CN108020235A - Method and system based on taxi empirical data dynamic access path weight value - Google Patents
Method and system based on taxi empirical data dynamic access path weight value Download PDFInfo
- Publication number
- CN108020235A CN108020235A CN201711069850.7A CN201711069850A CN108020235A CN 108020235 A CN108020235 A CN 108020235A CN 201711069850 A CN201711069850 A CN 201711069850A CN 108020235 A CN108020235 A CN 108020235A
- Authority
- CN
- China
- Prior art keywords
- node
- grid
- taxi
- path
- period
- 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
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
Abstract
A kind of path setting method and system based on taxi empirical data, including：First acquisition module obtains default node and all taxi car datas；Sort module will hire out car data classification according to the period；Second acquisition module obtains the routing information that a taxi described in a period passes through the default node；One period of first statistical module counts all taxis pass through the routing information collection in path between two neighboring node；Second statistical module counts all periods all taxis pass through the routing information collection in path between two neighboring node；Computing module calculates the correspondence weights in path according to routing information collection；First memory module is according to the corresponding weights in path described in period classification storage and the path.
Description
This case is using the applying date as 2015-07-27, and Application No. 2015104441915 is entitled《Passed through based on taxi
Test the path setting method and system of data》Patent application be female case divisional application.
Technical field
The present invention relates to field of electronic navigation, more particularly to a kind of path setting method based on taxi empirical data and
System.
Background technology
With the development and improvement of living standard of science and technology, path navigation becomes the indispensable step of people's trip,
Traditional air navigation aid generally use obtains initial position and target location, choose according to the map initial position and target location away from
From nearest route as navigation way, however, under the road conditions of big city complexity, distance is no longer that simple navigation considers.
The patent document of Application No. 201010566504.1 discloses a kind of guidance method, mobile terminal and road conditions
Navigation server, is grouped according to the road conditions request message of the location data of more than one mobile terminal and destination mobile terminal
Fusion calculation, obtains position data, bearing data and the speed identification information of more than one mobile terminal group to be shown and sends
To destination mobile terminal；Entire road is subdivided into the set in multiple sections by this method, gathers each section in road respectively
Specific road conditions.
But same path is simply subdivided into multiple sections by such scheme, more navigation way choosings are not provided with
Select, and many section road conditions of same path are similar, segment multiple sections and analysis is calculated to each section increase some and do not have
Necessary workload.
In addition, under the road conditions of city complexity, people are gone on a journey by the way of taxi mostly, and experienced taxi
Driver most will appreciate that the jam situation of road conditions, and can find most quick and unobstructed path, if taxi driver can be made full use of
Experience guidance path is set, for helping the user such as private car fast to arrive at and improves the road conditions tool of urban road
It is significant.
The content of the invention
The technical problems to be solved by the invention are：Under the road conditions of city complexity, not only comprehensive but also rational path is set,
Reliable data are provided for navigation.
In order to solve the above-mentioned technical problem, the technical solution adopted by the present invention is：
A kind of path setting method based on taxi empirical data, including：
Obtain default node and all taxi car datas；
Car data classification will be hired out according to the period；
Obtain the routing information that a taxi described in a period passes through the default node；
Count routing information collection of a period all taxis by path between two neighboring node；
Count routing information collection of all taxis of all periods by path between two neighboring node；
The correspondence weights in path are calculated according to routing information collection；
According to the corresponding weights in path described in period classification storage and the path.
The beneficial effect of the above-mentioned path setting method based on taxi empirical data is：According to the period by taxi
Data classify and respectively obtain each period taxi pass through two neighboring default node routing information collection, and to it is each when
Between the routing information collection of section counted so that the routing information finally obtained is representative, can represent different time sections
Road conditions, calculate corresponding weights further according to the routing information collection of statistics so that each weights reflects a kind of path letter
Breath, each paths can learn its road conditions by weight computing, and the corresponding weights in last classification storage path and path are
Navigation way selection provides reliable data.
A kind of path based on taxi empirical data sets system, including：
First acquisition module, for obtaining default node and all taxi car datas；
Sort module, for car data classification will to be hired out according to the period；
Second acquisition module, the routing information of the default node is passed through for obtaining a taxi described in a period；
First statistical module, the path in path between two neighboring node is passed through for counting a period all taxis
Information collection；
Second statistical module, for counting road of all taxis of all periods by path between two neighboring node
Footpath information collection；
Computing module, for calculating the correspondence weights in path according to routing information collection；
First memory module, for according to the corresponding weights in path described in period classification storage and the path.
The above-mentioned path based on taxi empirical data sets the beneficial effect of system to be：First acquisition module obtains pre-
If node and taxi car data, set for path and provide data basis；Sort module is classified on a time period by car data is hired out,
And taxi in a period is obtained by the second acquisition module and, by the routing information of two neighboring default node, obtains one
The routing information of a period, and the routing information collection of each period is obtained through the first statistical module and the second statistical module,
So that the routing information finally obtained is representative, road conditions in different time periods can be represented, computing module is according to statistics
Routing information collection calculates corresponding weights so that each weights reflects a kind of routing information, and each paths can
Its road conditions is learnt by weight computing, the corresponding weights in memory module classification storage path and path, carry for navigation way selection
Reliable data are supplied.
Brief description of the drawings
Fig. 1 is the flow chart of path setting method of the embodiment of the present invention one based on taxi empirical data；
Fig. 2 is the flow of the default node of path setting method of the embodiment of the present invention one based on taxi empirical data
Figure；
Fig. 3 is the structure diagram that path of the embodiment of the present invention two based on taxi empirical data sets system.
Label declaration：
11st, the 3rd acquisition module；12nd, first module is chosen；13rd, the 4th acquisition module；14th, second module is chosen；15th,
Three choose module；16th, the second memory module；2nd, the first acquisition module；3rd, sort module；4th, the second acquisition module；41st, the 5th obtain
Modulus block；42nd, processing module；43rd, logging modle；5th, the first statistical module；6th, the second statistical module；7th, computing module；8th,
One memory module.
Embodiment
For the technology contents that the present invention will be described in detail, the objects and the effects, below in conjunction with embodiment and coordinate attached
Figure is explained.
The design of most critical of the present invention is：According to the period to hiring out car data classification, obtained according to car data is hired out
Routing information collection between the two neighboring node of different time sections, and according to the correspondence weights in routing information collection calculating path.
Explanation of technical terms of the present invention：
Fig. 1 and Fig. 2 is refer to,
A kind of path setting method based on taxi empirical data, including：
S2, obtain default node and all taxi car datas；
S3, according to the period will hire out car data classification；
S4, obtain the routing information that a taxi described in a period passes through the default node；
S5, one period of statistics all taxis pass through the routing information collection in path between two neighboring node；
S6, all period all taxis of statistics pass through the routing information collection in path between two neighboring node；
S7, the correspondence weights according to routing information collection calculating path；
S8, according to the corresponding weights in path described in period classification storage and the path.
As can be seen from the above description, the beneficial effect of the path setting method of the invention based on taxi empirical data is：
According to the period will hire out car data classify and respectively obtain each period taxi by two neighboring default node road
Footpath information collection, and the routing information collection of each period is counted so that the routing information finally obtained is representative,
Road conditions in different time periods can be represented, calculate corresponding weights further according to the routing information collection of statistics so that each weights
A kind of routing information is all reflected, each paths can learn its road conditions, last classification storage path by weight computing
Weights corresponding with path provide reliable data for navigation way selection.
Further, described " default node " specifically default step is：
S11, obtain map datum and hire out car data, and map is divided equally into basic grid；
S12, according to the throughput of basic grid taxi obtain node grid to be chosen；
S13, obtain map datum, and map is averagely divided into the number grid fewer than basic grid number；
S14, according to the number of node grid to be chosen in grid obtain node grid；
S15, according to the POI of node grid and map obtain default node；
S16, the default node of storage.
Seen from the above description, node grid to be chosen is obtained according to the throughput of taxi, it is obstructed reduces taxi
The area data crossed or seldom passed through handles the workload brought and avoids the wasting of resources, according to node to be chosen in big grid
The number of grid obtains node grid so that and node density is reasonably distributed, and default node is obtained in combination with the POI of map, from
And default node and map POI can be corresponded to, easy to find.
Further, described " obtaining the routing information that a taxi described in a period passes through the default node " is specific
For：
Obtain the data that a taxi passes through two neighboring node within a period；
The taxi is handled to pass through the data of two neighboring default node within the period and obtain two neighboring pre-
If node path information；
The taxi is recorded within the period by the routing information between two neighboring node.
Seen from the above description, data and processing of the taxi by two neighboring node within a period, energy are obtained
Access the routing information of two neighboring node in the period.
Further, described " obtaining node grid according to the number of node grid to be chosen in grid " is specially：
Grid of the number of node grid to be chosen less than 1 is removed；
Using grid of the number of node grid to be chosen equal to 1 as node grid；
The number of node grid to be chosen is more than to 1 grid quartering preset times；
Using the grid to be chosen of taxi throughput maximum in the grid of quartering preset times as node grid.
As can be seen from the above description, node grid is obtained according to the number of node grid to be chosen so that node density is distributed
Rationally.
Further, described " obtaining default node according to the POI of node grid and map " is specially：
Using the center of road in node grid of the POI number less than 1 as default node；
Using the POI in node grid of the POI number equal to 1 as default node；
Using POI nearest from node grid center in node grid of the POI number more than 1 as default node.
As can be seen from the above description, it is corresponding with the POI of map to preset node, easy to find.
It refer to Fig. 3,
A kind of path based on taxi empirical data sets system, including：
First acquisition module 2, for obtaining default node and all taxi car datas；
Sort module 3, for car data classification will to be hired out according to the period；
Second acquisition module 4, the routing information of the default node is passed through for obtaining a taxi described in a period；
First statistical module 5, the road in path between two neighboring node is passed through for counting a period all taxis
Footpath information collection；
Second statistical module 6, for counting all taxis of all periods by path between two neighboring node
Routing information collection；
Computing module 7, for calculating the correspondence weights in path according to routing information collection；
First memory module 8, for according to the corresponding weights in path described in period classification storage and the path.
The above-mentioned path based on taxi empirical data sets the beneficial effect of system to be：First acquisition module 2 obtains
Default node and taxi car data, set for path and provide data basis；Sort module 3 is divided on a time period by car data is hired out
Class, and taxi in a period is obtained by the second acquisition module 4 and, by the routing information of two neighboring default node, is obtained
To the routing information of a period, and obtain the path of each period through the first statistical module 5 and the second statistical module 6 and believe
Breath collection so that the routing information finally obtained is representative, can represent road conditions in different time periods, 7 basis of computing module
The routing information collection of statistics calculates corresponding weights so that each weights reflects a kind of routing information, each paths
Its road conditions, 8 classification storage path of memory module and the corresponding weights in path can be learnt by weight computing, are navigation way
Selection provides reliable data.
Further, the path based on taxi empirical data sets system to further include：
3rd acquisition module 11, for obtaining map datum and hiring out car data, by map partitioning into basic grid；
First chooses module 12, for obtaining node grid to be chosen according to the throughput of basic grid taxi；
4th acquisition module 13, is the number net fewer than basic grid number by map partitioning for obtaining map datum
Lattice；
Second chooses module 14, for obtaining node grid according to the number of node grid to be chosen in grid；
3rd chooses module 15, for obtaining default node according to the POI of node grid and map；
Second memory module 16, for storing default node.
As can be seen from the above description, the first selection module obtains node grid to be chosen according to the throughput of taxi, reduces
Taxi not by or the workload brought of the area data processing that seldom passes through and avoid the wasting of resources, second chooses mould
Root tuber obtains node grid according to the number of node grid to be chosen in grid so that node density is reasonably distributed, while the 3rd choosing
The POI that modulus agllutination closes map obtains default node, so that default node and map POI can be corresponded to, easy to find.
It is further, described that " the second acquisition module 4 " includes：
5th acquisition module 41, for obtaining a taxi within a period by the data of two neighboring node；
Processing module 42, passes through the data of two neighboring default node simultaneously for handling the taxi within the period
Obtain two neighboring default node path information；
Logging modle 43, the path passed through for recording the taxi within the period between two neighboring node are believed
Breath.
