CN111554094A - Journey prediction model based on vehicle journey, driving behavior and traffic information - Google Patents
Journey prediction model based on vehicle journey, driving behavior and traffic information Download PDFInfo
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- CN111554094A CN111554094A CN202010428025.7A CN202010428025A CN111554094A CN 111554094 A CN111554094 A CN 111554094A CN 202010428025 A CN202010428025 A CN 202010428025A CN 111554094 A CN111554094 A CN 111554094A
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- G—PHYSICS
- G08—SIGNALLING
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention provides a journey prediction model based on vehicle journey, driving behavior and traffic information, which comprises the following steps: s1, collecting and selecting data; s2, data cleaning is carried out; s3, data de-ordering standardization processing; s4, comparing different strokes to obtain preselections; the invention can accurately predict the travel track and provides a reliable travel prediction model for the development of intelligent traffic.
Description
Technical Field
The invention belongs to the technical field of intelligent traffic, and particularly relates to a journey prediction model based on vehicle journey, driving behavior and traffic information.
Background
With continuous progress of scientific technology, the development of artificial intelligence is gradually mature, and the prediction of the travel track of the vehicle is of great significance, but the accuracy of the prediction of the travel track in the prior art is low, so that the patent application provides a travel prediction model based on the travel of the vehicle, the driving behavior and the traffic information, and the travel prediction result can be accurately given.
Disclosure of Invention
In view of the above, in order to overcome the above-mentioned drawbacks, the present invention aims to propose a trip prediction model based on a vehicle trip, driving behavior and traffic information.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a trip prediction model based on vehicle trip, driving behavior, and traffic information, comprising:
s1, collecting and selecting data;
s2, data cleaning is carried out;
s3, data de-ordering standardization processing;
s4, comparing different strokes to obtain preselections;
further, the specific implementation method of step S1 is as follows:
the data acquisition comprises the steps of acquiring vehicle travel data, driving behavior data and traffic information data;
the vehicle travel data is used for acquiring form information of the vehicle;
the driving behavior data is used for judging whether the driving is performed by the vehicle owner;
the traffic information data is used to determine an optimal form of the route.
Further, the vehicle travel data comprises a driving speed, a total driving mileage, a driving mileage of the current driving and a vehicle remaining oil amount.
Furthermore, the driving behavior is the driving habit of the owner, including the driving speed and the continuous distribution of the distance between the vehicle and the solid line or the dotted line, and is mainly used for judging whether the owner drives the vehicle.
Further, the traffic information data includes road distribution, red road lamp distribution information, and distribution conditions of service institutions in the area;
the service organization comprises a service area and a gas station.
Further, the data cleaning in step S2 is used to screen out inaccurate gap information.
Further, in step S3, the data de-singulation and warranty processing is to scale the data according to a certain ratio, and then to perform normalization processing to remove unit limitation between the data; and combining step similarity operation to obtain a processed value.
Further, in step S4, for each possible entering road, a pre-selection is given, and the pre-selection is presented in the form of a percentage.
Compared with the prior art, the journey prediction model based on the vehicle journey, the driving behavior and the traffic information has the following advantages:
the journey prediction model based on the vehicle journey, the driving behavior and the traffic information can accurately predict the journey track, and provides a reliable journey prediction model for the development of intelligent traffic.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a trip prediction model based on vehicle trip, driving behavior and traffic information according to an embodiment of 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.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
As shown in fig. 1, a trip prediction model based on a trip, driving behavior and traffic information of a vehicle includes:
s1, collecting and selecting data;
s2, data cleaning is carried out;
s3, data de-ordering standardization processing;
s4, comparing different strokes to obtain preselections;
the specific implementation method of step S1 is as follows:
the data acquisition comprises the steps of acquiring vehicle travel data, driving behavior data and traffic information data;
the vehicle travel data is used for acquiring form information of the vehicle;
the driving behavior data is used for judging whether the driving is performed by the vehicle owner;
the traffic information data is used to determine an optimal form of the route.
