CN105387865A - Route planning method and system based on traffic road data - Google Patents
Route planning method and system based on traffic road data Download PDFInfo
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- CN105387865A CN105387865A CN201510672763.5A CN201510672763A CN105387865A CN 105387865 A CN105387865 A CN 105387865A CN 201510672763 A CN201510672763 A CN 201510672763A CN 105387865 A CN105387865 A CN 105387865A
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- 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
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
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Abstract
The invention provides a route planning method and system based on traffic road data. The method includes the steps that travel starting point and terminal point information and a time range are obtained; preset time intervals are set within the travel time range; according to the travel starting point and terminal point information, travel routes are planned, current and historic traffic road data of all the travel routes are obtained, current and historic jamming indexes of all the travel routers are calculated, whether the travel routed meeting a first preset condition exist are judged, and if yes, current time serves travel time, wherein the first preset condition is that when the difference value between the current jamming indexes and the historic jamming indexes exceeds a preset threshold value, the travel routes meeting a second preset condition are selected from the travel routes meeting the first preset condition serve as preferential travel routes, and the second preset condition is that the current jamming indexes are minimum, or the difference value between the current jamming indexes and the historic jamming indexes larger than the current jamming indexes is the maximum in the travel routes meeting the first preset condition. Travel is facilitated for a user, and the user experience is effectively improved.
Description
Technical field
The present invention relates to computer software fields, particularly relate to a kind of paths planning method based on traffic route data and system.
Background technology
Along with the development of communication navigation technology, path planning, Intelligent travel obtain the generally favor of people gradually.At present, navigation product, after cooking up various feasible trip route, calculates the general used time of each trip route often according to the historical traffic data of each trip route, selects afterwards for user.
But along with the continuous improvement of urban road construction, road conditions may change at any time, only consider that historical traffic data obviously can not reflect the situation of road exactly, for user provides best trip route and travel time.
Summary of the invention
The shortcoming of prior art in view of the above, the object of the present invention is to provide a kind of paths planning method based on traffic route data and system, only consider historical traffic road data in prior art for solving and cause the not accurate enough problem of trip route planned.
For achieving the above object and other relevant objects, the invention provides a kind of paths planning method based on traffic route data, comprising: obtain trip origin information, travel destination information and travel time scope.Every a predetermined time interval within the travel time, perform following steps: go out all trip route according to described trip origin information and travel destination information planning.Obtain Current traffic road data and the historical traffic road data of each described trip route respectively, calculate the cur-rent congestion exponential sum history congestion index of each described trip route according to this respectively.Judge whether to meet the first pre-conditioned trip route, if having, then using current time as the travel time, wherein said first pre-conditionedly comprises: cur-rent congestion index exceedes predetermined threshold value lower than the difference of history congestion index.Select to meet the second pre-conditioned trip route as preferred trip route meeting in described first pre-conditioned trip route, wherein, described second pre-conditioned comprise following in one: (1) cur-rent congestion index is minimum; (2) cur-rent congestion index lower than history congestion index difference meet in described first pre-conditioned trip route maximum.
Optionally, described Current traffic road data at least comprises current average speeds; Described historical traffic road data at least comprises history average speeds.
Optionally, when described Current traffic road data also comprises: when traffic accident information, roadupkeep information or road road closures information, delete corresponding trip route.
Optionally, described Current traffic road data and the historical traffic road data obtaining each described trip route respectively, calculate the cur-rent congestion exponential sum history congestion index of each described trip route according to this respectively, comprising: described trip route is become each section according to urban road grade classification.Obtain Current traffic road data and the historical traffic road data in each described section, calculate the cur-rent congestion exponential sum history congestion index in each described section according to this respectively.According to the ratio-dependent weight coefficient of length in each affiliated trip route in each section.Respectively the cur-rent congestion exponential sum history congestion index in each section in each described trip route is weighted to the cur-rent congestion exponential sum history congestion index obtaining each described trip route.
For achieving the above object and other relevant objects, the invention provides a kind of path planning system based on traffic route data, comprising: load module, for obtaining trip origin information, travel destination information and travel time scope.Processing module, within the travel time every a predetermined time interval, perform following steps: go out all trip route according to described trip origin information and travel destination information planning; Obtain Current traffic road data and the historical traffic road data of each described trip route respectively, calculate the cur-rent congestion exponential sum history congestion index of each described trip route according to this respectively; Judge whether to meet the first pre-conditioned trip route, if having, then using current time as the travel time, wherein said first pre-conditionedly comprises: cur-rent congestion index exceedes predetermined threshold value lower than the difference of history congestion index; Select to meet the second pre-conditioned trip route as preferred trip route meeting in described first pre-conditioned trip route, wherein, described second pre-conditioned comprise following in one: (1) cur-rent congestion index is minimum; (2) cur-rent congestion index lower than history congestion index difference meet in described first pre-conditioned trip route maximum.
Optionally, described Current traffic road data at least comprises current average speeds, and described historical traffic road data at least comprises history average speeds.
Optionally, described processing module also for, when described Current traffic road data also comprises: when traffic accident information, roadupkeep information or road road closures information, delete corresponding trip route.
Optionally, described Current traffic road data and the historical traffic road data obtaining each described trip route respectively, calculate the cur-rent congestion exponential sum history congestion index of each described trip route according to this respectively, comprising: described trip route is become each section according to urban road grade classification.Obtain Current traffic road data and the historical traffic road data in each described section, calculate the cur-rent congestion exponential sum history congestion index in each described section according to this respectively.According to the ratio-dependent weight coefficient of length in each affiliated trip route in each section.Respectively the cur-rent congestion exponential sum history congestion index in each section in each described trip route is weighted to the cur-rent congestion exponential sum history congestion index obtaining each described trip route.
For achieving the above object and other relevant objects, the present invention also provides a kind of mobile terminal, comprising: input block, for obtaining trip origin information, travel destination information and travel time scope.Processing unit, is connected with described input block, within the travel time every a predetermined time interval, perform following steps: go out all trip route according to described trip origin information and travel destination information planning; Obtain Current traffic road data and the historical traffic road data of each described trip route respectively, calculate the cur-rent congestion exponential sum history congestion index of each described trip route according to this respectively; Judge whether to meet the first pre-conditioned trip route, if having, then using current time as the travel time, wherein said first pre-conditionedly comprises: cur-rent congestion index exceedes predetermined threshold value lower than the history difference number referred to that blocks up; Select to meet the second pre-conditioned trip route as preferred trip route meeting in described first pre-conditioned trip route, wherein, described second pre-conditioned comprise following in one: (1) cur-rent congestion index is minimum; (2) cur-rent congestion index lower than history congestion index difference meet in described first pre-conditioned trip route maximum.
Optionally, described mobile terminal comprises: automatic navigator, smart mobile phone or computer.
As mentioned above, air navigation aid based on traffic data of the present invention and system, while cooking up trip route, the historical traffic data of comprehensive each trip route and real time traffic data select best trip route and travel time for user, facilitate user to go on a journey, promote Consumer's Experience.
Accompanying drawing explanation
Fig. 1 is shown as the paths planning method process flow diagram based on traffic route data in one embodiment of the invention.
Fig. 2 is shown as the traffic congestion index curve schematic diagram of trip route C1 in one embodiment of the invention.
Fig. 3 is shown as the path planning system module map based on traffic route data in one embodiment of the invention.
Element numbers explanation
1 based on the path planning system of traffic route data
11 load modules
12 processing modules
S1 ~ S7 step
Embodiment
Below by way of specific instantiation, embodiments of the present invention are described, those skilled in the art the content disclosed by this instructions can understand other advantages of the present invention and effect easily.The present invention can also be implemented or be applied by embodiments different in addition, and the every details in this instructions also can based on different viewpoints and application, carries out various modification or change not deviating under spirit of the present invention.It should be noted that, when not conflicting, the feature in following examples and embodiment can combine mutually.
It should be noted that, the diagram provided in following examples only illustrates basic conception of the present invention in a schematic way, then only the assembly relevant with the present invention is shown in graphic but not component count, shape and size when implementing according to reality is drawn, it is actual when implementing, and the kenel of each assembly, quantity and ratio can be a kind of change arbitrarily, and its assembly layout kenel also may be more complicated.
