CN111932898A - ETC portal system-based short-time flow prediction method - Google Patents
ETC portal system-based short-time flow prediction method Download PDFInfo
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- CN111932898A CN111932898A CN202011006344.5A CN202011006344A CN111932898A CN 111932898 A CN111932898 A CN 111932898A CN 202011006344 A CN202011006344 A CN 202011006344A CN 111932898 A CN111932898 A CN 111932898A
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- G—PHYSICS
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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Abstract
The invention relates to a short-time flow prediction method based on an ETC portal system in the technical field of traffic information, which comprises the steps of extracting historical vehicle traffic data in a road section to be predicted, and summarizing sample flow data according to lanes; respectively calculating the flow change floating value of each lane in different time periods of two or more adjacent days, and then calculating to obtain a change floating average value; selecting two or more adjacent ETC gantries in the same passing direction of the highway section to be predicted, and counting historical traffic flow data; acquiring RSU antenna data of each lane of the ETC portal in the same time period and converting the RSU antenna data into a bit traffic data matrix according to the ETC portal in the road section to be predicted, and calculating real-time flow of each lane of the road network in different time periods; the method can provide a short-time flow prediction method which is fast and convenient in data acquisition, high in flow data updating efficiency and fast and accurate in prediction and is based on an ETC portal system.
Description
Technical Field
The invention relates to the technical field of traffic information, in particular to a short-time flow prediction method based on an ETC portal system.
Background
Along with the continuous development of economy in China, the urbanization level is deepened day by day, the requirements of people on good life are improved day by day, and the expressway plays an increasingly important role as an important channel between cities. With the rapid increase in the number of private cars of residents and the increasing demand for materials in cities, the traffic flow of highways shows a continuously rapid increase. The traffic volume increased year by year not only makes the highway more congested, but also can increase the emergence probability of traffic accident at the same time. The existing traffic flow measuring and calculating method generally analyzes data generated by an intersection point established on a highway. In the conventional prediction, traffic data of corresponding points, including a traffic natural number time period sum value and an average vehicle speed of vehicle types, are collected by using traffic points established on an expressway, and data of adjacent traffic points are measured and calculated by a statistical method to perform modeling calculation. The defects that traffic flow is predicted through traffic data in the prior art comprise that the traffic data of a certain section is acquired by the traffic data and cannot be subdivided into lanes; the density of the cross-modulation point distribution is not uniform, and the fine calculation cannot be carried out.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a short-time flow prediction method which is quick and convenient in data acquisition, high in flow data updating efficiency and quick and accurate in prediction and is based on an ETC portal system.
Apache Flink is a framework and distributed processing engine for stateful computation of unbounded and bounded data streams.
The two-dimensional matrix is formed by that each element in the matrix is not a single number any more, but an ordered real number pair, and the geometric position of each element is formed by a point on a corresponding two-dimensional plane.
The RSU is an english abbreviation of Road Side Unit, and the transliteration means a roadside Unit, and is installed in the roadside in the ETC system, and the vehicle identity recognition device is realized by adopting dsrc (dedicated Short Range communication) technology and communicating with an On Board Unit (OBU).
In order to achieve the purpose, the invention adopts the following technical scheme.
A short-time flow prediction method based on an ETC portal system specifically comprises the following steps:
the method comprises the following steps: selecting and extracting sample ETC portal historical vehicle traffic data in different driving directions in a road section to be predicted from a road network data server, and summarizing sample traffic data of each lane according to the lanes;
step two: respectively calculating the traffic change floating value of each lane in two or more adjacent days in different time periods according to the traffic sample flow data of the first step, and then calculating to obtain a change floating average value;
step three: selecting two or more adjacent ETC gantries in the same passing direction of the highway section to be predicted, and counting the historical traffic flow data of each lane for more than one continuous week within a preset fixed time; acquiring RSU antenna data of each lane of the ETC portal in the same time period according to the ETC portal in the road section to be predicted;
step four: the acquired RSU antenna data is converted into a traffic data matrix, and the matrix is a two-dimensional matrix, so that the number of vehicles in each time period can be conveniently extracted in the subsequent steps;
step five: constructing a traffic data matrix into message queue data, receiving and updating the message queue data in real time by using an Apache flight flow type calculation frame, summarizing and grouping the data in time periods according to each lane corresponding to a portal, and calculating real-time traffic of each lane of a road network in different time periods;
step six: and multiplying the obtained lane real-time flow data by the corresponding lane historical flow floating average value in the second step to finally obtain a road section traffic flow predicted value in the time period to be predicted.
As a further improvement of the present invention, the RSU antenna data collected in step three specifically include vehicle model data, license plate number data, transit time data, and station data passed through in the vehicle flow data.
As a further improvement of the present invention, the two-dimensional matrix in the second step includes vehicle passing time and vehicle passing number.
As a further improvement of the present invention, the variable float value in the step six refers to a traffic flow rate increase value in different time periods of two or more adjacent days, and the rate increase value can be a positive value or a negative value.
Due to the application of the technical scheme, the technical scheme of the invention has the following beneficial effects: the technical scheme includes that firstly, historical statistical data of sample portals in a road network to be predicted are selected to conduct batch summary statistics, then data of the same sample portals in an adjacent time period are selected to conduct real-time traffic flow updating, and short-time flow of the ETC portals is predicted by combining real-time flow conditions of the adjacent time period and referring to the historical data, so that the prediction basis is sufficient, and the situation that the ETC portals approach to actual traffic conditions is guaranteed to be approached; according to the technical scheme, the average change floating value is calculated for the lane flow in different time periods according to the change conditions of the historical flow data in different time periods, and the average change floating value is used as a reference factor during measurement and calculation, so that a correction basis can be provided for flow analysis and judgment, the difference caused by accidental flow change can be reduced, and the accuracy of data prediction is effectively improved; according to the technical scheme, real-time traffic data generated by ETC gantries in the highway network are combined with a flink flow type calculation frame, so that the convenience of real-time traffic data acquisition and updating and the processing efficiency are improved, and the speed and the precision of short-time traffic prediction of the road network are greatly improved.
Detailed Description
The present invention will be described in further detail with reference to the following reaction schemes and specific examples.
A short-time flow prediction method based on an ETC portal system specifically comprises the following steps: the method comprises the following steps: selecting and extracting sample ETC portal historical vehicle traffic data in different driving directions in a road section to be predicted from a road network data server, and summarizing sample traffic data of each lane according to the lanes; step two: respectively calculating the traffic flow change floating values of each lane in two or more adjacent days in different time periods according to the traffic sample flow data in the step one, and then calculating to obtain a change floating average value; step three: selecting two or more adjacent ETC gantries in the same passing direction of the highway section to be predicted, and counting the historical traffic flow data of each lane for more than one continuous week within a preset fixed time; acquiring RSU antenna data of each lane of the ETC portal in the same time period according to the ETC portal in the road section to be predicted; step four: the acquired RSU antenna data is converted into a traffic data matrix, and the matrix is a two-dimensional matrix, so that the number of vehicles in each time period can be conveniently extracted in the subsequent steps; step five: constructing a traffic data matrix into message queue data, receiving and updating the message queue data in real time by using an Apache flight flow type calculation frame, summarizing and grouping the data in time periods according to each lane corresponding to a portal, and calculating real-time traffic of each lane of a road network in different time periods; step six: and multiplying the obtained lane real-time flow data by the corresponding lane historical flow floating average value in the second step to finally obtain a road section traffic flow predicted value in the time period to be predicted.
The RSU antenna data collected in the third step specifically comprises vehicle type data, license plate number data, passing time data and station data in the vehicle flow data; the two-dimensional matrix in the step two comprises vehicle passing time and vehicle passing quantity; the variable floating value in the step six refers to the traffic flow increase rate value in different time periods of two or more adjacent days, and the increase rate value can be a positive value or a negative value.
The above is only a specific application example of the present invention, and the protection scope of the present invention is not limited in any way. All the technical solutions formed by equivalent transformation or equivalent replacement fall within the protection scope of the present invention.
Claims (4)
1. A short-time flow prediction method based on an ETC portal system is characterized by specifically comprising the following steps:
the method comprises the following steps: selecting and extracting sample ETC portal historical vehicle traffic data in different driving directions in a road section to be predicted from a road network data server, and summarizing sample traffic data of each lane according to the lanes;
step two: respectively calculating the traffic change floating value of each lane in two or more adjacent days in different time periods according to the traffic sample flow data of the first step, and then calculating to obtain a change floating average value;
step three: selecting two or more adjacent ETC gantries in the same passing direction of the highway section to be predicted, and counting the historical traffic flow data of each lane for more than one continuous week within a preset fixed time; acquiring RSU antenna data of each lane of the ETC portal in the same time period according to the ETC portal in the road section to be predicted;
step four: the acquired RSU antenna data is converted into a traffic data matrix, and the matrix is a two-dimensional matrix, so that the number of vehicles in each time period can be conveniently extracted in the subsequent steps;
step five: constructing a traffic data matrix into message queue data, receiving and updating the message queue data in real time by using an Apache flight flow type calculation frame, summarizing and grouping the data in time periods according to each lane corresponding to a portal, and calculating real-time traffic of each lane of a road network in different time periods;
step six: and multiplying the obtained lane real-time flow data by the corresponding lane historical flow floating average value in the second step to finally obtain a road section traffic flow predicted value in the time period to be predicted.
2. The ETC portal system-based short-time flow prediction method according to claim 1, characterized in that: the RSU antenna data collected in the third step specifically comprises vehicle type data, license plate number data, passing time data and station data in the vehicle flow data.
3. The ETC portal system-based short-time flow prediction method according to claim 1, characterized in that: and the two-dimensional matrix in the second step comprises the vehicle passing time and the vehicle passing number.
4. The ETC portal system-based short-time flow prediction method according to claim 1, characterized in that: the changed floating value in the step six refers to the traffic flow increase rate value of two or more adjacent days in different time periods, and the increase rate value can be a positive value or a negative value.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113034904A (en) * | 2021-03-05 | 2021-06-25 | 交通运输部公路科学研究所 | ETC data-based traffic state estimation method and device |
| CN113159856A (en) * | 2021-04-30 | 2021-07-23 | 山东旗帜信息有限公司 | Toll station exit flow prediction method and system |
| CN113496314A (en) * | 2021-09-07 | 2021-10-12 | 南京感动科技有限公司 | Method for predicting road traffic flow by neural network model |
| CN113689694A (en) * | 2021-07-28 | 2021-11-23 | 山东中创软件商用中间件股份有限公司 | Traffic flow prediction method, device, equipment and readable storage medium |
| CN114005276A (en) * | 2021-10-25 | 2022-02-01 | 浙江综合交通大数据开发有限公司 | Expressway congestion early warning method based on multi-data source fusion |
| CN114023073A (en) * | 2022-01-06 | 2022-02-08 | 南京感动科技有限公司 | Expressway congestion prediction method based on vehicle behavior analysis |
| CN114463972A (en) * | 2022-01-26 | 2022-05-10 | 成都和乐信软件有限公司 | Road section interval traffic analysis and prediction method based on ETC portal communication data |
| CN114898574A (en) * | 2022-04-26 | 2022-08-12 | 安徽省交通控股集团有限公司 | Method and system for estimating traffic parameters |
| CN115240414A (en) * | 2022-07-19 | 2022-10-25 | 陕西蓝德智慧交通科技有限公司 | Traffic condition investigation method based on ETC portal system |
| CN115331439A (en) * | 2022-08-09 | 2022-11-11 | 山东旗帜信息有限公司 | Vehicle history image-based highway interchange traffic flow prediction method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113034904B (en) * | 2021-03-05 | 2022-06-24 | 交通运输部公路科学研究所 | ETC data-based traffic state estimation method and device |
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| CN113159856A (en) * | 2021-04-30 | 2021-07-23 | 山东旗帜信息有限公司 | Toll station exit flow prediction method and system |
| CN113689694B (en) * | 2021-07-28 | 2023-06-02 | 山东中创软件商用中间件股份有限公司 | Traffic flow prediction method, device, equipment and readable storage medium |
| CN113689694A (en) * | 2021-07-28 | 2021-11-23 | 山东中创软件商用中间件股份有限公司 | Traffic flow prediction method, device, equipment and readable storage medium |
| CN113496314B (en) * | 2021-09-07 | 2021-12-03 | 南京感动科技有限公司 | Method for predicting road traffic flow by neural network model |
| CN113496314A (en) * | 2021-09-07 | 2021-10-12 | 南京感动科技有限公司 | Method for predicting road traffic flow by neural network model |
| CN114005276A (en) * | 2021-10-25 | 2022-02-01 | 浙江综合交通大数据开发有限公司 | Expressway congestion early warning method based on multi-data source fusion |
| CN114023073A (en) * | 2022-01-06 | 2022-02-08 | 南京感动科技有限公司 | Expressway congestion prediction method based on vehicle behavior analysis |
| CN114463972A (en) * | 2022-01-26 | 2022-05-10 | 成都和乐信软件有限公司 | Road section interval traffic analysis and prediction method based on ETC portal communication data |
| CN114463972B (en) * | 2022-01-26 | 2024-02-27 | 成都和乐信软件有限公司 | Road section interval traffic analysis prediction method based on ETC portal communication data |
| CN114898574A (en) * | 2022-04-26 | 2022-08-12 | 安徽省交通控股集团有限公司 | Method and system for estimating traffic parameters |
| CN115240414A (en) * | 2022-07-19 | 2022-10-25 | 陕西蓝德智慧交通科技有限公司 | Traffic condition investigation method based on ETC portal system |
| CN115240414B (en) * | 2022-07-19 | 2024-04-02 | 陕西蓝德智慧交通科技有限公司 | Traffic condition investigation method based on ETC portal system |
| CN115331439A (en) * | 2022-08-09 | 2022-11-11 | 山东旗帜信息有限公司 | Vehicle history image-based highway interchange traffic flow prediction method and system |
| CN115331439B (en) * | 2022-08-09 | 2023-08-18 | 山东旗帜信息有限公司 | Expressway interchange traffic flow prediction method based on vehicle history image |
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