CN112434260A - Road traffic state detection method and device, storage medium and terminal - Google Patents
Road traffic state detection method and device, storage medium and terminal Download PDFInfo
- Publication number
- CN112434260A CN112434260A CN202011135464.5A CN202011135464A CN112434260A CN 112434260 A CN112434260 A CN 112434260A CN 202011135464 A CN202011135464 A CN 202011135464A CN 112434260 A CN112434260 A CN 112434260A
- Authority
- CN
- China
- Prior art keywords
- sampling period
- traffic
- current sampling
- period
- detected
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 55
- 238000005070 sampling Methods 0.000 claims abstract description 222
- 238000000034 method Methods 0.000 claims abstract description 61
- 230000002159 abnormal effect Effects 0.000 claims abstract description 48
- 238000012417 linear regression Methods 0.000 claims abstract description 41
- 230000005856 abnormality Effects 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 6
- 206010039203 Road traffic accident Diseases 0.000 abstract description 22
- 230000006870 function Effects 0.000 description 7
- 238000004891 communication Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 238000013145 classification model Methods 0.000 description 4
- 238000012549 training Methods 0.000 description 4
- 238000010276 construction Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/23—Updating
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2477—Temporal data queries
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Probability & Statistics with Applications (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Fuzzy Systems (AREA)
- Computational Linguistics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Operations Research (AREA)
- Life Sciences & Earth Sciences (AREA)
- Algebra (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a method, a device, a storage medium and a terminal for detecting traffic states of roads, wherein the method comprises the following steps: constructing a sampling time point set required by the traffic state detection of the 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; establishing a linear regression model based on the sampling time point set, the flow data and the vehicle average speed, and calculating the Cock 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 Cocky distance of the current sampling period. Therefore, by adopting the embodiment of the application, the traffic accident can be alarmed in time, and a more reasonable route is planned for the driver on the expressway, so that the subsequent influence degrees of congestion and the like caused by the accident are reduced, and the operation efficiency of the expressway network is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method, a device, a storage medium and a terminal for detecting a traffic state of a road.
Background
With the development of the ETC technology of the highway portal frame in China, the availability and the reliability of traffic information on the highway are gradually improved. Traffic on highways is often more stable than that in cities, but traffic accidents at high speeds cause more serious losses due to intercity traffic jams caused during the accident occurrence and cleaning stages. Therefore, it is necessary to timely detect a traffic accident on a highway and plan a more reasonable route for a driver based on the detected accident, thereby reducing the degree of traffic congestion caused by the accident.
Currently, three technologies are generally used for detecting a traffic accident. The first method is that a classification model is trained based on information such as speed, flow and lane occupancy detected by sensors such as a coil and a radar, but different classification models are required for different roads due to 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, and the model is easily affected by change of external conditions to cause model failure; meanwhile, the coverage rate of a sensor required for detection is low in China, and the sensor cannot be put into detection of national highway sections under the current condition. The second is based on advanced sensors such as cameras, but the popularity of such sensors is not high in China at present. And the third method is that a flow prediction model is constructed by collecting multi-dimensional information such as roads, time, weather and the like based on real-time flow data and static road information, and the predicted value and the true value of the flow are compared to judge the sudden change of the flow. However, the construction of the traffic prediction model requires the collection of relatively complex network topology information, when the road section relationship changes, the model will fail, and the training of the classifier still requires a large amount of historical accident data, which is difficult to popularize in national traffic network detection.
The three prior arts can not find the traffic accident in time and can not ensure the traffic accident to be processed in time, thereby reducing the operating efficiency of the highway network.
Disclosure of Invention
The embodiment of the application provides a method and a device for detecting traffic states of roads, 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 road, where the method includes:
constructing a sampling time point set required by the 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 a time period 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 vehicle average speed, and calculating the Cock 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 Cock distance of the current sampling period.
Optionally, the constructing a set of sampling time points required for detecting the traffic state of the to-be-detected ETC portal includes:
extracting a current sampling period of the ETC portal to be detected from a real-time database;
extracting a plurality of continuous historical sampling periods before the 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, according to the cook distance of the current sampling period, whether the traffic state of the ETC portal to be detected is abnormal or not includes:
and when the Cock distance of the current sampling period is greater than or equal to a preset Cock distance threshold value, judging that traffic abnormity occurs. And when the Cock distance of the current time period is smaller than a preset Cock distance threshold value, determining that no traffic abnormality occurs.
Optionally, updating the historical normal database according to the following steps, including:
and 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 vehicles 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 vehicles corresponding to the sampling period which is the longest distance away from the current sampling period.
