CN110189029A - A kind of bicycle cycling and parking demand appraisal procedure based on extensive mobile phone location data - Google Patents
A kind of bicycle cycling and parking demand appraisal procedure based on extensive mobile phone location data Download PDFInfo
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- CN110189029A CN110189029A CN201910468624.9A CN201910468624A CN110189029A CN 110189029 A CN110189029 A CN 110189029A CN 201910468624 A CN201910468624 A CN 201910468624A CN 110189029 A CN110189029 A CN 110189029A
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- H—ELECTRICITY
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- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
Abstract
The present invention relates to a kind of bicycle cycling and parking demand appraisal procedure based on extensive mobile phone location data screen bicycle trip requirements and calculate bicycle cycling and parking demand to go on a journey as analytical unit.It is stopped and is gone on a journey using SMoT model extraction user, individually to go on a journey as analytical unit, being screened according to road network trip distance is suitable for trip of the bicycle as mode of transportation；When calculating cycling trip demand, equally to go on a journey as analytical unit, bicycle cycling and parking demand are assessed according to the trip for reaching and setting out in base station range.The present invention takes the cycling trip demand that public transport is plugged into account simultaneously.For the transit trip mode in long range trip, by introducing public transport station point data, departure place and destination are calculated with nearest public traffic station at a distance from, extract the trip that suitable bicycle is plugged into and calculating is plugged into trip requirements.
Description
Technical field
The present invention relates to intelligent technical field of geographic information, it is especially a kind of based on extensive mobile phone location data voluntarily
Vehicle cycles and parking demand appraisal procedure.
Background technique
In recent years, many city management departments vigorously advocate slow-moving traffic trip mode, Lai Gaishan urban structure both at home and abroad
System.House and town and country construction portion, State Development and Reform Commission, Ministry of Finance's publication " about reinforcing urban walking and bicycle traffic
The instruction of system Construction " in point out, to accelerate the slow-moving traffics bases such as pavement, bike lane and bicycle parking facility
Infrastructure construction, makes the trip share rate of big and medium-sized cities slow-moving traffic 55% or more.Bicycle is as the important of slow-moving traffic
Component part is mainly asked towards plugging into for " last one kilometer " during short distance trip requirements and transit trip
Topic, thus there is more significant feature in upper and spatial distribution.
On the other hand, with the progress of science and technology, cell phone is gradually popularized in crowd, and height is dissolved into people
Daily life in.And cell phone communication depends on service provided by neighbouring mobile communication signal base station, based on base station location
The mobile phone location data that mode is collected into, which can be realized, carries out continuous position tracking to large-scale crowd.It is the data large sample, low
The feature of cost provides new observation visual angle to explore mankind's mobility feature, therefore is widely used in transport need assessment
And in the application such as Urban population activity pattern study.Contain resident trip information abundant in mobile phone location data, also includes
Information required for cycling trip need assessment.
Currently, cycling trip need assessment method is based primarily upon two class models: probabilistic model and time series models.Its
In, probabilistic model (such as logit model) is based primarily upon survey data, probe into user personal attribute (such as gender, the age,
Education level, income) and trip characteristics (such as away from lease point distance, trip purpose, travel time) etc. go out with bicycle
Relationship between row demand.And time series models are mainly for the public bicycles system with trip record.For example, public
In bicycle system, the history based on each website uses data, excavates rule present in historical data, uses regression analysis
Or the time series models such as ARIMA are fitted and predict to public bicycles demand.
But traditional method fails the integrated demand for effectively assessing city dweller's cycling trip.Itself main reason is that
Mainly to there is a formula public bicycles as research object, demand cannot represent traditional cycling trip need assessment method
Whole cycling trip demand；Secondly, can not reflect that potential cycling trip needs using data based on bicycle history
It asks.
In addition to above traditional cycling trip need assessment method, Xu et al. proposition is screened using mobile phone location data
Short distance Trip chain, and then assess cycling trip demand (Xu Y, Shaw S-L, Fang Z, et al.Estimating
Potential Demand of Bicycle Trips from Mobile Phone Data—An Anchor-Point
Based Approach[J].ISPRS International Journal of Geo-Information,2016,5(8):
131).Its specific technical solution is: 1) trajectory clustering: the data record of traverse user, the most base station of search connection number, and
Cluster merging is carried out to the base station within the 500m of its periphery；2) duty residence is extracted: selection 00:00 to duration during 07:00
Greater than 4 hours, 09:00 to the movable anchor point for continuing 6 hours during 18:00 respectively as location of living and work；3) it goes on a journey
Chain extracts and screening: obtaining Trip chain using duty residence segmentation user trajectory, calculates in each Trip chain between all base stations
Road network distance, and according to being filtered to Trip chain apart from maximum value (if the maximum value in 1km to retaining if between 5km and should go out
Row chain, does not otherwise retain)；4) cycling trip demand calculates: according to the Trip chain after screening, traversing all time serieses in chain
The unequal data record point of adjacent and longitude and latitude assigns cycling trip demand.
There are clearly disadvantageous in the specific trip requirements of assessment bicycle for the method for Xu: (1) screening short distance trip and refers to
When mark, as unit of Trip chain, the short distance trip requirements in Trip chain are had ignored；(2) specific trip number index is being calculated
When, primary trip is can be regarded as into the movement each time in a complete trip, fail to go on a journey accounts for as a whole；
(3) demand of plugging into transit trip is not considered.
Summary of the invention
In view of this, the purpose of the present invention is to propose to a kind of, the bicycle based on extensive mobile phone location data cycles and stops
Vehicle need assessment method, can overcome deficiency in the prior art.
The present invention is realized using following scheme: a kind of bicycle based on extensive mobile phone location data is cycled and is needed with parking
Appraisal procedure is sought, specifically includes the following steps:
Step S1: data preparation and data screening are carried out to extensive mobile phone location data；
Step S2: it carries out user and stops identification, from finding out certain time in a certain range region in initial trace RT
The trajectory segment of length, the trajectory segment are to stop S, and the part of stop is removed in initial trace, remaining trajectory segment
As move M；
Step S3: carrying out ownership goal trip and extract, when mobile distance meets certain distance threshold alpha and β, the movement
Constitute the target trip TM of user；
Step S4: the trip requirements of bicycle are divided into two aspects of cycling demand and parking demand, and one day is divided
For 24 hours, cycling trip conditions of demand in each period are calculated.
Further, step S1 specifically: to the record data in mobile phone location data, initial trace, trajectory segment, shifting
Dynamic, stop, trip and target trip are defined；
The record data r indicates to form the original recorded data of track, is expressed as four-tuple < Customs Assigned Number, the time,
Longitude, dimension >, such as following formula:
R=< OID, t, lon, lat >；
In formula, OID is object number, and t is the time, and lon and lat respectively indicate longitude and latitude；
The initial trace RT indicates the record data of object number having the same sequentially in time after arriving first
The sequence that mode organizes the formation of, such as following formula:
RT=[r1, r2 ..., rn]；
In formula, n is record number, to arbitrary 1≤i < j≤n, rj.time > ri.time, that is, records evening time of rj
In record ri；
The trajectory segment TS is the record subset of initial trace, and segmentation is a part of individual track, by multiple continuous
Record composition, trajectory segment is expressed as one hexa-atomic group, it is as follows:
RTTS=< UserID, start, end, type, RECORDS >；
In formula, UserID indicates Customs Assigned Number, and start and end are respectively the time for being segmented beginning and end, and type is point
The type of section, including stop S and mobile M；RECORDS is the original records collection of composition segmentation, no less than two elements；
The stop S is the subclass of trajectory segment, and indicating individual, there is no the mobile or activities in subrange, and
Duration is greater than the set value T0, is expressed as five-tuple < UserID, start, end, type, RECORDS a >, wherein
Type type is S；
The mobile M is the subclass of trajectory segment, is indicated in this time, and individual is expressed as one in mobile state
A five-tuple < UserId, start, end, type, RECORDS >, wherein type type is M；It is mobile to indicate between stop
Travel behaviour；
The target trip TM indicates that the trip of user is meeting corresponding screening conditions, such as following formula on:
M ∈ TM | α < DIS (m) < β }
In formula, m is mobile example, and DIS is distance function, is reference with practical road network distance, α and β respectively indicate use
The corresponding target trip parameter of bicycle trip mode is generated in filtering.
Further, step S2 specifically includes the following steps:
Step S21: since first record, judge between next record data j to first record to be determined
Whether distance is less than the word space threshold DO of setting；
Step S22:, will record if the space length (calculating Euclidean distance) between two records is less than distance threshold DO
Potential stop set Q is added in j；And continue to judge next data record, calculate its in set Q each record between
Distance is added in set Q if distance is respectively less than DO；
Step S23: if set Q is not the time interval in empty and set Q between first record and the last one record
From time threshold T0 is greater than, then the record in set Q constitutes a stop；
Step S24: since current record, step S21- step S23 is repeated, until all records are processed.
Preferably, in real life, when moving distance is too short or too long, bicycle is not suitable for being chosen as trip work
Tool.Starting point may select walking to too people are larger in short-term the distance between destination, and apart from it is too long when people can select
Select faster trip mode.Thus cycling trip has a certain distance feature, it may be assumed that when the distance length of trip is in α to β
When, bicycle is suitable for being selected as trip tool.Still further aspect when the distance between starting point to destination is longer, exists certainly
The demand of plugging into of driving and public transport.
Further, step S3 specifically: divide ownership goal trip TM for the trip of short distance target and target of plugging into
Trip；
The trip of short distance target refers to the trajectory segment for meeting daily cycling trip short distance feature in short-distance movement；
The step of extracting the trip of short distance target are as follows: the road network distance for calculating all mobile M of user screens outlet from the mobile M of user
Trajectory segment of the net distance between α to β, the trajectory segment form the trip of short distance target；
Target of plugging into trip refers in mobile over long distances to meet the trajectory segment that public transport is plugged into trip and generated；
Extraction plug into target trip the step of are as follows: the road network distance for calculating all mobile M of user filters out distance from the mobile M of user
Movement greater than β；For the record data position of mobile starting and ending, the nearest public transport station of difference detection range
Point；Calculate the road network distance between the movement starting point and end point and public traffic station, if distance length between α to β,
Then the trajectory segment of the record and public traffic station composition constitutes target trip of plugging into.
The setting of α and β needs the general features in conjunction with city dweller's cycling trip to set in the present invention.
Further, step S4 specifically includes the following steps:
Step S41: the single base station p frequency outflow to set out in 24 periodspWith arrival frequency inflowpTable respectively
It is shown as:
In formula,Indicate be base station p within i-th of period from the frequency of base station p,What is indicated is at i-th
Period reaches the frequency of base station p；
Step S42: can partially meet in view of within certain period, reaching bicycle provided by the trip of base station p
The moment avoids the parking demand of this Some vehicles by the cycling demand the p of base station；Therefore, base station p is at i-th
The cycling demand at quarterAnd parking demandCalculating is shown below respectively:
Step S43: repeating above-mentioned two step to all users, calculates each base station in one day under each period
Cycling demandAnd parking demandAnd then it obtains one day 24
The demand and its distribution for cycling and stopping under a period；
Step S44: cycling and parking demand of all base stations of adding up in the corresponding period obtain riding under one day 24 period
Vehicle and parking demand.
To sum up, the present invention screens bicycle trip requirements and calculates bicycle cycling and stop to go on a journey as analytical unit
Vehicle demand.It is stopped and is gone on a journey using SMoT model extraction user, individually to go on a journey as analytical unit, sieved according to road network trip distance
Select the target trip of suitable bicycle mode；When calculating cycling trip demand, equally to go on a journey for analytical unit, according to base
The trip with setting out is reached in range of standing to assess bicycle cycling and parking demand.The present invention takes public transport into account and plugs into simultaneously
Cycling trip demand.For the transit trip mode in long range trip, by introducing public transport station point data,
Departure place and destination are calculated at a distance from nearest public traffic station, the trip for being suitble to bicycle to plug into is extracted and calculating connects
Refute trip requirements.
Compared with prior art, the invention has the following beneficial effects:
1, the present invention is based on large-scale crowd position data, it is convenient and efficient low in cost that data are obtained, and can be reflected voluntarily
The potential demand of vehicle trip.The limitation of city dweller's entirety trip requirements cannot be reflected by overcoming Traditional measurements method.
2, the present invention, which extracts, stops and goes on a journey, and individually to go on a journey as analytical unit, avoids to regard transit point to stop and lead
The cycling trip demand of cause is over-evaluated, also avoid due to single hop trip distance is too long and filter out whole section of Trip chain so as to cause
Trip requirements underestimate, therefore effectively overcome in background technique Xu method bring trip requirements and judge by accident.
3, the present invention calculates bicycle and plugs into what is generated during transit trip by introducing public traffic station
Demand effectively compensates for the careless omission that Xu et al. does not consider the demand of plugging into cycling trip need assessment.
Detailed description of the invention
Fig. 1 is the functional block diagram of the embodiment of the present invention.
Fig. 2 is the analog subscriber activity trajectory figure of the embodiment of the present invention.
Fig. 3 is the existing method and the present embodiment method contrast schematic diagram of the embodiment of the present invention.Wherein (a) is existing side
Method (b) is the present embodiment method.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and embodiments.
It is noted that described further below be all exemplary, it is intended to provide further instruction to the application.Unless another
It indicates, all technical and scientific terms used herein has usual with the application person of an ordinary skill in the technical field
The identical meanings of understanding.
It should be noted that term used herein above is merely to describe specific embodiment, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singular
Also it is intended to include plural form, additionally, it should be understood that, when in the present specification using term "comprising" and/or " packet
Include " when, indicate existing characteristics, step, operation, device, component and/or their combination.
As shown in Figure 1, present embodiments providing a kind of bicycle cycling and parking based on extensive mobile phone location data
Need assessment method, specifically includes the following steps:
Step S1: data preparation and data screening are carried out to extensive mobile phone location data；
Step S2: it carries out user and stops identification, from finding out certain time in a certain range region in initial trace RT
The trajectory segment of length, the trajectory segment are to stop S, and the part of stop is removed in initial trace, remaining trajectory segment
As move M；
Step S3: carrying out ownership goal trip and extract, when mobile distance meets certain distance threshold alpha and β, the movement
Constitute the target trip TM of user；
Step S4: the trip requirements of bicycle are divided into two aspects of cycling demand and parking demand, and one day is divided
For 24 hours, cycling trip conditions of demand in each period are calculated.
In the present embodiment, step S1 specifically: to the record data in mobile phone location data, initial trace, track point
Section, mobile, stop, trip and target trip are defined；
The record data r indicates to form the original recorded data of track, is expressed as four-tuple < Customs Assigned Number, the time,
Longitude, dimension >, such as following formula:
R=< OID, t, lon, lat >；
In formula, OID is object number, and t is the time, and lon and lat respectively indicate longitude and latitude；
The initial trace RT indicates the record data of object number having the same sequentially in time after arriving first
The sequence that mode organizes the formation of, such as following formula:
RT=[r1, r2 ..., rn]；
In formula, n is record number, to arbitrary 1≤i < j≤n, rj.time > ri.time, that is, records evening time of rj
In record ri；
The trajectory segment TS is the record subset of initial trace, and segmentation is a part of individual track, by multiple continuous
Record composition, trajectory segment is expressed as one hexa-atomic group, it is as follows:
RTTS=< UserID, start, end, type, RECORDS >；
In formula, UserID indicates Customs Assigned Number, and start and end are respectively the time for being segmented beginning and end, and type is point
The type of section, including stop S and mobile M；RECORDS is the original records collection of composition segmentation, no less than two elements；
The stop S is the subclass of trajectory segment, and indicating individual, there is no the mobile or activities in subrange, and
Duration is greater than the set value T0, is expressed as five-tuple < UserID, start, end, type, RECORDS a >, wherein
Type type is S；
The mobile M is the subclass of trajectory segment, is indicated in this time, and individual is expressed as one in mobile state
A five-tuple < UserId, start, end, type, RECORDS >, wherein type type is M；It is mobile to indicate between stop
Travel behaviour；
The target trip TM indicates that the trip of user is meeting corresponding screening conditions, such as following formula on:
M ∈ TM | α < DIS (m) < β }
In formula, m is mobile example, and DIS is distance function, is reference with practical road network distance, α and β respectively indicate use
The corresponding target trip parameter of bicycle trip mode is generated in filtering.
In the present embodiment, step S2 specifically includes the following steps:
Step S21: since first record, judge between next record data j to first record to be determined
Whether distance is less than the word space threshold DO of setting；
Step S22:, will record if the space length (calculating Euclidean distance) between two records is less than distance threshold DO
Potential stop set Q is added in j；And continue to judge next data record, calculate its in set Q each record between
Distance is added in set Q if distance is respectively less than DO；
Step S23: if set Q is not the time interval in empty and set Q between first record and the last one record
From time threshold T0 is greater than, then the record in set Q constitutes a stop；
Step S24: since current record, step S21- step S23 is repeated, until all records are processed.
Preferably, in real life, when moving distance is too short or too long, bicycle maximum probability will not be chosen as going on a journey
Tool.Starting point may select walking to too people are larger in short-term the distance between destination, and apart from it is too long when people's meeting
Select faster trip mode.Thus cycling trip has a certain distance feature, it may be assumed that when the distance length of trip is in α
When to β, bicycle maximum probability can be selected as trip tool.Still further aspect, when the distance between starting point to destination is longer,
There are the demands of plugging into of bicycle and public transport.
In the present embodiment, step S3 specifically: ownership goal trip TM is divided and goes on a journey and plugs into for short distance target
Target trip；
The trip of short distance target refers to the trajectory segment for meeting daily cycling trip short distance feature in short-distance movement；
The step of extracting the trip of short distance target are as follows: the road network distance for calculating all mobile M of user screens outlet from the mobile M of user
Trajectory segment of the net distance between α to β, the trajectory segment form the trip of short distance target；
Target of plugging into trip refers in mobile over long distances to meet the trajectory segment that public transport is plugged into trip and generated；
Extraction plug into target trip the step of are as follows: the road network distance for calculating all mobile M of user filters out distance from the mobile M of user
Movement greater than β；For the record data position of mobile starting and ending, the nearest public transport station of difference detection range
Point；Calculate the road network between mobile starting point and end point and public traffic station, if distance length between α to β, the note
The trajectory segment of record and public traffic station composition constitutes target trip of plugging into.
In the present embodiment, the setting of α and β need the general features in conjunction with city dweller's cycling trip to set.
In the present embodiment, step S4 specifically includes the following steps:
Step S41: the single base station p frequency outflow to set out in 24 periodspWith arrival frequency inflowpTable respectively
It is shown as:
In formula,Indicate be base station p within i-th of period from the frequency of base station p,What is indicated is at i-th
Period reaches the frequency of base station p；
Step S42: can partially meet in view of within certain period, reaching bicycle provided by the trip of base station p
The moment avoids the parking demand of this Some vehicles by the cycling demand the p of base station；Therefore, base station p is at i-th
The cycling demand at quarterAnd parking demandCalculating is shown below respectively:
Step S43: repeating above-mentioned two step to all users, calculates each base station in one day under each period
Cycling demandAnd parking demandAnd then it obtains one day 24
The demand and its distribution for cycling and stopping under a period；
Step S44: cycling and parking demand of all base stations of adding up in the corresponding period obtain riding under one day 24 period
Vehicle and parking demand.
To sum up, the present embodiment to be to go on a journey as analytical unit, screen bicycle trip requirements and calculate bicycle cycle with
Parking demand.It is stopped and is gone on a journey using SMoT model extraction user, individually to go on a journey as analytical unit, according to road network trip distance
Screen the target trip of suitable bicycle mode；When calculating cycling trip demand, equally to go on a journey as analytical unit, according to
The trip with setting out is reached in base station range to assess bicycle cycling and parking demand.The present invention takes public transport into account and connects simultaneously
The cycling trip demand refuted.For the transit trip mode in long range trip, by introducing public transport station points
According to the trip for being suitble to bicycle to plug into and meter are extracted at a distance from nearest public traffic station in calculating departure place and destination
Calculation is plugged into trip requirements.
The present embodiment combination user's daily routines characteristic simulation trace information of one user, to test the present embodiment side
The validity of case.The results show that the method for the present embodiment can more accurately assess the trip requirements of bicycle, it is specific to introduce such as
Under.
As shown in Fig. 2, Fig. 2 is to combine one day activity trajectory of resident's daily routines characteristic simulation user, the user is early
On first send child to go to institute, nursery schools and childcare centres from family, then eaten near company too early go to company to start the morning after meal work, noon
It returns to the work that company starts afternoon afterwards after lunch near company, finally comes home from work.
As shown in figure 3, Fig. 3 is the comparison diagram of the present embodiment method and the method identification cycling trip result of Xu.In Fig. 3
Case study on implementation in, the parameter selection in the present embodiment is as follows: stopping the distance threshold (D0) and time threshold (T0) point of identification
It is not set as 500 meters and 10 minutes；And the distance parameter α and β of short distance trip filtering are respectively set as 1km and 5km.In Fig. 3
(a) be calculated result using Xu method, (b) in Fig. 3 is the calculating of method assessment cycling trip demand of the invention
As a result, result statistics is shown in Table 1.
As can be seen that being 1,2,3 whole as a Trip chain by row number out, since this goes out in the appraisal procedure of Xu
The range of row chain is greater than 5km, therefore can be ignored, therefore will be can be filtered by 1,2,3 section of Trip chain formed of going on a journey, and causes
Without cycling trip demand at position A, B, C.But actually this three sections are the trips for meeting cycling trip distance feature,
It should participate in the calculating of cycling trip demand.In addition, in the Trip chain formed of being gone on a journey by 4,5 sections, due to depositing for transit point E
The trip 4 of script is being isolated as position transfer twice, Xu method will can this time be moved respectively as two cycling trips
Demand is calculated, so as to cause result erroneous estimation cycling trip demand (this simulation experiment in, E only use
The location point that family is passed through in moving process can't generate bicycle trip requirements).
The method for comparing Xu, the method for the present embodiment can more reasonably identify the property of user trajectory segmentation, and identification is full
The trip of sufficient cycling trip distance feature, cycling trip is calculated at position A, B, C and D for the trip of this part needs
It asks, while avoiding calculating trip requirements to transit point.Still further aspect, goes on a journey for long range, and the method for the present embodiment is abundant
Consider important function of the bicycle in public transport is plugged into, what the suitable bicycle of extraction was plugged into after introducing public traffic station goes out
It goes and calculates trip requirements of plugging into.1 cycling trip demand is finally respectively calculated at website F and G.
1 two methods result of table compares
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or 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 counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
The above described is only a preferred embodiment of the present invention, being not that the invention has other forms of limitations, appoint
What those skilled in the art changed or be modified as possibly also with the technology contents of the disclosure above equivalent variations etc.
Imitate embodiment.But without departing from the technical solutions of the present invention, according to the technical essence of the invention to above embodiments institute
Any simple modification, equivalent variations and the remodeling made, still fall within the protection scope of technical solution of the present invention.
Claims (5)
1. a kind of bicycle based on extensive mobile phone location data cycles and parking demand appraisal procedure, it is characterised in that: packet
Include following steps:
Step S1: data preparation and data screening are carried out to extensive mobile phone location data；
Step S2: it carries out user and stops identification, from finding out certain time length in a certain range region in initial trace RT
Trajectory segment, which is to stop S, and the part of stop is removed in initial trace, remaining trajectory segment is
Mobile M；
Step S3: it carries out ownership goal trip and extracts, when mobile distance meets certain distance threshold alpha and β, which is constituted
The target trip TM of user；
Step S4: the trip requirements of bicycle are divided into two aspects of cycling demand and parking demand, and were divided into 24 for one day
A hour calculates cycling trip conditions of demand in each period.
It is assessed 2. a kind of bicycle based on extensive mobile phone location data according to claim 1 is cycled with parking demand
Method, it is characterised in that: step S1 specifically: to the record data in mobile phone location data, initial trace, trajectory segment, shifting
Dynamic, stop, trip and target trip are defined；
The record data r indicates to form the original recorded data of track, is expressed as four-tuple < Customs Assigned Number, the time, longitude,
Dimension >, such as following formula:
R=< OID, t, lon, lat >；
In formula, OID is object number, and t is the time, and lon and lat respectively indicate longitude and latitude；
The initial trace RT indicates the record data of the object number having the same mode after arriving first sequentially in time
The sequence organized the formation of, such as following formula:
RT=[r1, r2 ..., rn]；
In formula, n is record number, is later than note to the time of arbitrary 1≤i < j≤n, rj.time > ri.time, i.e. record rj
Record ri；
The trajectory segment TS is the record subset of initial trace, and segmentation is a part of individual track, by multiple continuous notes
Trajectory segment is expressed as one hexa-atomic group by record composition, as follows:
RTTS=< UserID, start, end, type, RECORDS >；
In formula, UserID indicates that Customs Assigned Number, start and end are the time for being segmented beginning and end respectively, and type is segmentation
Type, including stop S and mobile M；RECORDS is the original records collection of composition segmentation, no less than two elements；
The stop S is the subclass of trajectory segment, and indicating individual, there is no the mobile or activities in subrange, and continue
Time is greater than the set value T0, is expressed as five-tuple < UserID, start, end, type, RECORDS a >, wherein type class
Type is S；
The mobile M is the subclass of trajectory segment, is indicated in this time, and individual is expressed as one five in mobile state
Tuple < UserId, start, end, type, RECORDS >, wherein type type is M；The mobile trip indicated between stop
Behavior；
The target trip TM indicates that the trip of user is meeting corresponding screening conditions, such as following formula on:
M ∈ TM | α < DIS (m) < β }
In formula, m is mobile example, and DIS is distance function, is reference with practical road network distance, α and β respectively indicate for
Filter generates the corresponding target trip parameter of bicycle trip mode.
It is assessed 3. a kind of bicycle based on extensive mobile phone location data according to claim 1 is cycled with parking demand
Method, it is characterised in that: step S2 specifically includes the following steps:
Step S21: since first record, judge the distance between next record data j to first record to be determined
Whether the word space threshold DO of setting is less than；
Step S22: if the space length between two records is less than distance threshold DO, potential stop is added in record j and is collected
Close Q；And continue to judge next data record, itself and the distance between each record in set Q are calculated, if distance is small
In DO, then it is added in set Q；
Step S23: if set Q is not that first time gap recorded between the last one record is big in empty and set Q
In time threshold T0, then the record in set Q constitutes a stop；
Step S24: since current record, step S21- step S23 is repeated, until all records are processed.
It is assessed 4. a kind of bicycle based on extensive mobile phone location data according to claim 1 is cycled with parking demand
Method, it is characterised in that: step S3 specifically: divide to go out for the trip of short distance target and target of plugging by ownership goal trip TM
Row；
The step of extracting the trip of short distance target are as follows: the road network distance for calculating all mobile M of user is screened from the mobile M of user
Trajectory segment of the road network distance between α to β out, the trajectory segment form the trip of short distance target；
Extraction plug into target trip the step of are as follows: the road network distance for calculating all mobile M of user is filtered out from the mobile M of user
Distance is greater than the movement of β；For the record data position of mobile starting and ending, the respectively nearest public friendship of detection range
Logical website；Calculate the road network distance between mobile starting point and end point and public traffic station, if distance length α to β it
Between, then the trajectory segment of the record and public traffic station composition constitutes target trip of plugging into.
It is assessed 5. a kind of bicycle based on extensive mobile phone location data according to claim 1 is cycled with parking demand
Method, it is characterised in that: step S4 specifically includes the following steps:
Step S41: the single base station p frequency outflow to set out in 24 periodspWith arrival frequency inflowpIt respectively indicates
Are as follows:
In formula,Indicate be base station p within i-th of period from the frequency of base station p,What is indicated is at i-th
Section reaches the frequency of base station p；
Step S42: considering within certain period, when this can partially be met by reaching bicycle provided by the trip of base station p
It carves by the cycling demand the p of base station, while avoiding the parking demand of this Some vehicles；Therefore, base station p is at the i-th moment
Cycling demandAnd parking demandCalculating is shown below respectively:
Step S43: above-mentioned two step is repeated to all users, calculates cycling of each base station in one day under each period
DemandAnd parking demandAnd then when obtaining one day 24
The lower demand and its distribution cycled and stop of section；
Step S44: cycling and parking demand of all base stations of adding up in the corresponding period, obtain cycling under one day 24 period and
Parking demand.
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