CN108108448B - Method and system for generating nationwide road portrait - Google Patents
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- CN108108448B CN108108448B CN201711448184.8A CN201711448184A CN108108448B CN 108108448 B CN108108448 B CN 108108448B CN 201711448184 A CN201711448184 A CN 201711448184A CN 108108448 B CN108108448 B CN 108108448B
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Abstract
The invention provides a method and a system for generating nationwide road portraits, which are based on massive truck track point data and comprise the following steps: a data extraction step of extracting data; a data cleaning and preprocessing step, wherein the data is cleaned and preprocessed, and data in a fixed time period is screened; a statistical analysis step, calculating the average value of the maximum speed of each vehicle, the maximum height, the maximum length, the maximum width and the maximum rated load of the passing truck according to roads; a step of labeling, namely determining the speed limit, height limit, length limit, width limit and weight limit of each road; a step of storing labels, namely storing label results into a database; and a label updating step of updating the road label every month. The invention combines mass real truck track point data, increases the quantity and dimensionality of analysis data, and has scientific and reliable statistical result; the work of the label is updated every month, and the timeliness of the label is guaranteed.
Description
Technical Field
The invention relates to the technical field of big data and freight markets, in particular to a method and a system for generating a national road portrait based on mass truck track point data.
Background
At present, the road information data purchased from a third party only contains basic road attributes such as road ID and road length, and the speed limit attribute in the basic attributes is inaccurate, so that some business data analysis requirements cannot be met.
The Chinese patent publication No. CN102568195A provides a method and a system capable of accurately pre-judging the driving track and the destination of a vehicle. The method for pre-judging the vehicle running track comprises the following steps: installing a positioning terminal for a vehicle; acquiring a plurality of driving track historical records of a vehicle; acquiring a current running track of a vehicle; comparing the current running track of the vehicle with the historical running track record so as to pre-judge the running track of the vehicle in the next time period; and issuing a judgment result. According to the method, the past running records of the vehicle are counted, the running records are compared with the current running track of the vehicle, and the subsequent running track of the vehicle is judged, so that a cargo owner and a freight carrier can accurately find a proper vehicle to execute a transportation task, and an important reference means can be provided for a vehicle management enterprise, such as a dangerous goods transportation enterprise, to manage whether the running state of the governed vehicle is normal or not.
However, in the current owned road data, the speed limit attribute of the road is inaccurate, and the road data does not have the attributes of height limitation, width limitation, length limitation and weight limitation, so that the actual business data analysis requirements (such as whether the truck is overspeed or overloaded or not through insurance business analysis) cannot be met.
Disclosure of Invention
In order to solve the problems, the method and the device combine the truck track point data and the truck information to supplement the attribute of the road related to the service analysis requirement.
Specifically, the invention provides a method for generating a nationwide road portrait, which is based on massive freight car track point data and comprises the following steps:
a data extraction step of extracting data;
a data cleaning and preprocessing step, wherein the data is cleaned and preprocessed, and data in a fixed time period is screened;
a statistical analysis step, calculating the average value of the maximum speed of each vehicle, the maximum height, the maximum length, the maximum width and the maximum rated load of the passing truck according to roads;
a step of labeling, namely determining the speed limit, height limit, length limit, width limit and weight limit of each road;
a step of storing labels, namely storing label results into a database;
and a label updating step of updating the road label every month.
Preferably, the data comprises track point data of all trucks in the last year and basic information of all trucks, wherein the basic information comprises truck length, vehicle width, truck height and rated load.
Preferably, the data washing and preprocessing step comprises the following steps:
filtering the night time period data;
filtering error data and track point data which are not matched with a road;
extracting relevant information in the track point data, wherein the relevant information comprises GPS speed, road ID, a license plate of a truck, the length of the truck, the width of the truck, the height of the truck and rated load;
the data are sorted by link, and the data of the same link ID are arranged together.
Preferably, the method for calculating the average value of the maximum speed of each vehicle according to the road comprises the following steps: and filtering the data of the same road ID and the same license plate but with the GPS speed less than the maximum speed of the vehicle on the road, namely only retaining the data of the maximum speed of each truck on each road and calculating the average value of the maximum speed of each vehicle.