Optionally, after judging whether the traffic state of the ETC portal to be detected is abnormal according to the Cock distance of the current sampling period, the method further includes:
when traffic abnormity occurs, traffic abnormity information is generated and sent to relevant departments for early warning; and
and when the traffic abnormality does not occur, 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 constructing a set of sampling time points required for detecting the traffic state of the to-be-detected ETC portal, 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 the time, flow data and vehicle average speed of the sampling period marked as a normal traffic state, and sending the time period, flow data and vehicle average speed corresponding to each sampling period to a historical normal database;
when the number of sampling periods in the normal traffic state reaches a preset number, the historical normal database is constructed;
the constructing a real-time database comprises:
and acquiring the time, the flow data and the vehicle average speed of the sampling period marked as the normal traffic state, and sending 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 device for a road, including:
the time point set generating module is used for constructing a sampling time point set required by the 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 a time period 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 Cock distance calculation module is used for creating a linear regression model based on the sampling time point set, the flow data and the vehicle average speed, and calculating the Cock distance of the current sampling period through the linear regression model;
and the traffic abnormity judgment module is used for judging whether the traffic state of the ETC portal to be detected is abnormal according to the Cock 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-mentioned 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 above-mentioned method steps.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the embodiment of the application, the traffic state detection device of the highway firstly constructs a sampling time point set required by the traffic state detection of the ETC portal to be detected; the ETC portal traffic state detection 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 corresponding to the current sampling period and the plurality of continuous historical sampling periods and vehicle average speed are loaded, then a linear regression model is created based on the sampling time point set, the flow data and the vehicle average speed, the Kuck distance of the current sampling period is calculated through the linear regression model, and finally whether the ETC portal to be detected is abnormal or not is judged according to the Kuck distance of the current sampling period. According to the method, the traffic state is judged whether to be abnormal or not by acquiring the ETC portal flow to be detected and the average speed data of the vehicles on the highway, judging the idea of the abnormal value of the linear model and the recent historical flow and speed data of the position of the ETC portal to be detected on the basis of the Cock distance, constructing the speed-flow linear regression model, calculating the Cock distance of the latest time point under the linear model, and further judging whether the traffic state is abnormal, so that traffic accidents can be found in time to give an alarm in time, other vehicles on the highway can be reminded to plan more reasonable travel routes in time, congestion caused by the traffic accidents is reduced, and the running efficiency of the highway network 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 method for detecting a traffic state of a road according to an embodiment of the present application;
fig. 2 is a schematic process block diagram of a traffic state detection process of a road according to an embodiment of the present application;
fig. 3 is a schematic flow chart of another road traffic state detection method provided in the embodiment of the present application;
fig. 4 is a schematic structural diagram of a traffic state detection device for a road 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 only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
In the description of the present invention, it is to 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 meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
Up to now, in the detection of traffic accidents, three technical implementations are generally included. The first method is that a classification model is trained based on information such as speed, flow and lane occupancy detected by sensors such as a coil and a radar, but different classification models are required for different roads due to 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, and the model is easily affected by change of external conditions to cause model failure; meanwhile, the coverage rate of a sensor required for detection is low in China, and the sensor cannot be put into detection of national highway sections under the current condition. The second is based on advanced sensors such as cameras, but the popularity of such sensors is not high in China at present. And the third method is that a flow prediction model is constructed by collecting multi-dimensional information such as roads, time, weather and the like based on real-time flow data and static road information, and the predicted value and the true value of the flow are compared to judge the sudden change of the flow. However, the construction of the traffic prediction model requires the collection of relatively complex network topology information, when the road section relationship changes, the model will fail, and the training of the classifier still requires a large amount of historical accident data, which is difficult to popularize in national traffic network detection. The three prior arts can not find the traffic accident and can not ensure the timely treatment of the traffic accident, thereby reducing the operating efficiency of the highway network. Therefore, the present application provides a method, an apparatus, a storage medium, and a terminal for detecting a traffic state of a road, so as to solve the above-mentioned problems in the related art. According to the technical scheme, the ETC portal flow and the vehicle average speed data to be detected on the highway are acquired, the idea of the abnormal value of the linear model and the recent historical flow and speed data of the position of the ETC portal to be detected are judged based on the Kuck distance, the speed-flow linear regression model is constructed, the Kuck distance of the latest time point under the linear model is calculated, whether the traffic state is abnormal or not is judged, traffic accidents can be found in time to give an alarm in time, other vehicles on the highway are reminded to plan more reasonable travel routes in time, congestion caused by the traffic accidents is reduced, the operation efficiency of the highway network is improved, and detailed explanation is carried out by adopting an exemplary embodiment.
The following describes in detail a method for detecting a traffic state of a road according to an embodiment of the present application with reference to fig. 1 to 3. The method may be implemented in dependence on a computer program, which may be run on a traffic status detection device for a road based on the von neumann architecture. The computer program may be integrated into the application or may run as a separate tool-like application. The traffic state detection device of the road in the embodiment of the present application may be a user terminal, including but not limited to: personal computers, tablet computers, handheld devices, in-vehicle devices, wearable devices, computing devices or other processing devices connected to a wireless modem, and the like. The user terminals may be called different names in different networks, for example: user equipment, access terminal, subscriber unit, subscriber station, mobile station, remote terminal, mobile device, user terminal, wireless communication device, user agent or user equipment, cellular telephone, cordless telephone, Personal Digital Assistant (PDA), terminal equipment in a 5G network or future evolution network, and the like.