The vehicle travel data comprises the driving speed, the total driving mileage, the driving mileage of the current driving and the residual oil quantity of the vehicle.
The driving behavior is the driving habit of the owner, comprises the driving speed and the continuous distribution condition of the distance between the vehicle and the solid line or the dotted line, and is mainly used for judging whether the owner drives the vehicle.
The traffic information data comprises road distribution, red road lamp distribution information and distribution conditions of service organizations in the area;
the service organization comprises a service area and a gas station.
The data cleaning in step S2 is used to screen out inaccurate gap information.
In step S3, the de-ordering and de-registering process of the data is to scale the data according to a certain ratio, and then to standardize the data to remove unit limitation between the data; and combining step similarity operation to obtain a processed value.
In step S4, for each possible road, preselections are given, and the preselections are presented in percentage form.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. A trip prediction model based on vehicle trip, driving behavior, and traffic information, comprising:
s1, collecting and selecting data;
s2, data cleaning is carried out;
s3, data de-ordering standardization processing;
and S4, comparing the strokes to obtain preselections.
2. The trip prediction model based on vehicle trip, driving behavior and traffic information according to claim 1, wherein the specific implementation method of the step S1 is as follows:
the data acquisition comprises the steps of acquiring vehicle travel data, driving behavior data and traffic information data;
the vehicle travel data is used for acquiring form information of the vehicle;
the driving behavior data is used for judging whether the driving is performed by the vehicle owner;
the traffic information data is used to determine an optimal form of the route.
3. The trip prediction model based on vehicle trip, driving behavior and traffic information of claim 2, characterized in that: the vehicle travel data comprises the driving speed, the total driving mileage, the driving mileage of the current driving and the residual oil quantity of the vehicle.
4. The trip prediction model based on vehicle trip, driving behavior and traffic information of claim 2, characterized in that: the driving behavior is the driving habit of the owner, comprises the driving speed and the continuous distribution condition of the distance between the vehicle and the solid line or the dotted line, and is mainly used for judging whether the owner drives the vehicle.
5. The trip prediction model based on vehicle trip, driving behavior and traffic information of claim 2, characterized in that: the traffic information data comprises road distribution, red road lamp distribution information and distribution conditions of service organizations in the area;
the service organization comprises a service area and a gas station.
6. The trip prediction model based on vehicle trip, driving behavior and traffic information of claim 1, characterized in that: the data cleaning in step S2 is used to screen out inaccurate gap information.
7. The trip prediction model based on vehicle trip, driving behavior and traffic information of claim 1, characterized in that: in step S3, the de-ordering and de-registering process of the data is to scale the data according to a certain ratio, and then to standardize the data to remove unit limitation between the data; and combining step similarity operation to obtain a processed value.
8. The trip prediction model based on vehicle trip, driving behavior and traffic information of claim 1, characterized in that: in step S4, for each possible road, preselections are given, and the preselections are presented in percentage form.
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| CN202010428025.7A CN111554094A (en) | 2020-05-20 | 2020-05-20 | Journey prediction model based on vehicle journey, driving behavior and traffic information |
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| CN110379165A (en) * | 2019-07-26 | 2019-10-25 | 中国第一汽车股份有限公司 | A kind of road type prediction technique, device, equipment and storage medium |
| CN110909907A (en) * | 2018-09-18 | 2020-03-24 | 西安四维图新信息技术有限公司 | Method and device for predicting fuel consumption of truck and storage medium |
| CN111089601A (en) * | 2019-11-28 | 2020-05-01 | 上海蔚来汽车有限公司 | Vehicle energy supplement reminding method, device and system |
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2020
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Patent Citations (7)
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| CN106289297A (en) * | 2016-10-09 | 2017-01-04 | 思建科技有限公司 | A kind of vehicle Fuel Oil Remaining distance travelled computational methods and system |
| CN108665699A (en) * | 2017-03-30 | 2018-10-16 | 杭州海康威视数字技术股份有限公司 | There is the method and device in place in a kind of prediction vehicle |
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