Fig. 1 and Fig. 2 is refer to, the embodiment of the present invention one is：
S11, obtain map datum and hire out car data, and map is divided equally into basic grid；For example, obtain Foochow
All trip of taxi data of map datum and Shang Yizhounei Fuzhou Cities, based on the map of Fuzhou City is averagely divided
Grid, such as the basic grid of 1000*1000；Vehicle-mounted GPS positioning system is installed on taxi, every N seconds (general 10-
The data for 30s) passing car back return server, including the information such as car ID number, gps coordinate, speed, direction, time, server end
Trip data with regard to taxi can be got；
S12, according to the throughput of basic grid taxi obtain node grid to be chosen；For example go out in some basic grid
The throughput hired a car is more than preset value 5, then using this grid as node grid to be chosen, another basic grid taxi
Throughput is 2, and less than preset value, the basic grid is not as node grid to be chosen, and preset value can be other numerical value, depending on tool
Depending on the situation of body region；
S13, obtain map datum, and map is averagely divided into the number grid fewer than basic grid number；Obtain Foochow
The map datum in city, the number grid fewer than basic grid number is averagely divided into by the map of Fuzhou City, such as 250*250
Grid；
S14, according to the number of node grid to be chosen in grid obtain node grid；By the number of node grid to be chosen
Grid less than 1 removes；Using grid of the number of node grid to be chosen equal to 1 as node grid；Will node net be chosen
The number of lattice is more than 1 grid quartering preset times；Taxi throughput in the grid of quartering preset times is maximum
Grid to be chosen is as node grid；If the grid number to be chosen in some grid is 16, it is assumed that preset times 2, by the net
16 grids are obtained after the lattice quartering 2 times, using the grid of taxi throughput maximum in 16 grids as node grid；
S15, according to the POI of node grid and map obtain default node；By road in node grid of the POI number less than 1
The center on road is as default node；Using the POI in node grid of the POI number equal to 1 as default node；By POI
Number POI nearest from node grid center in the node grid more than 1 is as default node；Such as with reference to Fuzhou City
The POI of figure, does not have POI in some node grid, another using the center of the node grid words spoken by an actor from offstage road as default node
Only POI park south gate in a node grid, then using park south gate as default node, have in yet another node grid
Three POI parks north gate, Yong Hui supermarkets and West Lake bus station, wherein West Lake bus station is nearest from grid element center, then the simultaneous West Lake
Bus station is as default node；
S16, the default node of storage；Store all default nodes；
S2, obtain default node and all taxi car datas；Obtain above-mentioned storage default node and it is one week upper in
All trip of taxi data in Fuzhou City；
S3, according to the period will hire out car data classification；Classify according to date, period to above-mentioned taxi car data, such as
Working day, nonworkdays, peak period on and off duty, daytime, evening, morning etc.；
S4, obtain the routing information that a taxi described in a period passes through the default node；A taxi is obtained to exist
By the data of two neighboring node in one period；Handle the taxi and pass through two neighboring default section within the period
The data of point simultaneously obtain two neighboring default node path information；The taxi is recorded within the period by two neighboring
Routing information between node；For example, obtaining certain taxi, two neighboring node park is passed through in peak period on and off duty on weekdays
The data of south gate and park north gate and processing, calculate the average speed of taxi, analyze the throughput of taxi, obtain park south
Routing information between door and park north gate；And record the taxi two neighboring node is passed through in peak period on and off duty on weekdays
Between routing information；
S5, one period of statistics all taxis pass through the routing information collection in path between two neighboring node；Count work
Make routing information collection of all taxis in day peak period on and off duty by path between two neighboring node；
S6, all period all taxis of statistics pass through the routing information collection in path between two neighboring node；
S7, the correspondence weights according to routing information collection calculating path；Such as working day peak period taxi on and off duty passes through
It is M that path between adjacent node park south gate and park north gate, which corresponds to weight computing,；
S8, according to the corresponding weights in path described in period classification storage and the path.
Fig. 3 is refer to, the embodiment of the present invention two is：
A kind of path based on taxi empirical data sets system, including：
3rd acquisition module 11, for obtaining map datum and hiring out car data, by map partitioning into basic grid；
First chooses module 12, for obtaining node grid to be chosen according to the throughput of basic grid taxi；
4th acquisition module 13, is the number net fewer than basic grid number by map partitioning for obtaining map datum
Lattice；
Second chooses module 14, for obtaining node grid according to the number of node grid to be chosen in grid；
3rd chooses module 15, for obtaining default node according to the POI of node grid and map；
Second memory module 16, for storing default node；
First acquisition module 2, for obtaining default node and all taxi car datas；
Sort module 3, for car data classification will to be hired out according to the period；
Second acquisition module 4, the routing information of the default node is passed through for obtaining a taxi described in a period；
Including：5th acquisition module 41, for obtaining a taxi within a period by the data of two neighboring node；Handle mould
Block 42, for handle the taxi within the period by two neighboring default node data and obtain two neighboring pre-
If node path information；Logging modle 43, passes through between two neighboring node for recording the taxi within the period
Routing information；
First statistical module 5, the road in path between two neighboring node is passed through for counting a period all taxis
Footpath information collection；
Second statistical module 6, for counting all taxis of all periods by path between two neighboring node
Routing information collection；
Computing module 7, for calculating the correspondence weights in path according to routing information collection；
First memory module 8, for according to the corresponding weights in path described in period classification storage and the path.
In the present solution, it is applied to navigation system according to the corresponding weights in path and the path of period classification storage
System, navigation system is according to the current position of user, target location matched node, further according to the path and road between matched node
The corresponding weights in footpath choose optimal navigation way, but this programme application is not limited to navigation system.
In conclusion path setting method and system provided by the invention based on taxi empirical data, the 3rd obtains
Module obtains map datum and hires out car data, by map partitioning into basic grid；First chooses module according to by taxi
The few basic grid of throughput is removed to obtain node grid to be chosen；4th obtains mould and the second selection module, the 3rd selection
Module according to the POI of node grid distribution situation to be chosen and map obtain density rationally, the default node of position suitable；Second
The default node of memory module storage；First acquisition module obtains default node and all taxi car datas；Sort module, second
Acquisition module, the first statistical module and the second statistical module count all taxis according to the period and pass through two neighboring node
Between routing information；And the correspondence weights in path are calculated according to routing information collection by computing module；So that each weights
All reflect a kind of routing information, each paths can learn its road conditions by weight computing, the first memory module according to
The corresponding weights in path described in period classification storage and the path, data are provided for route guidance.
The foregoing is merely the embodiment of the present invention, is not intended to limit the scope of the invention, every to utilize this hair
The equivalents that bright specification and accompanying drawing content are made, are directly or indirectly used in relevant technical field, similarly include
In the scope of patent protection of the present invention.
Claims (6)
- A kind of 1. method to set up based on taxi empirical data dynamic access path weight value, it is characterised in that including：Obtain default node and all taxi car datas；Car data classification will be hired out according to the period；Obtain the routing information that a taxi described in a period passes through the default node；Count routing information collection of a period all taxis by path between two neighboring node；Count routing information collection of all taxis of all periods by path between two neighboring node；The correspondence weights in path are calculated according to routing information collection；According to the corresponding weights in path described in period classification storage and the path；Wherein, described " default node " specifically default step is：Map datum and all taxi car datas are obtained, by map partitioning into basic grid；Node grid to be chosen is obtained according to the throughput of basic grid taxi；Map partitioning is less equal than to the grid of basic grid number for number again；Node grid is obtained according to the number of node grid to be chosen in grid；Default node is obtained according to the POI of node grid and map.
- 2. the method to set up according to claim 1 based on taxi empirical data dynamic access path weight value, its feature It is, " the obtaining the routing information that a taxi described in a period passes through the default node " is specially：Obtain the data that a taxi passes through two neighboring node within a period；The taxi is handled to pass through the data of two neighboring default node within the period and obtain two neighboring default section Point routing information；The taxi is recorded within the period by the routing information between two neighboring node.
- 3. the method to set up according to claim 1 based on taxi empirical data dynamic access path weight value, its feature It is, it is described " according to node grid to be chosen in grid if map partitioning to be less than to the grid of basic grid number for number Number obtain node grid " be specially：Grid of the number of node grid to be chosen less than 1 is removed；Using grid of the number of node grid to be chosen equal to 1 as node grid；The number of node grid to be chosen is more than to 1 grid quartering preset times；Using the grid to be chosen of taxi throughput maximum in the grid of quartering preset times as node grid.
- 4. the method to set up according to claim 1 based on taxi empirical data dynamic access path weight value, its feature It is, " the obtaining default node according to the POI of node grid and map " is specially：Using the center of road in node grid of the POI number less than 1 as default node；Using the POI in node grid of the POI number equal to 1 as default node；Using POI nearest from node grid center in node grid of the POI number more than 1 as default node.
- A kind of 5. system based on taxi empirical data dynamic access path weight value, it is characterised in that including：First acquisition module, for obtaining default node and all taxi car datas；Sort module, for car data classification will to be hired out according to the period；Second acquisition module, the routing information of the default node is passed through for obtaining a taxi described in a period；First statistical module, the routing information in path between two neighboring node is passed through for counting a period all taxis Collection；Second statistical module, for counting path letter of all taxis of all periods by path between two neighboring node Breath collection；Computing module, for calculating the correspondence weights in path according to routing information collection；First memory module, for according to the corresponding weights in path described in period classification storage and the path；Further include：3rd acquisition module, for obtaining map datum and all taxi car datas, by map partitioning into basic grid；First chooses module, for obtaining node grid to be chosen according to the throughput of basic grid taxi；4th acquisition module, for map partitioning to be less equal than to the grid of basic grid number for number again；Second chooses module, for obtaining node grid according to the number of node grid to be chosen in grid；3rd chooses module, for obtaining default node according to the POI of node grid and map.
- 6. the system according to claim 5 based on taxi empirical data dynamic access path weight value, it is characterised in that " second acquisition module " includes：5th acquisition module, for obtaining a taxi within a period by the data of two neighboring node；Processing module, for handle the taxi within the period by two neighboring default node data and obtain phase Adjacent two default node path information；Logging modle, for recording the taxi within the period by the routing information between two neighboring node.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711069850.7A CN108020235B (en) | 2015-07-27 | 2015-07-27 | Method and system for dynamically acquiring path weight based on taxi experience data |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510444191.5A CN105067001B (en) | 2015-07-27 | 2015-07-27 | Path setting method and system based on taxi empirical data |
| CN201711069850.7A CN108020235B (en) | 2015-07-27 | 2015-07-27 | Method and system for dynamically acquiring path weight based on taxi experience data |
Related Parent Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510444191.5A Division CN105067001B (en) | 2015-07-27 | 2015-07-27 | Path setting method and system based on taxi empirical data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108020235A true CN108020235A (en) | 2018-05-11 |
| CN108020235B CN108020235B (en) | 2020-10-23 |
Family
ID=54496427
Family Applications (4)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711069146.1A Active CN108051011B (en) | 2015-07-27 | 2015-07-27 | Reliable navigation path setting method based on taxi experience data |
| CN201711069850.7A Active CN108020235B (en) | 2015-07-27 | 2015-07-27 | Method and system for dynamically acquiring path weight based on taxi experience data |
| CN201711069859.8A Active CN108088452B (en) | 2015-07-27 | 2015-07-27 | Path weight obtaining method |
| CN201510444191.5A Active CN105067001B (en) | 2015-07-27 | 2015-07-27 | Path setting method and system based on taxi empirical data |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711069146.1A Active CN108051011B (en) | 2015-07-27 | 2015-07-27 | Reliable navigation path setting method based on taxi experience data |
Family Applications After (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711069859.8A Active CN108088452B (en) | 2015-07-27 | 2015-07-27 | Path weight obtaining method |
| CN201510444191.5A Active CN105067001B (en) | 2015-07-27 | 2015-07-27 | Path setting method and system based on taxi empirical data |
Country Status (1)
| Country | Link |
|---|---|
| CN (4) | CN108051011B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111832768A (en) * | 2019-08-13 | 2020-10-27 | 北京嘀嘀无限科技发展有限公司 | POI feature generation method and device, electronic equipment and storage medium |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI646495B (en) * | 2017-07-31 | 2019-01-01 | 元智大學 | Route planning system for looking for passenger using for taxi and carsharing and method thereof |
| CN108985506A (en) * | 2018-07-03 | 2018-12-11 | 蔚来汽车有限公司 | Planning driving path recommended method, prediction technique, acquisition methods and its device |
| JP6810978B1 (en) * | 2020-03-16 | 2021-01-13 | 株式会社ピース企画 | Cluster generator, cluster generation method and cluster generation program |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE19954971A1 (en) * | 1999-11-16 | 2001-06-07 | Daimler Chrysler Ag | System, for affecting vehicle traffic flow, uses traffic position detectors to record and transmit data indicating traffic positions to a computer-aided traffic central office prior to its analysis regarding effect on traffic flow |
| CN101364345A (en) * | 2008-09-25 | 2009-02-11 | 北京航天智通科技有限公司 | Real-time dynamic information processing method based on car detecting technique |
| CN102110364A (en) * | 2009-12-28 | 2011-06-29 | 日电（中国）有限公司 | Traffic information processing method and traffic information processing device based on intersections and sections |
| CN103218672A (en) * | 2013-03-24 | 2013-07-24 | 吉林大学 | Taxi cruising action analytical method based on global position system (GPS) data grid statistic |
| CN103218915A (en) * | 2013-03-05 | 2013-07-24 | 中山大学 | Experience route generation method based on probe vehicle data |
| CN103646560A (en) * | 2013-11-27 | 2014-03-19 | 福建工程学院 | Extraction method of taxi driving track experience knowledge paths |
| CN103714708A (en) * | 2013-12-18 | 2014-04-09 | 福建工程学院 | Optimal path planning method based on split-time experience path of taxi |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002039775A (en) * | 2000-07-28 | 2002-02-06 | Neutral Corp | Traveling system for servicing vehicle |
| JP2002269669A (en) * | 2001-03-09 | 2002-09-20 | Matsushita Electric Ind Co Ltd | Fcd collection system, fcd collection method, and device thereof |
| US7188026B2 (en) * | 2003-05-12 | 2007-03-06 | Dash Navigation, Inc. | Hierarchical floating car data network |
| CN101373559B (en) * | 2007-08-24 | 2010-08-18 | 同济大学 | Method for evaluating city road net traffic state based on floating vehicle data |
| CN101908276A (en) * | 2010-07-29 | 2010-12-08 | 北京世纪高通科技有限公司 | Method and device for evaluating traffic information |
| CN102063793B (en) * | 2011-01-12 | 2012-10-31 | 上海炬宏信息技术有限公司 | Road condition information detection method and system |
| CN102110363B (en) * | 2011-03-30 | 2013-07-10 | 北京世纪高通科技有限公司 | Floating vehicle data processing method and device |
| CN103632540B (en) * | 2012-08-20 | 2015-11-25 | 同济大学 | Based on the major urban arterial highway traffic circulation information processing method of floating car data |
| CN103531023B (en) * | 2013-10-18 | 2016-01-20 | 北京世纪高通科技有限公司 | A kind of data processing method and device |
-
2015
- 2015-07-27 CN CN201711069146.1A patent/CN108051011B/en active Active
- 2015-07-27 CN CN201711069850.7A patent/CN108020235B/en active Active
- 2015-07-27 CN CN201711069859.8A patent/CN108088452B/en active Active
- 2015-07-27 CN CN201510444191.5A patent/CN105067001B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE19954971A1 (en) * | 1999-11-16 | 2001-06-07 | Daimler Chrysler Ag | System, for affecting vehicle traffic flow, uses traffic position detectors to record and transmit data indicating traffic positions to a computer-aided traffic central office prior to its analysis regarding effect on traffic flow |
| CN101364345A (en) * | 2008-09-25 | 2009-02-11 | 北京航天智通科技有限公司 | Real-time dynamic information processing method based on car detecting technique |
| CN102110364A (en) * | 2009-12-28 | 2011-06-29 | 日电（中国）有限公司 | Traffic information processing method and traffic information processing device based on intersections and sections |
| CN103218915A (en) * | 2013-03-05 | 2013-07-24 | 中山大学 | Experience route generation method based on probe vehicle data |
| CN103218672A (en) * | 2013-03-24 | 2013-07-24 | 吉林大学 | Taxi cruising action analytical method based on global position system (GPS) data grid statistic |
| CN103646560A (en) * | 2013-11-27 | 2014-03-19 | 福建工程学院 | Extraction method of taxi driving track experience knowledge paths |
| CN103714708A (en) * | 2013-12-18 | 2014-04-09 | 福建工程学院 | Optimal path planning method based on split-time experience path of taxi |
Non-Patent Citations (6)
| Title |
|---|
| PHITHAKKITNUKOON S.等: "Taxi-aware map: Identifying and predicting vacant taxis in the city", 《INTERNATIONAL JOINT CONFERENCE ON AMBIENT INTELLIGENCE》 * |
| 唐炉亮 等: "出租车经验知识建模与路径规划算法", 《测绘学报》 * |
| 江波: "基于浮动车数据的实时交通状态估计", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
| 王美玲 等: "浮动车地图匹配算法研究", 《测绘学报》 * |
| 胡继华 等: "融合出租车驾驶经验的层次路径规划方法", 《交通运输系统工程与信息》 * |
| 蒋新华 等: "基于浮动车数据的道路单向限行状态动态识别", 《计算机应用》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111832768A (en) * | 2019-08-13 | 2020-10-27 | 北京嘀嘀无限科技发展有限公司 | POI feature generation method and device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108051011A (en) | 2018-05-18 |
| CN108051011B (en) | 2020-10-23 |
| CN105067001B (en) | 2017-11-24 |
| CN108088452B (en) | 2020-08-18 |
| CN108088452A (en) | 2018-05-29 |
| CN105067001A (en) | 2015-11-18 |
| CN108020235B (en) | 2020-10-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103106280B (en) | A kind of range query method of uncertain space-time trajectory data under road network environment | |
| CN105067001B (en) | Path setting method and system based on taxi empirical data | |
| CN106383868A (en) | Road network-based spatio-temporal trajectory clustering method | |
| CN103035123B (en) | Abnormal data acquisition methods and system in a kind of traffic track data | |
| CN104167092A (en) | Method and device for determining taxi pick-up and drop-off hot spot region center | |
| CN108776852A (en) | Without stake vehicle dispatching method and system | |
| CN105608505A (en) | Cellular signaling data based track traffic travel mode identification method for resident | |
| CN102324128A (en) | Method for predicting OD (Origin-Destination) passenger flow among bus stations on basis of IC (Integrated Circuit)-card record and device | |
| Jiang et al. | SunChase: Energy-efficient route planning for solar-powered EVs | |
| CN105513337A (en) | Passenger flow volume prediction method and device | |
| CN112990976A (en) | Commercial network site selection method, system, equipment and medium based on open source data mining | |
| CN105741537B (en) | Vehicle seeks recognition methods and the system of objective state | |
| CN107656987A (en) | A kind of subway station function method for digging based on LDA models | |
| US10489431B2 (en) | Digital location-based data methods and product | |
| CN103198645A (en) | Rush hour bus route station jumping running method based on passenger trip distribution | |
| CN106875734A (en) | A kind of method and device for pushing parking route | |
| CN104269057A (en) | Bayonet sensor layout method based on floating car OD data | |
| CN108573604A (en) | Public transport OD data capture methods based on mobile terminal location and storage medium | |
| CN105608919B (en) | The determination method and device of interchange of position | |
| CN106251628B (en) | A kind of method and device of the traffic trip amount of determining motor vehicle | |
| CN109520499B (en) | Method for realizing regional real-time isochrones based on vehicle GPS track data | |
| CN105096589B (en) | Method of selecting representative node in traffic road, system and client | |
| CN113888867A (en) | Parking space recommendation method and system based on LSTM position prediction | |
| CN115083198B (en) | Multi-vehicle power resource scheduling method and device | |
| Chai et al. | The impacts of automated vehicles on Center city parking |
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 |