Referring to fig. 1, a schematic flow chart of a method for detecting a traffic state of a road is provided in an embodiment of the present application. As shown in fig. 1, the method of the embodiment of the present application may include the following steps:
s101, constructing a sampling time point set required by the 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 a time period corresponding to a plurality of continuous historical sampling periods;
wherein, the ETC portal is the electronic equipment who uses to carry out vehicle charge and vehicle information acquisition on highway, in this application, handles the present traffic state of highway that comes the analysis to wait to detect ETC portal traffic and correspond through the data that produce 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 the traffic state detection of the ETC portal to be detected, when the time point set is constructed, a time period corresponding to a current sampling period of the ETC portal to be detected is extracted from a real-time database, time periods corresponding to a plurality of continuous historical sampling periods before the time period corresponding to the current sampling period are extracted from a historical normal database, and the time periods corresponding to the current sampling period and the time periods corresponding to the continuous historical sampling periods are merged to generate a sampling time point set.
Further, in a possible implementation, the time period corresponding to a plurality of consecutive historical sampling periods refers to a time period corresponding to a plurality of consecutive historical sampling periods that are closest in time to the current sampling period.
Further, before a sampling time point set required by the traffic state detection of the ETC portal to be detected is constructed, a historical normal database and a real-time database are constructed, 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, the flow data and the average speed of vehicles of the sampling period marked as the traffic normal state are obtained, and the time period, the flow data and the average speed of the vehicles corresponding to each sampling period are sent to the historical normal database;
and when the number of sampling periods in the normal traffic state reaches a preset number, the historical normal database is constructed.
It should be noted that, when the historical normal database is constructed, that is, when the historical normal database is initialized, the traffic data of the sampling period marked as the normal traffic state is acquired, the marking as the normal traffic state may be artificially determined and marked, or an hour variation coefficient or a day variation coefficient of the traffic may be used as a determination standard, when the day variation coefficient is smaller than a certain threshold, 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 historical normal database until the number of the sampling periods of the historical normal database reaches a preset number.
. The historical normal database is used for storing data of flow, speed and time, wherein the traffic state of the historical normal database is normal in the past period of time.
When a real-time database is constructed, flow data of a plurality of ETC gantries are acquired in real time according to a preset period, time periods, flow data and vehicle average speed corresponding to the sampling period are sent to the real-time database to be stored, and for example, flow and speed data of each gantry position are acquired at 5-minute counting intervals. The flow and speed of the ith statistical period are denoted as c (i), v (i).
Further, when it is detected that the historical normal database is updated, and when the historical normal database receives the traffic data and the average speed of the vehicle corresponding to the sampling period without traffic abnormality, and the traffic state of the current sampling period is not abnormal, adding the time period, the traffic 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 traffic data and the average speed of the vehicle corresponding to the sampling period which is the longest from the current sampling period. Therefore, the data in the historical normal database can be guaranteed to be the data with the normal traffic state in the preset quantity closest to the current sampling period time, and the result of traffic abnormity analysis is more accurate.
The preset period is set by the user according to the actual application scenario, and is not limited herein. For example, the preset period may be a period of 5 minutes or a period of 10 minutes. The Cocky distance is a common distance in statistical analysis and is used for diagnosing whether abnormal data exists in various regression analyses, and in the embodiment of the application, the Cocky distance threshold is preferably 1.
Specifically, the sampling time point set T required for judging the real-time state of the portal is extracted from the historical normal database and the real-time database, and comprises:
(1) the most recent m time periods (cycles) are obtained from the historical normal database (m ≧ 30 in order to satisfy large sample requirements). It should be noted that, according to different requirements of the service on the sensitivity of the system, different values may be taken. The larger m, the more historical data the system contains, and is statistically more accurate. But as it grows, the system is more susceptible to overall changes in the linear model that occur over time. Based on a large amount of statistical data, the general requirements are better met, and in a preferred embodiment, m is more than or equal to 30 and less than or equal to 60.
(2) The current ith time period (cycle) 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 the vehicles passing through the ETC portal.
In a possible implementation manner, after the sampling time point set is obtained according to step S101, the user terminal needs to load the traffic data and the vehicle average speed corresponding to the current i-th time period, and load the traffic data and the vehicle average speed corresponding to m consecutive historical time periods.
S103, creating a linear regression model based on the sampling time point set, the flow data and the vehicle average speed, and calculating the Cock distance of the current sampling period through the linear regression model;
the linear regression is a statistical analysis method for determining the interdependent quantitative relationship between two or more variables by using regression analysis in mathematical statistics.
Generally, in the application, if the ETC portal position is in a normal traffic state, the flow rate and the speed are in a linear relation, and the parameters of a traffic system cannot be suddenly changed in a short time, the parameters of a linear model can be approximately regarded as unchanged. Although speed and flow do not follow a strict linear relationship based on the traffic basic map, after segmenting the basic map, each segment can be approximated by a linear model. It is assumed in this application that the road conditions do not change abruptly in a short period of time.
Therefore, in the embodiment of the present application, if the position of the portal is affected by a traffic accident and is blocked or severely blocked, the speed and the flow rate may suddenly deviate from the original linear model, and appear as an abnormal outlier suddenly deviating from the speed-flow fit line in the two-dimensional space of the speed and the flow rate, and the abnormal outlier has a large influence on the fit line. Therefore, the Coker distance of the point is calculated, and when the Coker distance is larger than the set threshold, the influence of the point on the original linear model exceeding the allowable range is shown under the preset precision, and the traffic state of the point can be judged to be abnormal.
