CN112184931A - Vehicle charging path optimization system and analysis method based on highway network - Google Patents
Vehicle charging path optimization system and analysis method based on highway network Download PDFInfo
- Publication number
- CN112184931A CN112184931A CN202011043216.8A CN202011043216A CN112184931A CN 112184931 A CN112184931 A CN 112184931A CN 202011043216 A CN202011043216 A CN 202011043216A CN 112184931 A CN112184931 A CN 112184931A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- data
- traffic data
- exit
- charging path
- 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
Images
Classifications
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07B—TICKET-ISSUING APPARATUS; FARE-REGISTERING APPARATUS; FRANKING APPARATUS
- G07B15/00—Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points
- G07B15/06—Arrangements for road pricing or congestion charging of vehicles or vehicle users, e.g. automatic toll systems
-
- G—PHYSICS
- G08—SIGNALLING
- 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
- G08G1/0125—Traffic data processing
-
- G—PHYSICS
- G08—SIGNALLING
- 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
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Business, Economics & Management (AREA)
- Finance (AREA)
- Traffic Control Systems (AREA)
- Devices For Checking Fares Or Tickets At Control Points (AREA)
Abstract
The invention discloses a vehicle charging path optimization system and an analysis method based on a highway network, relating to the analysis of vehicle charging paths of a highway free flow charging network; the system comprises: the vehicle charging path analysis subsystem, the vehicle traffic data acquisition terminal, the vehicle traffic data analysis subsystem and the GIS information processing subsystem are used for carrying out data interaction in an annular mode; the analysis method comprises the following steps: constructing a basic road network topological graph; collecting vehicle information and acquiring vehicle passing data; analyzing the vehicle passing data and classifying the vehicle passing data; screening vehicle passing data according to the optimal weight of the charging path, and drawing a vehicle entrance-exit priority charging path; and drawing the vehicle entrance-exit preferred charging path on a visual map, and completing the vehicle entrance-exit complete preferred charging path. The invention obtains the vehicle charging optimal path in the road network according to the charging weight of each road section, thereby ensuring that the freeway flow charging management is more accurate, real and reliable.
Description
Technical Field
The invention relates to the technical field of vehicle charging path analysis, in particular to a vehicle charging path optimization system and an analysis method based on a highway network.
Background
In the traditional highway toll management, an entrance exit station is arranged between different road sections, a card is taken at an entrance, a card is returned at an exit, and the vehicle passing fee is calculated according to the shortest distance between the two stations.
However, in the freeway toll collection management of the expressway which is being reformed and popularized at present, the traditional stations between different road sections are cancelled, and the traditional stations are converted into an electronic charging mode of whole network communication, so that a real and accurate passing path of a vehicle running in a road network needs to be obtained.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a vehicle charging path optimization system and an analysis method based on a highway network, which obtain the vehicle charging path optimization in the network according to the charging weight of each road section, thereby enabling the highway free flow charging management to be more accurate, real and reliable.
The invention discloses a vehicle charging path optimization system based on a highway network, which comprises the following components:
the GIS information processing subsystem is used for constructing a basic road network topological graph through the geographic position information of road facilities on the expressway;
the vehicle passing data acquisition terminal performs data interaction with the GIS information processing subsystem and is used for acquiring vehicle information and acquiring vehicle passing data;
the vehicle passing data analysis subsystem is in data interaction with the vehicle passing data acquisition terminal and is used for analyzing the vehicle passing data and classifying the vehicle passing data;
the vehicle charging path analysis subsystem performs data interaction with the vehicle traffic data analysis subsystem and the GIS information processing subsystem, and is used for screening vehicle traffic data and drawing a vehicle entrance-exit priority charging path according to the optimal weight of the charging path; and the vehicle entrance-exit preferred charging path is depicted on a GIS information processing subsystem visual map, and the vehicle entrance-longitude-exit complete preferred charging path is completed.
As a further improvement of the invention, the vehicle passing data acquisition terminal is installed on the road facility, and the road facility comprises an entrance toll station, an exit toll station, an entrance section boundary portal frame, an exit section boundary portal frame and a road section middle portal frame.
