CN105067001A - Route setting method based on taxi experience data and system thereof - Google Patents
Route setting method based on taxi experience data and system thereof Download PDFInfo
- Publication number
- CN105067001A CN105067001A CN201510444191.5A CN201510444191A CN105067001A CN 105067001 A CN105067001 A CN 105067001A CN 201510444191 A CN201510444191 A CN 201510444191A CN 105067001 A CN105067001 A CN 105067001A
- Authority
- CN
- China
- Prior art keywords
- grid
- node
- taxi
- path
- time 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
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Navigation (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to a route setting method based on taxi experience data and a system thereof, the method comprises the following steps: obtaining a preset point and all taxi data by a first acquisition module; classifying the taxi data by a classification module according to periods of time; obtaining the route information when a taxi passes through the preset point in a period of time; making statistics to obtain a route information set when all taxis pass through adjacent points in a period of time by a first statistics module; making statistics to obtain the route information set when all taxis pass through adjacent points in all periods of time by a second statistics module; calculating a weight number corresponding to the route according to the route information set by a calculating module; and performing sorted storing on the route and the weight number corresponding to the route according to the periods of time by a first storage module.
Description
Technical field
The present invention relates to field of electronic navigation, particularly relate to a kind of path setting method based on taxi empirical data and system.
Background technology
Along with development and the growth in the living standard of science and technology, path navigation becomes people and to go on a journey an indispensable step, traditional air navigation aid usually adopts and obtains initial position and target location, choose the nearest route of initial position and target location according to the map as navigation way, but, under the road conditions of big city complexity, distance is no longer that simple navigation is considered.
Application number be 201010566504.1 patent document disclose a kind of guidance method, mobile terminal and guidance server, carry out packet aggregation calculating according to the locator data of more than one mobile terminal and the road conditions request message of destination mobile terminal, obtain the position data of more than one mobile terminal group to be shown, directional data and speed identification information and send to destination mobile terminal; Entire road is subdivided into the set in multiple section by the method, gathers the concrete road conditions in each section in road respectively.
But same path is just subdivided into multiple section by such scheme, does not provide more navigation way to select, and same path a lot of section road conditions are similar, segment multiple section and increase some workloads unnecessary to the computational analysis of each section.
In addition, under the road conditions of city complexity, people adopt the mode of taxi to go on a journey mostly, and experienced taxi driver can understand the jam situation of road conditions, and can quick and the most unobstructed path be found, if the experience that can make full use of taxi driver arranges guidance path, fast arrive destination for helping the user such as private car and improve the road conditions of urban road significant.
Summary of the invention
Technical matters to be solved by this invention is: under the road conditions of city complexity, arranges not only comprehensive but also rational path, for navigation provides reliable data.
In order to solve the problems of the technologies described above, the technical solution used in the present invention is:
Based on a path setting method for taxi empirical data, comprising:
Obtain and preset node and all taxi data;
According to the time period by taxi Data classification;
Obtain the routing information of a taxi described in the time period through described default node;
Add up the routing information collection in time period all taxis path between adjacent two nodes;
Add up the routing information collection in all taxis of all time periods path between adjacent two nodes;
According to the corresponding weights of routing information collection calculating path;
Weights corresponding to described path and described path are stored according to time period classification.
The beneficial effect of the above-mentioned path setting method based on taxi empirical data is: according to the time period, taxi Data classification is also obtained the routing information collection of each time period taxi through adjacent two default nodes respectively, and the routing information collection of each time period is added up, make the routing information that finally obtains representative, the road conditions of different time sections can be represented, corresponding weights are calculated again according to the routing information collection of statistics, each weights is made to reflect a kind of routing information, each paths can learn its road conditions by weight computing, weights corresponding to store path and path of finally classifying are that navigation way is selected to provide reliable data.
Path based on taxi empirical data arranges a system, comprising:
First acquisition module, for obtaining default node and all taxi data;
Sort module, for according to the time period by taxi Data classification;
Second acquisition module, for obtaining the routing information of a taxi described in the time period through described default node;
First statistical module, for adding up the routing information collection in time period all taxis path between adjacent two nodes;
Second statistical module, for adding up the routing information collection in all taxis of all time periods path between adjacent two nodes;
Computing module, for the corresponding weights according to routing information collection calculating path;
First memory module, for storing weights corresponding to described path and described path according to time period classification.
The beneficial effect that the above-mentioned path based on taxi empirical data arranges system is: the first acquisition module obtains presets node and taxi data, provides data basis for path is arranged, taxi data are classified by sort module on a time period, and to obtain in the time period taxi through the routing information of adjacent two default nodes by the second acquisition module, obtain the routing information of a time period, and the routing information collection of each time period is obtained through the first statistical module and the second statistical module, make the routing information that finally obtains representative, the road conditions of different time sections can be represented, computing module calculates corresponding weights according to the routing information collection of statistics, each weights is made to reflect a kind of routing information, each paths can learn its road conditions by weight computing, memory module classification store path and weights corresponding to path, for navigation way is selected to provide reliable data.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the embodiment of the present invention one based on the path setting method of taxi empirical data;
Fig. 2 is the process flow diagram of the embodiment of the present invention one based on the default node of the path setting method of taxi empirical data;
The structural representation of Fig. 3 to be the embodiment of the present invention two based on the path of taxi empirical data arranged system.
Label declaration:
11, the 3rd acquisition module; 12, first module is chosen; 13, the 4th acquisition module; 14, second module is chosen; 15, the 3rd module is chosen; 16, the second memory module; 2, the first acquisition module; 3, sort module; 4, the second acquisition module; 41, the 5th acquisition module; 42, processing module; 43, logging modle; 5, the first statistical module; 6, the second statistical module; 7, computing module; 8, the first memory module.
Embodiment
By describing technology contents of the present invention in detail, realized object and effect, accompanying drawing is coordinated to be explained below in conjunction with embodiment.
The design of most critical of the present invention is: according to the time period to taxi Data classification, obtains adjacent two the internodal routing information collection of different time sections according to taxi data, and according to the corresponding weights of routing information collection calculating path.
The explanation of technical terms that the present invention relates to:
Please refer to Fig. 1 and Fig. 2,
Based on a path setting method for taxi empirical data, comprising:
Node and all taxi data are preset in S2, acquisition;
S3, according to the time period by taxi Data classification;
S4, obtain the routing information of a taxi described in the time period through described default node;
S5, add up the routing information collection in time period all taxis path between adjacent two nodes;
S6, add up the routing information collection in all taxis of all time periods path between adjacent two nodes;
S7, corresponding weights according to routing information collection calculating path;
S8, store weights corresponding to described path and described path according to time period classification.
From foregoing description, the beneficial effect that the present invention is based on the path setting method of taxi empirical data is: according to the time period, taxi Data classification is also obtained the routing information collection of each time period taxi through adjacent two default nodes respectively, and the routing information collection of each time period is added up, make the routing information that finally obtains representative, the road conditions of different time sections can be represented, corresponding weights are calculated again according to the routing information collection of statistics, each weights is made to reflect a kind of routing information, each paths can learn its road conditions by weight computing, weights corresponding to store path and path of finally classifying are that navigation way is selected to provide reliable data.
Further, described " presetting node " concrete foresee steps is:
S11, acquisition map datum and taxi data, be on average divided into basic grid by map;
S12, obtain node grid to be chosen according to the throughput of basic grid taxi;
S13, acquisition map datum, be on average divided into the grid that number is fewer than basic grid number by map;
S14, obtain node grid according to the number of node grid to be chosen in grid;
S15, obtain default node according to the POI of node grid and map;
Node is preset in S16, storage.
Seen from the above description, node grid to be chosen is obtained according to the throughput of taxi, decrease taxi not by or the workload brought of the area data process seldom passed through and avoid the wasting of resources, number according to node grid to be chosen in macrolattice obtains node grid, node density is made to distribute rationally, POI simultaneously in conjunction with map obtains default node, thus default node and map POI can be corresponding, are convenient to find.
Further, described " obtaining the routing information of a taxi described in the time period through described default node " is specially:
Obtain taxi data through adjacent two nodes within a time period;
Process this taxi and obtain adjacent two default node path information through the data of adjacent two default nodes within the described time period;
Record this taxi within the described time period through adjacent two internodal routing informations.
Seen from the above description, obtain taxi and process through the data of adjacent two nodes within a time period, can obtain the routing information of adjacent two nodes in this time period.
Further, described " number according to node grid to be chosen in grid obtains node grid " is specially:
The grid that the number of node grid to be chosen is less than 1 is removed;
The number of node grid to be chosen is equaled the grid of 1 as node grid;
The number of node grid to be chosen is greater than the grid quartern preset times of 1;
Using grid to be chosen maximum for taxi throughput in the grid of quartern preset times as node grid.
From foregoing description, the number according to node grid to be chosen obtains node grid, and node density is distributed rationally.
Further, described " POI according to node grid and map obtains default node " is specially:
POI number is less than the center of road in the node grid of 1 as default node;
POI number to be equaled in the node grid of 1 this POI as default node;
POI number is greater than POI nearest from node grid center in the node grid of 1 as default node.
From foregoing description, preset node corresponding with the POI of map, be convenient to find.
Please refer to Fig. 3,
Path based on taxi empirical data arranges a system, comprising:
First acquisition module 2, for obtaining default node and all taxi data;
Sort module 3, for according to the time period by taxi Data classification;
Second acquisition module 4, for obtaining the routing information of a taxi described in the time period through described default node;
First statistical module 5, for adding up the routing information collection in time period all taxis path between adjacent two nodes;
Second statistical module 6, for adding up the routing information collection in all taxis of all time periods path between adjacent two nodes;
Computing module 7, for the corresponding weights according to routing information collection calculating path;
First memory module 8, for storing weights corresponding to described path and described path according to time period classification.
The beneficial effect that the above-mentioned path based on taxi empirical data arranges system is: the first acquisition module 2 obtains presets node and taxi data, provides data basis for path is arranged, taxi data are classified by sort module 3 on a time period, and to obtain in the time period taxi through the routing information of adjacent two default nodes by the second acquisition module 4, obtain the routing information of a time period, and the routing information collection of each time period is obtained through the first statistical module 5 and the second statistical module 6, make the routing information that finally obtains representative, the road conditions of different time sections can be represented, computing module 7 calculates corresponding weights according to the routing information collection of statistics, each weights is made to reflect a kind of routing information, each paths can learn its road conditions by weight computing, memory module 8 is classified weights corresponding to store path and path, for navigation way is selected to provide reliable data.
Further, arrange system based on the path of taxi empirical data also to comprise:
3rd acquisition module 11, for obtaining map datum and taxi data, becomes basic grid by map partitioning;
First chooses module 12, for obtaining node grid to be chosen according to the throughput of basic grid taxi;
Map partitioning, for obtaining map datum, is the grid that number is fewer than basic grid number by the 4th acquisition module 13;
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.
From foregoing description, first chooses module obtains node grid to be chosen according to the throughput of taxi, decrease taxi not by or the workload brought of the area data process seldom passed through and avoid the wasting of resources, second chooses module obtains node grid according to the number of node grid to be chosen in grid, node density is made to distribute rationally, simultaneously the 3rd choose module and obtain default node in conjunction with the POI of map, thus preset node and map POI can be corresponding, be convenient to find.
Further, described " the second acquisition module 4 " comprising:
5th acquisition module 41, for obtaining taxi data through adjacent two nodes within a time period;
Processing module 42, obtains adjacent two default node path information through the data of adjacent two default nodes for the treatment of this taxi within the described time period;
Logging modle 43, for recording this taxi within the described time period through adjacent two internodal routing informations.
Please refer to Fig. 1 and Fig. 2, embodiments of the invention one are:
S11, acquisition map datum and taxi data, be on average divided into basic grid by map; Such as, obtain all trip of taxi data of the map datum of Foochow and Shang Yizhounei Fuzhou City, the map of Fuzhou City is on average divided into basic grid, the basic grid of such as 1000*1000; Vehicle-mounted GPS positioning system installed by taxi, the data passing car every N second (general 10-30s) back return server, comprise the information such as No. ID, car, gps coordinate, speed, direction, time, server end just can get the trip data of taxi;
S12, obtain node grid to be chosen according to the throughput of basic grid taxi; In such as certain basic grid, the throughput of taxi is greater than preset value 5, then using this grid as node grid to be chosen, the throughput of another basic grid taxi is 2, be less than preset value, this basic grid is not as node grid to be chosen, preset value can be other numerical value, depending on concrete region situation;
S13, acquisition map datum, be on average divided into the grid that number is fewer than basic grid number by map; Obtain the map datum of Fuzhou City, the map of Fuzhou City is on average divided into the grid that number is fewer than basic grid number, the grid of such as 250*250;
S14, obtain node grid according to the number of node grid to be chosen in grid; The grid that the number of node grid to be chosen is less than 1 is removed; The number of node grid to be chosen is equaled the grid of 1 as node grid; The number of node grid to be chosen is greater than the grid quartern preset times of 1; Using grid to be chosen maximum for taxi throughput in the grid of quartern preset times as node grid; If the grid number to be chosen in certain grid is 16, suppose that preset times is 2, obtain 16 grids, using grid maximum for taxi throughput in 16 grids as node grid by after this grid quartern 2 times;
S15, obtain default node according to the POI of node grid and map; POI number is less than the center of road in the node grid of 1 as default node; POI number to be equaled in the node grid of 1 this POI as default node; POI number is greater than POI nearest from node grid center in the node grid of 1 as default node; Such as, in conjunction with the POI of Fuzhou City's map, POI is not had in certain node grid, using the center of this node grid words spoken by an actor from offstage road as default node, only have south gate, a POI park in another node grid, then using south gate, park as default node, then have in a node grid and have north gate, three POI parks, forever brightness supermarket and West Lake bus station, wherein West Lake bus station is nearest from grid element center, and Ze Jian West Lake bus station is as default node;
Node is preset in S16, storage; Store all default nodes;
Node and all taxi data are preset in S2, acquisition; Obtain all trip of taxi data of the default node of above-mentioned storage and Shang Yizhounei Fuzhou City;
S3, according to the time period by taxi Data classification; According to date, period to above-mentioned taxi Data classification, such as working day, nonworkdays, peak period on and off duty, daytime, evening, morning etc.;
S4, obtain the routing information of a taxi described in the time period through described default node; Obtain taxi data through adjacent two nodes within a time period; Process this taxi and obtain adjacent two default node path information through the data of adjacent two default nodes within the described time period; Record this taxi within the described time period through adjacent two internodal routing informations; Such as, obtain certain the taxi data of peak period on and off duty through adjacent two south gates, node park and north gate, park processing on weekdays, calculate the average velocity of hiring a car, analyze the throughput of hiring a car, obtain the routing information between south gate, park and north gate, park; And record this taxi routing information of peak period on and off duty between adjacent two nodes on weekdays;
S5, add up the routing information collection in time period all taxis path between adjacent two nodes; The routing information collection in all taxis in statistical work day peak period on and off duty path between adjacent two nodes;
S6, add up the routing information collection in all taxis of all time periods path between adjacent two nodes;
S7, corresponding weights according to routing information collection calculating path; The corresponding weight computing in the path of peak period on and off duty taxi on such as working day between south gate, adjacent node park and north gate, park is M;
S8, store weights corresponding to described path and described path according to time period classification.
Please refer to Fig. 3, embodiments of the invention two are:
Path based on taxi empirical data arranges a system, comprising:
3rd acquisition module 11, for obtaining map datum and taxi data, becomes basic grid by map partitioning;
First chooses module 12, for obtaining node grid to be chosen according to the throughput of basic grid taxi;
Map partitioning, for obtaining map datum, is the grid that number is fewer than basic grid number by the 4th acquisition module 13;
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 data;
Sort module 3, for according to the time period by taxi Data classification;
Second acquisition module 4, for obtaining the routing information of a taxi described in the time period through described default node; Comprise: the 5th acquisition module 41, for obtaining taxi data through adjacent two nodes within a time period; Processing module 42, obtains adjacent two default node path information through the data of adjacent two default nodes for the treatment of this taxi within the described time period; Logging modle 43, for recording this taxi within the described time period through adjacent two internodal routing informations;
First statistical module 5, for adding up the routing information collection in time period all taxis path between adjacent two nodes;
Second statistical module 6, for adding up the routing information collection in all taxis of all time periods path between adjacent two nodes;
Computing module 7, for the corresponding weights according to routing information collection calculating path;
First memory module 8, for storing weights corresponding to described path and described path according to time period classification.
In this programme, weights corresponding to path and described path stored according to time period classification are applied to navigational system, navigational system is according to the current position of user, target location matched node, choose best navigation way according to weights corresponding to path and path between the node of coupling again, but this programme application is not limited to navigational system.
In sum, the path setting method based on taxi empirical data provided by the invention and system, the 3rd acquisition module obtains map datum and taxi data, and map partitioning is become basic grid; First chooses module obtains node grid to be chosen according to basic grid few for taxi throughput being removed; 4th obtains mould and second chooses module, the 3rd and chooses module and obtain reasonable, the favorably situated default node of density according to the POI of node grid distribution situation to be chosen and map; Second memory module stores presets node; First acquisition module obtains presets node and all taxi data; Sort module, the second acquisition module, the first statistical module and the second statistical module add up the routing information of all taxis between adjacent two nodes according to the time period; And by the corresponding weights of computing module according to routing information collection calculating path; Make each weights reflect a kind of routing information, each paths can learn its road conditions by weight computing, and the first memory module stores weights corresponding to described path and described path, for route guidance provides data according to time period classification.
The foregoing is only embodiments of the invention; not thereby the scope of the claims of the present invention is limited; every equivalents utilizing instructions of the present invention and accompanying drawing content to do, or be directly or indirectly used in relevant technical field, be all in like manner included in scope of patent protection of the present invention.
Claims (8)
1. based on a path setting method for taxi empirical data, it is characterized in that, comprising:
Obtain and preset node and all taxi data;
According to the time period by taxi Data classification;
Obtain the routing information of a taxi described in the time period through described default node;
Add up the routing information collection in time period all taxis path between adjacent two nodes;
Add up the routing information collection in all taxis of all time periods path between adjacent two nodes;
According to the corresponding weights of routing information collection calculating path;
Weights corresponding to described path and described path are stored according to time period classification.
2. the path setting method based on taxi empirical data according to claim 1, is characterized in that, described " presetting node " concrete foresee steps is:
Obtain map datum and taxi data, map is on average divided into basic grid;
Node grid to be chosen is obtained according to the throughput of basic grid taxi;
Obtain map datum, map is on average divided into the grid that number is fewer than basic grid number;
Number according to node grid to be chosen in grid obtains node grid;
POI according to node grid and map obtains default node;
Store and preset node.
3. the path setting method based on taxi empirical data according to claim 1, is characterized in that, described " obtaining the routing information of a taxi described in the time period through described default node " is specially:
Obtain taxi data through adjacent two nodes within a time period;
Process this taxi and obtain adjacent two default node path information through the data of adjacent two default nodes within the described time period;
Record this taxi within the described time period through adjacent two internodal routing informations.
4. the path setting method based on taxi empirical data according to claim 2, is characterized in that, described " number according to node grid to be chosen in grid obtains node grid " is specially:
The grid that the number of node grid to be chosen is less than 1 is removed;
The number of node grid to be chosen is equaled the grid of 1 as node grid;
The number of node grid to be chosen is greater than the grid quartern preset times of 1;
Using grid to be chosen maximum for taxi throughput in the grid of quartern preset times as node grid.
5. the path setting method based on taxi empirical data according to claim 2, is characterized in that, described " POI according to node grid and map obtains default node " is specially:
POI number is less than the center of road in the node grid of 1 as default node;
POI number to be equaled in the node grid of 1 this POI as default node;
POI number is greater than POI nearest from node grid center in the node grid of 1 as default node.
6. the path based on taxi empirical data arranges a system, it is characterized in that, comprising:
First acquisition module, for obtaining default node and all taxi data;
Sort module, for according to the time period by taxi Data classification;
Second acquisition module, for obtaining the routing information of a taxi described in the time period through described default node;
First statistical module, for adding up the routing information collection in time period all taxis path between adjacent two nodes;
Second statistical module, for adding up the routing information collection in all taxis of all time periods path between adjacent two nodes;
Computing module, for the corresponding weights according to routing information collection calculating path;
First memory module, for storing weights corresponding to described path and described path according to time period classification.
7. the path based on taxi empirical data according to claim 6 arranges system, it is characterized in that, also comprises:
3rd acquisition module, for obtaining map datum and taxi data, becomes basic grid by map partitioning;
First chooses module, for obtaining node grid to be chosen according to the throughput of basic grid taxi;
Map partitioning, for obtaining map datum, is the grid that number is fewer than basic grid number by the 4th acquisition module;
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;
Second memory module, for storing default node.
8. the path based on taxi empirical data according to claim 6 arranges system, it is characterized in that, described " the second acquisition module " comprising:
5th acquisition module, for obtaining taxi data through adjacent two nodes within a time period;
Processing module, obtains adjacent two default node path information through the data of adjacent two default nodes for the treatment of this taxi within the described time period;
Logging modle, for recording this taxi within the described time period through adjacent two internodal routing informations.
Priority Applications (4)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711069859.8A CN108088452B (en) | 2015-07-27 | 2015-07-27 | Path weight obtaining method |
| 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 |
| CN201711069146.1A CN108051011B (en) | 2015-07-27 | 2015-07-27 | Reliable navigation path setting method based on taxi experience data |
Applications Claiming Priority (1)
| 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 |
Related Child Applications (3)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711069850.7A Division CN108020235B (en) | 2015-07-27 | 2015-07-27 | Method and system for dynamically acquiring path weight based on taxi experience data |
| CN201711069146.1A Division CN108051011B (en) | 2015-07-27 | 2015-07-27 | Reliable navigation path setting method based on taxi experience data |
| CN201711069859.8A Division CN108088452B (en) | 2015-07-27 | 2015-07-27 | Path weight obtaining method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105067001A true CN105067001A (en) | 2015-11-18 |
| CN105067001B CN105067001B (en) | 2017-11-24 |
Family
ID=54496427
Family Applications (4)
| 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 |
| 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 |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711069859.8A Active CN108088452B (en) | 2015-07-27 | 2015-07-27 | Path weight obtaining method |
Family Applications After (2)
| 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 |
Country Status (1)
| Country | Link |
|---|---|
| CN (4) | CN108088452B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| 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 |
Families Citing this family (2)
| 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 |
| 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 (10)
| 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 |
| US20070271029A1 (en) * | 2003-05-12 | 2007-11-22 | Assimakis Tzamaloukas | Hierarchical floating car data network |
| CN101373559A (en) * | 2007-08-24 | 2009-02-25 | 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 |
| CN102063793A (en) * | 2011-01-12 | 2011-05-18 | 上海炬宏信息技术有限公司 | Road condition information detection method and system |
| CN102110363A (en) * | 2011-03-30 | 2011-06-29 | 北京世纪高通科技有限公司 | Floating vehicle data processing method and device |
| CN103531023A (en) * | 2013-10-18 | 2014-01-22 | 北京世纪高通科技有限公司 | Data processing method and device |
| CN103632540A (en) * | 2012-08-20 | 2014-03-12 | 同济大学 | An urban main road traffic operation information processing method based on floating 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 |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE19954971B4 (en) * | 1999-11-16 | 2004-08-12 | Daimlerchrysler Ag | System for influencing the traffic flow of vehicles |
| CN100580735C (en) * | 2008-09-25 | 2010-01-13 | 北京航天智通科技有限公司 | Real-time dynamic traffic information processing method based on car detecting technique |
| CN102110364B (en) * | 2009-12-28 | 2013-12-11 | 日电（中国）有限公司 | Traffic information processing method and traffic information processing device based on intersections and sections |
| CN103218915B (en) * | 2013-03-05 | 2015-01-28 | 中山大学 | Experience route generation method based on probe vehicle data |
| CN103218672B (en) * | 2013-03-24 | 2016-03-02 | 吉林大学 | A kind of taxi based on gps data lattice statistical cruises behavior analysis method |
| 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 |
-
2015
- 2015-07-27 CN CN201711069859.8A patent/CN108088452B/en active Active
- 2015-07-27 CN CN201510444191.5A patent/CN105067001B/en active Active
- 2015-07-27 CN CN201711069146.1A patent/CN108051011B/en active Active
- 2015-07-27 CN CN201711069850.7A patent/CN108020235B/en active Active
Patent Citations (10)
| 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 |
| US20070271029A1 (en) * | 2003-05-12 | 2007-11-22 | Assimakis Tzamaloukas | Hierarchical floating car data network |
| CN101373559A (en) * | 2007-08-24 | 2009-02-25 | 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 |
| CN102063793A (en) * | 2011-01-12 | 2011-05-18 | 上海炬宏信息技术有限公司 | Road condition information detection method and system |
| CN102110363A (en) * | 2011-03-30 | 2011-06-29 | 北京世纪高通科技有限公司 | Floating vehicle data processing method and device |
| CN103632540A (en) * | 2012-08-20 | 2014-03-12 | 同济大学 | An urban main road traffic operation information processing method based on floating vehicle data |
| CN103531023A (en) * | 2013-10-18 | 2014-01-22 | 北京世纪高通科技有限公司 | Data processing method and device |
| CN103646560A (en) * | 2013-11-27 | 2014-03-19 | 福建工程学院 | Extraction method of taxi driving track experience knowledge paths |
Non-Patent Citations (3)
| Title |
|---|
| 唐炉亮 等: "出租车经验知识建模与路径规划算法", 《测绘学报》 * |
| 胡继华 等: "基于出租车经验路径的城市可达性计算方法", 《地理科学进展》 * |
| 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》 * |
Cited By (2)
| 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 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108051011B (en) | 2020-10-23 |
| CN108020235A (en) | 2018-05-11 |
| CN108088452A (en) | 2018-05-29 |
| CN108051011A (en) | 2018-05-18 |
| CN105067001B (en) | 2017-11-24 |
| CN108088452B (en) | 2020-08-18 |
| CN108020235B (en) | 2020-10-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10147316B2 (en) | Method, apparatus and computer program product for indexing traffic lanes for signal control and traffic flow management | |
| CN103106280B (en) | A kind of range query method of uncertain space-time trajectory data under road network environment | |
| CN103035123B (en) | Abnormal data acquisition methods and system in a kind of traffic track data | |
| CN104331422A (en) | Road section type presumption method | |
| TW202016728A (en) | Method and system for determiningwhether there is target road facility at intersection | |
| CN103337189A (en) | Dynamic road segment division based vehicle route guidance method | |
| Jiang et al. | SunChase: Energy-efficient route planning for solar-powered EVs | |
| EP2831545B1 (en) | Digital location-based data methods and product | |
| CN107403550B (en) | Public transport road network data acquisition method and device and terminal equipment | |
| CN110491158A (en) | A kind of bus arrival time prediction technique and system based on multivariate data fusion | |
| CN105067001A (en) | Route setting method based on taxi experience data and system thereof | |
| CN102968900A (en) | Method for processing RFID (Radio Frequency Identification) traffic data | |
| CN106931981A (en) | A kind of generation method and device of remaining time of navigating | |
| CN109859505B (en) | Early warning processing method, device, server and medium for high-speed station | |
| CN108182800A (en) | A kind of freight traffic information processing method and equipment | |
| CN105608919B (en) | The determination method and device of interchange of position | |
| CN109520499B (en) | Method for realizing regional real-time isochrones based on vehicle GPS track data | |
| Garg et al. | Mining bus stops from raw GPS data of bus trajectories | |
| CN105279963A (en) | Method of acquiring road condition information and apparatus thereof | |
| KR101206570B1 (en) | Apparatus and method for generating a road map | |
| CN105096589A (en) | Method of selecting representative node in traffic road, system and client | |
| CN110986992A (en) | Navigation method and device for unmanned vending vehicle, electronic equipment and storage medium | |
| CN112669604B (en) | Urban traffic scheduling method and device | |
| Yu et al. | A bus-arrival time prediction model based on historical traffic patterns | |
| CN103514741A (en) | Traffic jam information processing system and mobile communication terminal |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |