CN107665375A - In generation, drives the time predictor method and device that driver reaches generation and drives passenger position - Google Patents
In generation, drives the time predictor method and device that driver reaches generation and drives passenger position Download PDFInfo
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Abstract
The present invention provides a kind of generation and drives driver and reach generation and drive the time predictor method of passenger position and device, method and include：Obtain the positional information of generation driving visitor；Passenger position information is driven according to generation and generation drives driver's positional information and obtained and estimates generation with driving driver in nearest generation of estimating for driving visitor, and generating and drive driver position and arrive for the first Estimative path distance and the first estimated time for driving passenger position；In acquisition generation, drives characteristic information from historical record；Determine the traffic information of current point in time；Characteristic information and traffic information are driven according to the first Estimative path distance, the first estimated time, generation calculate to obtain and estimate the second Estimative path distance and the second estimated time that generation drives driver position and drive to the generation passenger position, and by the second estimated time be sent to generation drive passenger terminal, so that passenger determines whether that sending generation drives order request according to the second Estimative path distance and the second estimated time, reach generation driving visitor and understand estimated time situation, and the necessity for sending request is analyzed, strengthen usage experience.
Description
Technical field
The present invention relates to generation to drive technical field, more particularly to a kind of generation drives driver's arrival and estimated for the time for driving passenger position
Method and device.
Background technology
Generation drive exactly when car owner voluntarily can not drive to arrive at, by professional human pilot drive car owner car by its
Deliver to appointed place and collect the behavior of certain expense.And slowly turn into hot blast for driving, make some inconvenient crowds more
Safety.
It is to be based on ground mostly to be driven at present in generation and drive driver's arrival on software to generation for the estimated time of objective (car owner) position of driving
The service class computation for managing position obtains.The estimated time that this application returns be actually the path that returns of path planning away from
From being multiplied by a fixed coefficient.But under some different road conditions, relatively large deviation occurs in the estimated time obtained according to this, makes generation
Drive visitor and lose reference value.
The content of the invention
A kind of in present invention offer generation, drives the time predictor method and device that driver reaches generation and drives passenger position, existing for solving
There is technology to drive driver and reach generation and the problem of relatively large deviation easily occur for the estimated time for driving passenger position.
On the one hand, a kind of in present invention offer generation, drives the time predictor method that driver reaches generation and drives passenger position, including：
Obtain the positional information of generation driving visitor；
The positional information of visitor is driven according to the generation and generation drives the positional information acquisition of driver and the generation drives objective straight line
In closest generation of estimating, drives driver, and drives driver position to generation driving seats reserved for guests or passengers using generation is estimated described in the calculating of the first algorithm
The the first Estimative path distance put and the first estimated time；
Driven according to described estimate for the identity information for driving driver from history for the generation driven in record in acquisition preset time period
Characteristic information；
Obtain the traffic information of current point in time；
Characteristic information is driven according to first Estimative path distance, the first estimated time, generation and traffic information uses second
Algorithm calculate described in estimate generation drive driver position to the generation drive the second Estimative path distance of passenger position and second estimate when
Between, and by second estimated time be sent to generation drive passenger terminal.
Preferably, in addition to：Obtaining for before driving passenger position information, obtaining for the positional information and identity for driving driver
Information.
Preferably, first algorithm, including：
The positional information of driver is driven according to the default generation and the generation drives objective positional information and uses path planning side
Method obtains the first Estimative path distance；
First estimated time was obtained according to the first Estimative path distance and preset travel speed.
Preferably, second algorithm is the machine learning algorithm using GBDT models.
Preferably, estimate to drive in record from history generation for the identity information for driving driver described in the basis and obtain preset time
In generation in section, drives characteristic information, including：
Generation is estimated described in acquisition and drives history average overall travel speed of the driver in preset time period；
Generation is estimated described in acquisition and drives history average preparation time of the driver in preset time period, the time is generation
Drive the time that the predeterminable area centered on order ground is left after driver's order；
Generation is estimated described in acquisition and drives driver on the driving trace in preset time period with the presence or absence of generation driving visitor's
Positional information；
Obtaining all generations in preset time period drives driver and estimates generation described and drive the history on driver position and averagely prepare
Time.
Preferably, the traffic information includes working day, nonworkdays, default working peak period, non-default working peak
Phase, in default generation, drives the peak traffic phase, non-default generation drives the one or more of peak traffic phase.
In second aspect, a kind of present invention offer generation, drive the time estimating device that driver reaches generation and drives passenger position, including：
First acquisition module, the positional information of visitor is driven for obtaining generation；
First processing module, for according to the generation drive visitor positional information and generation drive driver positional information obtain with
In the generation, drives objective air line distance nearest generation of estimating and drives driver, and drives driver position using generation is estimated described in the calculating of the first algorithm
The first Estimative path distance and the first estimated time of passenger position are driven to the generation；
Second acquisition module, it is pre- for acquisition in record is driven from history for the identity information for driving driver for being estimated according to
If in the generation in the period, drives characteristic information；
3rd acquisition module, for obtaining the traffic information of current point in time；
Second processing module, for according to first Estimative path distance, the first estimated time, generation drive characteristic information and
Traffic information using the second algorithm calculate described in estimate generation drive the second Estimative path that driver position drives passenger position to the generation
Distance and the second estimated time, and by second estimated time be sent to generation drive passenger terminal.
Preferably, in addition to：First acquisition module is additionally operable to：Obtaining for before driving passenger position information, obtaining
In generation, drives the positional information and identity information of driver.
Preferably, the first processing module is specifically used for：
The positional information of visitor is driven according to the generation and generation drives the positional information acquisition of driver and the generation drives objective straight line
In closest generation of estimating, drives driver；
The positional information of driver is driven according to the default generation and the generation drives objective positional information and uses path planning side
Method obtains the first Estimative path distance；
First estimated time was obtained according to the first Estimative path distance and preset travel speed.
Preferably, second algorithm is the machine learning algorithm using GBDT models.
Preferably, second acquisition module is specifically used for：
Generation is estimated described in acquisition and drives history average overall travel speed of the driver in preset time period；
Generation is estimated described in acquisition and drives history average preparation time of the driver in preset time period, the time is generation
Drive the time that the predeterminable area centered on order ground is left after driver's order；
Generation is estimated described in acquisition and drives driver on the driving trace in preset time period with the presence or absence of generation driving visitor's
Positional information；
Obtaining all generations in preset time period drives driver and estimates generation described and drive the history on driver position and averagely prepare
Time.
Preferably, the 3rd acquisition module is specifically used for：
Judge whether the current point in time belongs to working day；
Judge whether the current point in time belongs to default working peak period；
Judge whether the current point in time belongs to default generation and drive the peak traffic phase.
As shown from the above technical solution, in a kind of generation provided by the invention, drives driver's arrival and is estimated for the time for driving passenger position
Method and device, visitor is driven by generation and sent before generation drives order request, according to generation drive objective position obtain around with
In generation, drives objective air line distance nearest generation of estimating and drives driver, and according to the generation for estimating generation and driving driver drive characteristic information, traffic information,
First Estimative path distance and the first estimated time, which calculate, obtains the final estimated time, and recommends to be shown in generation driving visitor
In terminal, so that passenger determines whether that sending generation drives order request according to the final estimated time, reach generation driving visitor and clearly estimate
Time situation, and the necessity for sending request is analyzed, enhance the usage experience of generation driving visitor.
Brief description of the drawings
Fig. 1 is to drive the flow that driver reaches generation and drives the time predictor method of passenger position the generation that the embodiment of the present invention 1 provides
Schematic diagram；
Fig. 2 is to drive the structure that driver reaches generation and drives the time estimating device of passenger position the generation that the embodiment of the present invention 2 provides
Schematic diagram.
Embodiment
With reference to the accompanying drawings and examples, the embodiment of the present invention is described in further detail.Implement below
Example is used to illustrate the present invention, but is not limited to the scope of the present invention.
Fig. 1 shows that the embodiment of the present invention 1 provides a kind of generation and drives driver and reach generation and drive the time predictor method of passenger position,
Including：
S11, the positional information for obtaining generation driving visitor.
In this step, it is necessary to which explanation, generation drive objective (i.e. car owner) because some special statuss oneself can not drive
Vehicle, application need to be driven using generation on mobile terminals.The present age drives visitor and logs on generation when driving application, when service end detects login
Trigger action, obtain generation drive visitor positional information.
S12, objective positional information is driven according to the generation and driven for the positional information acquisition and the generation for driving driver objective
In air line distance nearest generation of estimating, drives driver, and using the first algorithm calculate described in estimate generation and drive driver position and drive to the generation
The the first Estimative path distance and the first estimated time that seats reserved for guests or passengers is put.
In this step, it is necessary to illustrate, before the positional information of generation driving visitor is obtained, each in generation, drives driver for acquisition
Positional information and identity information.In wherein each generation, drives driver and drives application by logging in generation accordingly, it is determined that corresponding position
Information and identity information.
In generation, drives visitor and finds for driver is driven, and can typically consider that the generation nearer from oneself drives driver first, when being estimated with shortening
Between.Therefore service end the positional information of visitor driven according to generation for getting and generation drive driver positional information obtain with it is straight for visitor is driven
In linear distance nearest generation of estimating, drives driver.The determination of air line distance can drive driver by generation and Dai drives objective position coordinates and passed through
Calculate and obtain, this technology is more ripe, will not be repeated here.Wherein, estimate generation and drive driver to drive objective predeterminable area scope in generation
In interior numerous generations, drive the generation closest with generation driving visitor searched out in driver and drive driver.
It is determined that estimate after generation drives driver, the positional information of visitor need to be driven according to generation and estimates generation and drives the positional information of driver and adopts
Generation, which is estimated, with the calculating of the first algorithm drives driver position to for the first Estimative path distance and the first estimated time for driving passenger position.
First algorithm includes the computational methods and the computational methods of estimated time of path distance, and the acquisition of the first Estimative path distance can adopt
Calculated and obtained with paths planning method, this technology is more ripe, will not be repeated here.The acquisition of first estimated time can use distance
Obtained with the ratio calculation of speed.A fixed coefficient can be also multiplied by using path distance to obtain, and do not explained in detail herein
It is bright.
S13, estimate according to and driven for the identity information for driving driver from history generation in record in acquisition preset time period
In generation, drives characteristic information.
In this step, it is necessary to explanation, due to have selected estimate generation drive driver, then the estimated time finally obtained with
Estimate generation and drive driver's correlation.Therefore, the generation driven and obtained in record in preset time period for the history generation for driving driver need to be estimated from this
Drive characteristic information, it may include：
Generation is estimated described in acquisition and drives history average overall travel speed of the driver in preset time period.No matter estimate generation and drive driver
Which kind of mode of transportation to reach the place of generation driving visitor using, the history generation for being recorded in oneself is driven in record, row distance of going forward side by side
With the average treatment of time, history average overall travel speed is obtained.
Generation is estimated described in acquisition and drives history average preparation time of the driver in preset time period.Driver is driven due to estimating generation
After order, the objective position of generation driving can not be left at any time, need to do some necessary preparations.Therefore in order to consider that different generations drive
Preparation that driver is done is different, and the time is different, could dictate that generation drives the predeterminable area left after driver's order centered on order ground
Time used.Such as in order centered on radius be 100 meters of regional extents.
Generation is estimated described in acquisition and drives driver on the driving trace in preset time period with the presence or absence of generation driving visitor's
Positional information.The acquisition that this generation drives characteristic information is to consider that generation drives the familiarity that driver drives objective position to generation.
Obtaining all generations in preset time period drives driver and estimates generation described and drive the history on driver position and averagely prepare
Time.Generation is estimated to drive driver position and perhaps there can be many generations and drive driver and from this place leave for generation to drive visitor institute in place
Put.Therefore this can be obtained estimate generation and drive other generations on driver position and drive history average preparation time used in driver.
S14, the traffic information for obtaining current point in time.
In this step, it is necessary to which explanation is, it is also necessary to which judgement estimates generation and drives driver position to where for driving visitor
The traffic information to be undergone between position.The traffic information includes working day, nonworkdays, presets working peak period, be non-
Default working peak period, in default generation, drives the peak traffic phase, non-default generation drives the one or more of peak traffic phase.But it is not limited to
This.
For example, default working day is Mon-Fri, nonworkdays is Saturday and Sunday.Current point in time is at night 8:
00, if the same day is Sunday, the traffic information of current point in time is nonworkdays.If the same day is Thursday, current point in time
Traffic information is working day.
For example, default working peak period is the morning 7: 00 to the morning 8: 30, if current point in time is the morning 8: 00, currently
The traffic information at time point is default working peak period；If current point in time is the morning 9: 00, the road conditions letter of current point in time
Cease for non-default working peak period.
For example, in default generation, drives the peak traffic phase as 9: 00- evening 11: 00 at night, if current point in time is at night 10: 00,
Then the traffic information of current point in time drives the peak traffic phase for default generation；If current point in time is at night 11: 30, current time
The traffic information of point drives the peak traffic phase for non-default generation.
Above-mentioned obtained traffic information is cited by the embodiment of the present invention, but the invention is not limited in this.
S15, according to first Estimative path distance, the first estimated time, generation drive characteristic information and traffic information and use
Second algorithm estimates generation and drives driver position and drive the second Estimative path distance of passenger position and second pre- to the generation described in calculating
Estimate the time, and be sent to generation drive passenger terminal.In this step, it is necessary to explanation, by the above-mentioned Estimative path being related to away from
Characteristic information and traffic information are driven as input information from, estimated time, generation, are carried out calculating acquisition using GBDT models and are estimated generation
Drive driver position and the second Estimative path distance and the second estimated time of objective position are driven to generation, and be sent to generation to drive
It is shown in passenger terminal and is seen for driving is objective.The GBDT models herein being referred to are a kind of decision Tree algorithms of iteration, should
Algorithm is made up of more decision trees, and the conclusion of all trees, which adds up, does final result.GBDT models are widely used in engineering
Habit field.The GBDT models of the application are the corresponding GBDT moulds that acquisition is trained to the training data being presently in city
Type.Different cities are corresponding with different GBDT models, i.e., for different cities, are obtained respectively with the historical data training in respective city
Model corresponding to obtaining.And those skilled in the art can know how using GBDT models to calculate the estimated time, herein no longer
Realization to specific algorithm is described.
Generation driving visitor can drive in generation checks that estimating generation drives driver's arrival generation in passenger terminal (such as mobile phone, tablet personal computer)
Drive the second estimated time of objective position, second estimated time is to estimate acquisition based on numerous actual conditions, its with
Above-mentioned first estimated time is different, and it is compared with the first estimated time closer to reality.
Generation drives visitor can finally be judged to drive order request to decide whether to send generation according to the second estimated time.
In generation described in the embodiment of the present invention 1, drives the time predictor method that driver reaches generation and drives passenger position, is driven by generation
Visitor sent before generation drives order request, according to generation drive objective position obtain around with generation to drive objective air line distance nearest
Estimate generation and drive driver, and characteristic information, traffic information, the first Estimative path distance and first are driven for the generation for driving driver according to estimating
Estimated time, which calculate, obtains the final estimated time, and recommends to be shown in for driving in passenger terminal, so that passenger is according to most
The whole estimated time determines whether that transmission generation drives order request, reaches generation driving visitor and understands estimated time situation, and analyzes transmission and ask
The necessity asked, enhance the usage experience of generation driving visitor.
Fig. 2 shows that a kind of generation that the embodiment of the present invention 2 provides drives driver's arrival and estimates dress for the time for driving passenger position
Put, including the first acquisition module 21, first processing module 22, the second acquisition module 23, the 3rd acquisition module 24 and second processing
Module 25, wherein：
First acquisition module 21, the positional information of visitor is driven for obtaining generation；
First processing module 22, the positional information that positional information and generation for driving visitor according to the generation drive driver obtain
Objective air line distance nearest generation of estimating is driven with the generation and drives driver, and drives driver position using generation is estimated described in the calculating of the first algorithm
Put the first Estimative path distance and the first estimated time that the generation drives passenger position；
Second acquisition module 23, obtained for estimating to drive in record from history generation for the identity information for driving driver according to
In generation in preset time period, drives characteristic information；
3rd acquisition module 24, for obtaining the traffic information of current point in time；
Second processing module 25, for driving characteristic information according to first Estimative path distance, the first estimated time, generation
With traffic information using the second algorithm calculate described in estimate generation and drive driver position and estimate road to the generation drives passenger position second
Footpath distance and the second estimated time, and be sent to generation drive passenger terminal.
In the process of implementation, in generation, drives objective (i.e. car owner) because some special statuss oneself can not drive vehicle, need to move
In dynamic terminal application is driven using generation.
The present age drives visitor and logged on for when driving application, after the first acquisition module 21 detects login trigger action, obtains generation
The positional information of visitor is driven, and positional information is sent to first processing module 22.
First processing module 22 receives the position signalling for driving visitor in generation, and positional information and the reception of visitor are driven according to generation
To multiple generations drive the positional information of driver and calculate and obtain with driving driver for driving objective air line distance nearest generation of estimating, and
Generation simultaneously estimates that generation drives position information and the first Estimative path distance for the objective position information of driving and first are pre-
Estimate the time.First Estimative path distance of acquisition and the first estimated time are sent to Second processing module by first processing module.
In addition, first processing module be additionally operable to obtain generation drive visitor positional information before, obtain generation drive driver positional information and
Identity information.
Due to estimate that estimating generation drives estimated time of driver's arrival for the objective position of driving, therefore the second acquisition module 23
It would know that to estimate and drive characteristic information for some generations for driving driver, the generation, which drives characteristic information, may include：Estimate generation and drive driver default
History average overall travel speed in period；Estimate generation and drive history average preparation time of the driver in preset time period, it is described
Time is to drive the time that the predeterminable area centered on order ground is left after driver's order in generation；Estimate generation and drive driver default
The positional information of visitor is driven on driving trace in period with the presence or absence of the generation；In all generations, drive driver in preset time period
Estimated described for the history average preparation time driven on driver position.Second acquisition module 23 will be sent to for characteristic information is driven
Second processing module 25.
Due to estimate that estimating generation drives estimated time of driver's arrival for the objective position of driving, therefore the 3rd acquisition module 24
Can current point in time traffic information, traffic information section includes：The current point in time whether belong to working day, it is described current when
Between put whether belong to default working peak period and whether current point in time belongs to default generation and drive the peak traffic phase.3rd acquisition module
Traffic information is sent to Second processing module 25.
The receive information of Second processing module 25, and feature letter is driven according to the first Estimative path distance, the first estimated time, generation
Breath and traffic information using GBDT models obtain described in estimate generation and drive driver position and estimated to the generation drives passenger position second
Path distance and the second estimated time, and be sent to generation and drive passenger terminal, so that passenger is according to the second Estimative path distance and the
Two estimated times determined whether that sending generation drove order request.
Generation driving visitor can drive in generation checks that estimating generation drives driver's arrival generation in passenger terminal (such as mobile phone, tablet personal computer)
Drive the second estimated time of objective position, second estimated time is to estimate acquisition based on numerous actual conditions, its with
Above-mentioned first estimated time is different, and it is compared with the first estimated time closer to reality.
Generation drives visitor can finally be judged to drive order request to decide whether to send generation according to the second estimated time.
Because the described device of the embodiment of the present invention 2 is identical with the principle of above-described embodiment methods described, in further detail
Explanation content will not be repeated here.
It should be noted that can be by hardware processor (hardware processor) come real in the embodiment of the present invention
Existing related function module.
In generation described in the embodiment of the present invention 2, drives the time estimating device that driver reaches generation and drives passenger position, is driven by generation
Visitor sent before generation drives order request, according to generation drive objective position obtain around with generation to drive objective air line distance nearest
Estimate generation and drive driver, and characteristic information, traffic information, the first Estimative path distance and first are driven for the generation for driving driver according to estimating
Estimated time, which calculates, obtains the final estimated time, and recommends to be shown in for driving in passenger terminal, so that passenger is according to final pre-
Estimate the time determine whether send generation drive order request, reach generation drive visitor understand estimated time situation, and analyze send request
Necessity, enhance the usage experience of generation driving visitor.
In addition, it will be appreciated by those of skill in the art that although some embodiments described herein include other embodiments
In included some features rather than further feature, but the combination of the feature of different embodiments means in of the invention
Within the scope of and form different embodiments.For example, in the following claims, embodiment claimed is appointed
One of meaning mode can use in any combination.
It should be noted that the present invention will be described rather than limits the invention for above-described embodiment, and ability
Field technique personnel can design alternative embodiment without departing from the scope of the appended claims.In the claims,
Any reference symbol between bracket should not be configured to limitations on claims.Word "comprising" does not exclude the presence of not
Element or step listed in the claims.Word "a" or "an" before element does not exclude the presence of multiple such
Element.The present invention can be by means of including the hardware of some different elements and being come by means of properly programmed computer real
It is existing.In if the unit claim of equipment for drying is listed, several in these devices can be by same hardware branch
To embody.The use of word first, second, and third does not indicate that any order.These words can be explained and run after fame
Claim.
One of ordinary skill in the art will appreciate that：Various embodiments above is merely illustrative of the technical solution of the present invention, and
It is non-that it is limited；Although the present invention is described in detail with reference to foregoing embodiments, one of ordinary skill in the art
It should be understood that：It can still modify to the technical scheme described in foregoing embodiments, either to which part or
All technical characteristic carries out equivalent substitution；And these modifications or replacement, the essence of appropriate technical solution is departed from this hair
Bright claim limited range.
Claims (12)
1. in a kind of generation, drives the time predictor method that driver reaches generation and drives passenger position, it is characterised in that including：
Obtain the positional information of generation driving visitor；
The positional information of visitor is driven according to the generation and generation drives the positional information acquisition of driver and the generation drives objective air line distance
It is nearest to estimate generation and drive driver, and using the first algorithm calculate described in estimate generation and drive driver position and drive passenger position to the generation
First Estimative path distance and the first estimated time；
Feature is driven for the generation driven in record in acquisition preset time period from history for the identity information for driving driver according to described estimate
Information；
Obtain the traffic information of current point in time；
According to first Estimative path distance, the first estimated time, in generation, drives characteristic information and traffic information uses the second algorithm
Generation is estimated described in calculating and drives the second Estimative path distance and the second estimated time that driver position drives passenger position to the generation, and
By second estimated time be sent to generation drive passenger terminal.
2. according to the method for claim 1, it is characterised in that also include：Obtaining for before driving passenger position information, obtaining
The positional information and identity information of driver is driven in substitution.
3. according to the method for claim 1, it is characterised in that first algorithm, including：
The positional information of driver is driven according to the default generation and the generation is driven objective positional information and obtained using paths planning method
Take the first Estimative path distance；
First estimated time was obtained according to the first Estimative path distance and preset travel speed.
4. according to the method for claim 1, it is characterised in that second algorithm is the machine learning using GBDT models
Algorithm.
5. according to the method for claim 1, it is characterised in that estimated described in the basis generation drive the identity information of driver from
In history generation, drives the generation obtained in record in preset time period and drives characteristic information, including：
Generation is estimated described in acquisition and drives history average overall travel speed of the driver in preset time period；
Generation is estimated described in acquisition and drives history average preparation time of the driver in preset time period, the time is to drive department in generation
The time of the predeterminable area centered on order ground is left after machine order；
Generation is estimated described in acquisition and drives driver drives visitor with the presence or absence of generation position on the driving trace in preset time period
Information；
In acquisition all generations in preset time period, drive driver and are estimated described for the history average preparation time driven on driver position.
6. according to the method for claim 1, it is characterised in that the traffic information includes working day, nonworkdays, preset
The peak traffic phase is driven in working peak period, non-default working peak period, default generation, non-default generation drives one kind or more of peak traffic phase
Kind.
7. in a kind of generation, drives the time estimating device that driver reaches generation and drives passenger position, it is characterised in that including：
First acquisition module, the positional information of visitor is driven for obtaining generation；
First processing module, for according to the generation drive visitor positional information and generation drive driver positional information obtain with it is described
Driver is driven for objective air line distance nearest generation of estimating is driven, and driver position is driven to institute using generation is estimated described in the calculating of the first algorithm
State for the first Estimative path distance and the first estimated time for driving passenger position；
Second acquisition module, for estimated according to generation drive the identity information of driver driven from history generation obtain default in record when
Between generation in section drive characteristic information；
3rd acquisition module, for obtaining the traffic information of current point in time；
Second processing module, for driving characteristic information and road conditions according to first Estimative path distance, the first estimated time, generation
Information using the second algorithm calculate described in estimate generation drive the second Estimative path distance that driver position drives passenger position to the generation
With the second estimated time, and by second estimated time be sent to generation drive passenger terminal.
8. device according to claim 7, it is characterised in that also include：First acquisition module is additionally operable to：Obtaining
Before generation drives passenger position information, obtain for the positional information and identity information for driving driver.
9. device according to claim 7, it is characterised in that the first processing module is specifically used for：
The positional information of visitor is driven according to the generation and generation drives the positional information acquisition of driver and the generation drives objective air line distance
In nearest generation of estimating, drives driver；
The positional information of driver is driven according to the default generation and the generation is driven objective positional information and obtained using paths planning method
Take the first Estimative path distance；
First estimated time was obtained according to the first Estimative path distance and preset travel speed.
10. device according to claim 7, it is characterised in that second algorithm is the engineering using GBDT models
Practise algorithm.
11. device according to claim 7, it is characterised in that second acquisition module is specifically used for：
Generation is estimated described in acquisition and drives history average overall travel speed of the driver in preset time period；
Generation is estimated described in acquisition and drives history average preparation time of the driver in preset time period, the time is to drive department in generation
The time of the predeterminable area centered on order ground is left after machine order；
Generation is estimated described in acquisition and drives driver drives visitor with the presence or absence of generation position on the driving trace in preset time period
Information；
In acquisition all generations in preset time period, drive driver and are estimated described for the history average preparation time driven on driver position.
12. device according to claim 7, it is characterised in that the 3rd acquisition module is specifically used for：
Judge whether the current point in time belongs to working day；
Judge whether the current point in time belongs to default working peak period；
Judge whether the current point in time belongs to default generation and drive the peak traffic phase.
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