CN103730005A - Method and system for predicting journey running time - Google Patents
Method and system for predicting journey running time Download PDFInfo
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- CN103730005A CN103730005A CN201410027120.0A CN201410027120A CN103730005A CN 103730005 A CN103730005 A CN 103730005A CN 201410027120 A CN201410027120 A CN 201410027120A CN 103730005 A CN103730005 A CN 103730005A
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Abstract
The invention discloses a method for predicting journey running time. The method comprises the steps of presetting a running speed Vset and a running route, and enabling the total distance of the running route to be Z km; equally dividing the running route into n sections, and enabling the position of a target vehicle to be a first section of the running route; obtaining a current running speed V of the target vehicle and an average running speed of vehicles in other sections, and if no running vehicle is in the corresponding sections, setting the running speed of the corresponding sections as the preset running speed Vset; calculating total time Sn needed for running for the journey by the equation that Sn=T1+T2+...+Tn, wherein meeting the equation that T1=Z/(n*V), and meeting the equation Tn=Z/(n*Vn). According to the method, by obtaining information such as the running speed on the running route, time prediction accuracy is effectively improved, and user experience is greatly improved.
Description
Technical field
?the present invention relates to a kind of Forecasting Methodology and system, specifically automobile Forecasting Methodology and the system of needed time of travelling in a certain section of distance.
Background technology
along with society and expanding economy, the raising of people's life, most of family all has an Automobile, for trip has at ordinary times brought convenience.Along with the increase of automobile pollution, during much highways, or even expressway all often there will be situation, the especially festivals or holidays of traffic congestion, situation is more severe.Due to situation about blocking up on road, cause people often to waste a large amount of time on the way, and owing to can not obtaining in advance the running time of corresponding distance, make the arrival destination that people can not be punctual, to people, brought inconvenience.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of Forecasting Methodology and system of distance running time, effectively promotes the precision of predicted time, promotes greatly user and experiences.
On the one hand, the invention provides a kind of Forecasting Methodology of distance running time, comprise the following steps:
Default road speed V
setand traffic route, the total kilometrage of this traffic route is Z km;
Traffic route is on average divided into n section, target carriage position is this traffic route the 1st section;
Obtain the current driving speed V of target carriage, and the average overall travel speed of the car in other segmentations, if there is no driving vehicle in corresponding segment, the travel speed of this corresponding segments is default road speed V
set;
Calculating this section of needed T.T. S of distance that travel
n=T
1+ T
2+ ... + T
n, wherein T
1=Z/ (n*V), T
n=Z/ (n*Vn).
Described method also comprises: default traffic congestion time T
setif the actual travel speed in a certain segmentation of target carriage is less than 10 km/H,, in traffic congestion state, works as T
nduring >Z/ (n*10), by T
nvalue be made as Z/ (n*10).
Described method also comprises: if T
n> T
set, by T
nvalue be made as T
set.
Described method also comprises: each section that traffic route is divided has traffic capacity μ, this μ=V/V
setif, μ > 1, μ assignment is 1.
On the other hand, the present invention also provides a kind of prognoses system of distance running time, comprises setting unit, for Offered target road speed.Traffic route and traffic congestion time; Division unit, for being divided into traffic route n section; Computing unit, travels the needed time of corresponding distance for calculating.
The present invention, by travel route being divided into n segmentation, obtains the travel speed of each segmentation, effectively promotes the precision of predicted time, promotes greatly user and experiences.
Accompanying drawing explanation
Accompanying drawing 1 is schematic flow sheet of the present invention.
Embodiment
For the ease of those skilled in the art's understanding, below in conjunction with accompanying drawing, the invention will be further described.
As shown in Figure 1, the present invention has disclosed a kind of Forecasting Methodology of distance running time, comprises the following steps:
S1, default road speed V
setand traffic route, the total kilometrage of this traffic route is Z km.Can be by Offered target road speed V on mobile phone terminal
setand traffic route, user can arrange according to type of vehicle, driving custom, as to pre-set road speed be 100km/H.Mobile phone terminal is by these information reportings that arrange to server, and server carries out registration process after receiving these information.
S2, is on average divided into n section by traffic route, target carriage position is this traffic route the 1st section.
S3, obtains the current driving speed V of target carriage, and the average overall travel speed of the car in other segmentations, if there is no driving vehicle in corresponding segment, the travel speed of this corresponding segments is default road speed V
set.Current driving speed is reported to server, and the mobile phone terminal in each segmentation can be by information reporting to server, server is processed according to the information receiving.If there are multiple driving vehicle reporting informations in some segmentations, get the mean value of its speed as the current driving speed in this section.In each segmentation, all can possess driving traffic capacity μ, this μ=V/V
setif, μ > 1, μ assignment is 1, as target line vehicle speed V
setfor 100km/H, current actual travel speed is 20km/H, μ=0.2, and the traffic capacity is lower, and the road speed in this section, surface can be comparatively slow, can allow user have a clear and intuitive understanding.
S4, calculates this section of needed T.T. S of distance that travel
n=T
1+ T
2+ ... + T
n, wherein T
1=Z/ (n*V), T
n=Z/ (n*Vn).According to the road speed in each segmentation of obtaining, can calculate current period expert and finish this distance needed prediction T.T..
Described method also comprises: default traffic congestion time T
set, this traffic congestion time can be set according to driving experience at ordinary times, as is made as 0.5 hour, or other.If the actual travel speed in a certain segmentation of target carriage is less than 10 km/H,, in traffic congestion state, work as T
nduring >Z/ (n*10), by T
nvalue be made as Z/ (n*10), when the actual speed of driving a vehicle is less than 10 km/H, all treating as road speed is 10 km/H.
Described method also comprises: if T
n> T
set, by T
nvalue be made as T
set.In a certain segmentation of dividing, if the running time calculating is greater than the traffic congestion time, the running time of visiting segmentation is counted to the traffic congestion time.
Described method also comprises: each section that traffic route is divided has traffic capacity μ, this μ=V/V
setif, μ > 1, μ assignment is 1, by the traffic capacity, can be found out, μ value is higher, shows that the traffic capacity is higher, illustrates that road up train is more smooth and easy; μ value is lower, shows that the traffic capacity is lower, illustrates that road up train is not very smooth and easy, and vehicle is more.
In addition, correction factor also can be set, the road speed in a certain segmentation be revised to Vn=V
set* β
n, according to current location, apart from prediction between the n of section, need running time S
n-1and the traffic capacity μ of section n
nproofread and correct V
n.Work as S
n-1>=T
settime, β
n=1, thinking travels has removed in the traffic congestion situation of n section before arriving n section, can use the travel speed that pre-sets of mobile phone terminal self while driving to n section.Work as S
n-1< T
settime, β
n=(T
set-S
n-1)/T
set+ S
n-1/ T
set,, as the running time S apart from section n
n-1approach at 0 o'clock, correction factor=μ
n; And at 0 < S
n-1< T
setbetween, progressively reduce μ
non the impact of correction factor.If calculate
,
.For T
1, T
2t
nif, the running time > T of a certain segmentation
set, assignment is T
set.
On the other hand, the present invention also provides a kind of prognoses system of distance running time, comprises setting unit, for Offered target road speed.Traffic route and traffic congestion time; Division unit, for being divided into traffic route n section; Computing unit, travels the needed time of corresponding distance for calculating.
Claims (6)
1. a Forecasting Methodology for distance running time, comprises the following steps:
Default road speed V
setand traffic route, the total kilometrage of this traffic route is Z km;
Traffic route is on average divided into n section, target carriage position is this traffic route the 1st section;
Obtain the current driving speed V of target carriage, and the average overall travel speed of the car in other segmentations, if there is no driving vehicle in corresponding segment, the travel speed of this corresponding segments is default road speed V
set;
Calculating this section of needed T.T. S of distance that travel
n=T
1+ T
2+ ... + T
n, wherein T
1=Z/ (n*V), T
n=Z/ (n*Vn).
2. the Forecasting Methodology of distance running time according to claim 1, is characterized in that, described method also comprises: default traffic congestion time T
setif the actual travel speed in a certain segmentation of target carriage is less than 10 km/H,, in traffic congestion state, works as T
nduring >Z/ (n*10), by T
nvalue be made as Z/ (n*10).
3. the Forecasting Methodology of distance running time according to claim 2, is characterized in that, described method also comprises: if T
n> T
set, by T
nvalue be made as T
set.
4. the Forecasting Methodology of distance running time according to claim 3, is characterized in that, described method also comprises: each section that traffic route is divided has traffic capacity μ, this μ=V/V
setif, μ > 1, μ assignment is 1.
5. a prognoses system for distance running time, is characterized in that, described system comprises setting unit, for Offered target road speed.
6. traffic route and the traffic congestion time; Division unit, for being divided into traffic route n section; Computing unit, travels the needed time of corresponding distance for calculating.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410027120.0A CN103730005B (en) | 2014-01-22 | 2014-01-22 | Method and system for predicting journey running time |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410027120.0A CN103730005B (en) | 2014-01-22 | 2014-01-22 | Method and system for predicting journey running time |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103730005A true CN103730005A (en) | 2014-04-16 |
| CN103730005B CN103730005B (en) | 2017-01-18 |
Family
ID=50454057
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410027120.0A Expired - Fee Related CN103730005B (en) | 2014-01-22 | 2014-01-22 | Method and system for predicting journey running time |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111681414A (en) * | 2020-04-30 | 2020-09-18 | 安徽科力信息产业有限责任公司 | Method and device for evaluating and predicting time precision required by vehicle to pass signal lamp intersection |
| CN111681413A (en) * | 2020-04-30 | 2020-09-18 | 安徽科力信息产业有限责任公司 | Method and device for predicting crossing time of motor vehicle passing signal lamp in real time |
| CN111681415A (en) * | 2020-04-30 | 2020-09-18 | 安徽科力信息产业有限责任公司 | Method and system for predicting number of motor vehicles on expressway in real time |
| CN112639904A (en) * | 2018-09-06 | 2021-04-09 | 本田技研工业株式会社 | Route subdividing device |
| CN113538908A (en) * | 2021-07-09 | 2021-10-22 | 大连海事大学 | Road condition partition system based on combined characteristic parameters |
| CN113538907A (en) * | 2021-07-09 | 2021-10-22 | 大连海事大学 | Traffic flow classification-based driving time estimation system |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109817010B (en) * | 2017-11-20 | 2020-11-10 | 北京嘀嘀无限科技发展有限公司 | ETA (extract transform and extract) estimation method and device based on network and statistical model |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007004290A (en) * | 2005-06-21 | 2007-01-11 | Aisin Aw Co Ltd | Travel time database creation device |
| JP2008046955A (en) * | 2006-08-18 | 2008-02-28 | Xanavi Informatics Corp | Predictive traffic information generation method, predictive traffic information generator, and traffic information display terminal |
| US20090030596A1 (en) * | 2007-07-25 | 2009-01-29 | Xanavi Informatics Corporation | Traffic Information Providing System, Apparatus, Method, And In-Vehicle Information Apparatus |
| CN101436347A (en) * | 2008-12-09 | 2009-05-20 | 北京交通大学 | Prediction method for rapid road travel time |
| CN102298839A (en) * | 2011-07-12 | 2011-12-28 | 北京世纪高通科技有限公司 | Method and device for computing OD travel time |
-
2014
- 2014-01-22 CN CN201410027120.0A patent/CN103730005B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007004290A (en) * | 2005-06-21 | 2007-01-11 | Aisin Aw Co Ltd | Travel time database creation device |
| JP2008046955A (en) * | 2006-08-18 | 2008-02-28 | Xanavi Informatics Corp | Predictive traffic information generation method, predictive traffic information generator, and traffic information display terminal |
| US20090030596A1 (en) * | 2007-07-25 | 2009-01-29 | Xanavi Informatics Corporation | Traffic Information Providing System, Apparatus, Method, And In-Vehicle Information Apparatus |
| CN101436347A (en) * | 2008-12-09 | 2009-05-20 | 北京交通大学 | Prediction method for rapid road travel time |
| CN102298839A (en) * | 2011-07-12 | 2011-12-28 | 北京世纪高通科技有限公司 | Method and device for computing OD travel time |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112639904A (en) * | 2018-09-06 | 2021-04-09 | 本田技研工业株式会社 | Route subdividing device |
| CN112639904B (en) * | 2018-09-06 | 2022-10-11 | 本田技研工业株式会社 | Route subdividing device |
| CN111681414A (en) * | 2020-04-30 | 2020-09-18 | 安徽科力信息产业有限责任公司 | Method and device for evaluating and predicting time precision required by vehicle to pass signal lamp intersection |
| CN111681413A (en) * | 2020-04-30 | 2020-09-18 | 安徽科力信息产业有限责任公司 | Method and device for predicting crossing time of motor vehicle passing signal lamp in real time |
| CN111681415A (en) * | 2020-04-30 | 2020-09-18 | 安徽科力信息产业有限责任公司 | Method and system for predicting number of motor vehicles on expressway in real time |
| CN111681414B (en) * | 2020-04-30 | 2021-12-03 | 安徽科力信息产业有限责任公司 | Method and device for evaluating and predicting time precision required by vehicle to pass signal lamp intersection |
| CN113538908A (en) * | 2021-07-09 | 2021-10-22 | 大连海事大学 | Road condition partition system based on combined characteristic parameters |
| CN113538907A (en) * | 2021-07-09 | 2021-10-22 | 大连海事大学 | Traffic flow classification-based driving time estimation system |
| CN113538907B (en) * | 2021-07-09 | 2022-05-03 | 大连海事大学 | Traffic flow classification-based driving time estimation system |
| CN113538908B (en) * | 2021-07-09 | 2022-05-17 | 大连海事大学 | Road condition partition system based on combined characteristic parameters |
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| Publication number | Publication date |
|---|---|
| CN103730005B (en) | 2017-01-18 |
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Address after: Changan town in Guangdong province Dongguan 523860 usha Beach Road No. 18 Patentee after: GUANGDONG OPPO MOBILE TELECOMMUNICATIONS Corp.,Ltd. Address before: Changan town in Guangdong province Dongguan 523860 usha Beach Road No. 18 Patentee before: GUANGDONG OPPO MOBILE TELECOMMUNICATIONS Corp.,Ltd. |
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Granted publication date: 20170118 |
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