CN107305590A - A kind of urban transportation trip characteristicses based on mobile phone signaling data determine method - Google Patents
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Abstract
Method is determined the invention discloses a kind of urban transportation trip characteristicses based on mobile phone signaling data, including：Mobile phone signaling data is pre-processed, rapid extraction available fields, form the storage format using ID as critical field；To the shift position of each user according to time sequence, denoising is carried out according to speed and the abnormal determining method of angle；Using DBSCAN clustering algorithms formation accumulation point, the daily all dwell points of each user and residence time are identified；Dwell point is classified, urban transportation generating capacity, road traffic simulation amount, per capita trip number of times, trip number of times, trip distance, commuter distance, inhabitation index and employment index is calculated.The present invention makes full use of mobile phone signaling data, rapidly, urban transportation generating capacity, road traffic simulation amount, per capita trip number of times, trip number of times, trip distance, commuter distance, inhabitation index and employment index are accurately calculated, data supporting is provided for the research of Urban Traffic Planning, traffic administration and perforator vein.
Description
Technical field
The invention belongs to traffic programme data analysis field, more particularly to a kind of urban transportation based on mobile phone signaling data
Trip characteristicses determine method.
Background technology
Urban transportation generating capacity, road traffic simulation amount, trip number of times, per capita trip number of times, trip distance, commuter away from
It is the important parameter for reflecting urban transportation trip requirements from, inhabitation index and employment index, is traffic programme, urban construction and city
The scientific basis of city's management.Currently, the quickening with urban construction speed and the constantly improve of function, traffic of the people to city
Planning, construction and management propose higher requirement.Tradition obtains urban transportation generating capacity, road traffic simulation amount, trip number of times, people
The method of trip number of times, trip distance, commuter distance, inhabitation index and employment index is mainly by door-to-door survey, roadside
There is low sample rate, cycle length, people in the artificial investigation methods such as inquiry, form investigation, vehicle license and monthly ticket survey, this mode
Power direct financial costs are larger, and due to sample rate it is low and the quality of data the problems such as, it is difficult to realize expected effect.
Mobile phone signaling data refer to cellphone subscriber making a phone call, send short messages, change in location and produce when periodically updating
Mobile position data, the development of popularization and wireless location technology recently as mobile phone, mobile phone signaling data constantly improve
And growth so that calculated using mobile phone positioning urban transportation generating capacity, road traffic simulation amount, trip number of times, per capita trip number of times,
Trip distance, commuter distance, the method for inhabitation index and employment index turn into it is a kind of may.Almost everybody is owned by now
One mobile phone, each communication common carrier has the user resources of magnanimity and the basic data of correlation, relative to conventional survey mode, undoubtedly
Can obtain more comprehensively, more accurately data, for carry out deeper into urban transportation generating capacity, road traffic simulation amount, trip number of times,
Trip distance, inhabitation index and employment index, which are calculated, provides good data basis.
Because daily mobile phone signaling data amount is big (one day about 700,000,000 record, 70G data), and file storage size
Difference is big, to improve treatment effeciency, we using by the original mobile phone signaling data of one day by the uniform separated side of file size
Formula is stored.
DBSCAN is the representational density-based algorithms of comparison.With division and hierarchy clustering method not
Together, cluster is defined as the maximum set of the connected point of density by it, can be being cluster with region division highdensity enough, and can
The cluster of arbitrary shape is found in noisy spatial database.The processing of this step both can further eliminate shake data
Influence to calculating, can also polymerize dwell point, be prepared for trip calculating.
Inhabitation index and employment index are referred to as duty and live index.Index is lived in duty can be with reflecting regional duty firmly balance.Duty is lived
Balance refers to, in a specific region, and the quantity of employed population, the quantity of job and duty are lived all big in the quantity of local area
Cause is equal, and most of resident can work nearby.Employment index and inhabitation index are bigger, show that the region is lived closer to duty flat
The maximum of weighing apparatus, employment index and inhabitation index is 1, and duty, which is lived, in theory balances optimal situation, is exactly employment index and residence
Firmly index is substantially equal to 1.
Not yet there is pertinent literature report at present.
The content of the invention
In view of this, technology of the invention solves problem：The deficiencies in the prior art are overcome to be based on mobile phone signaling there is provided one kind
The urban transportation trip characteristicses of data determine method, rapidly, accurately the urban transportation generating capacity based on mobile phone signaling data,
Road traffic simulation amount, per capita trip number of times, trip number of times, trip distance, commuter distance, inhabitation index and employment index calculating
Method, more real-time and accurate data supporting is provided for the research of Urban Traffic Planning, traffic administration and perforator vein.
The technical solution of the present invention：Mainly for urban transportation generating capacity, road traffic simulation amount, trip number of times, per capita
Trip number of times, trip distance, commuter distance, inhabitation index and employment index are calculated, and (1) is entered to mobile phone signaling data
Row pretreatment, rapid extraction available fields form the storage format using ID as critical field；(2) to the shifting of each user
Dynamic position data according to time sequence, denoising is carried out according to speed and the abnormal determining method of angle；(3) DBSCAN clustering algorithm shapes are utilized
Into accumulation point, identify the daily all dwell points of each user and stop the beginning and ending time；(4) dwell point is classified, counted
Calculate traffic generating capacity, road traffic simulation amount, trip number of times, trip distance, inhabitation index and employment index.
Realized by following steps：
(1) mobile phone signaling data is pre-processed, extracts available fields, formed with ID, i.e. user's unique mark
UID is the storage format of critical field, forms the mobile position data of user；
(2) to the mobile position data of each user according to time sequence, sentenced extremely according to velocity anomaly determining method and angle
Disconnected method carries out denoising, obtains the mobile position data after denoising；
(3) according to the mobile position data after step (2) denoising, carry out clustering using DBSCAN clustering algorithms and formed
Accumulation point, identifies the daily all dwell points of each user and stops the beginning and ending time；The dwell point and stop beginning and ending time
Recognition methods is according to timestamp successively sequence, by the time in same cluster number by same all mobile position datas of user's whole day
Continuous accumulation point, is divided into a dwell point, and the rising as the dwell point using the earliest time and latest time of dwell point
Only time；
(4) dwell point obtained according to step (3), is calculated as 1 trip by adjacent 2 dwell points, this 2 dwell points is pressed
Time order and function order in specific region, calculates traffic generating capacity and the traffic in the region successively as O, D point of this trip
Traffic attraction.
(5) according to O, D point of step (4), calculate specific region and always go on a journey number of times and number of times of going on a journey per capita.
(6) according to the dwell point beginning and ending time, judge the residence and employment ground (commute OD) of each user, calculate specific
Zone leveling trip distance and average commuter distance.
(7) in specific region, region employed population number, job number and the duty are calculated respectively and is lived all in local area people
Number, then calculates inhabitation index and employment index respectively.
In the step (4), the process for calculating one day trip number of times of user is：Calculate user's whole day dwell point number it
With if equal to 0, user's trip number of times is 0；If more than or equal to 1, subtracting 1 and obtaining user's trip number of times.
In the step (4), the process for calculating the traffic generating capacity of specific region is：Calculate all O points in the region
Quantity sum, obtains the traffic generating capacity in the region.
In the step (4), the process for calculating the road traffic simulation amount of specific region is：Calculate all D points in the region
Quantity sum, obtains the road traffic simulation amount in the region.
In the step (5), the process for calculating total trip number of times of specific region is：O points or D points are calculated in the region
Trip number of times sum, obtain total trip number of times in the region.
In the step (5), the process for calculating the number of places per capita of specific region is：The region always go on a journey number of times divided by should
Region number, obtains the trip number of times per capita in the region.
In the step (6), the process for calculating each trip distance is：Calculate the Man Ha between O, D point of this trip
Distance, obtains trip distance.
In the step (6), the process for calculating specific region average trip distance is：All users in the region are calculated to own
Trip distance sum, divided by the region are always gone on a journey number of times, obtain the zone leveling trip distance.
In the step (6), the process for judging single ID residences is：Judge user in one day (0,6) ∪ (21,
24) (0 is referred to:00-6:00 and 21:00-24:00) it whether there is dwell point in time interval, if it does, by the residence time most
Long dwell point as user residence.
In the step (6), the process for judging single ID employment ground is：(8,18) time in judging user on weekdays
There is dwell point in interval, and dwell point beginning and ending time difference was more than 4 hours, if in the presence of above-mentioned condition and stop will be met
Time most long dwell point as user employment.
In the step (6), the process for calculating the average commuter distance in specific region is：Calculate all users in the region
Residence and the manhatton distance sum on employment ground, divided by the zone user number, obtain the average commuter distance in the region.
In the step (7), the process for calculating specific region employed population number is：Residence is calculated in the region, and is deposited
Number of users sum on employment ground, obtains the region employed population number.
In the step (7), the process for calculating specific region job number is：Calculate user of the employment ground in the region
Quantity sum, obtains the region job number.
In the step (7), calculating specific region duty is lived all is in the process of local area number：Calculate residence and employment ground
In the number of users sum in the region, obtain the region duty and live all in local area number.
In the step (7), the process for calculating specific region inhabitation index is：The region duty is calculated to live all in local area number
With the ratio of employed population number (whether being obtained employment in local area), inhabitation index is obtained.
In the step (7), the process for calculating specific region employment index is：The region duty is calculated to live all in local area number
With the ratio of job number (whether being lived in local area), employment index is obtained.
The advantage of the present invention compared with prior art is：
(1) present invention makes full use of mobile phone signaling data, by above step, rapidly, accurately calculates city friendship
Logical generating capacity, road traffic simulation amount, per capita trip number of times, trip number of times, trip distance, commuter distance, inhabitation index and just
Industry index, data supporting is provided for urban population monitoring with traffic programme；
(2) basic data of the invention derives from cordless communication network, has velocity process with respect to methods such as sampling statisticses
Hurry up, the advantage that result precision is high.
Brief description of the drawings
Fig. 1 is the inventive method flow chart；
Fig. 2 is that medium velocity of the present invention judges principle schematic extremely；
Fig. 3 judges principle schematic extremely for angle in the present invention；
Fig. 4 is the DBSCAN cluster schematic diagrames in the present invention；
Fig. 5 is dwell point classification schematic diagram in the present invention；
Fig. 6 is specific region traffic generating capacity and road traffic simulation amount schematic diagram in the present invention；
Fig. 7 is trip distance schematic diagram in the present invention.
Embodiment
To make technology solution problem, technical scheme and the advantage of the present invention clearer, below in conjunction with the accompanying drawings to the present invention
It is described in further detail.
Urban transportation trip characteristicses provided in an embodiment of the present invention determine method in pretreatment stage, rapid extraction mobile phone letter
Useful field in data is made, is formed with the data storage form that ID (user's unique mark) is keyword, it is convenient following
Data cleansing and dwell point clustering.
As shown in figure 1, being determined for the urban transportation trip characteristicses provided in an embodiment of the present invention based on mobile phone signaling data
Method flow diagram, including：
Step 101, mobile phone signaling data is pre-processed
To improve treatment effeciency, the original mobile phone signaling data of one day is uniformly stored separately by file size, we make
Method is：256 different files are stored into by two after ID (UID) cryptographic Hash；Then multiple threads mistake
The data of undesirable (field missing, longitude and latitude be not abnormal in zoning, timestamp) are rejected in journey, following storage is formed
Mode：
I-th of file data form:(i, UID, timestamp, longitude, latitude, cell, event).
Wherein i for UID Hash encryption after the 121st~128 hashed value (i.e.)。
Step 102, to the mobile position data of each user according to time sequence, and carry out data cleansing.It is different including speed
Often cleaning and angle are cleaned extremely.
Because the problem of mobile phone has drift in position fixing process, will carry out data cleansing before treatment, remove
Shake and the influence drifted about to data.First to the mobile position data of each user according to time sequence, then walked as follows
Suddenly：
Step 1021, velocity anomaly cleaning
1st, continuous 3 location points are read first, then calculate two neighboring location point apart from d and time difference t, such as scheme
Shown in 2, tri- location points of A, B, C are the coordinate points in the same user mobile phone signaling data that in chronological sequence order is produced, point
Not Ji Suan intermediate point B to former and later two points A, C translational speed vABAnd vBC, wherein vAB=dAB/tAB, vBC=dBC/tBC。
V=min { vAB,vBC}。
If the 2, (i.e. the speed of intermediate point to former and later two points is both greater than 120km/h to v ＞ 120, recurs speed different
Often), then velocity anomaly point of the B location point as user is judged, by the position point deletion.
Step 1022, angle are cleaned extremely
1st, same continuous 4 location points of user are read, the angle that three location points are formed before and after calculating, such as Fig. 3 institutes
Show, tetra- location points of A, B, C, D are the coordinate points in the same user mobile phone signaling data that in chronological sequence order is produced, respectively
The angle ∠ that calculating A, B, C and B, tri- location points of C, D are formedABCAnd ∠BCD。
α=max { ∠ABC,∠BCD}。
If the 2, α ＜ π/4 (i.e. angle a and angle b are all higher than 135 °), judges that location point C produces as user during traveling
Raw abnormity point, is deleted C by angle unusual determination method.
Speed and angle abnormal point deletion of the two above step to all users is repeated, shake and drift are removed to reach
Influence to data.
Step 103, clustered using DBSCAN, identify all dwell points of user and beginning and ending time
Be primarily based on DBSCAN clustering algorithms to after cleaning data set carry out clustering, then according to cluster result come
Differentiate dwell point.A series of location points of daily same user are obtained by data cleansing, each location point includes three parameters：
UTC time t, longitude lon, latitude lat.Three parameters are inputted to DBSCAN algorithms：Location point, it is minimum comprising points MinPts,
Search field radius Eps.Identification dwell point detailed process be：
Step 1031, DBSCAN cluster to form accumulation point
Choose exemplified by first location point, the distance for first finding first location point and remaining location point is less than 300 meters
Point, then judges that the position that it is included in accumulation point is counted less than minimum comprising points 2, therefore export the class of first location point
Type is external point.Circulate successively, find the position points that some location point includes in accumulation point be more than it is minimum comprising points
During MinPts, point centered on the point classification of this location point is exported, accumulation point is identified as.
The 1st, the critical zone (300 meters are set in this algorithm) that one radius is s, s distance differences between adjacent base station are set
Intermediate value round determination.Since first location point in a user grouping, all location points and first position are calculated
Point apart from D.
The 2nd, if all distances are both less than 300 meters, or, distance be less than 300 meters of points be more than it is minimum comprising points 2,
Then it may determine that as accumulation point, the cluster number of label aggregation point.If any one distance is more than 300 meters, next position is brought into
Put the above-mentioned calculating process of a repetition.
As shown in figure 4, A1、A2、A3、A4、A5Cluster is formed, mark cluster number is A；B1、B2Cluster is formed, mark cluster number is B.
Step 1032, dwell point and its beginning and ending time identification
Same all mobile position datas of user's whole day are successively sorted according to timestamp, the time in same cluster number is connected
Continuous accumulation point, is divided into a dwell point, and the earliest time and latest time that these are put are used as the start-stop of the dwell point
Time.As shown in figure 5, { A1、A2、A3Be cluster A a dwell point, at the beginning of it between be tA1, the end time is tA3, it
Residence time is tA3-tA1；B1、B2For a cluster B dwell point, at the beginning of it between be tB1, the end time is tB2.It stops
It is t to stay the timeB2-tB1。
After dwell point and residence time identification, by ID merging data, following storage mode is formed：
Wherein non-dwell point is labeled as 0.
Step 104, calculating traffic generating capacity and road traffic simulation amount
Adjacent 2 dwell points of user are 1 trip, and in chronological sequence order is used as this to 2 dwell points of trip successively
O, D point of trip, as shown in fig. 6, traffic generating capacity refers to the travel amount using specific region to go out beginning-of-line O, road traffic simulation amount
Refer to the travel amount using specific region as travel destination D.
Step 1041, calculating traffic generating capacity
Traffic generating capacity is beginning-of-line O to go on a journey in the U of specific region total amount.First determine whether the starting point O of ith tripi
Whether in the region, if it is add up Jia 1, then travel through the traffic hair that the cumulative amount that all trips obtain is the region
Raw amount TG.Calculation is：
Step 1042, calculating road traffic simulation amount
Road traffic simulation amount is that travel destination D goes on a journey total amount in the U of specific region.First determine whether the terminal D of ith tripi
Whether in the region, if it is add up Jia 1, then travel through the traffic suction that the cumulative amount that all trips obtain is the region
The amount of drawing TA.Calculation is：
Step 105, calculate places number and number of times of going on a journey per capita
Trip number of times is specific region traffic trip total degree, and be specially that all users are daily in the region stops from one
Number of times of the point to another dwell point.
Step 1051, calculate places number
Number of times of going on a journey is the trip total amount for beginning-of-line O or travel destination D wherein any point in the U of specific region, first
First judge the starting point O of ith tripiOr terminal DiWhether wherein any point adds up Jia 1 in the region, if it is, then
The cumulative amount that all trips of traversal are obtained is the trip number of times TS in the region, and calculation is：
Step 1052, calculating are gone on a journey number of times per capita
Specific region U trip number of times TS divided by region number N, obtains the region and goes on a journey per capita number of times m：
Step 106, calculating trip distance and commuter distance
Trip distance be specific region average trip distance, the i.e. manhatton distance of all trips of all users in the region it
With divided by the region trip number of times.Commuter distance is all user residences in the zone leveling Commuting Distance, the i.e. region
With the manhatton distance average value on employment ground.
Step 1061, calculating trip distance
As shown in fig. 7, manhatton distance in the U of specific region between two dwell point O, D points of all trips is averaged
It is worth for trip distance, if the O point coordinates of ith trip is (xOi,yOi), D point coordinates is (xDi,yDi), then trip distance d is：
Step 1062, judge user residence and employment ground
Judge that (0,6) ∪ (21,24) of user's first in one day (refers to 0:00-6:00 and 21:00-24:00) time zone
In whether there is dwell point, if it is present residence time most long dwell point is designated as into O' as the residence of user
(xO',yO'), otherwise residence is labeled as 0.
It whether there is dwell point, and dwell point beginning and ending time in (8,18) time interval in judging user on weekdays
Difference is more than 4 hours, if in the presence of will meet above-mentioned condition and residence time most long dwell point is used as the employment of user, note
For D'(xD',yD'), otherwise employment ground is labeled as 0.
Step 1063, calculating commuter distance
The average value of manhatton distance in the U of specific region between the residence of all users and employment ground is commuter
Distance, if the residence O' point coordinates of i-th of user is (xO'i,yO'i), employment ground D' point coordinates is (xD'i,yD'i), then go on a journey
It is apart from d'：
Step 107, calculating inhabitation index and employment index
Step 1071, calculating inhabitation index
Inhabitation index is that the number all in the U of specific region and the ratio of employed population number are lived in duty.First, if i-th of use
The residence at family is O'i, employment ground is D'i, residence is calculated in the region, and there is the number of users on employment ground, obtain just
Industry population α：
Secondly, the number of users of residence and employment ground in the region is calculated, must be assumed office all in the region
Number β：
Inhabitation index R：
Step 1072, calculating employment index
Employment index be duty live all specific region U number β and job number γ ratio.First, if i-th of use
It is the employment at family D'i, number of users of the employment ground in the region is calculated, job number γ is obtained：
Employment index W：
Above example is provided just for the sake of the description purpose of the present invention, and is not intended to limit the scope of the present invention.This
The scope of invention is defined by the following claims.The various equivalent substitutions that do not depart from spirit and principles of the present invention and make and repair
Change, all should cover within the scope of the present invention.
Claims (10)
1. a kind of urban transportation trip characteristicses based on mobile phone signaling data determine method, it is characterised in that：To dwell point and stop
Stay the beginning and ending time to be classified, calculate urban transportation generating capacity, road traffic simulation amount, per capita trip number of times, trip number of times, trip
Distance, commuter distance, inhabitation index and employment index；The traffic generating capacity (Trip Generation) is to calculate special
It is the travel amount for beginning-of-line to determine region；The road traffic simulation amount (Trip Attraction) is to calculate specific region to go on a journey
The travel amount of terminal；The trip number of times is specific region trip total amount (Trip Summation)；The number of times of going on a journey per capita
For the travel amount per capita in specific region；The trip distance be specific region in trip distance per capita, that is, go out beginning-of-line or
Whole trips of the travel destination in specific region, go out beginning-of-line to the average value of the manhatton distance of travel destination；It is described logical
Diligent trip distance is the whole use of the commuter distance per capita in specific region, i.e. residence or employment ground in specific region
Family, average value of the residence to the manhatton distance on employment ground；The inhabitation index lives the number all in specific region for duty
With the ratio of employed population number；The employment index is that the number all in specific region and the ratio of job number are lived in duty.
2. the urban transportation trip characteristicses according to claim 1 based on mobile phone signaling data determine method, its feature exists
In：It is described calculate traffic generating capacity method be：Adjacent 2 dwell points of user are 1 trip, and 2 dwell points of trip are on time
Between sequencing successively as O, D point of this trip, traffic generating capacity is that trips of the beginning-of-line O in the U of specific region total
Amount, first determines whether the starting point O of ith tripiWhether in the region, if it is add up Jia 1, then travel through all trips
Obtained cumulative amount is the traffic generating capacity TG in the region, and calculation is：
3. the urban transportation trip characteristicses according to claim 1 based on mobile phone signaling data determine method, its feature exists
In：It is described calculate road traffic simulation amount method be：Adjacent 2 dwell points of user are 1 trip, and 2 dwell points of trip are on time
Between sequencing successively as this trip O, D point, road traffic simulation amount be trips of the travel destination D in the U of specific region it is total
Amount, first determines whether the terminal D of ith tripiWhether in the region, if it is add up Jia 1, then travel through all trips
Obtained cumulative amount is the road traffic simulation amount TA in the region, and calculation is：
4. the urban transportation trip characteristicses according to claim 1 based on mobile phone signaling data determine method, its feature exists
In：It is described calculate places number method be：Adjacent 2 dwell points of user are 1 trip, and 2 dwell points of trip are temporally
Sequencing is successively as O, D point of this trip, and trip number of times is beginning-of-line OiOr travel destination DiWherein any point exists
Whether the trip total amount in the U of specific region, first determine whether beginning or end wherein any point of ith trip in the region
It is interior, if it is add up Jia 1, then travel through the trip number of times TS that the cumulative amount that all trips obtain is the region, calculating side
Formula is：
5. the urban transportation trip characteristicses according to claim 1 based on mobile phone signaling data determine method, its feature exists
In：The go on a journey method of number of times of the calculating specific region U people is：The trip number of times TS in the region divided by region number N, is obtained
Gone on a journey per capita number of times m to the region：
6. the urban transportation trip characteristicses according to claim 1 based on mobile phone signaling data determine method, its feature exists
In：It is described calculate trip distance method be：Manhattan in the U of specific region between two dwell point O, D points of all trips
The average value of distance is trip distance, if the O of ith tripiPoint coordinates is (xOi,yOi), DiPoint coordinates is (xDi,yDi), then go out
Row distance d is：
7. the urban transportation trip characteristicses according to claim 1 based on mobile phone signaling data determine method, its feature exists
In：The judgement user residence and employment ground method are：Judge that (0,6) ∪ (21,24) of user's first in one day (refers to 0:
00-6:00 and 21:00-24:00) it whether there is dwell point in time interval, if it is present the residence time most long is stopped
Stationary point is designated as O'(x as the residence of userO',yO'), otherwise residence is labeled as 0；
It whether there is dwell point in (8,18) time interval in judging user on weekdays, and dwell point beginning and ending time difference is big
In 4 hours, if in the presence of will meet above-mentioned condition and residence time most long dwell point be as the employment of user, be designated as D'
(xD',yD'), otherwise employment ground is labeled as 0.
8. the urban transportation trip characteristicses according to claim 1 based on mobile phone signaling data determine method, its feature exists
In：It is described calculate commuter distance method be：Man Ha in the U of specific region between the residence of all users and employment ground
The average value for distance of pausing is commuter distance, if the residence O' of i-th of useriPoint coordinates is (xO'i,yO'i), employment ground D'i
Point coordinates is (xD'i,yD'i), then commuter is apart from d'：
9. the urban transportation trip characteristicses according to claim 1 based on mobile phone signaling data determine method, its feature exists
In：The method of the inhabitation index of the calculating specific region U is：First, if the residence of i-th of user is O'i, employment ground is
D'i, residence is calculated in the region, and there is the number of users on employment ground, obtain employed population number α：
Secondly, the number of users of residence and employment ground in the region, the number that must be assumed office all in the region are calculated
β：
Inhabitation index R：
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10. the urban transportation trip characteristicses according to claim 1 based on mobile phone signaling data determine method, its feature exists
In：The employment index of the calculating specific region U is：Numbers of users of the employment ground D' in the region is calculated, hilllock of obtaining employment is obtained
Digit γ：
Employment index W：
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<mfrac>
<mi>&beta;</mi>
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3
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| Publication number | Priority date | Publication date | Assignee | Title |
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101807222A (en) * | 2010-02-09 | 2010-08-18 | 武汉大学 | Station-based urban public traffic network optimized configuration method |
| CN102333274A (en) * | 2010-07-12 | 2012-01-25 | 同济大学 | Cell phone signal data based-method for processing commuting information and apparatus thereof |
| CN102595323A (en) * | 2012-03-20 | 2012-07-18 | 北京交通发展研究中心 | Method for obtaining resident travel characteristic parameter based on mobile phone positioning data |
| US20120323691A1 (en) * | 2011-06-15 | 2012-12-20 | Smart Destinations, Inc. | Systems and methods for location-based marketing for attraction access |
| CN104899443A (en) * | 2015-06-05 | 2015-09-09 | 陆化普 | Method and system for evaluating current travel demand and predicting travel demand in future |
| CN105117789A (en) * | 2015-07-29 | 2015-12-02 | 西南交通大学 | Resident trip mode comprehensive judging method based on handset signaling data |
| CN105142106A (en) * | 2015-07-29 | 2015-12-09 | 西南交通大学 | Traveler home-work location identification and trip chain depicting method based on mobile phone signaling data |
| CN105513348A (en) * | 2015-11-27 | 2016-04-20 | 西南交通大学 | Mobile phone signaling trip chain-based OD matrix acquisition method |
-
2017
- 2017-06-14 CN CN201710448907.8A patent/CN107305590B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101807222A (en) * | 2010-02-09 | 2010-08-18 | 武汉大学 | Station-based urban public traffic network optimized configuration method |
| CN102333274A (en) * | 2010-07-12 | 2012-01-25 | 同济大学 | Cell phone signal data based-method for processing commuting information and apparatus thereof |
| US20120323691A1 (en) * | 2011-06-15 | 2012-12-20 | Smart Destinations, Inc. | Systems and methods for location-based marketing for attraction access |
| CN102595323A (en) * | 2012-03-20 | 2012-07-18 | 北京交通发展研究中心 | Method for obtaining resident travel characteristic parameter based on mobile phone positioning data |
| CN104899443A (en) * | 2015-06-05 | 2015-09-09 | 陆化普 | Method and system for evaluating current travel demand and predicting travel demand in future |
| CN105117789A (en) * | 2015-07-29 | 2015-12-02 | 西南交通大学 | Resident trip mode comprehensive judging method based on handset signaling data |
| CN105142106A (en) * | 2015-07-29 | 2015-12-09 | 西南交通大学 | Traveler home-work location identification and trip chain depicting method based on mobile phone signaling data |
| CN105513348A (en) * | 2015-11-27 | 2016-04-20 | 西南交通大学 | Mobile phone signaling trip chain-based OD matrix acquisition method |
Non-Patent Citations (1)
| Title |
|---|
| 赖见辉: ""基于移动通信定位数据的交通信息提取及分析方法研究"", 《中国博士学位论文全文数据库 工程科技辑II》 * |
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