Preferably, the method for determining the speed limit, the height limit, the length limit, the width limit and the weight limit of each road comprises the following steps:
comparing the average value of the maximum speed of each vehicle with the national road speed limit standard, and downwards matching a national standard value closest to the average value of the maximum speed of each vehicle as a speed limit value;
comparing the maximum vehicle height passed by the road with national road height limit standards, if the road height limit standard of a certain country is greater than the maximum vehicle height value, taking the national road height limit standard value as a height limit value, and otherwise, taking the maximum vehicle height value as the height limit value, and calculating the weight limit value of the road in the same way;
and taking the maximum length and the maximum width obtained in the step of statistical analysis as the length limit value and the width limit value of the road.
Preferably, the step of storing the tag result into the database includes storing the data into the database according to the formats of the road ID, the speed limit value, the height limit value, the length limit value, the width limit value and the weight limit value.
Preferably, the tag updating step includes the steps of:
counting, namely calculating a new speed limit value, a new height limit value, a new length limit value, a new width limit value and a new weight limit value at the beginning of each month;
and an updating step, comparing the newly calculated label with the old label stored before, and replacing the old label with the new label if the new label is inconsistent with the old label.
According to another aspect of the present invention, the present invention further provides a system for generating a national road representation, based on a mass of truck track point data, comprising the following modules connected in sequence:
the data extraction module is used for extracting data;
the data cleaning and preprocessing module is used for cleaning and preprocessing the data and screening the data in a fixed time period;
the statistical analysis module is used for calculating the average value of the maximum speed of each vehicle, the maximum height, the maximum length, the maximum width and the maximum rated load of the passing truck according to the road;
the labeling module is used for determining the speed limit, height limit, length limit, width limit and weight limit of each road;
the storage label module is used for storing the label result into a database;
and the label updating module is used for updating the road labels every month.
Preferably, the data comprises track point data of all trucks in the last year and basic information of all trucks, wherein the basic information comprises truck length, vehicle width, truck height and rated load.
Preferably, the data cleaning and preprocessing module comprises the following units:
the first filtering unit is used for filtering the night time period data;
the second filtering unit is used for filtering error data and track point data which are not matched with the road;
the extraction unit is used for extracting relevant information in the track point data, wherein the relevant information comprises GPS speed, road ID, a license plate of a truck, the length of the truck, the width of the truck, the height of the truck and rated load;
and the sorting unit is used for sorting the data according to the roads and arranging the data of the same road ID together.
Preferably, the method for calculating the average value of the maximum speed of each vehicle according to the road comprises the following steps: and filtering the data of the same road ID and the same license plate but with the GPS speed less than the maximum speed of the vehicle on the road, namely only retaining the data of the maximum speed of each truck on each road and calculating the average value of the maximum speed of each vehicle.
Preferably, the method for determining the speed limit, the height limit, the length limit, the width limit and the weight limit of each road comprises the following steps:
comparing the average value of the maximum speed of each vehicle with the national road speed limit standard, and downwards matching a national standard value closest to the average value of the maximum speed of each vehicle as a speed limit value;
comparing the maximum vehicle height passed by the road with national road height limit standards, if the road height limit standard of a certain country is greater than the maximum vehicle height value, taking the national road height limit standard value as a height limit value, and otherwise, taking the maximum vehicle height value as the height limit value, and calculating the weight limit value of the road in the same way;
and taking the maximum length and the maximum width obtained in the step of statistical analysis as the length limit value and the width limit value of the road.
Preferably, the step of storing the tag result into the database includes storing the data into the database according to the formats of the road ID, the speed limit value, the height limit value, the length limit value, the width limit value and the weight limit value.
Preferably, the tag updating module includes the following units:
the statistical unit is used for calculating a new speed limit value, a new height limit value, a new length limit value, a new width limit value and a new weight limit value at the beginning of each month;
and the updating unit is used for comparing the newly calculated label with the old label stored before, and replacing the old label with the new label if the new label is inconsistent with the old label.
