CN111598347A - Road transport vehicle ultra-long stroke segmentation optimization method - Google Patents
Road transport vehicle ultra-long stroke segmentation optimization method Download PDFInfo
- Publication number
- CN111598347A CN111598347A CN202010432548.9A CN202010432548A CN111598347A CN 111598347 A CN111598347 A CN 111598347A CN 202010432548 A CN202010432548 A CN 202010432548A CN 111598347 A CN111598347 A CN 111598347A
- Authority
- CN
- China
- Prior art keywords
- travel
- ultra
- satellite positioning
- data
- segmentation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000011218 segmentation Effects 0.000 title claims abstract description 53
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000005457 optimization Methods 0.000 title claims abstract description 15
- 230000002159 abnormal effect Effects 0.000 claims abstract description 22
- 238000001914 filtration Methods 0.000 claims abstract description 11
- 238000012216 screening Methods 0.000 claims abstract description 10
- 238000007781 pre-processing Methods 0.000 claims abstract description 4
- 238000001514 detection method Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 2
- 230000006399 behavior Effects 0.000 abstract 2
- 230000014509 gene expression Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0835—Relationships between shipper or supplier and carriers
- G06Q10/08355—Routing methods
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a road transport vehicle ultra-long stroke segmentation optimization method, which comprises the steps of obtaining vehicle satellite positioning track data; detecting, filtering and preprocessing abnormal points of data; carrying out stroke segmentation on the track data; screening for an ultra-long stroke; and (5) cutting with an ultra-long stroke. According to the method, the trajectory data obtained by the terminal is used, the data is cleaned and filtered, then the travel is segmented, the segmented travel is screened for the ultra-long travel, and finally the ultra-long travel is segmented again. The method can be applied to various applications related to driving behaviors, the ultra-long stroke is screened out and then is re-segmented, the reasonable stroke segmentation can ensure the accuracy of the stroke information, and the driving behaviors and the driving risks of the user reflected in the stroke information can be more real and reliable.
Description
Technical Field
The invention relates to a vehicle ultra-long stroke segmentation optimization method, in particular to a road transport vehicle ultra-long stroke segmentation optimization method, and belongs to the technical field of vehicle stroke segmentation optimization.
Background
With the widespread installation of the road transport vehicle satellite positioning system terminal, the collection and storage capacity of the road vehicle track data is obviously improved. The vehicle trajectory routing processing is a necessary means for analyzing the driving behavior of the user and evaluating the driving risk section. The information obtained by analyzing the vehicle track journey data can provide reliable basis for describing user figures for insurance companies and for government traffic supervision. With the development of big data, the improvement of artificial intelligence and 5G technology, the application scenes of vehicle travel information are more and more.
Since the vehicle trajectory is generated by the position point information obtained by the satellite positioning system, the satellite positioning device is affected by factors such as its accuracy and environment when acquiring data, and there is a certain difference between the acquired data and actual data. The conventional vehicle track data stroke segmentation method is suitable for segmentation of most strokes, but the problem of overlong stroke after partial segmentation is found. The travel information such as travel mileage, travel duration and the like of the travel can be abnormal due to the ultra-long travel, and the abnormal phenomenon can cause the analysis result to be incorrect and generate misleading to an information user. According to the method, the track data of the road transport vehicle is obtained, the abnormal data is filtered and preprocessed, the travel segmentation is carried out, the ultra-long travel is screened according to the travel starting and stopping time, and the travel segmentation is carried out on the ultra-long travel.
Disclosure of Invention
The invention aims to solve the problems and provide an ultra-long stroke segmentation optimization method for a road transport vehicle.
The invention realizes the purpose through the following technical scheme: a road transport vehicle ultra-long stroke segmentation optimization method comprises the following steps:
(1) acquiring track data of vehicle satellite positioning;
(2) detecting, filtering and preprocessing abnormal points of the data;
(3) carrying out stroke segmentation on the track data;
(4) screening the overlong stroke;
(5) and carrying out stroke segmentation on the ultra-long stroke.