In a possible implementation manner, firstly, a linear regression model is created according to flow data and 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 kuck distance D 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 modeli。
Specifically, the Cock distance DiThe calculation formula is as follows:
wherein the parameters
The calculation formula is as follows:
wherein { v (i) | i ∈ T } samples the velocity actual values for all time points in the set of time points T,
Wherein e isiFor the i-point residual,
as an estimate of the standard deviation of the sample, HiiI point leverage.
Further, parameter HiiThe calculation formula of (2) is as follows:
wherein n isTThe number of time points in the sampling time point set T. { c (i) | i ∈ T } sample the actual values of traffic at all time points in the set of time points T.
Further, the parameter
The calculation formula of (2) is as follows:
wherein the parameters
The calculation formula of (2) is as follows:
wherein the parameters
Meter (2)The calculation formula is as follows:
wherein the parameters
Parameter(s)
Parameter(s)
And S104, judging whether the traffic state of the ETC portal to be detected is abnormal or not according to the Cock distance of the current sampling period.
In one possible implementation, the Kork distance D may be calculated based on step S103iAnd when the Coker distance of the time period corresponding to the current sampling period is smaller than the preset Coker distance threshold, determining that no traffic abnormality occurs. When traffic abnormity occurs, traffic abnormity information is generated and sent to relevant departments for early warning; and when the traffic abnormality does not occur, 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 particular, based on the calculated DiAnd a preset Cock distance threshold value d, judging the portal position traffic state in the ith statistical period, and updating a flow and speed database under the historical normal condition.
If D isiAnd d is greater than or equal to d, the portal traffic state in the ith statistical period is abnormal.
If D isi<And d, the portal traffic state in the ith statistical cycle is normal.
Preferably, the couk distance threshold is selected to judge the linear regression abnormal outliers by d-1.
For example, as shown in fig. 2, fig. 2 is a schematic process diagram of a road traffic state detection process provided in an embodiment of the present application, when a highway on which an ETC portal is located is started to detect a traffic state, first, speed and flow data of each portal is obtained, and then, normal flow data is determined from the speed and flow data of each portal and stored in a historical normal database, and a real-time database is constructed. And then 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 data information in the set T, calculating a Cock distance Di according to the linear regression model, and determining that the traffic state at the current moment i is abnormal when the Di is greater than or equal to a preset Cock distance d. And when the Di is smaller than the preset Cock distance d, determining that the traffic state at the current moment i is normal, and after the traffic state is normal, sending the traffic data at the current moment i to a historical normal database for storage.
In the embodiment of the application, the traffic state detection device of the highway firstly constructs a sampling time point set required by the traffic state detection of the ETC portal to be detected; the ETC portal traffic state detection 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 corresponding to the current sampling period and the plurality of continuous historical sampling periods and vehicle average speed are loaded, then a linear regression model is created based on the sampling time point set, the flow data and the vehicle average speed, the Kuck distance of the current sampling period is calculated through the linear regression model, and finally whether the ETC portal to be detected is abnormal or not is judged according to the Kuck distance of the current sampling period. According to the method, the traffic state is judged whether to be abnormal or not by acquiring the ETC portal flow to be detected and the vehicle average speed data on the road, judging the idea of the abnormal value of the linear model and the recent historical flow and speed data of the position of the portal to be detected based on the Cock distance, constructing the speed-flow linear regression model, calculating the Cock distance of the latest time point under the linear model, and further judging whether the traffic state is abnormal, so that traffic accidents can be found in time to give an alarm in time, other vehicles on the highway are reminded to plan more reasonable travel routes in time, congestion caused by the traffic accidents is reduced, and the operation efficiency of the highway network is improved.
Please refer to fig. 3, which is a flowchart illustrating a method for detecting a traffic status of a road according to an embodiment of the present application. The present embodiment is exemplified by applying the method for detecting the traffic state of the road to the user terminal.
The method for detecting the traffic state of the road can comprise the following steps:
s201, constructing a historical normal database and a real-time database;
s202, extracting the current sampling period of the 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 vehicle average speed, and calculating the Cock distance of the current sampling period through the linear regression model;
s207, when the Coker distance of the current sampling period is larger than or equal to a preset Coker distance threshold value, judging that traffic abnormity occurs, and when the Coker distance of the current sampling period is smaller than the preset Coker distance threshold value, judging that no traffic abnormity occurs;
s208, when traffic abnormity occurs, generating traffic abnormity information and sending the traffic abnormity information to a relevant department for early warning; and when the traffic abnormality does not occur, 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, the traffic state detection device of the highway firstly constructs a sampling time point set required by the traffic state detection of the ETC portal to be detected; the ETC portal traffic state detection 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 corresponding to the current sampling period and the plurality of continuous historical sampling periods and vehicle average speed are loaded, then a linear regression model is created based on the sampling time point set, the flow data and the vehicle average speed, the Kuck distance of the current sampling period is calculated through the linear regression model, and finally whether the ETC portal to be detected is abnormal or not is judged according to the Kuck distance of the current sampling period. According to the method, the traffic state is judged whether to be abnormal or not by acquiring the ETC portal flow to be detected and the vehicle average speed data on the road, judging the idea of the abnormal value of the linear model and the recent historical flow and speed data of the position of the portal to be detected based on the Cock distance, constructing the speed-flow linear regression model, calculating the Cock distance of the latest time point under the linear model, and further judging whether the traffic state is abnormal, so that traffic accidents can be found in time to give an alarm in time, other vehicles on the highway are reminded to plan more reasonable travel routes in time, congestion caused by the traffic accidents is reduced, and the operation efficiency of the highway network is improved.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made 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 road according to an exemplary embodiment of the invention is shown. The road traffic state detection device can be implemented by software, hardware or a combination of the two to form all or part of the terminal. The device 1 comprises a time point set generating module 10, a parameter loading module 20, a Cuk distance calculating module 30 and a traffic abnormity judging module 40.
