CN108776852A - Without stake vehicle dispatching method and system - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000012549 training Methods 0.000 claims abstract description 19
- 238000004590 computer program Methods 0.000 claims description 9
- 238000000605 extraction Methods 0.000 claims description 9
- 238000009826 distribution Methods 0.000 claims description 7
- 238000003860 storage Methods 0.000 claims description 7
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000003066 decision tree Methods 0.000 claims description 3
- 238000013075 data extraction Methods 0.000 claims description 2
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
Present disclose provides a kind of no stake vehicle dispatching method and systems, are related to data processing field.This method includes：The data characteristics in each target stop preset range is extracted based on the history multi-source data in region；Using data characteristics as the input parameter of vehicle Demand Forecast Model, using the vehicle demand of each target stop as the output parameter of vehicle Demand Forecast Model, training vehicle Demand Forecast Model；Based on trained vehicle Demand Forecast Model, the vehicle demand of each target stop is predicted；Vehicle scheduling strategy is determined according to the vehicle demand of each target stop.The disclosure can more efficiently carry out no stake vehicle scheduling.
Description
Technical field
This disclosure relates to data processing field more particularly to a kind of no stake vehicle dispatching method and system.
Background technology
As a kind of Green Travel mode, bicycle is deep always to be liked by government and people.Bicycle is selected to carry out short
Distance trip, it is not only more convenient, additionally it is possible to take exercises, reduce motor vehicle exhaust emission, alleviate traffic congestion.
Place setting Cycle Hire point of traditional public bicycles system more than the volume of the flow of passengers, lessee need in bicycle
Lease point lease bicycle, gives back bicycle to any Cycle Hire point after use.In recent years, it has emerged in large numbers large quantities of
Without stake public bicycles system.Unlike traditional public bicycles system, no stake bicycle system allows user in office
Where bicycle is leased by side, and bicycle is returned to any place.Due to no fixed lease place, no stake bicycle energy
User is allowed to use more convenient.
Traditional has a bicycle mainly in each lease point carry out bicycle scheduling, and allows user without stake bicycle
It cycles, return the car in any place, more stringent requirements are proposed to its scheduling problem.If there is no efficient scheduling strategy, no stake
Bicycle just has serious packing phenomenon in some places, to cause traffic congestion, results in waste of resources.On the other hand,
It will appear the situation that supply falls short of demand in certain regions being in great demand of riding, to influence the trip experience of user.
Invention content
The disclosure technical problem to be solved is to provide a kind of no stake vehicle dispatching method and system, can more added with
Effect ground is carried out without stake vehicle scheduling.
On the one hand according to the disclosure, a kind of no stake vehicle dispatching method is proposed, including：Based on the history multi-source number in region
According to the data characteristics extracted in each target stop preset range；Using data characteristics as the input of vehicle Demand Forecast Model
Parameter, using the vehicle demand of each target stop as the output parameter of vehicle Demand Forecast Model, training vehicle demand
Prediction model；Based on trained vehicle Demand Forecast Model, the vehicle demand of each target stop is predicted；According to each
The vehicle demand of target stop determines vehicle scheduling strategy.
Optionally, it is based on trained vehicle Demand Forecast Model, predicts the vehicle demand packet of each target stop
It includes：The data characteristics in each target stop preset time period is extracted based on the multi-source data in region；Data characteristics is defeated
Enter the vehicle demand that each target stop is predicted to trained vehicle Demand Forecast Model.
Optionally, this method further includes：It is poly- that space is carried out to history vehicle distributed data, interest point data and road net data
Class calculates, and determines parking area；The target stop of each parking area is determined according to interest point data and road net data.
Optionally, this method further includes：Based on optimum target in the optimal vehicle scheduling strategy of vehicle scheduling policy selection.
Optionally, optimum target includes each vehicle from launching to being borrowed total time is most short, each target parking
Point needs the vehicle fleet dispatched according at least one measured during minimum or scheduled vehicle needs the sum of mobile total distance minimum.
Optionally, multi-source data includes air quality data, public transport data on flows, weather data, vehicle distribution number
According to, interest point data and road net data.
