CN115188186A - Method for monitoring traffic flow in area - Google Patents
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Abstract
The invention relates to the technical field of intelligent traffic, and particularly discloses a method for monitoring traffic flow in a region, which comprises the following steps: determining a flow upper limit threshold and a flow lower limit threshold of the current period of the area to be monitored according to the traffic flow monitoring data of the previous period; acquiring real-time traffic flow data of an area to be monitored, and respectively comparing the real-time traffic flow data with the flow upper limit threshold and the flow lower limit threshold; and if the real-time traffic flow data of the current period is between the lower flow threshold and the upper flow threshold, determining that the current traffic flow data is normal, otherwise, determining that the current traffic flow is abnormal. According to the method for monitoring the traffic flow in the area, provided by the invention, the geographic space is partitioned, and the traffic flow monitoring data of the gates and the GPS in the area are comprehensively partitioned, so that the traffic flow in the area is more comprehensively depicted, and the method is more accurate than a single-point or single-source flow detection method.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a method for monitoring traffic flow in an area.
Background
The phenomenon of traffic flow aggregation in a short time easily causes traffic paralysis in an area, which not only causes traffic delay of travelers, but also can further cause traffic safety accidents.
Therefore, it is very important for traffic management departments to increase the police force in time to guide blockage dredging and maintain order when problems occur by monitoring the traffic flow in the district in real time. At present, most flow detection algorithms are based on a single data source and independent flow monitoring equipment, and cannot form comprehensive and detailed perception on the traffic flow in an area, so that the flow abnormity detection capability is poor, and the early warning method has great limitation in the aspects of real-time performance and accuracy.
Therefore, how to improve the accuracy and real-time performance of traffic flow monitoring becomes a technical problem to be solved urgently by those skilled in the art.
Disclosure of Invention
The invention provides a method for monitoring traffic flow in an area, which solves the problems of low accuracy and poor real-time performance of traffic flow monitoring in the related technology.
As one aspect of the present invention, there is provided a method for monitoring traffic flow in a region, comprising:
determining a flow upper limit threshold and a flow lower limit threshold of the current period of the area to be monitored according to the traffic flow monitoring data of the previous period;
acquiring real-time traffic flow data of an area to be monitored, and respectively comparing the real-time traffic flow data with the upper flow threshold and the lower flow threshold;
if the real-time traffic flow data of the current period is between the lower flow threshold and the upper flow threshold, determining that the current traffic flow data is normal, otherwise, determining that the current traffic flow is abnormal;
the method for determining the upper limit threshold value and the lower limit threshold value of the current period of the area to be monitored according to the traffic flow monitoring data of the previous period comprises the following steps:
carrying out region division according to map information of a region to be monitored to obtain a plurality of regions to be monitored;
matching the bayonet traffic data and the GPS data of the area to be monitored in the previous period with the plurality of areas to be monitored to obtain bayonet traffic data corresponding to each area to be monitored;
and carrying out quartile calculation according to the traffic data of the gates of the areas to be monitored and the flow time sequence, and determining the upper threshold value and the lower threshold value of the traffic flow of the areas to be monitored.
Further, area division is performed according to map information of the area to be monitored, and a plurality of areas to be monitored are obtained, wherein the area division includes:
acquiring map information of an area to be monitored;
carrying out region division on map information of a region to be monitored to obtain a plurality of regions to be monitored;
and coding each region to be monitored to obtain the region code to be monitored.
Further, the area division is performed on the map information of the area to be monitored to obtain a plurality of areas to be monitored, and the method comprises the following steps:
dividing map information of an area to be monitored into N-N areas to be monitored according to the Morton code, wherein N is a natural number which is greater than or equal to 1.
Further, encoding each region to be monitored to obtain a region code to be monitored, including:
and coding each region to be monitored according to the Morton code to obtain the region code to be monitored.
Further, matching the bayonet traffic data and the GPS data of the area to be monitored in the previous period with the plurality of areas to be monitored to obtain the bayonet traffic data corresponding to each area to be monitored, including:
encoding the checkpoint flow data and the GPS data of the area to be monitored in the previous period based on the Morton code to obtain checkpoint flow data encoding information and GPS data encoding information;
matching the bayonet traffic data coding information and the GPS data coding information with the to-be-monitored area code to match the bayonet traffic data and the GPS data to the corresponding to-be-monitored area;
and filling corresponding bayonet flow data and GPS data into each area to be monitored.
Further, matching the bayonet traffic data and the GPS data of the area to be monitored in the previous period with the plurality of areas to be monitored to obtain the bayonet traffic data corresponding to each area to be monitored, further comprising:
and when the corresponding bayonet traffic data or GPS data is lacked in the area to be monitored, completing the area to be monitored which is lacked in the bayonet traffic data or GPS data according to a bilinear interpolation method.
Carrying out quartile calculation according to the traffic time sequence and the bayonet flow data of the area to be monitored, and determining the traffic flow upper limit threshold and the traffic flow lower limit threshold of the area to be monitored, wherein the quartile calculation comprises the following steps:
extracting a bayonet traffic data and GPS data set of each moment of an area to be monitored;
splitting the bayonet traffic data and the GPS data set of the area to be monitored at each moment to obtain the bayonet traffic data set of the area to be monitored at each moment and the GPS data set of the area to be monitored at each moment;
and calculating according to the bayonet flow data set of each moment of the area to be monitored and the GPS data set of each moment of the area to be monitored by combining quartiles to determine the traffic flow upper limit threshold and the traffic flow lower limit threshold of the area to be monitored.
Further, calculating according to the bayonet flow data set of each moment of the area to be monitored and the GPS data set of each moment of the area to be monitored in combination with the quartile number to determine the traffic flow upper limit threshold and the traffic flow lower limit threshold of the area to be monitored, including:
respectively calculating the upper quartile and the lower quartile of a bayonet flow data set at each moment of the area to be monitored to obtain an upper quartile flow result and a lower quartile flow result;
determining a traffic flow upper limit threshold value and a traffic flow lower limit threshold value according to the flow upper quartile result and the flow quartile result;
respectively calculating the upper quartile and the lower quartile of the GPS data set of each moment of the area to be monitored to obtain a GPS data upper quartile result and a GPS data lower quartile result;
and determining an upper speed threshold and a lower speed threshold according to the upper quartile result of the GPS data and the lower quartile result of the GPS data.
Further, acquiring real-time traffic flow data of the area to be monitored, and comparing the real-time traffic flow data with the flow upper limit threshold and the flow lower limit threshold respectively, wherein the steps of:
acquiring real-time traffic flow and real-time GPS data of a current period of an area to be monitored;
and comparing the real-time traffic flow of the current period of the area to be monitored with the traffic flow upper limit threshold and the traffic flow lower limit threshold respectively, and comparing the real-time GPS data of the current period of the area to be monitored with the vehicle speed upper limit threshold and the vehicle speed lower limit threshold respectively.
Further, if the real-time traffic flow data of the current period is between the lower flow threshold and the upper flow threshold, determining that the current traffic flow data is normal, otherwise, determining that the current traffic flow is abnormal, including:
if the real-time traffic flow of the current period of the area to be monitored is greater than or equal to the traffic flow upper limit threshold and the real-time vehicle speed is greater than or equal to the vehicle speed upper limit threshold, or if the real-time traffic flow of the current period of the area to be monitored is less than or equal to the traffic flow lower limit threshold and the real-time vehicle speed is less than or equal to the vehicle speed lower limit threshold, determining that the current traffic flow is abnormal;
otherwise, judging that the traffic flow in the current period is normal.
The method for monitoring the traffic flow in the area comprises the steps of firstly extracting historical data, determining a normal range of the flow through a data partition matching method based on map partition Morton codes, a space-time matrix construction based on fusion bayonet flow data and GPS data, a matrix null completion method based on bilinear interpolation, and a flow upper and lower limit threshold determination method based on quartile, and finally judging whether the flow is abnormal or not based on a real-time detection method of the abnormal traffic flow in the area. Therefore, the method for monitoring the traffic flow in the area divides the geographic space, and comprehensively divides the traffic flow monitoring data of the gates and the GPS in the area, so that the traffic flow in the area is more comprehensively depicted, and the method is more accurate than a single-point or single-source flow detection method.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a method for monitoring traffic flow in a region according to the present invention.
FIG. 2 is a schematic diagram of Morton encoding provided by the present invention.
FIG. 3 is a schematic diagram of spatio-temporal matrix construction provided by the present invention.
Fig. 4 is a schematic diagram of bilinear interpolation provided by the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make those skilled in the art better understand the technical solutions of the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged as appropriate in order to facilitate the embodiments of the invention described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this embodiment, a method for monitoring traffic flow in a region is provided, and fig. 1 is a flowchart of a method for monitoring traffic flow in a region according to an embodiment of the present invention, as shown in fig. 1, including:
s100, determining a flow upper limit threshold and a flow lower limit threshold of a current period of an area to be monitored according to traffic flow monitoring data of a previous period;
specifically, the embodiment of the present invention may include:
s110, dividing areas according to map information of the areas to be monitored to obtain a plurality of areas to be monitored;
in the embodiment of the present invention, the method specifically includes:
acquiring map information of an area to be monitored;
dividing the map information of the area to be monitored to obtain a plurality of areas to be monitored;
and coding each region to be monitored to obtain the region code to be monitored.
In the embodiment of the present invention, the area division is performed on the map information of the area to be monitored to obtain a plurality of areas to be monitored, including:
and dividing the map information of the area to be monitored into N-by-N areas to be monitored according to the Morton code, wherein N is a natural number which is greater than or equal to 1.
It should be noted that, encoding each to-be-monitored region to obtain a to-be-monitored region code includes:
and coding each region to be monitored according to the Morton code to obtain the region code to be monitored.
It should be understood that the map is partitioned by the morton code, and the two types of data are matched to the corresponding map sections according to the longitude and latitude information of the checkpoint data and the GPS data. As shown in fig. 2, the principle of morton code is to divide the whole map or a certain area, and make binary coding according to longitude and latitude, the binary coding is coded in a manner of dividing longitude and latitude equally in turn; or the longitude and the latitude are divided into two stages respectively, then the binary codes are combined in a crossed mode, and finally the binary codes are converted into decimal codes.
S120, matching the bayonet traffic data and the GPS data of the area to be monitored in the previous period with the plurality of areas to be monitored to obtain bayonet traffic data corresponding to each area to be monitored;
in the embodiment of the present invention, the method may specifically include:
encoding the checkpoint flow data and the GPS data of the area to be monitored in the previous period based on the Morton code to obtain checkpoint flow data encoding information and GPS data encoding information;
matching the bayonet traffic data coding information and the GPS data coding information with the region code to be monitored so as to match the bayonet traffic data and the GPS data to the corresponding region to be monitored;
and filling corresponding bayonet flow data and GPS data into each area to be monitored.
It should be understood that, in the embodiment of the present invention, after the map partition of the data set is completed, the corresponding bayonet traffic data and GPS data (specifically, vehicle speed) are filled into each block, a certain time interval is set, the data set is divided into M parts in a time range, then the map is divided into N × N dimensional blocks based on the morton code, and the data and the blocks are spatially corresponding to each other one by one, so that M N × N dimensional space-time matrices can be constructed.
And calculating the value of each element in the matrix according to the bayonet flow data and the GPS data of each block. Assuming that the time span of the historical data is T, dividing the T into M moments every T time, wherein the j row matrix elements and the k column matrix elements in the time-space matrix of the ith moment correspond to l bayonets, the matrix elements correspond to z GPS data, the bayonet flow information of the elements is the sum of l bayonet flows, the GPS flow speed information is the sum of z GPS data, and if no corresponding bayonets or GPS data exist in the block, null values are filled. Fig. 3 shows the way of calculating Am i j elements of the space-time matrix a.
In the embodiment of the present invention, the matching of the checkpoint traffic data and the GPS data of the area to be monitored in the previous period with the multiple areas to be monitored to obtain the checkpoint traffic data corresponding to each area to be monitored, further includes:
and when the corresponding bayonet traffic data or GPS data is lacked in the area to be monitored, completing the area to be monitored which is lacked in the bayonet traffic data or GPS data according to a bilinear interpolation method.
It should be understood that after the construction of the flow-velocity space-time matrix is completed, due to the limitation of the bayonet data and the GPS data, a lot of missing data must be generated, and the missing value filling is completed by using a bilinear interpolation formula.
Bilinear interpolation is a relatively common two-dimensional linear interpolation method, and the basic principle is shown in FIG. 4, assuming that Q is known 11 、Q 21 、Q 12 And Q 22 Now, the missing value P is requested. Firstly, linear interpolation is carried out in the x direction to obtain R1 and R2 pixel points, and then linear interpolation is carried out again in the y direction based on the R1 and the R2.
First, get R 1 And R 2 ：
Then, the following P:
s130, according to the bayonet flow data of the area to be monitored, quartile calculation is carried out according to the flow time sequence, and the traffic flow upper limit threshold value and the traffic flow lower limit threshold value of the area to be monitored are determined.
In the embodiment of the invention, the method comprises the following steps:
extracting a bayonet traffic data and GPS data set of each moment of an area to be monitored;
splitting the bayonet traffic data and the GPS data set of the area to be monitored at each moment to obtain a bayonet traffic data set of the area to be monitored at each moment and a GPS data set of the area to be monitored at each moment;
and calculating according to the bayonet flow data set of each moment of the area to be monitored and the GPS data set of each moment of the area to be monitored by combining quartiles to determine the traffic flow upper limit threshold and the traffic flow lower limit threshold of the area to be monitored.
Further specifically, the method for determining the upper limit threshold value and the lower limit threshold value of the traffic flow of the area to be monitored by combining a quartile with a bayonet flow data set at each moment of the area to be monitored and a GPS data set at each moment of the area to be monitored includes:
respectively calculating the upper quartile and the lower quartile of the bayonet traffic data set at each moment of the area to be monitored to obtain an upper quartile result and a lower quartile result of the traffic;
determining a traffic flow upper limit threshold value and a traffic flow lower limit threshold value according to the flow upper quartile result and the flow quartile result;
respectively calculating the upper quartile and the lower quartile of a GPS data set of each moment of the area to be monitored to obtain a GPS data upper quartile result and a GPS data lower quartile result;
and determining an upper speed threshold and a lower speed threshold according to the upper quartile result of the GPS data and the lower quartile result of the GPS data.
Calculating the upper limit and the lower limit of the flow time sequence at the same position by using a quartile as a threshold value for judging whether the flow is abnormal, and extracting each time matrix for the region ij
Splitting the bayonet traffic and the GPS traffic of the matrix to obtain Q t And V t Two one-dimensional matrices, wherein Q t ＝[q 0 ,q 1 ,q 2 ,…q M ]，V t ＝[v 0 ,v 1 ,v 2 ,…v M ]. Separately compute the set Q t The upper quartile of (a) is q t 3, the lower quartile is q t 1, defined IOR as q t 3-q t 1, setting a flow comparison upper limit threshold q tup Is q t 3+1.5 IOR, flow rate vs lower threshold q tdo Is q t 1-1.5 IOR. In the same way, calculate set V t Respectively have upper and lower threshold values of v tup And v tdo 。
S200, acquiring real-time traffic flow data of an area to be monitored, and comparing the real-time traffic flow data with the flow upper limit threshold and the flow lower limit threshold respectively;
in the embodiment of the present invention, the method may specifically include:
acquiring real-time traffic flow and real-time GPS data of a current period of an area to be monitored;
and comparing the real-time traffic flow of the current period of the area to be monitored with the traffic flow upper limit threshold and the traffic flow lower limit threshold respectively, and comparing the real-time GPS data of the current period of the area to be monitored with the vehicle speed upper limit threshold and the vehicle speed lower limit threshold respectively.
S300, if the real-time traffic flow data of the current period is between the lower flow threshold and the upper flow threshold, determining that the current traffic flow data is normal, otherwise, determining that the current traffic flow is abnormal;
in the embodiment of the present invention, the method may specifically include:
if the real-time traffic flow of the current period of the area to be monitored is greater than or equal to the traffic flow upper limit threshold and the real-time vehicle speed is greater than or equal to the vehicle speed upper limit threshold, or if the real-time traffic flow of the current period of the area to be monitored is less than or equal to the traffic flow lower limit threshold and the real-time vehicle speed is less than or equal to the vehicle speed lower limit threshold, determining that the current traffic flow is abnormal;
otherwise, judging that the traffic flow in the current period is normal.
It should be understood that, after the prediction is started, every 15 minutes is set as a time, assuming that the initial prediction time is 0, T is the current time, and 96 times a day is taken as a period T, the flow at the current time is monitored in real time, so as to compare the upper and lower flow limit thresholds and further determine whether the regional flow is abnormal. Suppose that the current time is t, and the current q is t And v t Comparing q in real time for the current bayonet monitoring flow and GPS monitoring flow t 、v t And upper and lower threshold values q tup 、q tdo 、v tup 、v tdo v tdo If (q) t ≥q tup And v is t ≥v tup ) Or (q) t ≤q tdo And v is t ≤v tdo ) If so, the flow rate is determined to be abnormal. And entering the next period T after the flow comparison in the period T is finished, and continuously executing the steps, thereby continuously circulating.
In summary, the method for monitoring traffic flow in an area provided by the embodiment of the invention includes the steps of firstly extracting historical data, determining a normal range of flow through a data partition matching method based on map partition Morton codes, a spatio-temporal matrix construction of fusion bayonet flow data and GPS data, a matrix null completion method based on bilinear interpolation, and a flow upper and lower limit threshold determination method based on quartile, and finally judging whether the flow is abnormal or not based on a real-time detection method of abnormal traffic flow in the area. Therefore, the method for monitoring the traffic flow in the area provided by the embodiment of the invention can be used for comprehensively depicting the traffic flow in the area by partitioning the geographic space and comprehensively partitioning the traffic monitoring data of the gates and the GPS in the area, and is more accurate than a single-point or single-source flow detection method.
It will be understood that the above embodiments are merely exemplary embodiments taken to illustrate the principles of the present invention, which is not limited thereto. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and scope of the invention, and such modifications and improvements are also considered to be within the scope of the invention.
Claims (10)
1. A method for monitoring traffic flow in a region, comprising:
determining a flow upper limit threshold and a flow lower limit threshold of the current period of the area to be monitored according to the traffic flow monitoring data of the previous period;
acquiring real-time traffic flow data of an area to be monitored, and respectively comparing the real-time traffic flow data with the upper flow threshold and the lower flow threshold;
if the real-time traffic flow data of the current period is between the flow lower limit threshold and the flow upper limit threshold, determining that the current traffic flow data is normal, otherwise, determining that the current traffic flow is abnormal;
the method for determining the upper flow threshold and the lower flow threshold of the current period of the area to be monitored according to the traffic flow monitoring data of the previous period comprises the following steps:
carrying out region division according to map information of a region to be monitored to obtain a plurality of regions to be monitored;
matching the bayonet traffic data and the GPS data of the area to be monitored in the previous period with the plurality of areas to be monitored to obtain bayonet traffic data corresponding to each area to be monitored;
and performing quartile calculation according to the traffic data of the bayonet of the area to be monitored and the flow time sequence, and determining a traffic flow upper limit threshold and a traffic flow lower limit threshold of the area to be monitored.
2. The method for monitoring the traffic flow in the area according to claim 1, wherein the area division is performed according to the map information of the area to be monitored to obtain a plurality of areas to be monitored, and the method comprises the following steps:
obtaining map information of an area to be monitored;
carrying out region division on map information of a region to be monitored to obtain a plurality of regions to be monitored;
and coding each region to be monitored to obtain the region code to be monitored.
3. The method for monitoring the traffic flow in the area according to claim 2, wherein the step of dividing the map information of the area to be monitored into a plurality of areas to be monitored comprises the steps of:
dividing map information of an area to be monitored into N-N areas to be monitored according to the Morton code, wherein N is a natural number which is greater than or equal to 1.
4. The method for monitoring the traffic flow in the area according to claim 2, wherein the step of coding each area to be monitored to obtain the code of the area to be monitored comprises the following steps:
and coding each region to be monitored according to the Morton code to obtain the region code to be monitored.
5. The method for monitoring traffic flow in an area according to claim 2, wherein the step of matching the gate flow data and the GPS data of the area to be monitored in the previous period with the plurality of areas to be monitored to obtain gate flow data corresponding to each area to be monitored comprises the steps of:
encoding the checkpoint flow data and the GPS data of the area to be monitored in the previous period based on the Morton code to obtain checkpoint flow data encoding information and GPS data encoding information;
matching the bayonet traffic data coding information and the GPS data coding information with the region code to be monitored so as to match the bayonet traffic data and the GPS data to the corresponding region to be monitored;
and filling corresponding bayonet flow data and GPS data into each area to be monitored.
6. The method for monitoring the traffic flow in the area according to claim 5, wherein the gate flow data and the GPS data of the area to be monitored in the previous period are matched with the plurality of areas to be monitored to obtain the gate flow data corresponding to each area to be monitored, further comprising:
and when the corresponding bayonet traffic data or GPS data is lacked in the area to be monitored, completing the area to be monitored which is lacked in the bayonet traffic data or GPS data according to a bilinear interpolation method.
7. The method for monitoring the traffic flow in the region according to claim 2, wherein the step of performing quartile calculation according to the flow time sequence according to the bayonet flow data of the region to be monitored and determining the traffic flow upper limit threshold and the traffic flow lower limit threshold of the region to be monitored comprises the following steps:
extracting a bayonet traffic data and GPS data set of each moment of an area to be monitored;
splitting the bayonet traffic data and the GPS data set of the area to be monitored at each moment to obtain a bayonet traffic data set of the area to be monitored at each moment and a GPS data set of the area to be monitored at each moment;
and calculating according to the bayonet flow data set of each moment of the area to be monitored and the GPS data set of each moment of the area to be monitored by combining quartiles so as to determine the traffic flow upper limit threshold and the traffic flow lower limit threshold of the area to be monitored.
8. The method for monitoring the traffic flow in the area according to claim 7, wherein the calculation is performed according to the checkpoint flow data set at each time of the area to be monitored and the GPS data set at each time of the area to be monitored in combination with the quartile number to determine the traffic flow upper limit threshold value and the traffic flow lower limit threshold value of the area to be monitored, and the method comprises the following steps:
respectively calculating the upper quartile and the lower quartile of a bayonet flow data set at each moment of the area to be monitored to obtain an upper quartile flow result and a lower quartile flow result;
determining a traffic flow upper limit threshold value and a traffic flow lower limit threshold value according to the flow upper quartile result and the flow quartile result;
respectively calculating the upper quartile and the lower quartile of the GPS data set of each moment of the area to be monitored to obtain a GPS data upper quartile result and a GPS data lower quartile result;
and determining an upper speed threshold and a lower speed threshold according to the upper quartile result of the GPS data and the lower quartile result of the GPS data.
9. The method for monitoring the traffic flow in the area according to claim 8, wherein the step of obtaining real-time traffic flow data of the area to be monitored and comparing the real-time traffic flow data with the upper flow threshold and the lower flow threshold respectively comprises the following steps:
acquiring real-time traffic flow and real-time GPS data of a current period of an area to be monitored;
and comparing the real-time traffic flow of the current period of the area to be monitored with the traffic flow upper limit threshold and the traffic flow lower limit threshold respectively, and comparing the real-time GPS data of the current period of the area to be monitored with the vehicle speed upper limit threshold and the vehicle speed lower limit threshold respectively.
10. The method for monitoring the traffic flow in the area according to claim 9, wherein if the real-time traffic flow data of the current period is between the lower flow threshold and the upper flow threshold, it is determined that the current traffic flow data is normal, otherwise it is determined that the current traffic flow is abnormal, including:
if the real-time traffic flow of the current period of the area to be monitored is greater than or equal to the traffic flow upper limit threshold and the real-time vehicle speed is greater than or equal to the vehicle speed upper limit threshold, or if the real-time traffic flow of the current period of the area to be monitored is less than or equal to the traffic flow lower limit threshold and the real-time vehicle speed is less than or equal to the vehicle speed lower limit threshold, determining that the current traffic flow is abnormal;
otherwise, judging that the traffic flow in the current period is normal.
Priority Applications (1)
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Citations (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH03209599A (en) * | 1990-01-11 | 1991-09-12 | Nippon Signal Co Ltd:The | Detector for abnormal traffic flow |
| DE19944077A1 (en) * | 1999-09-14 | 2001-03-22 | Daimler Chrysler Ag | Traffic condition supervision in traffic network, undertaking inquiry of current position and/or prognosis of future position of flank between area of free traffic and area of synchronized traffic continuously |
| CN102968901A (en) * | 2012-11-30 | 2013-03-13 | 青岛海信网络科技股份有限公司 | Method for acquiring regional congestion information and regional congestion analyzing device |
| US20160153804A1 (en) * | 2014-12-02 | 2016-06-02 | Here Global B.V. | Monitoring and Visualizing Traffic Surprises |
| CN107424416A (en) * | 2017-06-19 | 2017-12-01 | 深圳市创艺工业技术有限公司 | Road section traffic volume flow monitoring system based on unmanned plane |
| CN108629990A (en) * | 2018-06-14 | 2018-10-09 | 重庆同济同枥信息技术有限公司 | A kind of real-time dynamic timing method and system based on multi-source data |
| CN109409713A (en) * | 2018-10-15 | 2019-03-01 | 北京交通大学 | Road network appraisal procedure based on Bayesian model and three times standard deviation criterion |
| CN109935080A (en) * | 2019-04-10 | 2019-06-25 | 武汉大学 | The monitoring system and method that a kind of vehicle flowrate on traffic route calculates in real time |
| CN109979193A (en) * | 2019-02-19 | 2019-07-05 | 中电海康集团有限公司 | A kind of data exception diagnostic method based on Markov model |
| CN110363255A (en) * | 2019-08-19 | 2019-10-22 | 公安部交通管理科学研究所 | A kind of Speed Limitation on Freeway current-limiting method based on deep learning algorithm |
| US20200097351A1 (en) * | 2018-09-26 | 2020-03-26 | Palo Alto Research Center Incorporated | System and method for binned inter-quartile range analysis in anomaly detection of a data series |
| CN111179592A (en) * | 2019-12-31 | 2020-05-19 | 合肥工业大学 | Urban traffic prediction method and system based on spatio-temporal data flow fusion analysis |
| CN111583648A (en) * | 2020-05-14 | 2020-08-25 | 公安部交通管理科学研究所 | Urban traffic flow prediction method fusing GPS data and checkpoint flow data |
| CN111613049A (en) * | 2019-02-26 | 2020-09-01 | 北京嘀嘀无限科技发展有限公司 | Road state monitoring method and device |
| CN112330074A (en) * | 2020-12-02 | 2021-02-05 | 公安部交通管理科学研究所 | Bayonet traffic early warning method based on mobile police service |
| WO2021052031A1 (en) * | 2019-09-20 | 2021-03-25 | 苏宁云计算有限公司 | Statistical interquartile range-based commodity inventory risk early warning method and system, and computer readable storage medium |
| CN113190997A (en) * | 2021-04-29 | 2021-07-30 | 贵州数据宝网络科技有限公司 | Big data terminal data restoration method and system |
| CN113808399A (en) * | 2021-09-15 | 2021-12-17 | 谦亨信息化技术与系统(苏州)有限公司 | Intelligent traffic management method and system based on big data |
| CN114155703A (en) * | 2021-09-18 | 2022-03-08 | 阿里云计算有限公司 | Traffic control method and device |
| US20220148316A1 (en) * | 2020-09-14 | 2022-05-12 | Shanghai Sensetime Intelligent Technology Co., Ltd. | Method and device for determining road operation condition, and storage medium |
| CN114495497A (en) * | 2022-01-19 | 2022-05-13 | 山东大学 | Method and system for distinguishing and interpolating traffic abnormal data |
| CN114495498A (en) * | 2022-01-20 | 2022-05-13 | 青岛海信网络科技股份有限公司 | Traffic data distribution effectiveness judging method and device |
-
2022
- 2022-06-28 CN CN202210740114.4A patent/CN115188186B/en active Active