Refer to Fig. 1, the invention provides a kind of paths planning method based on traffic route data, comprise the steps:
Step S1: obtain trip origin information, travel destination information and travel time scope.It should be noted that, trip origin information, travel destination information can be concrete title or coordinate position.
Step S2: every a predetermined time interval within the travel time, performs step S3.Predetermined time interval is less, more can reflect present road situation in real time.
Step S3: go out all trip route according to described trip origin information and travel destination information planning, can utilize the instrument such as navigation software or navigating instrument to complete.
Step S4: the Current traffic road data and the historical traffic road data that obtain each described trip route respectively, calculates the cur-rent congestion exponential sum history congestion index of each described trip route according to this respectively, is specially:
1) described trip route is become each section according to urban road grade classification.
2) obtain Current traffic road data and the historical traffic road data in each described section, calculate the cur-rent congestion exponential sum history congestion index in each described section according to this respectively.
3) according to the ratio-dependent weight coefficient of length in each affiliated trip route in each section.
4) respectively the cur-rent congestion exponential sum history congestion index obtaining each described trip route is weighted to the cur-rent congestion exponential sum history congestion index in each section in each described trip route.
In one embodiment, described Current traffic road data at least comprises current average speeds, and described historical traffic road data at least comprises history average speeds.It should be noted that, the calculating of congestion index is prior art, and those skilled in the art can be calculated by average speeds.Preferably, definition congestion index is " unimpeded " at 0-20, and 20-40 is " substantially unimpeded ", 40-60 is " slightly blocking up ", 60-80 is " moderate is blocked up ", and 80-100 is " heavy congestion ", and its jam level corresponding is respectively 1 grade, 2 grades, 3 grades, 4 grades, 5 grades.The jam level that the average speeds scope in each section is corresponding may be defined as following form:
| 1 | 2 | 3 | 4 | 5 | |
| Through street | ＞65 | (50，65] | (35，50] | (20，35] | ≤20 |
| Trunk roads | ＞45 | (35，45] | (25，35] | (15，25] | ≤15 |
| Secondary distributor road | ＞35 | (25，35] | (15，25] | (10，15] | ≤10 |
| Branch road | ＞35 | (25，35] | (15，25] | (10，15] | ≤10 |
In one embodiment, when described Current traffic road data also comprises: when traffic accident information, roadupkeep information or road road closures information, delete corresponding trip route.That is, there is impassable situation in current trip route, namely get rid of this path.
Step S5: judge whether to meet the first pre-conditioned trip route, if having, then performs step S6; If nothing, then wouldn't operate, wait and enter next predetermined time interval.Wherein, described first pre-conditionedly comprises: cur-rent congestion index exceedes predetermined threshold value lower than history congestion index.
Step S6: using current time as the travel time.
Step S7: select to meet the second pre-conditioned trip route as preferred trip route meeting in described first pre-conditioned trip route.Wherein, described second pre-conditioned comprise following in one:
1) cur-rent congestion index is minimum.
2) cur-rent congestion index lower than history congestion index difference meet in described first pre-conditioned trip route maximum.
The implementation of this method will be described by a preferred embodiment below, illustrate with Fig. 2:
Obtaining trip origin information is A, and travel destination information is B, and travel time scope is 6:00 ~ 09:00.Predetermined time interval is 5 minutes.Cook up all trip route according to trip origin information A and travel destination information B and have 3: C1, C2 and C3.Respectively C1, C2 are become each section with C3 according to urban road grade classification, comprising: through street, trunk roads, secondary distributor road and branch road.Obtain Current traffic road data and the historical traffic road data in each section that C1 comprises, such as: current average speeds, history average speeds, and calculate the cur-rent congestion exponential sum history congestion index in each section respectively.When described Current traffic road data also comprises: when traffic accident information, roadupkeep information or road road closures information, delete trip route C1; Otherwise, then C1 is retained.
According to the ratio-dependent weight coefficient of length in C1 in each section.Suppose that C1 is 10 kilometers, comprising: 2 kilometers, a through street, trunk roads 4 kilometers, one secondary distributor road 3 kilometers and a branch road 1 kilometer, then the weight coefficient of through street is 0.2, and the weight coefficient of trunk roads is 0.4, the weight coefficient of secondary distributor road is 0.3, and the weight coefficient of branch road is 0.1.Respectively the cur-rent congestion exponential sum history congestion index in section each in C1 is weighted to the cur-rent congestion exponential sum history congestion index obtaining C1.
Identical process is done to C2, C3, in the present road traffic data of this hypothesis C2 and C3, includes traffic accident information and road road closures information, thus do not do to retain.Fig. 2 shows cur-rent congestion index curve and the history congestion index curve of the trip route C1 calculated, and visible, the current time is 08:10, and cur-rent congestion index is 23, and history congestion index is 30.
Judge the trip route whether C1 meets cur-rent congestion index and exceed predetermined threshold value lower than history congestion index, such as, predetermined threshold value is the cur-rent congestion index of 10, C1 is 7 lower than history congestion index, is no more than 10, then think that C1 does not meet first pre-conditioned.Again such as, predetermined threshold value is the cur-rent congestion index of 4, C1 is 7 lower than history congestion index, more than 4, then thinks that C1 meets first pre-conditioned, current time point is decided to be the travel time.
It should be noted that, do not comprise in the trip route of traffic accident information, roadupkeep information or road road closures information etc. in all planning, as long as there have a trip route to meet to be first pre-conditioned, the current time can be determined to be decided to be the travel time, if all do not meet the first pre-conditioned trip route, then wouldn't process, wait for the arrival of next prefixed time interval.
After having had the travel time, start to determine trip route, if pre-conditionedly trip route cannot be selected according to second, then wouldn't go on a journey, be specially: continue to judge whether to meet the second pre-conditioned trip route meeting in described first pre-conditioned trip route, if have, as preferred trip route, wherein, described second is pre-conditionedly: cur-rent congestion index is minimum, or meeting in first pre-conditioned all trip route, cur-rent congestion index is maximum lower than the difference of history congestion index.Assuming that the final traffic path selected is C1, and the travel time is 08:10.
Refer to Fig. 3, similar to said method embodiment principle, the invention provides a kind of path planning system 1 based on traffic route data, comprising: load module 11 and processing module 12.Because the technical characteristic in embodiment of the method also can be applied in native system embodiment, thus it is no longer repeated.
Load module 11 obtains trip origin information, travel destination information and travel time scope.Processing module 12 every a predetermined time interval, performs following steps: go out all trip route according to described trip origin information and travel destination information planning within the travel time.Obtain Current traffic road data and the historical traffic road data of each described trip route respectively, calculate the cur-rent congestion exponential sum history congestion index of each described trip route according to this respectively, preferably, described Current traffic road data at least comprises current average speeds, and described historical traffic road data at least comprises history average speeds.Judge whether to meet the first pre-conditioned trip route, if have, then using current time as the travel time, wherein said first pre-conditionedly comprises: cur-rent congestion index exceedes predetermined threshold value lower than the difference of history congestion index, select to meet the second pre-conditioned trip route as preferred trip route meeting in described first pre-conditioned trip route, wherein, described second pre-conditioned comprise following in one: (1) cur-rent congestion index minimum (2) cur-rent congestion index lower than history congestion index difference meet in described first pre-conditioned trip route maximum.
In one embodiment, when described Current traffic road data also comprises: when traffic accident information, roadupkeep information or road road closures information, described processing module 12 is also for deleting corresponding trip route.
In one embodiment, described Current traffic road data and the historical traffic road data obtaining each described trip route respectively, calculate the cur-rent congestion exponential sum history congestion index of each described trip route according to this respectively, be specially: described trip route is become each section according to urban road grade classification, obtain Current traffic road data and the historical traffic road data in each described section, calculate the cur-rent congestion exponential sum history congestion index in each described section according to this respectively, according to the ratio-dependent weight coefficient of length in each affiliated trip route in each section, respectively the cur-rent congestion exponential sum history congestion index in each section in each described trip route is weighted to the cur-rent congestion exponential sum history congestion index obtaining each described trip route.
Similar to said method embodiment principle, the present invention also provides a kind of mobile terminal (not shown), comprising: input block, and the processing unit be connected with input block.Mobile terminal includes but not limited to automatic navigator, smart mobile phone or apparatus such as computer.Input block includes but not limited to the equipment such as touch-screen, mouse, keyboard.Processing unit can be CPU, MCU or SOC and circuit realiration etc. thereof.Because the technical characteristic in embodiment of the method can apply in this hardware embodiment, thus it is no longer repeated.
Input block obtains trip origin information, travel destination information and travel time scope.Processing unit within the travel time every a predetermined time interval, perform following steps: go out all trip route according to described trip origin information and travel destination information planning, obtain Current traffic road data and the historical traffic road data of each described trip route respectively, calculate the cur-rent congestion exponential sum history congestion index of each described trip route according to this respectively, judge whether to meet the first pre-conditioned trip route, if have, then using current time as the travel time, wherein said first pre-conditionedly comprises: cur-rent congestion index exceedes predetermined threshold value lower than the difference of history congestion index, select to meet the second pre-conditioned trip route as preferred trip route meeting in described first pre-conditioned trip route, wherein, described second pre-conditioned comprise following in one: (1) cur-rent congestion index minimum (2) cur-rent congestion index lower than history congestion index difference meet in described first pre-conditioned trip route maximum.
In sum, paths planning method based on traffic route data of the present invention and system, by binding analysis Current traffic road data and historical traffic data, for trip user provides best travel time and trip route intelligently, user is facilitated to go on a journey, promote Consumer's Experience, effectively overcome various shortcoming of the prior art and tool high industrial utilization.
Above-described embodiment is illustrative principle of the present invention and effect thereof only, but not for limiting the present invention.Any person skilled in the art scholar all without prejudice under spirit of the present invention and category, can modify above-described embodiment or changes.Therefore, such as have in art usually know the knowledgeable do not depart from complete under disclosed spirit and technological thought all equivalence modify or change, must be contained by claim of the present invention.
Claims (10)
1. based on a paths planning method for traffic route data, it is characterized in that, comprising:
Obtain trip origin information, travel destination information and travel time scope;
Every a predetermined time interval within the travel time, perform following steps:
All trip route are gone out according to described trip origin information and travel destination information planning;
Obtain Current traffic road data and the historical traffic road data of each described trip route respectively, calculate the cur-rent congestion exponential sum history congestion index of each described trip route according to this respectively;
Judge whether to meet the first pre-conditioned trip route, if having, then using current time as the travel time, wherein said first pre-conditionedly comprises: cur-rent congestion index exceedes predetermined threshold value lower than the difference of history congestion index;
Select to meet the second pre-conditioned trip route as preferred trip route meeting in described first pre-conditioned trip route, wherein, described second pre-conditioned comprise following in one: (1) cur-rent congestion index is minimum; (2) cur-rent congestion index lower than history congestion index difference meet in described first pre-conditioned trip route maximum.
2. the paths planning method based on traffic route data according to claim 1, is characterized in that, described Current traffic road data at least comprises current average speeds; Described historical traffic road data at least comprises history average speeds.
3. the paths planning method based on traffic route data according to claim 2, is characterized in that, when described Current traffic road data also comprises: when traffic accident information, roadupkeep information or road road closures information, delete corresponding trip route.
4. the paths planning method based on traffic route data according to claim 1, it is characterized in that, described Current traffic road data and the historical traffic road data obtaining each described trip route respectively, calculate the cur-rent congestion exponential sum history congestion index of each described trip route according to this respectively, comprising:
Described trip route is become each section according to urban road grade classification;
Obtain Current traffic road data and the historical traffic road data in each described section, calculate the cur-rent congestion exponential sum history congestion index in each described section according to this respectively;
According to the ratio-dependent weight coefficient of length in each affiliated trip route in each section;
Respectively the cur-rent congestion exponential sum history congestion index in each section in each described trip route is weighted to the cur-rent congestion exponential sum history congestion index obtaining each described trip route.
5. based on a path planning system for traffic route data, it is characterized in that, comprising:
Load module, for obtaining trip origin information, travel destination information and travel time scope;
Processing module, within the travel time every a predetermined time interval, perform following steps:
All trip route are gone out according to described trip origin information and travel destination information planning;
Obtain Current traffic road data and the historical traffic road data of each described trip route respectively, calculate the cur-rent congestion exponential sum history congestion index of each described trip route according to this respectively;
Judge whether to meet the first pre-conditioned trip route, if having, then using current time as the travel time, wherein said first pre-conditionedly comprises: cur-rent congestion index exceedes predetermined threshold value lower than the difference of history congestion index;
Select to meet the second pre-conditioned trip route as preferred trip route meeting in described first pre-conditioned trip route, wherein, described second pre-conditioned comprise following in one: (1) cur-rent congestion index is minimum; (2) cur-rent congestion index lower than history congestion index difference meet in described first pre-conditioned trip route maximum.
6. the path planning system based on traffic route data according to claim 5, is characterized in that, described Current traffic road data at least comprises current average speeds; Described historical traffic road data at least comprises history average speeds.
7. the path planning system based on traffic route data according to claim 6, it is characterized in that, described processing module also for, when described Current traffic road data also comprises: when traffic accident information, roadupkeep information or road road closures information, delete corresponding trip route.
8. the path planning system based on traffic route data according to claim 5, it is characterized in that, described Current traffic road data and the historical traffic road data obtaining each described trip route respectively, calculate the cur-rent congestion exponential sum history congestion index of each described trip route according to this respectively, comprising:
Described trip route is become each section according to urban road grade classification;
Obtain Current traffic road data and the historical traffic road data in each described section, calculate the cur-rent congestion exponential sum history congestion index in each described section according to this respectively;
According to the ratio-dependent weight coefficient of length in each affiliated trip route in each section;
Respectively the cur-rent congestion exponential sum history congestion index in each section in each described trip route is weighted to the cur-rent congestion exponential sum history congestion index obtaining each described trip route.
9. a mobile terminal, is characterized in that, comprising:
Input block, for obtaining trip origin information, travel destination information and travel time scope;
Processing unit, is connected with described input block, within the travel time every a predetermined time interval, perform following steps:
All trip route are gone out according to described trip origin information and travel destination information planning;
Obtain Current traffic road data and the historical traffic road data of each described trip route respectively, calculate the cur-rent congestion exponential sum history congestion index of each described trip route according to this respectively;
Judge whether to meet the first pre-conditioned trip route, if having, then using current time as the travel time, wherein said first pre-conditionedly comprises: cur-rent congestion index exceedes predetermined threshold value lower than the difference of history congestion index;
Select to meet the second pre-conditioned trip route as preferred trip route meeting in described first pre-conditioned trip route, wherein, described second pre-conditioned comprise following in one: (1) cur-rent congestion index is minimum; (2) cur-rent congestion index lower than history congestion index difference meet in described first pre-conditioned trip route maximum.
10. mobile terminal according to claim 9, is characterized in that, described mobile terminal comprises: automatic navigator, smart mobile phone or computer.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1517680A (en) * | 2003-01-10 | 2004-08-04 | ������������ʽ���� | Navigation server and navigation display method |
| CN103177570A (en) * | 2012-12-25 | 2013-06-26 | 北京交通发展研究中心 | Method for predicting traffic jam indexes for rush hours in morning and evening |
| CN103514743A (en) * | 2013-09-28 | 2014-01-15 | 上海电科智能系统股份有限公司 | Method for recognizing abnormal traffic state characteristics of real-time index data matching memory range |
| JP5560026B2 (en) * | 2009-12-02 | 2014-07-23 | 株式会社ゼンリンデータコム | Map display device, map display method, and map display program |
| CN104157142A (en) * | 2014-08-27 | 2014-11-19 | 河海大学 | Urban path travel time forecasting method based on floating vehicle data |
| CN104567897A (en) * | 2013-10-16 | 2015-04-29 | 大陆汽车投资（上海）有限公司 | Road condition forecast combined path planning method and navigation device |
| CN104882020A (en) * | 2015-06-05 | 2015-09-02 | 刘光明 | Method for predicting traffic conditions and driving time |
| CN104931065A (en) * | 2015-06-05 | 2015-09-23 | 刘光明 | Map with relative traffic condition and drive route |
-
2015
- 2015-10-16 CN CN201510672763.5A patent/CN105387865B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1517680A (en) * | 2003-01-10 | 2004-08-04 | ������������ʽ���� | Navigation server and navigation display method |
| JP5560026B2 (en) * | 2009-12-02 | 2014-07-23 | 株式会社ゼンリンデータコム | Map display device, map display method, and map display program |
| CN103177570A (en) * | 2012-12-25 | 2013-06-26 | 北京交通发展研究中心 | Method for predicting traffic jam indexes for rush hours in morning and evening |
| CN103514743A (en) * | 2013-09-28 | 2014-01-15 | 上海电科智能系统股份有限公司 | Method for recognizing abnormal traffic state characteristics of real-time index data matching memory range |
| CN104567897A (en) * | 2013-10-16 | 2015-04-29 | 大陆汽车投资（上海）有限公司 | Road condition forecast combined path planning method and navigation device |
| CN104157142A (en) * | 2014-08-27 | 2014-11-19 | 河海大学 | Urban path travel time forecasting method based on floating vehicle data |
| CN104882020A (en) * | 2015-06-05 | 2015-09-02 | 刘光明 | Method for predicting traffic conditions and driving time |
| CN104931065A (en) * | 2015-06-05 | 2015-09-23 | 刘光明 | Map with relative traffic condition and drive route |
Cited By (25)
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|---|---|---|---|---|
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| CN107957267B (en) * | 2016-10-14 | 2021-07-30 | 阿里巴巴（中国）有限公司 | Method and device for determining navigation path prompt information |
| CN106530702B (en) * | 2016-11-24 | 2019-05-03 | 广东顺德中山大学卡内基梅隆大学国际联合研究院 | A kind of stochastic and dynamic network traffic planing method based on traffic index |
| CN106530702A (en) * | 2016-11-24 | 2017-03-22 | 广东顺德中山大学卡内基梅隆大学国际联合研究院 | Random dynamic network traffic planning method based on traffic exponent |
| CN106767871A (en) * | 2016-12-27 | 2017-05-31 | 上海斐讯数据通信技术有限公司 | A kind of navigation system and its application method of the preset mode based on high in the clouds |
| WO2018165848A1 (en) * | 2017-03-14 | 2018-09-20 | 深圳市南北汽车美容有限公司 | Method for analysing real-time road conditions to recommend travel time, and navigation system |
| CN108627162A (en) * | 2017-03-21 | 2018-10-09 | 高德信息技术有限公司 | A kind of navigation routine determines method and device |
| CN107123264A (en) * | 2017-05-31 | 2017-09-01 | 温州市鹿城区中津先进科技研究院 | A kind of method that abnormal congestion is judged based on traffic big data |
| CN110285820A (en) * | 2018-03-19 | 2019-09-27 | 上海博泰悦臻网络技术服务有限公司 | A kind of trip road condition predicting method and system for vehicle device |
| CN108981739A (en) * | 2018-06-08 | 2018-12-11 | 南方科技大学 | Path planning method, device, server and storage medium |
| CN108981739B (en) * | 2018-06-08 | 2022-02-22 | 南方科技大学 | Path planning method, device, server and storage medium |
| CN108830426A (en) * | 2018-07-03 | 2018-11-16 | 华平智慧信息技术（深圳）有限公司 | Rescue layout of roads method and Related product |
| CN109816151A (en) * | 2018-12-29 | 2019-05-28 | 天津五八到家科技有限公司 | Transit route planning, replay method, server and storage medium |
| CN110184867A (en) * | 2019-06-10 | 2019-08-30 | 葛志凯 | Alleviate traffic congestion and reduces the roading method and system of vehicle exhaust emission |
| CN110276951A (en) * | 2019-06-26 | 2019-09-24 | 朱志强 | One kind being based on mobile Internet Congestion prediction method |
| CN110276951B (en) * | 2019-06-26 | 2020-11-13 | 朱志强 | Traffic jam early warning method based on mobile internet |
| CN110823236A (en) * | 2019-10-12 | 2020-02-21 | 百度在线网络技术（北京）有限公司 | Path planning method and device, electronic equipment and storage medium |
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| CN111397625A (en) * | 2020-03-30 | 2020-07-10 | 腾讯科技（深圳）有限公司 | Vehicle navigation method and related device |
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