Optionally, this method further includes：By air quality data, public transport data on flows, weather data and vehicle point
Cloth data are divided into working days evidence and festivals or holidays data；Working days evidence and festivals or holidays data are spaced to schedule respectively
It is divided；Using the ready-portioned working days evidence of each target stop and festivals or holidays data as vehicle Demand Forecast Model
Input parameter.
Optionally, vehicle Demand Forecast Model is using artificial neural network ANN model, Bayesian model, decision-tree model
With at least one of depth network model model.
According to another aspect of the present disclosure, it is also proposed that a kind of no stake vehicle dispatch system, including：Data characteristics extraction is single
Member, for extracting the data characteristics in each target stop preset range based on the history multi-source data in region；Predict mould
Type training unit is used for using data characteristics as the input parameter of vehicle Demand Forecast Model, by the vehicle of each target stop
Output parameter of the demand as vehicle Demand Forecast Model, training vehicle Demand Forecast Model；Demand Forecast unit is used
In based on trained vehicle Demand Forecast Model, the vehicle demand of each target stop is predicted；Scheduling strategy generates single
Member, for determining vehicle scheduling strategy according to the vehicle demand of each target stop.
Optionally, data characteristics extraction unit is additionally operable to pre- based on each target stop of multi-source data extraction in region
If the data characteristics in the period；Demand Forecast unit is used to data characteristics being input to trained vehicle requirement forecasting mould
Type predicts the vehicle demand of each target stop.
Optionally, which further includes：Target area determination unit, for history vehicle distributed data, interest point
Space clustering calculating is carried out according to road net data, determines parking area；Target stop determination unit, for according to interest point
According to the target stop for determining each parking area with road net data.
Optionally, which further includes：Optimal policy selecting unit, for being based on optimum target in vehicle scheduling strategy
Select optimal vehicle scheduling strategy.
Optionally, optimum target includes each vehicle from launching to being borrowed total time is most short, each target parking
Point needs the vehicle fleet dispatched according at least one measured during minimum or scheduled vehicle needs the sum of mobile total distance minimum.
Optionally, multi-source data includes air quality data, public transport data on flows, weather data, vehicle distribution number
According to, interest point data and road net data.
Optionally, which further includes：Time division unit, for by air quality data, public transport data on flows,
Weather data and vehicle distributed data are divided into working days evidence and festivals or holidays data, and working days evidence and festivals or holidays data are distinguished
Interval is divided to schedule；Wherein, by the ready-portioned working days evidence of each target stop and festivals or holidays data
Input parameter as vehicle Demand Forecast Model.
Optionally, multi-source data includes air quality data, public transport data on flows, weather data, vehicle distribution number
According to, interest point data and road net data.
According to another aspect of the present disclosure, it is also proposed that a kind of no stake vehicle dispatch system, including：Memory；And coupling
To the processor of memory, processor is configured as based on for example above-mentioned no stake vehicle scheduling of instruction execution for being stored in memory
Method.
According to another aspect of the present disclosure, it is also proposed that a kind of computer readable storage medium is stored thereon with computer journey
The step of sequence instructs, which realizes above-mentioned no stake vehicle dispatching method when being executed by processor.
Compared with the relevant technologies, the embodiment of the present disclosure trains vehicle Demand Forecast Model using multi-source data, is then based on
Trained vehicle Demand Forecast Model predicts the vehicle demand of each target stop, according to each target stop
Vehicle demand determines vehicle scheduling strategy, so as to more efficiently carry out no stake vehicle scheduling.
By referring to the drawings to the detailed description of the exemplary embodiment of the disclosure, the other feature of the disclosure and its
Advantage will become apparent.
Description of the drawings
The attached drawing of a part for constitution instruction describes embodiment of the disclosure, and is used to solve together with the description
Release the principle of the disclosure.
The disclosure can be more clearly understood according to following detailed description with reference to attached drawing, wherein：
Fig. 1 is the flow diagram of the one embodiment of the disclosure without stake vehicle dispatching method.
Fig. 2 is the flow diagram of another embodiment of the disclosure without stake vehicle dispatching method.
Fig. 3 is the variation of different time sections bicycle usage amount.
Fig. 4 is that bicycle uses accounting under different air qualities.
Fig. 5 is different weather bicycle usage amount accounting.
Fig. 6 is the structural schematic diagram of the one embodiment of the disclosure without stake vehicle dispatch system.
Fig. 7 is the structural schematic diagram of another embodiment of the disclosure without stake vehicle dispatch system.
Fig. 8 is the structural schematic diagram of another embodiment of the disclosure without stake vehicle dispatch system.
Fig. 9 is the structural schematic diagram of further embodiment of the disclosure without stake vehicle dispatch system.
Specific implementation mode
The various exemplary embodiments of the disclosure are described in detail now with reference to attached drawing.It should be noted that：Unless in addition having
Body illustrates that the unlimited system of component and the positioned opposite of step, numerical expression and the numerical value otherwise illustrated in these embodiments is originally
Scope of disclosure.
Simultaneously, it should be appreciated that for ease of description, the size of attached various pieces shown in the drawings is not according to reality
Proportionate relationship draw.
It is illustrative to the description only actually of at least one exemplary embodiment below, is never used as to the disclosure
And its application or any restrictions that use.
Technology, method and apparatus known to person of ordinary skill in the relevant may be not discussed in detail, but suitable
In the case of, the technology, method and apparatus should be considered as authorizing part of specification.
In shown here and discussion all examples, any occurrence should be construed as merely illustrative, without
It is as limitation.Therefore, the other examples of exemplary embodiment can have different values.
It should be noted that：Similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined, then it need not be further discussed in subsequent attached drawing in a attached drawing.
To make the purpose, technical scheme and advantage of the disclosure be more clearly understood, below in conjunction with specific embodiment, and reference
The disclosure is further described in attached drawing.
Existing bicycle dispatching method has been based primarily upon a public bicycles system.Relative to there is a public bicycles system
System, more, position of counting out to the bicycle parking of no stake bicycle system is not fixed, the capacity each parked a little does not limit
System, therefore, based in the scheduling for thering is an algorithm for bicycle scheduling to cannot be used directly for no stake bicycle.Without stake bicycle conduct
A kind of new Lease way is studied and few about its scheduling scheme.The scheduling of current major no stake public bicycles is main
Distributed data based on bicycle can not accurately predict the demand of riding of following each position user, to can not effectively into
Row scheduling.
Fig. 1 is the flow diagram of the one embodiment of the disclosure without stake vehicle dispatching method.
In step 110, the data in each target stop preset range are extracted based on the history multi-source data in region
Feature.Wherein, multi-source data be, for example, air quality data, public transport data on flows, weather data, vehicle distributed data,
POI (Point of Interest, point of interest) data and road net data etc..Wherein, POI data and road net data belong to static
Data, i.e. data are not changed at any time.Air quality data, public transport data on flows, weather data and bicycle point
Cloth data etc. are dynamic data, i.e. data changed with the time.The data characteristics of extraction is for example including Characteristics of Air Quality, people
Flow, weather characteristics, bicycle distribution characteristics, POI distribution characteristics, road network dispatch feature etc..
In step 120, using data characteristics as the input parameter of vehicle Demand Forecast Model, by each target stop
Output parameter of the vehicle demand as vehicle Demand Forecast Model, training vehicle Demand Forecast Model.Wherein, vehicle demand is pre-
It surveys model and ANN (Artificial Neural Network, artificial neural network) model, Bayesian model, decision may be used
Tree-model, depth network model etc. can also use the combination of multiple models.
It in one embodiment, can be using data characteristics as the input parameter of vehicle Demand Forecast Model, by each mesh
Output parameter of the vehicle demand of stop as vehicle Demand Forecast Model is marked, repetitive exercise vehicle requirement forecasting mould is passed through
Type.The process of repetitive exercise vehicle Demand Forecast Model is, for example,：By the output result of vehicle Demand Forecast Model with accordingly adopt
The vehicle demand of the target stop of collection is compared, and judges whether comparison result meets the vehicle demand loss letter of structure
Several requirements, iterates, and optimizes and adjust the parameter of vehicle Demand Forecast Model so that comparison result finally meets structure
The requirement of the vehicle demand loss function of vehicle Demand Forecast Model, preserves the vehicle Demand Forecast Model.
In step 130, it is based on trained vehicle Demand Forecast Model, predicts the vehicle demand of each target stop
Amount.
In step 140, vehicle scheduling strategy is determined according to the vehicle demand of each target stop.Such as A targets are stopped
The existing vehicle of vehicle point is 10, but actual demand amount is 20, and the existing vehicle of B target points vehicle point is 50, but actual demand amount
It it is 15, the existing vehicle of C target stops is 40, but actual demand amount is 25.It then can be from B targets stop to A mesh
Mark stop is dispatched buses, and can also be dispatched buses from C targets stop to A target stops, can also be by two targets of B, C
Stop is dispatched buses to A target stops.
In this embodiment, vehicle Demand Forecast Model is trained using multi-source data, being then based on trained vehicle needs
Prediction model is sought, predicts the vehicle demand of each target stop, is determined according to the vehicle demand of each target stop
Vehicle scheduling strategy, so as to more efficiently carry out no stake vehicle scheduling.
Fig. 2 is the flow diagram of another embodiment of the disclosure without stake vehicle dispatching method.
In step 210, space clustering calculating is carried out to history bicycle distributed data, interest point data and road net data,
Determine parking area.For example, DBSCAN (Density-Based Spatial Clustering of may be used
Applications with Noise have noisy density clustering method), the clusters such as K-Means (K mean values) calculate
Method find hire a car, limited a parking area that demand of returning the car is more.
In step 220, the target stop of each parking area is determined according to interest point data and road net data.For every
A parking area finds the one or more target stops for allowing to park bicycle in conjunction with road network and POI data.Thus will
A unlimited target location is converted to limited a target stop, and entire dispatching algorithm carries out voluntarily between limit target stop
Vehicle is dispatched.
In step 230, based on the history air quality data in region, public transport data on flows, weather data, voluntarily
Vehicle distributed data, interest point data and road net data extract the data characteristics in each target stop preset range.Wherein, needle
To each target stop, the feature of data in a segment limit around it can be extracted.Data characteristics can be divided into static nature
And behavioral characteristics.Static nature refers to that data are not changed at any time, such as road network and POI.Behavioral characteristics refer to that data are read
Count the variation with the time, such as air quality data, public transport data on flows, weather data and bicycle distributed data.
Wherein, different time sections bicycle usage amount is different.The cities the Tu3Wei Mou part of in September, 2016 each period is without stake public affairs
The usage amount of bicycle altogether.As seen from the figure, in the use of morning peak (7 points to 8 points) and evening peak (17 points to 19 points) bicycle
Amount increases, and seldom in night (23 points to second day 5 points) bicycle usage amount.Therefore, it in the scheduling process of bicycle, answers
The consideration time factor, meanwhile, before peak time morning and evening, it should dispatch bicycle, to meet the need of riding of more people
It asks.Therefore, for behavioral characteristics, data can carry out fragment by generation time, for example, dividing data by working day and festivals or holidays
24 slices can be divided by the hour for each part at two parts.In this way, sharing 48 slice of data, wherein working day 24
It is a, festivals or holidays 24.
Fig. 4 give certain city part of in September, 2016 without stake public bicycles in the case that different air qualities per hour
Usage amount accounting situation.There is figure it is found that in the case where air quality is good, people prefer to use bicycle, because of air
In the case of ropy, people can reduce outdoor activity, and then the usage amount of bicycle also can be reduced accordingly.Therefore, in training
When vehicle Demand Forecast Model, need to consider air quality data.For air quality data, air quality spy can be extracted
Sign.Such as the air quality situation of each target point of each isochronous surface is calculated, such as：PM2.5,PM10,SO2,NO2,CO,O3
The concentration of equal pollutants.
The lease of no stake public bicycles/give back place be distributed in mostly subway, by bus stop, around market,
By cell doorway and office building.Therefore, during bicycle is dispatched, we should consider emphatically subway, bus
It stands, the bicycle by market, cell and office building.In addition, that us can be helped to predict is each for the brushing card data of subway, public transport etc.
Subway exports and the demand by bus of bus stop.Therefore, it in training vehicle Demand Forecast Model, needs to consider public transport
Data on flows extracts flow of the people around each target stop.Furthermore it is also possible to according to signal base station data, WIFI connections
Data, taxi track data etc. calculate flow of the people data.
Fig. 5 is shown certain city part of in September, 2016 and is averaged hourly make in fine day and rainy day without stake public bicycles
Dosage accounting situation.As shown in Figure 5, the usage amount of rainy day bicycle is far longer than in the usage amount of fine day bicycle.In addition, the winter
The usage amount of its bicycle is far less than the usage amount of summer bicycle.Therefore, it in training vehicle Demand Forecast Model, needs
Consider weather data.Weather characteristics are extracted for weather data, for example, the day of each target stop of each isochronous surface is vaporous
Condition (cloudy, fine, rain etc.), temperature, wind speed, wind direction, humidity etc..
For bicycle distributed data, the distribution characteristics of history bicycle can be extracted, such as：Each isochronous surface is each
Bicycle sum, each car around target stop is from launching to the time span etc. ridden away.
For interest point data and road net data, the POI/ road network dispatch features of δ kilometers of surrounding can be extracted, such as：Respectively
The number of class POI, the length in various grade sections, intersection number, number of traffic lights etc..
In step 240, using data characteristics as the input parameter of vehicle Demand Forecast Model, by each target stop
Output parameter of the bicycle demand as vehicle Demand Forecast Model, training vehicle Demand Forecast Model.Wherein it is possible to by every
The input parameter of the ready-portioned working days evidence of a target stop and festivals or holidays data as vehicle Demand Forecast Model.
In step 250, based on the air quality data in region, public transport data on flows, weather data, bicycle point
Cloth data, interest point data and road net data extract the data characteristics in each target stop preset time period.That is, extracting every
The feature of a nearest a period of time data of target stop.
In step 260, data characteristics is input to trained vehicle Demand Forecast Model, predicts each target stop
Bicycle demand.
In step 270, bicycle scheduling strategy is determined according to the bicycle demand of each target stop.
In step 280, optimal bicycle scheduling strategy is selected in bicycle scheduling strategy based on optimum target.For example,
When selecting optimal bicycle scheduling strategy, consider each bicycle from launching to total time for being borrowed is most short, each target
Stop needs the bicycle total amount of data dispatched minimum or scheduled bicycle needs the sum of mobile total distance minimum.
In this embodiment, the target stop of bicycle scheduling is determined using the method for space clustering so that unlimited
Target location is converted into limited a target stop.In addition, in this embodiment, interest point data, road net data, public transport
The historical rethinking data training vehicle Demand Forecast Model of data on flows, weather data, air quality data and bicycle,
Then the vehicle demand for predicting each target stop determines vehicle scheduling according to the vehicle demand of each target stop
Strategy can effectively solve the problem that the scheduling problem of no stake public bicycles.Once Successful utilization can reduce bicycle operator
Dispatch cost, promote the utilization rate of bicycle, additionally it is possible to alleviation put carelessly as bicycle and caused by traffic congestion, more just
Persons who happens to be on hand for an errand go on a journey.
Fig. 6 is the structural schematic diagram of the one embodiment of the disclosure without stake vehicle dispatch system.The system includes data spy
Levy extraction unit 610, prediction model training unit 620, Demand Forecast unit 630 and scheduling strategy generation unit 640.
Data characteristics extraction unit, for extracting each predetermined model of target stop based on the history multi-source data in region
Enclose interior data characteristics.Wherein, multi-source data is, for example, air quality data, public transport data on flows, weather data, vehicle
Distributed data, POI data and road net data etc..
Prediction model training unit 620 is used for using data characteristics as the input parameter of vehicle Demand Forecast Model, will be every
Output parameter of the vehicle demand of a target stop as vehicle Demand Forecast Model, training vehicle Demand Forecast Model.
Wherein, vehicle Demand Forecast Model may be used ANN (Artificial Neural Network, artificial neural network) model,
Bayesian model, decision-tree model, depth network model etc., can also be the group of multiple models.
Demand Forecast unit 630 is used to be based on trained vehicle Demand Forecast Model, predicts each target stop
Vehicle demand.
Scheduling strategy generation unit 640 is used to determine vehicle scheduling plan according to the vehicle demand of each target stop
Slightly.
In this embodiment, vehicle Demand Forecast Model is trained using multi-source data, being then based on trained vehicle needs
Prediction model is sought, predicts the vehicle demand of each target stop, is determined according to the vehicle demand of each target stop
Vehicle scheduling strategy, so as to more efficiently carry out no stake vehicle scheduling.
Fig. 7 is the structural schematic diagram of another embodiment of the disclosure without stake vehicle dispatch system.The system further includes mesh
Mark area determination unit 710 and target stop determination unit 720.
Target area determination unit 710 is used to carry out history bicycle distributed data, interest point data and road net data
Space clustering calculates, and determines parking area.
Target stop determination unit 720 is used to determine the mesh of each parking area according to interest point data and road net data
Mark stop.For each parking area, the one or more targets for allowing to park bicycle are found in conjunction with road network and POI data
Stop.
Data characteristics extraction unit 610 is used in the off-line learning stage, based on history air quality data, the public affairs in region
Traffic flow data, weather data, bicycle distributed data, interest point data and road net data extract each target stop altogether
Data characteristics in preset range.In addition, data characteristics extraction unit 610 was additionally operable in the on-line prediction stage, based in region
Air quality data, public transport data on flows, weather data, bicycle distributed data, interest point data and road net data
Extract the data characteristics in each target stop preset time period.That is, extracting the nearest a period of time number of each target stop
According to feature.
In one embodiment, in the scheduling process of bicycle, it is contemplated that time factor.The system further includes the time
Division unit 730, for dividing the air quality data, public transport data on flows, weather data and vehicle distributed data
For working days evidence and festivals or holidays data, working days evidence and festivals or holidays data are spaced to schedule respectively and drawn
Point.
Prediction model training unit 620 is used for using data characteristics as the input parameter of vehicle Demand Forecast Model, will be every
Output parameter of the bicycle demand of a target stop as vehicle Demand Forecast Model, training vehicle requirement forecasting mould
Type.Wherein it is possible to using the ready-portioned working days evidence of each target stop and festivals or holidays data as vehicle requirement forecasting mould
The input parameter of type.
Demand Forecast unit 630 is used to data characteristics being input to trained vehicle Demand Forecast Model, and prediction is every
The bicycle demand of a target stop.
Scheduling strategy generation unit 640 is used to determine that bicycle is dispatched according to the bicycle demand of each target stop
Strategy.
In another embodiment, which can also include optimal policy selecting unit 740, for based on optimization mesh
It is marked on the optimal vehicle scheduling strategy of vehicle scheduling policy selection.For example, when selecting optimal bicycle scheduling strategy, consider each
Bicycle from launching to total time for being borrowed is most short, each target stop needs the bicycle total amount of data dispatched minimum or
Scheduled bicycle needs the sum of mobile total distance minimum.
In this embodiment, the target stop of bicycle scheduling is determined using the method for space clustering so that unlimited
Target location is converted into limited a target stop.In addition, in this embodiment, interest point data, road net data, public transport
The historical rethinking data training vehicle Demand Forecast Model of data on flows, weather data, air quality data and bicycle,
Then the vehicle demand for predicting each target stop determines vehicle scheduling according to the vehicle demand of each target stop
Strategy can effectively solve the problem that the scheduling problem of no stake public bicycles.Once Successful utilization can reduce bicycle operator
Dispatch cost, promote the utilization rate of bicycle, additionally it is possible to alleviation put carelessly as bicycle and caused by traffic congestion, more just
Persons who happens to be on hand for an errand go on a journey.
Fig. 8 is the structural schematic diagram of another embodiment of the disclosure without stake vehicle dispatch system.The system includes storage
Device 810 and processor 820.Wherein：
Memory 810 can be disk, flash memory or other any non-volatile memory mediums.Memory is for storing Fig. 1-
Instruction in embodiment corresponding to 2.Processor 820 is coupled to memory 810, one or more integrated circuits can be used as real
It applies, such as microprocessor or microcontroller.The processor 820 is for executing the instruction stored in memory.
It in one embodiment, can be with as shown in figure 9, the system 900 includes memory 910 and processor 920.Processing
Device 920 is coupled to memory 910 by BUS buses 930.The system 900 can also be connected to outside by memory interface 940 and deposit
Storage device 950 can also be connected to network or an other department of computer science to call external data by network interface 960
System (not shown).It no longer describes in detail herein.
In this embodiment, it is instructed by memory stores data, then above-metioned instruction is handled by processor, it can be effective
It improves without stake vehicle scheduling efficiency.
In another embodiment, a kind of computer readable storage medium, is stored thereon with computer program instructions, this refers to
The step of order realizes the method in embodiment corresponding to Fig. 1-2 when being executed by processor.It should be understood by those skilled in the art that,
Embodiment of the disclosure can be provided as method, apparatus or computer program product.Therefore, complete hardware reality can be used in the disclosure
Apply example, the form of complete software embodiment or embodiment combining software and hardware aspects.Moreover, the disclosure can be used one
It is a or it is multiple wherein include computer usable program code computer can use non-transient storage medium (include but not limited to
Magnetic disk storage, CD-ROM, optical memory etc.) on the form of computer program product implemented.
The disclosure is reference according to the method for the embodiment of the present disclosure, the flow chart of equipment (system) and computer program product
And/or block diagram describes.It should be understood that each flow in flowchart and/or the block diagram can be realized by computer program instructions
And/or the combination of the flow and/or box in box and flowchart and/or the block diagram.These computer programs can be provided to refer to
Enable the processor of all-purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to generate
One machine so that by the instruction that computer or the processor of other programmable data processing devices execute generate for realizing
The device for the function of being specified in one flow of flow chart or multiple flows and/or one box of block diagram or multiple boxes.
These computer program instructions, which may also be stored in, can guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works so that instruction generation stored in the computer readable memory includes referring to
Enable the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one box of block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device so that count
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, in computer or
The instruction executed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in a box or multiple boxes.
So far, the disclosure is described in detail.In order to avoid covering the design of the disclosure, it is public that this field institute is not described
Some details known.Those skilled in the art as described above, can be appreciated how to implement technology disclosed herein completely
Scheme.
Although some specific embodiments of the disclosure are described in detail by example, the skill of this field
Art personnel it should be understood that above example merely to illustrate, rather than in order to limit the scope of the present disclosure.The skill of this field
Art personnel are it should be understood that can modify to above example in the case where not departing from the scope of the present disclosure and spirit.This public affairs
The range opened is defined by the following claims.
Claims (18)
1. a kind of no stake vehicle dispatching method, including：
The data characteristics in each target stop preset range is extracted based on the history multi-source data in region；
Using data characteristics as the input parameter of vehicle Demand Forecast Model, by the vehicle demand of each target stop
As the output parameter of the vehicle Demand Forecast Model, the training vehicle Demand Forecast Model；
Based on the trained vehicle Demand Forecast Model, the vehicle demand of each target stop is predicted；
Vehicle scheduling strategy is determined according to the vehicle demand of each target stop.
2. no stake vehicle dispatching method according to claim 1, wherein be based on the trained vehicle requirement forecasting mould
Type predicts that the vehicle demand of each target stop includes：
The data characteristics in each target stop preset time period is extracted based on the multi-source data in region；
Data characteristics is input to the trained vehicle Demand Forecast Model, predicts the vehicle of each target stop
Demand.
3. no stake vehicle dispatching method according to claim 1, further includes：
Space clustering calculating is carried out to history vehicle distributed data, interest point data and road net data, determines parking area；
The target stop of each parking area is determined according to interest point data and road net data.
4. no stake vehicle dispatching method according to claim 1, further includes：
Based on optimum target in the optimal vehicle scheduling strategy of the vehicle scheduling policy selection.
5. no stake vehicle dispatching method according to claim 4, wherein
Total time that the optimum target includes each vehicle from launching to being borrowed is most short, each target stop needs to adjust
The vehicle fleet of degree is according at least one measured during minimum or scheduled vehicle needs the sum of mobile total distance minimum.
6. any described without stake vehicle dispatching method according to claim 1-5, wherein
The multi-source data includes air quality data, public transport data on flows, weather data, vehicle distributed data, interest
Point data and road net data.
7. no stake vehicle dispatching method according to claim 6, further includes：
The air quality data, public transport data on flows, weather data and vehicle distributed data are divided into working days evidence
With festivals or holidays data；
By the working days evidence and the festivals or holidays data, interval divides to schedule respectively；
It will each the ready-portioned working days evidence of target stop and the festivals or holidays data be needed as the vehicle
Seek the input parameter of prediction model.
8. any described without stake vehicle dispatching method according to claim 1-5, wherein
The vehicle Demand Forecast Model uses artificial neural network ANN model, Bayesian model, decision-tree model and depth net
At least one of network model model.
9. a kind of no stake vehicle dispatch system, including：
Data characteristics extraction unit, for being extracted in each target stop preset range based on the history multi-source data in region
Data characteristics；
Prediction model training unit is used for using data characteristics as the input parameter of vehicle Demand Forecast Model, will be each described
Output parameter of the vehicle demand of target stop as the vehicle Demand Forecast Model, the training vehicle requirement forecasting
Model；
Demand Forecast unit, for being based on the trained vehicle Demand Forecast Model, each target parking of prediction
The vehicle demand of point；
Scheduling strategy generation unit, for determining vehicle scheduling strategy according to the vehicle demand of each target stop.
10. no stake vehicle dispatch system according to claim 9, wherein
The data characteristics extraction unit is additionally operable to default based on each target stop of multi-source data extraction in region
Data characteristics in period；
The Demand Forecast unit is used to data characteristics being input to the trained vehicle Demand Forecast Model, and prediction is every
The vehicle demand of a target stop.
11. no stake vehicle dispatch system according to claim 9, further includes：
Target area determination unit, for carrying out space clustering to history vehicle distributed data, interest point data and road net data
It calculates, determines parking area；
Target stop determination unit, for determining that the target of each parking area is stopped according to interest point data and road net data
Point.
12. no stake vehicle dispatch system according to claim 9, further includes：
Optimal policy selecting unit, for being based on optimum target in the optimal vehicle scheduling plan of the vehicle scheduling policy selection
Slightly.
13. no stake vehicle dispatch system according to claim 12, wherein
Total time that the optimum target includes each vehicle from launching to being borrowed is most short, each target stop needs to adjust
The vehicle fleet of degree is according at least one measured during minimum or scheduled vehicle needs the sum of mobile total distance minimum.
14. any described without stake vehicle dispatch system according to claim 9-13, wherein
The multi-source data includes air quality data, public transport data on flows, weather data, vehicle distributed data, interest
Point data and road net data.
15. no stake vehicle dispatch system according to claim 14, further includes：
Time division unit is used for the air quality data, public transport data on flows, weather data and vehicle distribution number
According to working days evidence and festivals or holidays data is divided into, the working days evidence and the festivals or holidays data are distinguished to schedule
Interval is divided；It wherein, will each ready-portioned working days evidence of target stop and the festivals or holidays data
Input parameter as the vehicle Demand Forecast Model.
16. any described without stake vehicle dispatch system according to claim 9-13, wherein
The multi-source data includes air quality data, public transport data on flows, weather data, vehicle distributed data, interest
Point data and road net data.
17. a kind of no stake vehicle dispatch system, including：
Memory；And
It is coupled to the processor of the memory, the processor is configured as based on the instruction execution for being stored in the memory
If claim 1 to 8 any one of them is without stake vehicle dispatching method.
18. a kind of computer readable storage medium, is stored thereon with computer program instructions, real when which is executed by processor
The step of existing claim 1 to 8 any one of them is without stake vehicle dispatching method.
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