Patent Citations (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH03209599A (en) * | 1990-01-11 | 1991-09-12 | Nippon Signal Co Ltd:The | Detector for abnormal traffic flow |
| DE19944077A1 (en) * | 1999-09-14 | 2001-03-22 | Daimler Chrysler Ag | Traffic condition supervision in traffic network, undertaking inquiry of current position and/or prognosis of future position of flank between area of free traffic and area of synchronized traffic continuously |
| CN102968901A (en) * | 2012-11-30 | 2013-03-13 | 青岛海信网络科技股份有限公司 | Method for acquiring regional congestion information and regional congestion analyzing device |
| US20160153804A1 (en) * | 2014-12-02 | 2016-06-02 | Here Global B.V. | Monitoring and Visualizing Traffic Surprises |
| CN107424416A (en) * | 2017-06-19 | 2017-12-01 | 深圳市创艺工业技术有限公司 | Road section traffic volume flow monitoring system based on unmanned plane |
| CN108629990A (en) * | 2018-06-14 | 2018-10-09 | 重庆同济同枥信息技术有限公司 | A kind of real-time dynamic timing method and system based on multi-source data |
| US20200097351A1 (en) * | 2018-09-26 | 2020-03-26 | Palo Alto Research Center Incorporated | System and method for binned inter-quartile range analysis in anomaly detection of a data series |
| CN109409713A (en) * | 2018-10-15 | 2019-03-01 | 北京交通大学 | Road network appraisal procedure based on Bayesian model and three times standard deviation criterion |
| CN109979193A (en) * | 2019-02-19 | 2019-07-05 | 中电海康集团有限公司 | A kind of data exception diagnostic method based on Markov model |
| CN111613049A (en) * | 2019-02-26 | 2020-09-01 | 北京嘀嘀无限科技发展有限公司 | Road state monitoring method and device |
| CN109935080A (en) * | 2019-04-10 | 2019-06-25 | 武汉大学 | The monitoring system and method that a kind of vehicle flowrate on traffic route calculates in real time |
| CN110363255A (en) * | 2019-08-19 | 2019-10-22 | 公安部交通管理科学研究所 | A kind of Speed Limitation on Freeway current-limiting method based on deep learning algorithm |
| WO2021052031A1 (en) * | 2019-09-20 | 2021-03-25 | 苏宁云计算有限公司 | Statistical interquartile range-based commodity inventory risk early warning method and system, and computer readable storage medium |
| CN111179592A (en) * | 2019-12-31 | 2020-05-19 | 合肥工业大学 | Urban traffic prediction method and system based on spatio-temporal data flow fusion analysis |
| CN111583648A (en) * | 2020-05-14 | 2020-08-25 | 公安部交通管理科学研究所 | Urban traffic flow prediction method fusing GPS data and checkpoint flow data |
| US20220148316A1 (en) * | 2020-09-14 | 2022-05-12 | Shanghai Sensetime Intelligent Technology Co., Ltd. | Method and device for determining road operation condition, and storage medium |
| CN112330074A (en) * | 2020-12-02 | 2021-02-05 | 公安部交通管理科学研究所 | Bayonet traffic early warning method based on mobile police service |
| CN113190997A (en) * | 2021-04-29 | 2021-07-30 | 贵州数据宝网络科技有限公司 | Big data terminal data restoration method and system |
| CN113808399A (en) * | 2021-09-15 | 2021-12-17 | 谦亨信息化技术与系统(苏州)有限公司 | Intelligent traffic management method and system based on big data |
| CN114155703A (en) * | 2021-09-18 | 2022-03-08 | 阿里云计算有限公司 | Traffic control method and device |
| CN114495497A (en) * | 2022-01-19 | 2022-05-13 | 山东大学 | Method and system for distinguishing and interpolating traffic abnormal data |
| CN114495498A (en) * | 2022-01-20 | 2022-05-13 | 青岛海信网络科技股份有限公司 | Traffic data distribution effectiveness judging method and device |
Non-Patent Citations (1)
| Title |
|---|
| 侯策 等: "道路车流量不均匀的交通灯控制系统研究", 现代信息科技, vol. 4, no. 9, pages 10 - 15 * |
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