The invention combines mass real truck track point data, increases the quantity and dimensionality of analysis data, and has scientific and reliable statistical result; the work of the label is updated every month, and the timeliness of the label is guaranteed.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart of a method of generating a national road representation based on a large amount of truck track point data according to the present invention;
FIG. 2 is a flow chart of the data cleaning and preprocessing steps of the present invention;
FIG. 3 is a flow chart of the labeling step of the present invention;
FIG. 4 is a block diagram of a system for generating a national road representation based on a large amount of truck track point data in accordance with the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
FIG. 1 is a matching flow chart of the method for generating a national road representation based on mass freight car track point data of the present invention, comprising the steps of:
A1. a data extraction step: extracting data;
A2. data cleaning and preprocessing: the method is used for improving the quality of modeling data and screening data in a fixed time period;
A3. the statistical analysis step is used for calculating the average value of the maximum speed of each vehicle, the maximum height, the maximum length, the maximum width and the maximum rated load of the passing truck according to the road;
A4. labeling: the device is used for determining the speed limit, the height limit, the length limit, the width limit and the weight limit of each road;
A5. and a step of storing the label: for storing the tag results in a database;
A6. and (3) updating the label: for updating the road label monthly.
Therefore, the method combines mass real truck track point data, increases the quantity and dimensionality of analysis data, and is scientific and reliable in statistical result; the work of the label is updated every month, and the timeliness of the label is guaranteed.
Example 1
As shown in FIG. 1, the invention for generating a nationwide road portrait based on massive truck track point data comprises the following steps:
1. and (6) data extraction. This step requires two data to be extracted:
and extracting all freight car track point data in the last year. The extraction source may be a free truck track acquisition platform as disclosed in the art, or may be semi-open source data such as a medium delivery platform.
And extracting basic information of all trucks, including information such as truck length, vehicle width, truck height, rated load and the like. The extraction source can be a free truck information acquisition platform disclosed in the field, and can also be data of a semi-open source such as a delivery platform.
2. And (4) cleaning and preprocessing data. As shown in fig. 2, this step specifically includes the following four steps:
filtering nighttime period data, e.g., filtering out nighttime 10: 00-6 in the morning: data between 00;
filtering error data and track point data which are not matched with the road;
extracting relevant information such as GPS speed, road ID, wagon license plate, wagon length, vehicle width, vehicle height, rated load and the like in the track point data (if a plurality of road IDs exist, the last one is taken, because the GPS speed is generated on the last road);
the data are sorted by link, and the data of the same link ID are arranged together.
3. And (5) carrying out statistical analysis. The statistics include:
and (4) carrying out statistics on the maximum speed mean value: and filtering the data of the same road ID and the same license plate but with the GPS speed less than the maximum speed of the vehicle on the road, namely only retaining the data of the maximum speed of each truck on each road and calculating the average value of the maximum speed of each vehicle.
And (3) other statistics: and counting the maximum height, the maximum length, the maximum width and the maximum rated load of the passing truck according to the road.
4. And (5) labeling. The label includes:
limiting the speed: comparing the average value of the maximum speed with the national road speed limit standard, and downwards matching a national standard value closest to the average value of the maximum speed to serve as a speed limit value; as shown in FIG. 3, the maximum speed mean is 105.77km/h, and then the speed limit is found by matching downwards to be 100km/h as the speed limit value.
Height and weight limitation: and comparing the maximum vehicle height passed by the road with the national road height limit standard, if the national road height limit standard is greater than the maximum vehicle height value, taking the national road height limit standard value as a height limit value, and otherwise, taking the maximum vehicle height value as the height limit value. The weight limit value of the road can be calculated in the same way.
Length limitation and width limitation: since the country has no standard for limiting the length and width of the truck, the maximum length and width values counted before are used as the length and width limit values of the road.
5. And storing the label.
And storing the data into a database according to the formats of the road ID, the speed limit value, the height limit value, the length limit value, the width limit value and the weight limit value.
6. And updating the label.
Counting: and (4) calculating a new speed limit value, a new height limit value, a new length limit value, a new width limit value and a new weight limit value at the beginning of each month according to the method in the step 1-4.
Updating: the newly computed label is compared to the old label previously stored, and if the new and old labels are not consistent, the old label is replaced with the new label.
As shown in FIG. 4, according to another aspect of the invention, the invention also provides a system 100 for generating a national road representation based on a mass of truck track point data, comprising the following modules connected in sequence:
a data extraction module 110 for extracting data; the data comprises track point data of all trucks in the last year and basic information of all trucks, wherein the basic information comprises truck length, vehicle width, truck height and rated load.
A data cleaning and preprocessing module 120, configured to clean and preprocess the data, and screen data in a fixed time period; preferably, the data washing and preprocessing module 120 includes the following units: a first filtering unit 121 for filtering the night time period data; the second filtering unit 122 is used for filtering error data and track point data which are not matched with a road; the extracting unit 123 is configured to extract relevant information in the trajectory point data, where the relevant information includes a GPS speed, a road ID, a number plate of a truck, a truck length, a vehicle width, a vehicle height, and a rated load; and a sorting unit 124 for sorting the data by road, and arranging the data of the same road ID together.
And the statistical analysis module 130 is used for calculating the average value of the maximum speed of each vehicle, the maximum height, the maximum length, the maximum width and the maximum rated load of the passing truck according to the road. And filtering the data of the same road ID and the same license plate but with the GPS speed less than the maximum speed of the vehicle on the road, namely only retaining the data of the maximum speed of each truck on each road and calculating the average value of the maximum speed of each vehicle.
And the labeling module 140 is used for determining the speed limit, the height limit, the length limit, the width limit and the weight limit of each road. Limiting the speed: comparing the average value of the maximum speed with the national road speed limit standard, and downwards matching a national standard value closest to the average value of the maximum speed to serve as a speed limit value; as shown in FIG. 3, the maximum speed mean is 105.77km/h, and then the speed limit is found by matching downwards to be 100km/h as the speed limit value. Height and weight limitation: and comparing the maximum vehicle height passed by the road with the national road height limit standard, if the national road height limit standard is greater than the maximum vehicle height value, taking the national road height limit standard value as a height limit value, and otherwise, taking the maximum vehicle height value as the height limit value. The weight limit value of the road can be calculated in the same way. Length limitation and width limitation: since the country has no standard for limiting the length and width of the truck, the maximum length and width values counted before are used as the length and width limit values of the road.
A tag storage module 150 for storing the tag result in a database; and storing the data into a database according to the formats of the road ID, the speed limit value, the height limit value, the length limit value, the width limit value and the weight limit value.
And a tag updating module 160 for updating the road tags every month. The tag update module 160 includes the following elements: the statistical unit 161 is configured to calculate a new speed limit value, a new height limit value, a new length limit value, a new width limit value, and a new weight limit value at the beginning of each month; an updating unit 162 for comparing the newly calculated label with an old label stored before, and if the new and old labels are not identical, replacing the old label with the new label.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A method for generating a nationwide road portrait based on massive freight car track point data is characterized by comprising the following steps:
a data extraction step of extracting data;
a data cleaning and preprocessing step, wherein the data is cleaned and preprocessed, and data in a fixed time period is screened; filtering the night time period data; filtering error data and track point data which are not matched with a road; extracting relevant information in the track point data, wherein the relevant information comprises GPS speed, road ID, a license plate of a truck, the length of the truck, the width of the truck, the height of the truck and rated load; arranging data according to the road, and arranging the data of the same road ID together;
a statistical analysis step, calculating the average value of the maximum speed of each vehicle, the maximum height, the maximum length, the maximum width and the maximum rated load of the passing truck according to roads;
a step of labeling, namely determining the speed limit, height limit, length limit, width limit and weight limit of each road; comparing the average value of the maximum speed of each vehicle with the national road speed limit standard, and downwards matching a national standard value closest to the average value of the maximum speed of each vehicle as a speed limit value; comparing the maximum vehicle height passed by the road with national road height limit standards, if the road height limit standard of a certain country is greater than the maximum vehicle height value, taking the national road height limit standard value as a height limit value, and otherwise, taking the maximum vehicle height value as the height limit value, and calculating the weight limit value of the road in the same way; taking the maximum length and the maximum width obtained in the step of statistical analysis as a length limit value and a width limit value of the road;
a step of storing labels, namely storing label results into a database;
and a label updating step of updating the road label every month.
2. The method of generating a national road representation as claimed in claim 1, wherein:
the data comprises track point data of all trucks in the last year and basic information of all trucks, wherein the basic information comprises truck length, vehicle width, truck height and rated load.
3. The method of generating a national road representation as claimed in claim 2, wherein:
the method for calculating the average value of the maximum speed of each vehicle according to the road comprises the following steps: and filtering the data of the same road ID and the same license plate but with the GPS speed less than the maximum speed of the vehicle on the road, namely only retaining the data of the maximum speed of each truck on each road and calculating the average value of the maximum speed of each vehicle.
4. The method of generating a national road representation as claimed in claim 3, wherein:
and storing the label result into a database comprises storing the data into the database according to the formats of the road ID, the speed limit value, the height limit value, the length limit value, the width limit value and the weight limit value.
5. The method of generating a national road representation as claimed in claim 4, wherein:
the tag updating step includes the steps of:
counting, namely calculating a new speed limit value, a new height limit value, a new length limit value, a new width limit value and a new weight limit value at the beginning of each month;
and an updating step, comparing the newly calculated label with the old label stored before, and replacing the old label with the new label if the new label is inconsistent with the old label.
6. A system for generating a nationwide road portrait based on massive freight car track point data is characterized by comprising the following modules connected in sequence:
the data extraction module is used for extracting data;
the data cleaning and preprocessing module is used for cleaning and preprocessing the data and screening the data in a fixed time period; the data cleaning and preprocessing module comprises the following units: the first filtering unit is used for filtering the night time period data; the second filtering unit is used for filtering error data and track point data which are not matched with the road; the extraction unit is used for extracting relevant information in the track point data, wherein the relevant information comprises GPS speed, road ID, a license plate of a truck, the length of the truck, the width of the truck, the height of the truck and rated load; a sorting unit for sorting data by road, and arranging data of the same road ID together;
the statistical analysis module is used for calculating the average value of the maximum speed of each vehicle, the maximum height, the maximum length, the maximum width and the maximum rated load of the passing truck according to the road;
the labeling module is used for determining the speed limit, height limit, length limit, width limit and weight limit of each road; comparing the average value of the maximum speed of each vehicle with the national road speed limit standard, and downwards matching a national standard value closest to the average value of the maximum speed of each vehicle as a speed limit value; comparing the maximum vehicle height passed by the road with national road height limit standards, if the road height limit standard of a certain country is greater than the maximum vehicle height value, taking the national road height limit standard value as a height limit value, and otherwise, taking the maximum vehicle height value as the height limit value, and calculating the weight limit value of the road in the same way; taking the maximum length and the maximum width obtained in the step of statistical analysis as a length limit value and a width limit value of the road;
the storage label module is used for storing the label result into a database;
and the label updating module is used for updating the road labels every month.
7. The system for generating a national road representation as claimed in claim 6, wherein:
the data comprises track point data of all trucks in the last year and basic information of all trucks, wherein the basic information comprises truck length, vehicle width, truck height and rated load.
8. The system for generating a national road representation as claimed in claim 7, wherein:
the method for calculating the average value of the maximum speed of each vehicle according to the road comprises the following steps: and filtering the data of the same road ID and the same license plate but with the GPS speed less than the maximum speed of the vehicle on the road, namely only retaining the data of the maximum speed of each truck on each road and calculating the average value of the maximum speed of each vehicle.
9. The system for generating a national road representation as claimed in claim 8, wherein:
and storing the label result into a database comprises storing the data into the database according to the formats of the road ID, the speed limit value, the height limit value, the length limit value, the width limit value and the weight limit value.
10. The system for generating a national road representation as claimed in claim 9, wherein:
the tag update module comprises the following elements:
the statistical unit is used for calculating a new speed limit value, a new height limit value, a new length limit value, a new width limit value and a new weight limit value at the beginning of each month;
and the updating unit is used for comparing the newly calculated label with the old label stored before, and replacing the old label with the new label if the new label is inconsistent with the old label.
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| CN110798805B (en) * | 2018-08-02 | 2021-07-20 | 北京四维图新科技股份有限公司 | Data processing method and device based on GPS track and storage medium |
| CN111047862B (en) * | 2019-12-09 | 2021-06-29 | 北京中交兴路信息科技有限公司 | Method for acquiring road attribute |
| CN111477028B (en) * | 2020-04-28 | 2022-05-24 | 北京百度网讯科技有限公司 | Method and device for generating information in automatic driving |
| CN113177780A (en) * | 2021-05-12 | 2021-07-27 | 中移智行网络科技有限公司 | Data processing method and device, network equipment and readable storage medium |
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| CN106846869A (en) * | 2017-02-21 | 2017-06-13 | 驼队重卡（北京）物流信息技术有限责任公司 | A kind of update method of lorry navigation road auxiliary information |
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| CN108108448A (en) | 2018-06-01 |
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