As a further scheme of the invention: the acquiring of the trajectory data for the satellite positioning of the vehicle includes:
satellite positioning latitude, satellite positioning longitude, satellite positioning time, satellite positioning direction, satellite positioning speed, satellite positioning precision and satellite number.
As a further scheme of the invention: the data abnormal point detection, filtration and pretreatment comprises the following steps:
and filtering the track data of the vehicle satellite positioning, and rejecting data points with abnormal satellite positioning longitude and latitude, abnormal satellite positioning time, abnormal satellite positioning direction, abnormal satellite positioning speed and poor satellite positioning precision.
As a further scheme of the invention: the process of segmenting the track data comprises the following steps:
and the segmented road transport vehicle travel data can be obtained through satellite positioning track data according to the road transport vehicle travel segmentation rule. From the above calculation, 2 information indexes of the trip start-stop time can be obtained.
As a further scheme of the invention: the ultra-long stroke screening comprises:
and acquiring the information of the starting and ending time of the travel, and calculating the running time of the travel. Screening the overlong travel according to the travel time length of the travel, and if the travel time length of the travel is less than a certain value, judging that the travel of the vehicle is a non-overlong travel; if the running time is longer than a certain value, the travel of the vehicle is judged to be an ultra-long travel, and the next stage of segmentation is carried out. And executing the operation to screen out the ultra-long running stroke.
As a further scheme of the invention: the ultra-long stroke segmentation comprises the following steps:
acquiring ultra-long travel track data, sequencing the gps point data from low to high according to the starting time, filtering and removing the duplicate of the sequenced gps point data, performing travel segmentation on the duplicated gps point data, and finally screening the segmented travel to obtain the effective travel.
The invention has the beneficial effects that: the method for optimizing the road transport vehicle ultra-long stroke segmentation is reasonable in design, and the acquired satellite positioning data is filtered and preprocessed, so that the influence on the stroke segmentation caused by the data quality problem is reduced. Further, the vehicle trajectory data is subjected to trip segmentation. Further, the invention screens ultra-long strokes. The invention cuts the ultra-long stroke.
Drawings
FIG. 1 is a schematic flow chart of a method for optimizing the segmentation of a road transport vehicle for an ultra-long travel route according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a process of a trip segmentation method according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of an ultra-long stroke segmentation optimization method according to an embodiment of the present invention.
Detailed Description
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.
Referring to fig. 1 to 3, a method for optimizing the super-long travel segmentation of a road transport vehicle includes the following steps:
step A, acquiring satellite positioning track data:
in this example, a satellite positioning module (including driving inside a vehicle, a vehicle-mounted smart device, a smart phone, etc.) is started, and a data connection is established with the computing terminal through the satellite positioning module. The method can acquire the satellite positioning latitude, the satellite positioning longitude, the satellite positioning time, the satellite positioning direction, the satellite positioning speed, the satellite positioning precision, the satellite number and other trajectory data;
b, performing data anomaly point detection, filtration and pretreatment:
in this example, anomaly detection and preprocessing are performed on the acquired data. And eliminating data points with abnormal satellite positioning longitude and latitude, abnormal satellite positioning time, abnormal satellite positioning direction, abnormal satellite positioning speed and poor satellite positioning precision. The abnormal data points can cause the deviation of characteristic calculation, so that the accuracy of judging the illegal plugging and unplugging condition of the terminal is lost;
step C, path segmentation of the track data:
by analyzing the travel of the road transport vehicle, the phenomenon that part of the travel is over-long is found, and the phenomenon can cause deviation on the result of the travel division. The track data of the road transport vehicles are large in data quantity and the arrangement sequence may have a disorder phenomenon, so that the track data need to be reordered according to time. The quality of the trajectory data of the road transport vehicle has an important influence on the travel data, so that the trajectory data needs to be quality filtered. The track data of the road transport vehicle may have a repetitive phenomenon, so that the track data needs to be deduplicated according to longitude and latitude information. The mode expressions of adjacent time intervals of the track data of the road transport vehicles are inconsistent, and the time interval of the segmentation journey needs to be set according to the ratio of the mode expressions; the specific steps of the above contents refer to fig. 2.
Step D, screening for an ultra-long stroke:
the travel time length can be calculated through the travel starting and stopping time information, and if the travel time length does not exceed a certain value, the travel is considered to be a non-ultra-long travel; and if the running time exceeds a certain value, the travel is considered to be an overlong travel. Acquiring the travel track data and performing next-stage segmentation;
e, ultra-long stroke segmentation:
by re-segmenting the ultra-long stroke, the segmentation of the ultra-long stroke can be optimized. The track data of the road transport vehicles may be similar, so that the track data needs to be removed according to distance intervals. The mode expressions of adjacent time intervals of the track data of the road transport vehicles are inconsistent, and the time interval of the segmentation journey needs to be set according to the ratio of the mode expressions. The journey of road transport vehicle needs to contain a certain amount of track points, need reject the journey according to the data of track point in the journey. The travel information of the road transport vehicle can reflect the effectiveness of the travel, and the travel needs to be eliminated according to the starting and stopping longitude and latitude information of the travel; the specific steps of the above are shown in fig. 3.
As shown in fig. 2, in the present embodiment, the method includes the following steps:
1) sorting according to st from low to high;
2) calculating the number of all gps points of the vehicle track data, if the number is less than 2, not judging the vehicle, and if the number is more than or equal to 2, executing the next step;
3) deleting gps points with acc (satellite positioning accuracy) of 0;
4) deleting gps points whose status (satellite positioning status) is 0;
5) deleting the gps points with sat less than or equal to 0;
6) if the ith gps point sat is earlier than the (i-1) th gps point sat, deleting the ith gps point until the sequence of all gps points sat in the vehicle track data is increased;
7) deleting the gps point with locationStatus (according to 808 protocol analysis value) of 0(0 represents unreliable satellite positioning);
8) if the longitude and latitude values of the ith gps point are the same as the longitude and latitude values of the ith-1 th gps point, deleting the ith gps point until the longitude or latitude of two adjacent gps points are different;
9) and calculating the time interval of adjacent gps points of the vehicle track data, and counting the ratio of the mode of the time interval, wherein if the ratio of the mode is more than x%, the stroke segmentation is carried out in t1min, and if the ratio of the mode is less than or equal to x%, the stroke segmentation is carried out in t2 min.
As shown in fig. 3, in the present embodiment, the method includes the following steps:
1) calculating the linear distance between adjacent gps points, and deleting the ith gps point if the linear distance between the ith gps point and the (i-1) th point is less than the gps point of s1 m;
2) calculating the time interval of adjacent gps points of the vehicle track data, and counting the ratio of the mode of the time interval, if the ratio of the mode is greater than x%, performing the stroke segmentation in t1min, and if the ratio of the mode is less than or equal to x%, performing the stroke segmentation in t2 min;
3) deleting strokes of which the number of gps points is less than or equal to n;
4) and (4) calculating the straight distance of the starting point and the ending point of the travel, and deleting the travel if the distance is less than s2 m.
The working principle is as follows: when the method for optimizing the ultra-long stroke segmentation of the road transport vehicle is used, firstly satellite positioning track data is obtained, then detection, filtration and pretreatment are carried out according to abnormal points, then stroke segmentation is carried out on the track data, then the ultra-long stroke is screened, and finally the ultra-long stroke is segmented. According to the method, after relevant data are obtained and cleaned and preprocessed, stroke segmentation is carried out according to trajectory data. On the premise of segmenting the ultra-long stroke, the method is a reliable basis for analyzing the vehicle stroke characteristics.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (6)
1. A road transport vehicle ultra-long stroke segmentation optimization method is characterized by comprising the following steps: the method comprises the following steps:
(1) acquiring track data of vehicle satellite positioning;
(2) detecting, filtering and preprocessing abnormal points of the data;
(3) carrying out stroke segmentation on the track data;
(4) screening the overlong stroke;
(5) and carrying out stroke segmentation on the ultra-long stroke.
2. The road transport vehicle ultra-long stroke segmentation optimization method according to claim 1, characterized in that: the acquiring of the trajectory data for the satellite positioning of the vehicle includes:
satellite positioning latitude, satellite positioning longitude, satellite positioning time, satellite positioning direction, satellite positioning speed, satellite positioning precision and satellite number.
3. The road transport vehicle ultra-long stroke segmentation optimization method according to claim 1, characterized in that: the data abnormal point detection, filtration and pretreatment comprises the following steps:
and filtering the track data of the vehicle satellite positioning, and rejecting data points with abnormal satellite positioning longitude and latitude, abnormal satellite positioning time, abnormal satellite positioning direction, abnormal satellite positioning speed and poor satellite positioning precision.
4. The road transport vehicle ultra-long stroke segmentation optimization method according to claim 1, characterized in that: the process of segmenting the track data comprises the following steps:
and the segmented road transport vehicle travel data can be obtained through satellite positioning track data according to the road transport vehicle travel segmentation rule. From the above calculation, 2 information indexes of the trip start-stop time can be obtained.
5. The road transport vehicle ultra-long stroke segmentation optimization method according to claim 1, characterized in that: the ultra-long stroke screening comprises:
and acquiring the information of the starting and ending time of the travel, and calculating the running time of the travel. Screening the overlong travel according to the travel time length of the travel, and if the travel time length of the travel is less than a certain value, judging that the travel of the vehicle is a non-overlong travel; if the running time is longer than a certain value, the travel of the vehicle is judged to be an ultra-long travel, and the next stage of segmentation is carried out. And executing the operation to screen out the ultra-long running stroke.
6. The road transport vehicle ultra-long stroke segmentation optimization method according to claim 1, characterized in that: the ultra-long stroke segmentation comprises the following steps:
acquiring ultra-long travel track data, sequencing the gps point data from low to high according to the starting time, filtering and removing the duplicate of the sequenced gps point data, performing travel segmentation on the duplicated gps point data, and finally screening the segmented travel to obtain the effective travel.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010432548.9A CN111598347B (en) | 2020-05-20 | 2020-05-20 | Ultra-long travel segmentation optimization method for road transport vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010432548.9A CN111598347B (en) | 2020-05-20 | 2020-05-20 | Ultra-long travel segmentation optimization method for road transport vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111598347A true CN111598347A (en) | 2020-08-28 |
| CN111598347B CN111598347B (en) | 2024-02-09 |
Family
ID=72190422
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010432548.9A Active CN111598347B (en) | 2020-05-20 | 2020-05-20 | Ultra-long travel segmentation optimization method for road transport vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111598347B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112153573A (en) * | 2020-09-28 | 2020-12-29 | 平安国际融资租赁有限公司 | Segmentation method and device based on position track, computer equipment and storage medium |
| CN112380311A (en) * | 2020-11-27 | 2021-02-19 | 上海评驾科技有限公司 | POI (Point of interest) identification method based on travel track |
| CN114973670A (en) * | 2022-05-23 | 2022-08-30 | 斑马网络技术有限公司 | Method, device and equipment for determining travel |
| CN115080683A (en) * | 2022-08-24 | 2022-09-20 | 天津所托瑞安汽车科技有限公司 | Vehicle journey processing method, device and storage medium |
| CN116486639A (en) * | 2023-06-14 | 2023-07-25 | 眉山环天智慧科技有限公司 | Vehicle supervision method based on remote sensing and Beidou satellite data analysis |
Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5548515A (en) * | 1990-10-09 | 1996-08-20 | Pilley; Harold R. | Method and system for airport control and management |
| JP2009109233A (en) * | 2007-10-26 | 2009-05-21 | Aisin Aw Co Ltd | Map display apparatus and program |
| KR20130039902A (en) * | 2011-10-13 | 2013-04-23 | 최은영 | Travel photo data processing system and method |
| CN103109161A (en) * | 2010-06-22 | 2013-05-15 | 通腾科技股份有限公司 | Navigation device & method |
| US20130179057A1 (en) * | 2012-01-09 | 2013-07-11 | Airbiquity Inc. | Electric vehicle charging network services |
| JP5248696B1 (en) * | 2012-05-25 | 2013-07-31 | 株式会社東芝 | Electronic device, handwritten document creation method, and handwritten document creation program |
| CN104933869A (en) * | 2015-05-19 | 2015-09-23 | 交通运输部公路科学研究所 | Anti-noise type satellite positioning data-based method for identifying vehicle violation behaviors |
| CN104966129A (en) * | 2015-06-08 | 2015-10-07 | 浙江大学 | Method for separating vehicle running track |
| US20150314793A1 (en) * | 2012-12-03 | 2015-11-05 | Audi Ag | Method for traffic-flow-conditioned adaptation of stopping processes to a synthetically modulated speed profile along a route travelled along by a vehicle and control device for carrying out the method |
| US20160187283A1 (en) * | 2013-06-07 | 2016-06-30 | Lifescan Scotland Limited | Electrochemical-based analytical test strip with a soluble electrochemically-active coating opposite a bare electrode |
| KR20170059352A (en) * | 2015-11-20 | 2017-05-30 | (주)블루포인트 | System for Reminding Parking Location, and Vehicle Information Collection Device Suitable for the Same |
| CN108364457A (en) * | 2018-01-31 | 2018-08-03 | 长安大学 | A kind of commercial car method for detecting fatigue driving based on GPS |
| US20180367651A1 (en) * | 2016-02-16 | 2018-12-20 | Huawei Technologies Co., Ltd. | Stream control transmission protocol SCTP-based communications method and system, and appartus |
| CN109195095A (en) * | 2018-09-04 | 2019-01-11 | 广东翼卡车联网服务有限公司 | A kind of driving trace cutting method and device based on vehicle GPS |
| US10246037B1 (en) * | 2018-07-16 | 2019-04-02 | Cambridge Mobile Telematics Inc. | Vehicle telematics of vehicle crashes |
| CN110118976A (en) * | 2019-04-18 | 2019-08-13 | 广州斯沃德科技有限公司 | A kind of driving trace method for drafting, device, terminal device and readable storage medium storing program for executing |
| CN110197588A (en) * | 2019-06-03 | 2019-09-03 | 长安大学 | A kind of truck driving behavior appraisal procedure and device based on GPS track data |
| CN110276954A (en) * | 2019-06-28 | 2019-09-24 | 青岛无车承运服务中心有限公司 | Vehicle driving behavior integration methods of marking based on BEI-DOU position system |
| CN110276953A (en) * | 2019-06-28 | 2019-09-24 | 青岛无车承运服务中心有限公司 | Rule-breaking vehicle travel risk analysis method based on BEI-DOU position system |
| CN110298516A (en) * | 2019-07-04 | 2019-10-01 | 南京行者易智能交通科技有限公司 | A kind of method, apparatus, mobile end equipment and the server of the too long public bus network of fractionation based on passenger flow OD data |
-
2020
- 2020-05-20 CN CN202010432548.9A patent/CN111598347B/en active Active
Patent Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5548515A (en) * | 1990-10-09 | 1996-08-20 | Pilley; Harold R. | Method and system for airport control and management |
| JP2009109233A (en) * | 2007-10-26 | 2009-05-21 | Aisin Aw Co Ltd | Map display apparatus and program |
| CN103109161A (en) * | 2010-06-22 | 2013-05-15 | 通腾科技股份有限公司 | Navigation device & method |
| KR20130039902A (en) * | 2011-10-13 | 2013-04-23 | 최은영 | Travel photo data processing system and method |
| US20130179057A1 (en) * | 2012-01-09 | 2013-07-11 | Airbiquity Inc. | Electric vehicle charging network services |
| JP5248696B1 (en) * | 2012-05-25 | 2013-07-31 | 株式会社東芝 | Electronic device, handwritten document creation method, and handwritten document creation program |
| US20150314793A1 (en) * | 2012-12-03 | 2015-11-05 | Audi Ag | Method for traffic-flow-conditioned adaptation of stopping processes to a synthetically modulated speed profile along a route travelled along by a vehicle and control device for carrying out the method |
| US20160187283A1 (en) * | 2013-06-07 | 2016-06-30 | Lifescan Scotland Limited | Electrochemical-based analytical test strip with a soluble electrochemically-active coating opposite a bare electrode |
| CN104933869A (en) * | 2015-05-19 | 2015-09-23 | 交通运输部公路科学研究所 | Anti-noise type satellite positioning data-based method for identifying vehicle violation behaviors |
| CN104966129A (en) * | 2015-06-08 | 2015-10-07 | 浙江大学 | Method for separating vehicle running track |
| KR20170059352A (en) * | 2015-11-20 | 2017-05-30 | (주)블루포인트 | System for Reminding Parking Location, and Vehicle Information Collection Device Suitable for the Same |
| US20180367651A1 (en) * | 2016-02-16 | 2018-12-20 | Huawei Technologies Co., Ltd. | Stream control transmission protocol SCTP-based communications method and system, and appartus |
| CN108364457A (en) * | 2018-01-31 | 2018-08-03 | 长安大学 | A kind of commercial car method for detecting fatigue driving based on GPS |
| US10246037B1 (en) * | 2018-07-16 | 2019-04-02 | Cambridge Mobile Telematics Inc. | Vehicle telematics of vehicle crashes |
| CN109195095A (en) * | 2018-09-04 | 2019-01-11 | 广东翼卡车联网服务有限公司 | A kind of driving trace cutting method and device based on vehicle GPS |
| CN110118976A (en) * | 2019-04-18 | 2019-08-13 | 广州斯沃德科技有限公司 | A kind of driving trace method for drafting, device, terminal device and readable storage medium storing program for executing |
| CN110197588A (en) * | 2019-06-03 | 2019-09-03 | 长安大学 | A kind of truck driving behavior appraisal procedure and device based on GPS track data |
| CN110276954A (en) * | 2019-06-28 | 2019-09-24 | 青岛无车承运服务中心有限公司 | Vehicle driving behavior integration methods of marking based on BEI-DOU position system |
| CN110276953A (en) * | 2019-06-28 | 2019-09-24 | 青岛无车承运服务中心有限公司 | Rule-breaking vehicle travel risk analysis method based on BEI-DOU position system |
| CN110298516A (en) * | 2019-07-04 | 2019-10-01 | 南京行者易智能交通科技有限公司 | A kind of method, apparatus, mobile end equipment and the server of the too long public bus network of fractionation based on passenger flow OD data |
Non-Patent Citations (2)
| Title |
|---|
| BO ZHAO;MINGTAO NI;PEIRU FAN;: "Powermitter:Data Exfiltration from Air-Gapped Computer through Switching Power Supply", 中国通信, no. 02 * |
| 刘应吉;赵侃;***;夏鸿文;: "基于卫星定位数据的违规驾驶行为辨识方法", 公路交通科技, no. 11 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112153573A (en) * | 2020-09-28 | 2020-12-29 | 平安国际融资租赁有限公司 | Segmentation method and device based on position track, computer equipment and storage medium |
| CN112153573B (en) * | 2020-09-28 | 2023-04-07 | 平安国际融资租赁有限公司 | Segmentation method and device based on position track, computer equipment and storage medium |
| CN112380311A (en) * | 2020-11-27 | 2021-02-19 | 上海评驾科技有限公司 | POI (Point of interest) identification method based on travel track |
| CN112380311B (en) * | 2020-11-27 | 2024-04-02 | 上海评驾科技有限公司 | POI (Point of interest) identification method based on travel track |
| CN114973670A (en) * | 2022-05-23 | 2022-08-30 | 斑马网络技术有限公司 | Method, device and equipment for determining travel |
| CN114973670B (en) * | 2022-05-23 | 2024-04-09 | 斑马网络技术有限公司 | Stroke determination method, device and equipment |
| CN115080683A (en) * | 2022-08-24 | 2022-09-20 | 天津所托瑞安汽车科技有限公司 | Vehicle journey processing method, device and storage medium |
| CN115080683B (en) * | 2022-08-24 | 2022-11-25 | 天津所托瑞安汽车科技有限公司 | Vehicle journey processing method, device and storage medium |
| CN116486639A (en) * | 2023-06-14 | 2023-07-25 | 眉山环天智慧科技有限公司 | Vehicle supervision method based on remote sensing and Beidou satellite data analysis |
| CN116486639B (en) * | 2023-06-14 | 2023-09-29 | 眉山环天智慧科技有限公司 | Vehicle supervision method based on remote sensing and Beidou satellite data analysis |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111598347B (en) | 2024-02-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111598347A (en) | Road transport vehicle ultra-long stroke segmentation optimization method | |
| CN111046049B (en) | Truck GPS track data compression method | |
| CN107590999B (en) | Traffic state discrimination method based on checkpoint data | |
| Borkar et al. | An efficient method to generate ground truth for evaluating lane detection systems | |
| CN112837542B (en) | Method and device for counting traffic volume of highway section, storage medium and terminal | |
| CN111400533B (en) | Image screening method, device, electronic equipment and storage medium | |
| CN114664087B (en) | Method, device, equipment and medium for recognizing up-down high speed of vehicle based on track | |
| CN113553482A (en) | Stay point identification and trip chain construction system, algorithm, equipment and storage medium | |
| CN110443319B (en) | Track duplicate removal method and device and storage medium | |
| CN115862331A (en) | Vehicle travel track reconstruction method considering bayonet network topological structure | |
| CN114997777A (en) | Vehicle movement feature identification method based on track information | |
| CN111425281B (en) | Diesel vehicle refueling or urea adding behavior detection method and system | |
| CN111724607B (en) | Steering lamp use detection method and device, computer equipment and storage medium | |
| CN115841765B (en) | Vehicle position blind area monitoring method and device, electronic equipment and readable storage medium | |
| CN111474565A (en) | Method for judging illegal plugging condition of road transport vehicle satellite positioning system terminal | |
| CN104732062A (en) | On-road user socialization attribute automatic judgment method based on characteristic event, movement behavior, traveling track and geographic position | |
| CN116071726A (en) | Road inspection system and method based on edge calculation | |
| CN115440037A (en) | Traffic flow data acquisition method and device, electronic equipment and storage medium | |
| CN113033713B (en) | Accident fragment identification method, device, equipment and readable storage medium | |
| CN102903235B (en) | Method and device for evaluating quality of real-time road condition | |
| CN113611130A (en) | Method, system and storage medium for acquiring traffic flow of local and transit trucks | |
| CN114494986A (en) | Road scene recognition method and device | |
| CN111369803B (en) | Marginal bayonet detection method and device and computer readable storage medium | |
| CN110544378A (en) | method for judging traffic jam condition of mobile phone user | |
| CN115209342B (en) | Subway driver identification method, system and readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |