a traffic state detection device of a highway firstly constructs a sampling time point set required by traffic state detection of an ETC portal to be detected; the method comprises the steps that a sampling time point set comprises a time period corresponding to a current sampling period of an ETC portal to be detected and a time period corresponding to a plurality of continuous historical sampling periods, flow data and vehicle average speed corresponding to the current sampling period and the plurality of continuous historical sampling periods are loaded, then a linear regression model is built based on the sampling time point set, the flow data and the vehicle average speed, the Cook distance of the current sampling period is calculated through the linear regression model, and finally whether the traffic state of the ETC portal to be detected is abnormal is judged according to the Cook distance of the current sampling period. According to the application, through acquiring ETC portal flow and vehicle average speed data to be detected on a highway, judging the idea of abnormal value of the linear model and recent historical flow and speed data of the portal position to be detected based on the Cooks distance, constructing a speed-flow linear regression model, calculating the Cooks distance of the latest time point under the linear model, and judging whether the traffic state is abnormal or not, thereby timely finding out traffic accidents to give an alarm, timely reminding other vehicles on the highway to plan more reasonable travel routes, reducing congestion caused by the traffic accidents, and further improving the operation efficiency of the expressway network.
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 a traffic state detection device for a highway according to an exemplary embodiment of the present invention is shown. The road traffic state detection device may be implemented as all or part of a terminal by software, hardware or a combination of both. The device 1 comprises a time point set generation module 10, a parameter loading module 20, a Cooky distance calculation module 30 and a traffic abnormality judgment module 40.
The time point set generating module 10 is used for constructing a sampling time point set required by traffic state detection of the ETC portal to be detected; the sampling time point set comprises a time period corresponding to the current sampling period of the ETC portal to be detected and time periods corresponding to a plurality of continuous historical sampling periods;
the parameter loading module 20 is configured to load flow data and an average speed of the vehicle corresponding to a time period corresponding to a current sampling period and a time period corresponding to a plurality of continuous historical sampling periods;
the coulomb distance calculation module 30 is configured to create a linear regression model based on the set of sampling time points, the flow data, and the average speed of the vehicle, and calculate, through the linear regression model, the coulomb distance of the time period corresponding to the current sampling period;
and the traffic abnormality judging module 40 is used for judging whether the traffic state of the ETC portal to be detected is abnormal or not according to the Cooky distance of the time period corresponding to the current sampling period.
It should be noted that, when the road traffic state detection device provided in the above embodiment performs the road traffic state detection method, only the division of the above functional modules is used for illustration, in practical application, the above 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 state detection device of the highway provided by the above embodiment and the traffic state detection method embodiment of the highway belong to the same concept, which embody detailed implementation procedures and are 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 state detection device of a highway firstly constructs a sampling time point set required by traffic state detection of an ETC portal to be detected; the method comprises the steps that a sampling time point set comprises a time period corresponding to a current sampling period of an ETC portal to be detected and a time period corresponding to a plurality of continuous historical sampling periods, flow data and vehicle average speed corresponding to the current sampling period and the plurality of continuous historical sampling periods are loaded, then a linear regression model is built based on the sampling time point set, the flow data and the vehicle average speed, the Cook distance of the current sampling period is calculated through the linear regression model, and finally whether the traffic state of the ETC portal to be detected is abnormal is judged according to the Cook distance of the current sampling period. According to the application, through acquiring ETC portal flow and vehicle average speed data to be detected on a highway, judging the idea of abnormal value of the linear model and recent historical flow and speed data of the portal position to be detected based on the Cooks distance, constructing a speed-flow linear regression model, calculating the Cooks distance of the latest time point under the linear model, and judging whether the traffic state is abnormal or not, thereby timely finding out traffic accidents to give an alarm, timely reminding other vehicles on the highway to plan more reasonable travel routes, reducing congestion caused by the traffic accidents, and further improving the operation efficiency of the expressway network.
The invention also provides a computer readable medium, on which program instructions are stored, which when executed by a processor implement the road traffic state detection method provided by the above-mentioned respective method embodiments. The invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the method for detecting traffic conditions of a road according to the above-mentioned 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 a traffic state detection application of a road 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 traffic state detection application of the highway stored in the memory 1005, and specifically perform the following operations:
Constructing a sampling time point set required by traffic state detection of an ETC portal to be detected; the sampling time point set comprises a time period corresponding to the current sampling period of the ETC portal to be detected and time periods corresponding to a plurality of continuous historical sampling periods;
Loading flow data and vehicle average speed corresponding to the current sampling period and a plurality of continuous historical sampling periods;
Creating a linear regression model based on the sampling time point set, the flow data and the average speed of the vehicle, and calculating the Cookie distance of the current sampling period through the linear regression model;
And judging whether the traffic state of the ETC portal to be detected is abnormal or not according to the Cook distance of the current sampling period.
In one embodiment, the processor 1001, when executing the set of sampling time points required to construct the traffic state detection of the ETC portal to be detected, specifically performs the following operations:
Extracting the current sampling period of the ETC portal to be detected from a real-time database;
Extracting a plurality of continuous historical sampling periods before a time period corresponding to the current sampling period from a historical normal database;
and combining the current sampling period and the plurality of continuous historical sampling periods to generate a sampling time point set.
In one embodiment, the processor 1001, when executing the determination of whether the traffic state of the ETC portal to be detected is abnormal according to the kuke distance of the time period corresponding to the current sampling period, specifically executes the following operations:
when the Cookry distance of the time period corresponding to the current sampling period is greater than or equal to a preset Cookry distance threshold value, determining that traffic abnormality occurs
In one embodiment, the processor 1001, when executing the determination of whether the traffic state of the ETC portal to be detected is abnormal according to the kuke distance of the time period corresponding to the current sampling period, specifically executes the following operations:
And when the Cookry distance of the time period corresponding to the current sampling period is smaller than a preset Cookry distance threshold value, determining that no traffic abnormality occurs.
In one embodiment, the processor 1001 further performs the following operations when performing the determination of whether the traffic state of the ETC portal to be detected is abnormal according to the kuke distance of the period corresponding to the current sampling period:
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 flow data corresponding to the time period corresponding to the current sampling period and the average speed of the vehicle to a historical normal database for storage.
In one embodiment, the processor 1001, before executing the set of sampling points in time required to construct the traffic state detection of the ETC portal to be detected, further performs the following operations:
collecting flow data of the ETC portal to be detected according to a preset period;
when the sampling period of the flow data of the ETC portal to be detected reaches the set sampling period number, calculating the Cook distance corresponding to the flow data of the ETC portal to be detected with the set sampling period number;
When the Cookie distance of the flow data of the ETC portal to be detected is larger than or equal to a preset Cookie distance threshold, the flow data of the ETC portal to be detected with the set sampling period number is sent to the history normal database for storage;
and acquiring flow data of the ETC portal frames in real time according to a preset sampling period, and sending the flow data to a real-time database for storage.
In the embodiment of the application, a traffic state detection device of a highway firstly constructs a sampling time point set required by traffic state detection of an ETC portal to be detected; the method comprises the steps that a sampling time point set comprises a time period corresponding to a current sampling period of an ETC portal to be detected and a time period corresponding to a plurality of continuous historical sampling periods, flow data and vehicle average speed corresponding to the current sampling period and the plurality of continuous historical sampling periods are loaded, then a linear regression model is built based on the sampling time point set, the flow data and the vehicle average speed, the Cook distance of the current sampling period is calculated through the linear regression model, and finally whether the traffic state of the ETC portal to be detected is abnormal is judged according to the Cook distance of the current sampling period. According to the application, through acquiring ETC portal flow and vehicle average speed data to be detected on a highway, judging the idea of abnormal value of the linear model and recent historical flow and speed data of the portal position to be detected based on the Cooks distance, constructing a speed-flow linear regression model, calculating the Cooks distance of the latest time point under the linear model, and judging whether the traffic state is abnormal or not, thereby timely finding out traffic accidents to give an alarm, timely reminding other vehicles on the highway to plan more reasonable travel routes, reducing congestion caused by the traffic accidents, and further improving the operation efficiency of the expressway network.
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. A method for detecting traffic conditions on a highway, the method comprising:
Constructing a sampling time point set required by traffic state detection of an ETC portal to be detected; the sampling time point set comprises a time period corresponding to the current sampling period of the ETC portal to be detected and time periods corresponding to a plurality of continuous historical sampling periods;
Loading flow data and vehicle average speed corresponding to the current sampling period and a plurality of continuous historical sampling periods;
Creating a linear regression model based on the sampling time point set, the flow data and the average speed of the vehicle, and calculating the Cookie distance of the current sampling period through the linear regression model; wherein,
The calculating the kuke distance of the current sampling period through the linear regression model comprises the following steps:
Creating a linear regression model according to the flow data and the average speed data in each time period in the sampling time point set T and T, and calculating a Cookry distance D i of (c (i), v (i)) added into the ith statistical period by the linear regression model;
And judging whether the traffic state of the ETC portal to be detected is abnormal or not according to the Cook distance of the current sampling period.
2. The method according to claim 1, wherein said constructing a set of sampling points in time required for traffic condition detection of an ETC portal to be detected comprises:
Extracting the current sampling period of the ETC portal to be detected from a real-time database;
Extracting a plurality of continuous historical sampling periods before a time period corresponding to the current sampling period from a historical normal database;
and combining the current sampling period and the plurality of continuous historical sampling periods to generate a sampling time point set.
3. The method according to claim 1, wherein the determining whether the traffic state of the ETC portal to be detected is abnormal according to the kuke distance of the current sampling period includes:
when the Cooks distance of the current sampling period is greater than or equal to a preset Cooks distance threshold value, judging that traffic abnormality occurs;
And when the Cookie distance of the current sampling period is smaller than a preset Cookie distance threshold, judging that the traffic is not abnormal.
4. The method of claim 2, wherein updating the historical normal database comprises:
If the traffic state of the current sampling period is not abnormal, adding the time period, the flow data and the average speed of the vehicle corresponding to the current sampling period into the historical normal database, and deleting the time period, the flow data and the average speed of the vehicle corresponding to the sampling period which is the farthest from the current sampling period.
5. The method according to claim 1, wherein after the determining whether the traffic state of the ETC portal to be detected is abnormal according to the kuke distance of the current sampling period, further comprises:
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 flow data corresponding to the current sampling period and the average speed of the vehicle to a historical normal database for storage.
6. The method according to claim 1, wherein prior to constructing the set of sampling time points required for traffic condition detection of the ETC portal to be detected, further comprising:
and constructing a historical normal database and a real-time database.
7. The method of claim 6, wherein the constructing a historical normal database comprises:
collecting flow data of the ETC portal to be detected according to a preset period;
Acquiring time, flow data and vehicle average speed of the sampling period marked as the traffic normal state, and sending the time period, the flow data and the vehicle average speed corresponding to each sampling period to a history normal database;
when the number of sampling periods in the normal traffic state reaches a preset number in an accumulated manner, the construction of the historical normal database is completed;
and/or
The constructing the real-time database comprises the following steps:
And acquiring the flow data and the vehicle average speed of the ETC portal to be detected in real time according to a preset sampling period, and transmitting the time period, the flow data and the vehicle average speed corresponding to the sampling period to a real-time database for storage.
8. A traffic state detection device for a highway, the device comprising:
The time point set generation module is used for constructing a sampling time point set required by traffic state detection of the ETC portal to be detected; the sampling time point set comprises a time period corresponding to the current sampling period of the ETC portal to be detected and time periods corresponding to a plurality of continuous historical sampling periods;
the parameter loading module is used for loading flow data and vehicle average speed corresponding to the current sampling period and a plurality of continuous historical sampling periods;
The Cookie distance calculation module is used for creating a linear regression model based on the sampling time point set, the flow data and the average speed of the vehicle, and calculating the Cookie distance of the current sampling period through the linear regression model; wherein,
The calculating the kuke distance of the current sampling period through the linear regression model comprises the following steps:
Creating a linear regression model according to the flow data and the average speed data in each time period in the sampling time point set T and T, and calculating a Cookry distance D i of (c (i), v (i)) added into the ith statistical period by the linear regression model;
And the traffic abnormality judging module is used for judging whether the traffic state of the ETC portal to be detected is abnormal or not according to the Cook distance of the current sampling period.
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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| CN114613137B (en) * | 2022-03-07 | 2023-02-21 | 同盾科技有限公司 | Congestion index determination method, device, medium and equipment applied to expressway |
| CN116504076A (en) * | 2023-06-19 | 2023-07-28 | 贵州宏信达高新科技有限责任公司 | Expressway traffic flow prediction method based on ETC portal data |
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