The invention also discloses a vehicle charging path optimization analysis method based on the vehicle charging path optimization system, which comprises the following steps:
constructing a basic road network topological graph;
collecting vehicle information and acquiring vehicle passing data;
analyzing the vehicle passing data and classifying the vehicle passing data;
screening vehicle passing data according to the optimal weight of the charging path, and drawing a vehicle entrance-exit priority charging path; wherein, the charging path priority weight is shortest distance or least charging;
and drawing the vehicle entrance-exit preferred charging path on a visual map, and completing the vehicle entrance-exit complete preferred charging path.
As a further improvement of the present invention, the constructing of the basic road network topological graph includes:
inputting the space longitude and latitude positioning information of all entrance toll stations, exit toll stations, entrance section boundary gantries, exit section boundary gantries and section middle gantries of the expressway in a GIS space information system;
forming unique nodes of a road network by each entrance toll station, each exit toll station, each entry road section boundary portal frame, each exit road section boundary portal frame and each road section middle portal frame, and constructing a road network topology through the nodes;
according to the single-row running principle that the expressway can not turn around, adjacent nodes form reachable connecting lines;
and drawing a shortest distance priority road network topology and another minimum charging priority road network topology according to the charging path preference weight.
As a further improvement of the present invention, the acquiring vehicle information and obtaining vehicle passing data includes:
collecting characteristic images of vehicles driving through road network nodes, identifying license plates and license plate colors of the vehicles, identifying vehicle types, and recording vehicle passing time, driving direction and road network node types; the road network node types comprise an entrance toll station, an exit toll station, an entrance section boundary portal frame, an exit section boundary portal frame and a road section middle portal frame;
and each road network node outputs a traffic data packet by taking a vehicle as a unit.
As a further improvement of the invention, the vehicle passing data is analyzed and classified; the method comprises the following steps:
converging vehicle traffic data of each road network node, and dividing the vehicle traffic data into two categories of boundary vehicle traffic data and non-boundary vehicle traffic data to obtain a boundary vehicle traffic data set and a non-boundary vehicle traffic data set;
and dividing the boundary vehicle passing data into boundary vehicle passing data and boundary vehicle passing data to obtain an entrance vehicle passing data subset and an exit vehicle passing data subset.
As a further improvement of the invention, according to the preferred weight of the charging path, vehicle passing data is screened, and a vehicle entrance-exit priority charging path is drawn; the method comprises the following steps:
any vehicle passing data is extracted from the boundary vehicle passing data set, and whether the data is entrance vehicle passing data or exit vehicle passing data is judged;
matching the same vehicle traffic data at the egress vehicle traffic data subset using the ingress vehicle traffic data; matching the same vehicle traffic data at the ingress vehicle traffic data subset using the egress vehicle traffic data;
and obtaining a road network node result matched with the traffic data of the vehicles entering and leaving the exit, and describing the preferred charging path of the vehicles entering and leaving the exit according to the priority weight of the charging path.
As a further refinement of the invention, said using the ingress vehicle traffic data, matching the same vehicle traffic data at the egress vehicle traffic data subset; matching the same vehicle traffic data at the ingress vehicle traffic data subset using the egress vehicle traffic data; the method comprises the following steps:
filtering data with inconsistent vehicle information;
filtering data of which the entry time is greater than the exit time or the exit time is less than the entry time;
filtering road network nodes which are inaccessible on GIS spatial information, and driving in a single direction is irreversible;
and filtering data of abnormal running speeds among the road network nodes, and dividing the distance between two road network nodes by the vehicle in-out time difference to obtain the average running speed, wherein the abnormal running speed is abnormal traffic data.
As a further improvement of the present invention, the vehicle entering-exiting preferred charging path is depicted on a visual map, and the vehicle entering-passing-exiting complete preferred charging path is completed; the method comprises the following steps:
screening the non-boundary vehicle traffic data set for matching vehicle traffic data using ingress-egress vehicle traffic data matching vehicle traffic data;
and supplementing segment road section nodes of the vehicle entering-exiting preferred charging path by using the screened matched correct vehicle passing data to obtain a complete vehicle entering-passing-exiting preferred charging path.
As a further refinement of the present invention, the screening of the non-boundary vehicle traffic data set for matching vehicle traffic data using matched vehicle traffic data of ingress-egress vehicle traffic data; the method comprises the following steps:
filtering data with inconsistent vehicle information;
filtering data of which the passing time is not in the in-out time period or data of which the out-time is less than the in-time;
the screened data are arranged in an ascending order according to the time from a single road network node to an entrance road network node, and a data sequence is obtained;
extracting a first piece of data of each road network node data sequence from the data sequences to form a road network node data sequence;
in the road network node data sequence, filtering road network nodes which are inaccessible on GIS spatial information, and driving in a unidirectional mode is irreversible;
and checking data of abnormal running speed, and dividing the distance between two network nodes by the vehicle in-out time difference to obtain the average running speed, wherein the abnormal running speed is abnormal traffic data.
Compared with the prior art, the invention has the beneficial effects that:
the invention constructs the highway toll road network on the GIS spatial information system visual map, outputs the optimal charging path of the passing vehicle in the road network, and has the advantages of analysis optimization, integrity and reliability of the vehicle charging path.
Drawings
FIG. 1 is a block diagram of a preferred system for vehicle billing path based on highway network according to an embodiment of the present invention;
fig. 2 is a flowchart of a preferred analysis method for vehicle charging paths based on a highway network according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The invention is described in further detail below with reference to the attached drawing figures:
as shown in fig. 1, the present invention provides a vehicle charging path optimization system based on a highway network, comprising: the system comprises a vehicle charging path analysis subsystem, a vehicle traffic data acquisition terminal, a vehicle traffic data analysis subsystem and a GIS information processing subsystem; wherein:
the GIS information processing subsystem is used for constructing a basic road network topological graph through the geographic position information of road facilities on the expressway; the road facility comprises an entrance toll station, an exit toll station, an entrance section boundary portal frame, an exit section boundary portal frame and a road section middle portal frame; the specific construction method comprises the following steps:
inputting the space longitude and latitude positioning information of all entrance toll stations, exit toll stations, entrance section boundary gantries, exit section boundary gantries and section middle gantries of the expressway in a GIS space information system; forming unique nodes of a road network by each entrance toll station, each exit toll station, each entry road section boundary portal frame, each exit road section boundary portal frame and each road section middle portal frame, and constructing a road network topology through the nodes; according to the single-row running principle that the expressway can not turn around, adjacent nodes form reachable connecting lines; and drawing a shortest distance priority road network topology and another minimum charging priority road network topology according to the charging path preference weight.
The vehicle traffic data acquisition terminal and the GIS information processing subsystem carry out data interaction through a set protocol and are used for acquiring vehicle information and acquiring vehicle traffic data; the vehicle passing data acquisition terminal is installed on road facilities. The specific implementation method comprises the following steps:
collecting characteristic images of vehicles driving through road network nodes, identifying license plates and license plate colors of the vehicles, identifying vehicle types, and recording vehicle passing time, driving direction and road network node types; the road network node types comprise an entrance toll station, an exit toll station, an entrance section boundary portal frame, an exit section boundary portal frame and a road section middle portal frame; and each road network node outputs a traffic data packet by taking a vehicle as a unit.
The vehicle traffic data analysis subsystem and the vehicle traffic data acquisition terminal perform data interaction through a set protocol and are used for analyzing vehicle traffic data and classifying the vehicle traffic data; the specific implementation method comprises the following steps:
converging vehicle traffic data of each road network node, and dividing the vehicle traffic data into two categories of boundary vehicle traffic data and non-boundary vehicle traffic data to obtain a boundary vehicle traffic data set and a non-boundary vehicle traffic data set; and dividing the boundary vehicle passing data into boundary vehicle passing data and boundary vehicle passing data to obtain an entrance vehicle passing data subset and an exit vehicle passing data subset.
The vehicle charging path analysis subsystem, the vehicle traffic data analysis subsystem and the GIS information processing subsystem carry out data interaction through a set protocol and are used for screening vehicle traffic data and drawing a vehicle entrance-exit priority charging path according to the optimal weight of the charging path; and depicting the vehicle entrance-exit preferred charging path on a GIS information processing subsystem visual map, and completing the vehicle entrance-longitude-exit complete preferred charging path. Wherein the content of the first and second substances,
the specific method for drawing the vehicle entrance-exit priority charging path comprises the following steps:
any vehicle passing data is extracted from the boundary vehicle passing data set, and whether the data is entrance vehicle passing data or exit vehicle passing data is judged; matching the same vehicle traffic data at the egress vehicle traffic data subset using the ingress vehicle traffic data; matching the same vehicle traffic data at the ingress vehicle traffic data subset using the egress vehicle traffic data; and obtaining a road network node result matched with the traffic data of the vehicles entering and leaving the exit, and describing the preferred charging path of the vehicles entering and leaving the exit according to the priority weight of the charging path.
The concrete method for completing the vehicle entering-passing-exiting complete preferred charging path comprises the following steps: screening the non-boundary vehicle traffic data set for matched vehicle traffic data using the matched vehicle traffic data of the ingress-egress vehicle traffic data; and supplementing segment road section nodes of the vehicle entering-exiting preferred charging path by using the screened matched correct vehicle passing data to obtain the vehicle entering-passing-exiting complete preferred charging path.
As shown in fig. 2, the present invention also discloses a vehicle charging path optimization analysis method based on the vehicle charging path optimization system, which includes:
step 1, constructing a basic road network topological graph;
the method specifically comprises the following steps:
step 11, inputting the space longitude and latitude positioning information of all entrance toll stations, exit toll stations, entrance section boundary gantries, exit section boundary gantries and road section middle gantries of the expressway in a GIS (geographic information System);
step 12, forming unique nodes of a road network by each entrance toll station, each exit toll station, each entrance section boundary portal frame, each exit section boundary portal frame and each road section middle portal frame, and constructing a road network topology through the nodes;
step 13, according to a single-row driving principle that the expressway can not turn around, adjacent nodes form reachable connecting lines;
and step 14, drawing a shortest distance priority road network topology and another shortest charging priority road network topology according to the charging path preference weight.
Step 2, collecting vehicle information and acquiring vehicle passing data;
the method specifically comprises the following steps:
step 21: the intelligent vehicle passing data acquisition device is arranged on each road network node and is used for acquiring vehicle characteristic images of vehicles passing through the road network nodes, identifying license plates and license plate colors of the vehicles, identifying vehicle types, and recording vehicle passing time, driving directions and road network node types (entrance toll stations, exit toll stations, boundary portal frames of road sections and intermediate portal frames of road sections);
step 22: and each road network node outputs a traffic data packet by taking a vehicle as a unit.
Step 3, analyzing the vehicle passing data and classifying the vehicle passing data;
the method specifically comprises the following steps:
step 31, converging vehicle traffic data of each road network node, and dividing the vehicle traffic data into two categories of boundary vehicle traffic data and non-boundary vehicle traffic data to obtain a boundary vehicle traffic data set and a non-boundary vehicle traffic data set;
and 32, dividing the boundary vehicle passing data into boundary vehicle passing data and boundary vehicle passing data to obtain an entrance vehicle passing data subset and an exit vehicle passing data subset.
Step 4, screening vehicle passing data according to the optimal weight of the charging path, and drawing a vehicle entrance-exit priority charging path; wherein, the priority weight of the charging path is the shortest distance or the least charging;
the method specifically comprises the following steps:
step 41, extracting any vehicle passing data from the boundary vehicle passing data set, and judging whether the vehicle passing data is entrance vehicle passing data or exit vehicle passing data;
step 42, matching the same vehicle passing data in the exit vehicle passing data subset by using the entrance vehicle passing data; matching the same vehicle traffic data at the ingress vehicle traffic data subset using the egress vehicle traffic data; wherein the content of the first and second substances,
further comprising:
step 421, filtering data (license plate number, license plate color and vehicle type) with inconsistent vehicle information;
step 422, filtering data of which the entry time is greater than the exit time or data of which the exit time is less than the entry time;
step 423, filtering the inaccessible road network nodes on the GIS spatial information, and driving in a single direction irreversibly;
step 424, filtering data of abnormal driving speeds among road network nodes, dividing the distance between two road network nodes by the vehicle in-out time difference to obtain the average driving speed, wherein the abnormal driving speeds are abnormal traffic data and can be filtered;
and 43, obtaining a road network node result matched with the traffic data of the vehicles entering and leaving the entrance and exit, and describing a preferred charging path of the vehicles entering and leaving the entrance and exit according to the priority weight of the charging path.
Step 5, drawing the vehicle entrance-exit preferred charging path on a visual map, and completing the vehicle entrance-exit complete preferred charging path;
the method specifically comprises the following steps:
step 51, screening matched vehicle passing data in a non-boundary vehicle passing data set by using vehicle passing data matched with the vehicle passing data of the entrance-exit;
further comprising:
step 511, filtering data (license plate number, license plate color and vehicle type) with inconsistent vehicle information;
step 512, filtering data with the passage time not in the in-out time period or data with the out-out time less than the in-out time;
the data screened in the step 513 and the step 512 are arranged in an ascending order according to the time from the single road network node to the entrance road network node;
step 514, in the step 513, extracting a first piece of data of each road network node data sequence to form a road network node data sequence;
step 515, in the data sequence of step 514, filtering the inaccessible road network nodes on the GIS spatial information, and driving in a unidirectional mode is irreversible;
step 516, checking abnormal data of the running vehicle speed, and dividing the distance between two network nodes by the vehicle in-out time difference to obtain the average running vehicle speed, wherein the abnormal data of the vehicle speed is abnormal traffic data and can be filtered;
and step 52, supplementing segment road section nodes of the vehicle entering-exiting preferred charging path by using the screened matched correct vehicle passing data, and obtaining a complete vehicle entering-passing-exiting preferred charging path.
The invention has the advantages that:
the invention constructs the highway toll road network on the GIS spatial information system visual map, outputs the optimal charging path of the passing vehicle in the road network, and has the advantages of analysis optimization, integrity and reliability of the vehicle charging path.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A vehicle charging path optimization system based on a highway network is characterized by comprising the following components:
the GIS information processing subsystem is used for constructing a basic road network topological graph through the geographic position information of road facilities on the expressway;
the vehicle passing data acquisition terminal performs data interaction with the GIS information processing subsystem and is used for acquiring vehicle information and acquiring vehicle passing data;
the vehicle passing data analysis subsystem is in data interaction with the vehicle passing data acquisition terminal and is used for analyzing the vehicle passing data and classifying the vehicle passing data;
the vehicle charging path analysis subsystem performs data interaction with the vehicle traffic data analysis subsystem and the GIS information processing subsystem, and is used for screening vehicle traffic data and drawing a vehicle entrance-exit priority charging path according to the optimal weight of the charging path; and the vehicle entrance-exit preferred charging path is depicted on a GIS information processing subsystem visual map, and the vehicle entrance-longitude-exit complete preferred charging path is completed.
2. The vehicle billing path optimization system of claim 1 wherein the vehicle transit data collection terminal is mounted on the roadway facility, the roadway facility including an entrance toll booth, an exit toll booth, an entrance roadway boundary portal, an exit roadway boundary portal, and a road section intermediate portal.
3. A vehicle charging path preference analysis method based on the vehicle charging path preference system according to any one of claims 1 to 2, comprising:
constructing a basic road network topological graph;
collecting vehicle information and acquiring vehicle passing data;
analyzing the vehicle passing data and classifying the vehicle passing data;
screening vehicle passing data according to the optimal weight of the charging path, and drawing a vehicle entrance-exit priority charging path; wherein, the charging path priority weight is shortest distance or least charging;
and drawing the vehicle entrance-exit preferred charging path on a visual map, and completing the vehicle entrance-exit complete preferred charging path.
4. The method according to claim 3, wherein the constructing a basic road network topology map comprises:
inputting the space longitude and latitude positioning information of all entrance toll stations, exit toll stations, entrance section boundary gantries, exit section boundary gantries and section middle gantries of the expressway in a GIS space information system;
forming unique nodes of a road network by each entrance toll station, each exit toll station, each entry road section boundary portal frame, each exit road section boundary portal frame and each road section middle portal frame, and constructing a road network topology through the nodes;
according to the single-row running principle that the expressway can not turn around, adjacent nodes form reachable connecting lines;
and drawing a shortest distance priority road network topology and another minimum charging priority road network topology according to the charging path preference weight.
5. The method for analyzing vehicle billing path preference according to claim 4, wherein the collecting vehicle information and obtaining vehicle traffic data comprises:
collecting characteristic images of vehicles driving through road network nodes, identifying license plates and license plate colors of the vehicles, identifying vehicle types, and recording vehicle passing time, driving direction and road network node types; the road network node types comprise an entrance toll station, an exit toll station, an entrance section boundary portal frame, an exit section boundary portal frame and a road section middle portal frame;
and each road network node outputs a traffic data packet by taking a vehicle as a unit.
6. The vehicle billing path preference analysis method of claim 5 wherein the analyzing the vehicle traffic data classifies the vehicle traffic data; the method comprises the following steps:
converging vehicle traffic data of each road network node, and dividing the vehicle traffic data into two categories of boundary vehicle traffic data and non-boundary vehicle traffic data to obtain a boundary vehicle traffic data set and a non-boundary vehicle traffic data set;
and dividing the boundary vehicle passing data into boundary vehicle passing data and boundary vehicle passing data to obtain an entrance vehicle passing data subset and an exit vehicle passing data subset.
7. The vehicle charging path preference analysis method according to claim 6, wherein the vehicle traffic data is screened according to the charging path preference weight, and a vehicle entrance-exit priority charging path is drawn; the method comprises the following steps:
any vehicle passing data is extracted from the boundary vehicle passing data set, and whether the data is entrance vehicle passing data or exit vehicle passing data is judged;
matching the same vehicle traffic data at the egress vehicle traffic data subset using the ingress vehicle traffic data; matching the same vehicle traffic data at the ingress vehicle traffic data subset using the egress vehicle traffic data;
and obtaining a road network node result matched with the traffic data of the vehicles entering and leaving the exit, and describing the preferred charging path of the vehicles entering and leaving the exit according to the priority weight of the charging path.
8. The vehicle billing path preference analysis method of claim 7 wherein the using the ingress vehicle traffic data matches the same vehicle traffic data at the egress vehicle traffic data subset; matching the same vehicle traffic data at the ingress vehicle traffic data subset using the egress vehicle traffic data; the method comprises the following steps:
filtering data with inconsistent vehicle information;
filtering data of which the entry time is greater than the exit time or the exit time is less than the entry time;
filtering road network nodes which are inaccessible on GIS spatial information, and driving in a single direction is irreversible;
and filtering data of abnormal running speeds among the road network nodes, and dividing the distance between two road network nodes by the vehicle in-out time difference to obtain the average running speed, wherein the abnormal running speed is abnormal traffic data.
9. The vehicle charging path preferred analysis method according to claim 7, wherein the vehicle entry-exit preferred charging path is depicted on a visual map, and a vehicle entry-via-exit complete preferred charging path is completed; the method comprises the following steps:
screening the non-boundary vehicle traffic data set for matching vehicle traffic data using ingress-egress vehicle traffic data matching vehicle traffic data;
and supplementing segment road section nodes of the vehicle entering-exiting preferred charging path by using the screened matched correct vehicle passing data to obtain a complete vehicle entering-passing-exiting preferred charging path.
10. The vehicle billing path preference analysis method of claim 9 wherein the matched vehicle traffic data is filtered in the non-boundary vehicle traffic data set using ingress-egress vehicle traffic data matched vehicle traffic data; the method comprises the following steps:
filtering data with inconsistent vehicle information;
filtering data of which the passing time is not in the in-out time period or data of which the out-time is less than the in-time;
the screened data are arranged in an ascending order according to the time from a single road network node to an entrance road network node, and a data sequence is obtained;
extracting a first piece of data of each road network node data sequence from the data sequences to form a road network node data sequence;
in the road network node data sequence, filtering road network nodes which are inaccessible on GIS spatial information, and driving in a unidirectional mode is irreversible;
and checking data of abnormal running speed, and dividing the distance between two network nodes by the vehicle in-out time difference to obtain the average running speed, wherein the abnormal running speed is abnormal traffic data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011043216.8A CN112184931B (en) | 2020-09-28 | 2020-09-28 | Vehicle charging path optimization system and analysis method based on highway network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011043216.8A CN112184931B (en) | 2020-09-28 | 2020-09-28 | Vehicle charging path optimization system and analysis method based on highway network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112184931A true CN112184931A (en) | 2021-01-05 |
| CN112184931B CN112184931B (en) | 2022-09-13 |
Family
ID=73946842
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011043216.8A Active CN112184931B (en) | 2020-09-28 | 2020-09-28 | Vehicle charging path optimization system and analysis method based on highway network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112184931B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112925820A (en) * | 2021-02-02 | 2021-06-08 | 重庆首讯科技股份有限公司 | Method, device and system for identifying vehicle evasion toll |
| CN113096389A (en) * | 2021-03-23 | 2021-07-09 | 北京交通大学 | Multi-source data-based national highway network topology construction method |
| CN114758493A (en) * | 2022-03-21 | 2022-07-15 | 山东省交通规划设计院集团有限公司 | Expressway traffic flow monitoring method and system based on data fusion |
| CN115273258A (en) * | 2022-07-06 | 2022-11-01 | 太极计算机股份有限公司 | High-speed fee calculation method, device and system |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002035477A1 (en) * | 2000-10-27 | 2002-05-02 | Vodafone Holding Gmbh | Method for charging tolls |
| CN101859312A (en) * | 2010-04-20 | 2010-10-13 | 长安大学 | Highway network topological structure data model and path calculation method |
| CN107644460A (en) * | 2017-10-11 | 2018-01-30 | 广州交嵌信息技术有限公司 | Freeway path identifying system and method based on cloud |
| CN108986241A (en) * | 2018-06-14 | 2018-12-11 | 北京诚达交通科技有限公司 | A kind of highway driving Path Recognition monitoring system and method |
| CN109506669A (en) * | 2018-12-28 | 2019-03-22 | 斑马网络技术有限公司 | Dynamic path planning method, device, system and storage medium |
| CN110634194A (en) * | 2019-10-16 | 2019-12-31 | 江苏量动信息科技有限公司 | Vehicle passing charging method, charging terminal equipment and charging system |
| CN110992499A (en) * | 2019-11-20 | 2020-04-10 | 北京握奇智能科技有限公司 | High-speed charging method and system based on block chain |
| CN110991442A (en) * | 2019-09-02 | 2020-04-10 | 南京感动科技有限公司 | High-accuracy highway license plate cloud identification method |
| CN111044058A (en) * | 2018-10-11 | 2020-04-21 | 北京嘀嘀无限科技发展有限公司 | Route planning method, route planning device, computer device, and storage medium |
| CN111063042A (en) * | 2019-12-23 | 2020-04-24 | 湖北省交通科学研究所 | Highway free flow toll collection path fitting system based on big data |
| CN111292533A (en) * | 2020-02-11 | 2020-06-16 | 北京交通大学 | Method for estimating flow of arbitrary section of highway at any time period based on multi-source data |
| CN111710166A (en) * | 2020-08-20 | 2020-09-25 | 南京感动科技有限公司 | Path fitting method based on ETC portal frame |
-
2020
- 2020-09-28 CN CN202011043216.8A patent/CN112184931B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002035477A1 (en) * | 2000-10-27 | 2002-05-02 | Vodafone Holding Gmbh | Method for charging tolls |
| CN101859312A (en) * | 2010-04-20 | 2010-10-13 | 长安大学 | Highway network topological structure data model and path calculation method |
| CN107644460A (en) * | 2017-10-11 | 2018-01-30 | 广州交嵌信息技术有限公司 | Freeway path identifying system and method based on cloud |
| CN108986241A (en) * | 2018-06-14 | 2018-12-11 | 北京诚达交通科技有限公司 | A kind of highway driving Path Recognition monitoring system and method |
| CN111044058A (en) * | 2018-10-11 | 2020-04-21 | 北京嘀嘀无限科技发展有限公司 | Route planning method, route planning device, computer device, and storage medium |
| CN109506669A (en) * | 2018-12-28 | 2019-03-22 | 斑马网络技术有限公司 | Dynamic path planning method, device, system and storage medium |
| CN110991442A (en) * | 2019-09-02 | 2020-04-10 | 南京感动科技有限公司 | High-accuracy highway license plate cloud identification method |
| CN110634194A (en) * | 2019-10-16 | 2019-12-31 | 江苏量动信息科技有限公司 | Vehicle passing charging method, charging terminal equipment and charging system |
| CN110992499A (en) * | 2019-11-20 | 2020-04-10 | 北京握奇智能科技有限公司 | High-speed charging method and system based on block chain |
| CN111063042A (en) * | 2019-12-23 | 2020-04-24 | 湖北省交通科学研究所 | Highway free flow toll collection path fitting system based on big data |
| CN111292533A (en) * | 2020-02-11 | 2020-06-16 | 北京交通大学 | Method for estimating flow of arbitrary section of highway at any time period based on multi-source data |
| CN111710166A (en) * | 2020-08-20 | 2020-09-25 | 南京感动科技有限公司 | Path fitting method based on ETC portal frame |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112925820A (en) * | 2021-02-02 | 2021-06-08 | 重庆首讯科技股份有限公司 | Method, device and system for identifying vehicle evasion toll |
| CN113096389A (en) * | 2021-03-23 | 2021-07-09 | 北京交通大学 | Multi-source data-based national highway network topology construction method |
| CN113096389B (en) * | 2021-03-23 | 2022-05-17 | 北京交通大学 | Multi-source data-based national highway network topology construction method |
| CN114758493A (en) * | 2022-03-21 | 2022-07-15 | 山东省交通规划设计院集团有限公司 | Expressway traffic flow monitoring method and system based on data fusion |
| CN114758493B (en) * | 2022-03-21 | 2024-03-12 | 山东省交通规划设计院集团有限公司 | Expressway traffic flow monitoring method and system based on data fusion |
| CN115273258A (en) * | 2022-07-06 | 2022-11-01 | 太极计算机股份有限公司 | High-speed fee calculation method, device and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112184931B (en) | 2022-09-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112184931B (en) | Vehicle charging path optimization system and analysis method based on highway network | |
| US9564048B2 (en) | Origin destination estimation based on vehicle trajectory data | |
| CN101965601B (en) | Driving support device and driving support method | |
| KR100820467B1 (en) | a traffic estimating system and the method considered road type | |
| CN110264734A (en) | A kind of vehicle on highway auditing system and its working method | |
| WO2021082464A1 (en) | Method and device for predicting destination of vehicle | |
| CN112200351A (en) | Urban area passenger flow volume prediction method based on mobile phone signaling data | |
| CN113570864B (en) | Method and device for matching running path of electric bicycle and storage medium | |
| CN109872533A (en) | A kind of lane grade real-time traffic information processing method based on spatial data | |
| CN110807926B (en) | Road impedance prediction method and device based on hybrid traffic | |
| CN105869402B (en) | Express highway section speed modification method based on polymorphic type floating car data | |
| CN110969861B (en) | Vehicle identification method, device, equipment and computer storage medium | |
| CN114898571A (en) | ETC big data-based highway all-section vehicle speed measuring method | |
| Tsubota et al. | Comparative analysis of traffic state estimation: cumulative counts-based and trajectory-based methods | |
| CN114446064A (en) | Method, device, storage medium and terminal for analyzing traffic of expressway service area | |
| CN115797403A (en) | Traffic accident prediction method and device, storage medium and electronic device | |
| CN110675631A (en) | Traffic flow traceability analysis method and system | |
| Růžička et al. | Big data application for urban transport solutions | |
| Elleuch et al. | Towards an efficient traffic congestion prediction method based on neural networks and big GPS data | |
| CN110211380B (en) | Multisource traffic data fusion expressway congestion interval detection method | |
| Gong et al. | Estimating link travel time with sparse GPS data on highway corridors | |
| CN115294791A (en) | Intelligent traffic guidance system for smart city | |
| CN113345238A (en) | Traffic jam analysis system based on urban public transport network perception | |
| CN109409731B (en) | Highway holiday travel feature identification method fusing section detection traffic data and crowdsourcing data | |
| Jomrich | Dynamic maps for highly automated driving-generation, distribution and provision |
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 |