The time point set generating module 10 is used for constructing a sampling time point set required by the 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 a time period corresponding to a plurality of continuous historical sampling periods;
the parameter loading module 20 is configured to load flow data and an average vehicle speed corresponding to a time period corresponding to a current sampling period and a time period corresponding to a plurality of consecutive historical sampling periods;
the Cock distance calculation module 30 is used for creating a linear regression model based on the sampling time point set, the flow data and the vehicle average speed, and calculating the Cock distance of the time period corresponding to the current sampling period through the linear regression model;
and the traffic abnormity judgment module 40 is used for judging whether the traffic state of the ETC portal to be detected is abnormal according to the Kuck distance of the time period corresponding to the current sampling period.
It should be noted that, when the traffic state detection device for a road provided in the above embodiment executes the traffic state detection method for a road, the above division of each functional module is merely used as an example, and in practical applications, the above function distribution 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 above described functions. In addition, the traffic state detection device for the road and the traffic state detection method for the road provided by the embodiments belong to the same concept, and the detailed implementation process is shown in the method embodiments and is not described herein again.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the embodiment of the application, the traffic state detection device of the highway firstly constructs a sampling time point set required by the traffic state detection of the ETC portal to be detected; the ETC portal traffic state detection 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 corresponding to the current sampling period and the plurality of continuous historical sampling periods and vehicle average speed are loaded, then a linear regression model is created based on the sampling time point set, the flow data and the vehicle average speed, the Kuck distance of the current sampling period is calculated through the linear regression model, and finally whether the ETC portal to be detected is abnormal or not is judged according to the Kuck distance of the current sampling period. According to the method, the traffic state is judged whether to be abnormal or not by acquiring the ETC portal flow to be detected and the vehicle average speed data on the road, judging the idea of the abnormal value of the linear model and the recent historical flow and speed data of the position of the portal to be detected based on the Cock distance, constructing the speed-flow linear regression model, calculating the Cock distance of the latest time point under the linear model, and further judging whether the traffic state is abnormal, so that traffic accidents can be found in time to give an alarm in time, other vehicles on the highway are reminded to plan more reasonable travel routes in time, congestion caused by the traffic accidents is reduced, and the operation efficiency of the highway network is improved.
The present invention also provides a computer readable medium, on which program instructions are stored, which when executed by a processor implement the method for detecting traffic status of a road provided by the above-mentioned method embodiments. The invention also provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of traffic status detection of a roadway of the various method embodiments described above.
Please refer to fig. 5, which provides a schematic structural diagram of a terminal according to 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, memory 1005, at least one communication bus 1002.
Wherein a communication bus 1002 is used to enable connective 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 also 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.
The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set 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 various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may optionally be at least one memory device located remotely from the processor 1001. As shown in fig. 5, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a traffic state detection application program for roads.
In the terminal 1000 shown in fig. 5, the user interface 1003 is mainly used as an interface for providing input for a user, and acquiring data input by the user; and the processor 1001 may be configured to call the traffic status detection application program of the road stored in the memory 1005, and specifically perform the following operations:
constructing a sampling time point set required by the 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 a time period 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 vehicle average speed, and calculating the Cock 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 Cock distance of the current sampling period.
In one embodiment, the processor 1001, when executing the set of sampling time points required for the construction of a traffic status detection of the ETC portal to be detected, specifically performs the following operations:
extracting a current sampling period of the ETC portal to be detected from a real-time database;
extracting a plurality of continuous historical sampling periods before the 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 an embodiment, when the processor 1001 determines whether the traffic state of the ETC portal to be detected is abnormal according to the cuk distance of the time period corresponding to the current sampling period, the following operations are specifically performed:
when the Cock distance of the time period corresponding to the current sampling period is larger than or equal to the preset Cock distance threshold value, determining that traffic abnormity occurs
In an embodiment, when the processor 1001 determines whether the traffic state of the ETC portal to be detected is abnormal according to the cuk distance of the time period corresponding to the current sampling period, the following operations are specifically performed:
and when the Cock distance of the time period corresponding to the current sampling period is smaller than a preset Cock distance threshold value, determining that no traffic abnormality occurs.
In one embodiment, the processor 1001 further performs the following operations after determining whether the traffic state of the ETC portal to be detected is abnormal according to the cuk distance of the time period corresponding to the current sampling period:
when traffic abnormity occurs, traffic abnormity information is generated and sent to relevant departments for early warning; and
and when the traffic abnormality does not occur, 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, when prior to performing the set of sampling points in time required to construct a traffic status 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 corresponding Cock distance of the flow data of the ETC portal to be detected with the set sampling period number;
when the Cock distance of the flow data of the ETC portal to be detected is larger than or equal to a preset Cock distance threshold value, sending the flow data of the ETC portal to be detected with the set sampling period number to the historical normal database for storage;
the flow data of a plurality of ETC gantries are collected in real time according to a preset sampling period and are sent to a real-time database for storage.
In the embodiment of the application, the traffic state detection device of the highway firstly constructs a sampling time point set required by the traffic state detection of the ETC portal to be detected; the ETC portal traffic state detection 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 corresponding to the current sampling period and the plurality of continuous historical sampling periods and vehicle average speed are loaded, then a linear regression model is created based on the sampling time point set, the flow data and the vehicle average speed, the Kuck distance of the current sampling period is calculated through the linear regression model, and finally whether the ETC portal to be detected is abnormal or not is judged according to the Kuck distance of the current sampling period. According to the method, the traffic state is judged whether to be abnormal or not by acquiring the ETC portal flow to be detected and the vehicle average speed data on the road, judging the idea of the abnormal value of the linear model and the recent historical flow and speed data of the position of the portal to be detected based on the Cock distance, constructing the speed-flow linear regression model, calculating the Cock distance of the latest time point under the linear model, and further judging whether the traffic state is abnormal, so that traffic accidents can be found in time to give an alarm in time, other vehicles on the highway are reminded to plan more reasonable travel routes in time, congestion caused by the traffic accidents is reduced, and the operation efficiency of the highway network is improved.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware that is related to instructions of a computer program, and the program can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.
Claims (10)
1. A method for detecting traffic conditions of a road, the method comprising:
constructing a sampling time point set required by the 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 a time period 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 vehicle average speed, and calculating the Cock 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 Cock distance of the current sampling period.
2. The method according to claim 1, wherein the constructing of the set of sampling time points required for the traffic status detection of the ETC portal to be detected comprises:
extracting a current sampling period of the ETC portal to be detected from a real-time database;
extracting a plurality of continuous historical sampling periods before the 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 judging whether the traffic state of the ETC portal to be detected is abnormal or not according to the Cock distance of the current sampling period comprises:
and when the Cock distance of the current sampling period is greater than or equal to a preset Cock distance threshold value, judging that traffic abnormity occurs.
And when the Cock distance of the current sampling period is smaller than a preset Cock distance threshold value, judging that the traffic is not abnormal.
4. The method of claim 2, wherein updating the historical normal database comprises:
and 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 vehicles 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 vehicles corresponding to the sampling period which is the longest distance away from the current sampling period.
5. The method according to claim 1, wherein after judging whether the traffic state of the ETC portal to be detected is abnormal according to the Cock distance of the current sampling period, the method further comprises the following steps:
when traffic abnormity occurs, traffic abnormity information is generated and sent to relevant departments for early warning; and
and when the traffic abnormality does not occur, sending the flow data corresponding to the current sampling period and the average speed of the vehicle to the historical normal database for storage.
6. The method according to claim 1, wherein before the establishing the set of sampling time points required for traffic status 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 building the historical normal database comprises:
collecting flow data of the ETC portal to be detected according to a preset period;
acquiring the time, flow data and vehicle average speed of the sampling period marked as a normal traffic state, and sending the time period, flow data and vehicle average speed corresponding to each sampling period to a historical normal database;
when the number of sampling periods in the normal traffic state reaches a preset number, the historical normal database is constructed;
and/or
The constructing a real-time database comprises:
and acquiring the flow data and the average vehicle speed of the ETC portal frame to be detected in real time according to a preset sampling period, and sending the time period, the flow data and the average vehicle speed corresponding to the sampling period to a real-time database for storage.
8. A traffic state detection device for a road, the device comprising:
the time point set generating module is used for constructing a sampling time point set required by the 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 a time period 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 Cock distance calculation module is used for creating a linear regression model based on the sampling time point set, the flow data and the vehicle average speed, and calculating the Cock distance of the current sampling period through the linear regression model;
and the traffic abnormity judgment module is used for judging whether the traffic state of the ETC portal to be detected is abnormal according to the Cock distance of the current sampling period.
9. A computer storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor and to perform the method steps according to 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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011135464.5A CN112434260B (en) | 2020-10-21 | 2020-10-21 | Road traffic state detection method, device, storage medium and terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011135464.5A CN112434260B (en) | 2020-10-21 | 2020-10-21 | Road traffic state detection method, device, storage medium and terminal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112434260A true CN112434260A (en) | 2021-03-02 |
| CN112434260B CN112434260B (en) | 2024-06-14 |
Family
ID=74695866
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011135464.5A Active CN112434260B (en) | 2020-10-21 | 2020-10-21 | Road traffic state detection method, device, storage medium and terminal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112434260B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114550478A (en) * | 2022-02-28 | 2022-05-27 | 福建省高速公路信息科技有限公司 | ETC-based highway safety automatic driving recommendation method |
| CN114613137A (en) * | 2022-03-07 | 2022-06-10 | 同盾科技有限公司 | Congestion index determination method, device, medium and equipment applied to expressway |
| CN114706112A (en) * | 2022-03-03 | 2022-07-05 | 北京中交兴路信息科技有限公司 | Method, device, electronic device and medium for detecting vehicle positioning |
| 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 |
| CN117877274A (en) * | 2024-03-13 | 2024-04-12 | 四川智慧高速科技有限公司 | ETC-based provincial expressway network traffic induction method |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060182451A1 (en) * | 2005-01-18 | 2006-08-17 | Hisashi Shoji | Abnormality determining apparatus, image forming apparatus, copying machine, and information obtaining method |
| CN101923778A (en) * | 2009-09-11 | 2010-12-22 | 中山大学 | Detection method of highway traffic congestion state based on video |
| CN102117731A (en) * | 2009-12-31 | 2011-07-06 | 中芯国际集成电路制造(上海)有限公司 | Method and device for monitoring measurement data in process production flow of semiconductor |
| CN104484996A (en) * | 2014-12-18 | 2015-04-01 | 江苏省交通规划设计院股份有限公司 | Road segment traffic state distinguishing method based on multi-source data |
| CN104821080A (en) * | 2015-03-02 | 2015-08-05 | 北京理工大学 | Intelligent vehicle traveling speed and time predication method based on macro city traffic flow |
| CN106790029A (en) * | 2016-12-15 | 2017-05-31 | 宝德科技集团股份有限公司 | A kind of big data acquisition methods and system based on identifying code |
| CN107657377A (en) * | 2017-09-26 | 2018-02-02 | 大连理工大学 | A kind of public transportation lane policy evaluation method returned based on breakpoint |
| CN108229592A (en) * | 2018-03-27 | 2018-06-29 | 四川大学 | Outlier detection method and device based on GMDH neuroids |
| CN108847022A (en) * | 2018-06-08 | 2018-11-20 | 浙江银江智慧交通集团有限公司 | A kind of rejecting outliers method of microwave traffic data collection equipment |
| CN109284320A (en) * | 2018-08-15 | 2019-01-29 | 上海明析数据科技有限公司 | Automatic returning diagnostic method in big data platform |
| JP2019028608A (en) * | 2017-07-27 | 2019-02-21 | 積水化学工業株式会社 | Information processing system, information processing device, and program |
| CN109785629A (en) * | 2019-02-28 | 2019-05-21 | 北京交通大学 | A kind of short-term traffic flow forecast method |
| CN109920250A (en) * | 2019-05-10 | 2019-06-21 | 李天学 | Dynamic prediction urban road intelligent traffic administration system method |
| CN111511880A (en) * | 2017-12-26 | 2020-08-07 | 株式会社Posco | Attached mineral measuring device of coke bin |
| CN111613053A (en) * | 2020-04-21 | 2020-09-01 | 北京掌行通信息技术有限公司 | Traffic disturbance detection and analysis method, device, storage medium and terminal |
| CN111785019A (en) * | 2020-06-22 | 2020-10-16 | 北京千方科技股份有限公司 | Vehicle traffic data generation method and system based on V2X and storage medium |
-
2020
- 2020-10-21 CN CN202011135464.5A patent/CN112434260B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060182451A1 (en) * | 2005-01-18 | 2006-08-17 | Hisashi Shoji | Abnormality determining apparatus, image forming apparatus, copying machine, and information obtaining method |
| CN101923778A (en) * | 2009-09-11 | 2010-12-22 | 中山大学 | Detection method of highway traffic congestion state based on video |
| CN102117731A (en) * | 2009-12-31 | 2011-07-06 | 中芯国际集成电路制造(上海)有限公司 | Method and device for monitoring measurement data in process production flow of semiconductor |
| CN104484996A (en) * | 2014-12-18 | 2015-04-01 | 江苏省交通规划设计院股份有限公司 | Road segment traffic state distinguishing method based on multi-source data |
| CN104821080A (en) * | 2015-03-02 | 2015-08-05 | 北京理工大学 | Intelligent vehicle traveling speed and time predication method based on macro city traffic flow |
| CN106790029A (en) * | 2016-12-15 | 2017-05-31 | 宝德科技集团股份有限公司 | A kind of big data acquisition methods and system based on identifying code |
| JP2019028608A (en) * | 2017-07-27 | 2019-02-21 | 積水化学工業株式会社 | Information processing system, information processing device, and program |
| CN107657377A (en) * | 2017-09-26 | 2018-02-02 | 大连理工大学 | A kind of public transportation lane policy evaluation method returned based on breakpoint |
| CN111511880A (en) * | 2017-12-26 | 2020-08-07 | 株式会社Posco | Attached mineral measuring device of coke bin |
| CN108229592A (en) * | 2018-03-27 | 2018-06-29 | 四川大学 | Outlier detection method and device based on GMDH neuroids |
| CN108847022A (en) * | 2018-06-08 | 2018-11-20 | 浙江银江智慧交通集团有限公司 | A kind of rejecting outliers method of microwave traffic data collection equipment |
| CN109284320A (en) * | 2018-08-15 | 2019-01-29 | 上海明析数据科技有限公司 | Automatic returning diagnostic method in big data platform |
| CN109785629A (en) * | 2019-02-28 | 2019-05-21 | 北京交通大学 | A kind of short-term traffic flow forecast method |
| CN109920250A (en) * | 2019-05-10 | 2019-06-21 | 李天学 | Dynamic prediction urban road intelligent traffic administration system method |
| CN111613053A (en) * | 2020-04-21 | 2020-09-01 | 北京掌行通信息技术有限公司 | Traffic disturbance detection and analysis method, device, storage medium and terminal |
| CN111785019A (en) * | 2020-06-22 | 2020-10-16 | 北京千方科技股份有限公司 | Vehicle traffic data generation method and system based on V2X and storage medium |
Non-Patent Citations (8)
| Title |
|---|
| YUCHUAN DU等: "Velocity Control Strategies to Improve Automated Vehicle Driving Comfort", IEEE, vol. 10, no. 1, pages 8, XP011676133, DOI: 10.1109/MITS.2017.2776148 * |
| 崔俊富等: "残差在线性回归分析中的作用研究", 牡丹江大学学报, vol. 29, no. 10, pages 84 * |
| 张军谋: "甘肃省入境客流演变及流量预测研究", 中国优秀硕士学位论文全文数据库经济与管理科学辑, no. 7, pages 153 - 98 * |
| 张辉等: "基于交通流理论的检测数据多元函数补全方法", 2019年中国城市交通规划年会, 16 October 2019 (2019-10-16), pages 1 * |
| 杨珍珍等: "数据驱动的动态路径优化和停车诱导模型与算法", 中国博士学位论文全文数据库工程科技Ⅱ辑, no. 1, 15 January 2020 (2020-01-15), pages 034 - 76 * |
| 胡凡: "公路客运量预测方法对比分析", 中国优秀硕士学位论文全文数据库工程科技Ⅱ辑, no. 12, 15 December 2015 (2015-12-15), pages 034 - 177 * |
| 胡凡: "公路客运量预测方法对比分析", 中国优秀硕士学位论文全文数据库工程科技Ⅱ辑, no. 12, pages 034 - 177 * |
| 陈长坤;武艳;辛梦阳;李明;: "基于广义线性模型的干线公路交通事故预测", 公路与汽运, no. 06, pages 56 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114550478A (en) * | 2022-02-28 | 2022-05-27 | 福建省高速公路信息科技有限公司 | ETC-based highway safety automatic driving recommendation method |
| CN114550478B (en) * | 2022-02-28 | 2024-04-09 | 福建省高速公路信息科技有限公司 | ETC-based highway safe automatic driving recommendation method |
| CN114706112A (en) * | 2022-03-03 | 2022-07-05 | 北京中交兴路信息科技有限公司 | Method, device, electronic device and medium for detecting vehicle positioning |
| CN114613137A (en) * | 2022-03-07 | 2022-06-10 | 同盾科技有限公司 | Congestion index determination method, device, medium and equipment applied to expressway |
| 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 |
| CN117877274A (en) * | 2024-03-13 | 2024-04-12 | 四川智慧高速科技有限公司 | ETC-based provincial expressway network traffic induction method |
| CN117877274B (en) * | 2024-03-13 | 2024-05-14 | 四川智慧高速科技有限公司 | ETC-based provincial expressway network traffic induction method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112434260B (en) | 2024-06-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112434260B (en) | Road traffic state detection method, device, storage medium and terminal | |
| CN112863172B (en) | Highway traffic running state judgment method, early warning method, device and terminal | |
| CN109919347B (en) | Road condition generation method, related device and equipment | |
| CN110415516B (en) | Urban traffic flow prediction method and medium based on graph convolution neural network | |
| CN108986465B (en) | Method, system and terminal equipment for detecting traffic flow | |
| CN112434075B (en) | ETC portal-based traffic abnormality detection method and device, storage medium and terminal | |
| CN113838284A (en) | Vehicle early warning method and device on accident-prone road section, storage medium and terminal | |
| CN112734242A (en) | Method and device for analyzing availability of vehicle running track data, storage medium and terminal | |
| CN113570867B (en) | Urban traffic state prediction method, device, equipment and readable storage medium | |
| CN104424812A (en) | Bus arrival time prediction system and method | |
| CN114120650B (en) | Method and device for generating test results | |
| CN112734956B (en) | ETC portal determination method and device and storage medium | |
| CN113190538A (en) | Road construction method and device based on track data, storage medium and terminal | |
| CN111540027A (en) | Detection method, detection device, electronic equipment and storage medium | |
| CN115019242A (en) | Abnormal event detection method and device for traffic scene and processing equipment | |
| CN114103987A (en) | Vehicle endurance early warning method and device and electronic equipment | |
| CN114662583A (en) | Emergency event prevention and control scheduling method and device, electronic equipment and storage medium | |
| CN114625744A (en) | Updating method and device of electronic map | |
| CN116311946B (en) | Method, system, terminal and storage medium for displaying real-time traffic situation | |
| CN115440037B (en) | Traffic flow data acquisition method and device, electronic equipment and storage medium | |
| EP4141386A1 (en) | Road data monitoring method and apparatus, electronic device and storage medium | |
| CN106781470B (en) | Method and device for processing running speed of urban road | |
| CN115830870A (en) | Toll station data processing method and device, computer equipment and storage medium | |
| CN112836626B (en) | Accident determining method and device, model training method and device and electronic equipment | |
| CN113283670A (en) | Bus arrival time prediction method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |