CN105117789A - Resident trip mode comprehensive judging method based on handset signaling data - Google Patents
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Abstract
The invention discloses a method of comprehensively judging a resident trip mode based on handset signaling data, which belongs to the transport planning and management data analyzing field. A data source is from handset signaling data provided by mobile network service providers. Data cleaning, integrating and position conversion are further performed. A resident trip mode is further judged by mobile space-time path describing and stopover point identifying. The method which can effectively discriminate seven common trip modes including walking, bicycling, routine bus, electric vehicle, self-driving, taxi and rail transit thus acquires trip mode information of residents. A data basis is further provided for fields of special traffic programs, comprehensive traffic programs and intelligent traffic systems of cities, etc.
Description
Technical field
The invention belongs to traffic programme data analysis field, be specifically related to a kind of method of the resident trip mode comprehensive distinguishing based on mobile phone signaling data.
Background technology
Resident trip information plays vital effect in traffic programme, traffic control and management etc., it disclose the rule of urban land use, business activity, humane custom, public transport network management, be widely used in the field such as urban integrated traffic planning, intelligent transportation system.But existing resident trip survey is all according to the method such as survey, telephone questionnaire of the accreditation of Institute of Traffic Engineers of the U.S., European Transport association, traffic management department of China or employer's organization, generally there is the problems such as cost is high, workload is large, data processing cycle is long, content subjectivity strong, data out of true in these classic methods.Along with the significantly decline of sharply expansion, the use cost of cellphone subscriber, interest concessions mobile phone Mobile data carries out data mining, and extracting the accurately complete resident trip information of acquisition becomes possibility.In mobile phone Mobile Data Mining, how identifying user have employed that trip mode in what stage, is the place of the heavy difficult point of research now.
Prior art one related to the present invention
The technical scheme of prior art one
Patent of invention: based on the Commute travel mode identification method of AGPS technology
Applicant: Beijing Jiaotong University
Inventor: Qian great Lin Luo Yi Yan Peng Lishanshanli becomes magnificent Dong Qian
The shortcoming of prior art one
1. the mobile phone state information data of data source for providing from GPS module in mobile phone.And for cellphone GPS signal, there is strict requirement.The identification can carrying out trip mode met the demands, otherwise None-identified.
2. trip mode recognition methods is by BP neural network, needs a large amount of sample training, and the model trained then could be utilized to carry out trip mode identification.Algorithm is complicated, operand is too large, not easily promotes.
3. the trip mode identified is walking, bus, car, cannot effectively identify for track traffic.
Prior art two related to the present invention
The technical scheme of prior art two
Patent of invention: a kind of trip mode recognition methods based on mobile phone signal data
Applicant: Shanghai Mei Hui softcom limited
Inventor: the flat Ran Bin of Sun Li light old Ming Weiqiu Wei Yiqiu will army Liu Sheng
The shortcoming of prior art two
1. data source is cell phone network signal.
2. the trip mode identified is track traffic, routine bus system, private motor vehicles, bicycle and walking, but lacks the identification of taxi and electric motor car.
Prior art three related to the present invention
The technical scheme of prior art three
Patent of invention: a kind of road trip mode method of discrimination based on smart mobile phone and system
Applicant: China Aerospace System Engineering Corporation
Inventor: the bright Zhang Dan of the law of the country rock Rayleigh army luxuriant chaste tree of Wang Zhenhua ten thousand long woods scape pool great waves list Ya Wennie
The shortcoming of prior art three
1. data source is the mobile phone state information data with GPS module
2. the trip mode identified is walking, by bus, cannot identifies for traffic methods such as track traffics.
Prior art four related to the present invention
The technical scheme of prior art four
Patent of invention: a kind of traffic trip mode identification method based on mobile phone location
Applicant: Guangzhou Institute of Geography
Inventor: Li Yong Zhou Handong
The shortcoming of prior art four
1. data source is active data and passive data.Active data is GPS and AGPS data, and passive data are the data of the location technology based on mobile network.
2. the trip mode identified is walking, public transport, self driving/taxi, cannot identify for other trip modes such as track traffics.
Summary of the invention
The present invention is directed to prior art data source to obtain and be limited to GPS or the network information and method of discrimination and fail to reflect the information such as track traffic and a kind of propose resident trip mode comprehensive distinguishing based on mobile phone signaling data method.
For overcoming the above problems, technical method of the present invention is to provide a kind of method of the resident trip mode comprehensive distinguishing based on mobile phone signaling data, comprises the following steps:
Step 1: by existing travel modal situation, extract line mode feature, obtain the prior probability of trip mode according to available sample;
Step 2: the mobile phone signaling data obtaining certain user once complete trip, and by the identification with dwell point of portraying in Mobile Space-time path, extract the single trip mode subchain of user;
Step 3: by the average velocity of trip mode subchain, trip mode is differentiated for the first time, the result of differentiation is Vehicle emission or bicycle trip;
Step 4: by average velocity, trip duration and trip distance three attributes, differentiate bicycle trip, the result of differentiation is walking or cycling trip;
Step 5: the trip mode subchain belonging to Vehicle emission mode mated with GIS gauze, if trip mode is mated with GIS Metro Network, is just determined as track traffic trip; If trip mode is mated with GIS public transport network, be just determined as regular public traffic trip; If do not mated with GIS public transport network with GIS Metro Network, just forward step 6 to;
Step 6: by average velocity, maximal rate and trip duration three attributes, differentiate remaining possible trip mode, the result of differentiation is electric motor car, self driving or taxi.
As preferably, step 1 comprises:
1) the extraction feature of travel modal;
2) the choosing of the discrimination properties that differentiates of walking and bicycle;
3) walking and bicycle prior probability build;
4) choosing of the discrimination properties that electric motor car, self driving and taxi differentiate;
5) electric motor car, self driving and taxi prior probability build.
As preferably, step 2 also comprises:
By the segmentation of dwell point, each trip mode subchain is made only to comprise a kind of trip mode.
As preferably, step 3 is specially:
If average velocity is less than threshold speed c
v1, then bicycle trip is determined as; If average velocity is more than or equal to threshold speed c
v1, then Vehicle emission is determined as.
As preferably, step 4 specifically comprises:
The differentiation walking obtained according to step 1 and the prior probability of bicycle, according to formula p (x|y
i) p (y
i) classification differentiated is calculated, and choose type calculating p (x|y
i) p (y
i) in maximal term as differentiate trip mode; Wherein, the attribute differentiated in x is average velocity, trip duration and trip distance here, y
ifor the type differentiated, be walking and bicycle here.
As preferably, step 6 comprises:
In conjunction with the prior probability of electric motor car, self driving and trip of taxi, utilize formula p (x|y
i) p (y
i) classification differentiated is calculated, and choose type calculating p (x|y
i) p (y
i) in maximal term as differentiate trip mode; Wherein, the attribute differentiated in x is average velocity, maximal rate and trip duration here, y
ifor the type differentiated, be electric motor car, self driving and taxi here.
The present invention can increase a dimension for the resident trip acquisition of information work based on mobile phone signaling data in sum, and can realize following effect:
1. the present invention utilize source data provide for Mobile Network Operator, meet the mobile phone signaling data of state's laws about individual privacy.There is the features such as obtain manner is simple, procurement cost is low, message sample is large.
2. recognition methods by means of the trip characteristics of existing mode of transportation, does not need too much sample training.Algorithm simply, easily realizes.
3. the trip mode of identification of the present invention contains the major way of Urban Residential Trip substantially.I.e. walking, bicycle, regular public traffic, electric motor car, self driving, taxi and track traffic.
4. provide data basis for Urban Traffic Planning.
5. for city construction planning and building provides data basis.
Accompanying drawing explanation
Fig. 1 is main-process stream schematic diagram of the present invention;
The Bayesian Decision Tree of Fig. 2 method of discrimination.
Embodiment
For making object of the present invention, technical scheme and advantage clearly understand, to develop simultaneously embodiment referring to accompanying drawing, the present invention is described in further details.
The mobile phone signaling data that the object of the invention is to be provided by Mobile Network Operator sentences method for distinguishing to user's trip mode.Its data source is the mobile phone signaling data that Mobile Network Operator provides, and is then converted processed by data cleansing, integrated and position.And by the identification with dwell point of portraying in Mobile Space-time path, by method of the present invention, resident trip mode is differentiated.The method can realize effective differentiation of walking, bicycle, regular public traffic, electric motor car, self driving, taxi and track traffic seven kinds of common trip modes, thus the trip mode information of resident can be obtained, for the fields such as the special traffic programme in city, comprehensive transport plan and intelligent transportation system provide data basis.
Mobile phone signaling data in the present invention refers to, among mobile communication process, answer the call when occurring to beat, send short messages, switching on and shutting down, across LAC region, refresh at regular intervals time, by mobile phone call bill data (CDR) and the mobile phone traffic data (TDR) of mobile phone operators record.
Step 1 helps existing travel modal situation, extracts line mode feature, obtains the prior probability of trip mode according to available sample.
Definition x={a
1, a
2, a
mbe Trip chain to be discriminated, and each a is a discrimination properties of x.C={y
1, y
2y
nthe set of type of travel modal for differentiating, and each y trip mode that to be of C concrete.
For in the differentiation process of walking and bicycle:
X={ average velocity, trip duration, trip distance }, C={ walking, bicycle }
The prior probability that needs obtain is:
Table 1 walking and cycling trip differentiate prior probability
In differentiation process for electric motor car, self driving and taxi: x={ average velocity, maximal rate, trip duration }, C={ electric motor car, self driving, taxi }
The prior probability that needs obtain is:
Table 2 electric motor car, self driving and trip of taxi differentiate prior probability
Step 2 obtains the mobile phone signaling data of certain user once complete trip, and by the identification with dwell point of portraying in Mobile Space-time path, extracts the single trip mode subchain of resident.Wherein by the segmentation of dwell point, each trip mode subchain is made only to comprise a kind of trip mode.
Trip mode, by the average velocity of trip mode subchain, differentiates by step 3 for the first time, and the result of differentiation is Vehicle emission or bicycle trip.If average velocity is less than threshold speed c
v1, then bicycle trip is determined as.If average velocity is more than or equal to threshold speed c
v1, then Vehicle emission is determined as.
Step 4 is by average velocity, trip duration and trip distance three attributes, and differentiate bicycle trip, the result of differentiation is walking or cycling trip.Be specially the prior probability of differentiation walking and the bicycle obtained according to step 1, according to formula p (x|y
i) p (y
i) classification differentiated carried out calculating (attribute differentiated in x be average velocity, go on a journey duration and trip distance here.Yi is the type differentiated, is walking and bicycle here).Differentiate that result is chosen type and calculated p (x|y
i) p (y
i) in maximal term.
The trip mode subchain that step 5 belongs to Vehicle emission mode is mated with GIS gauze.If trip mode is mated with GIS Metro Network, be just determined as track traffic trip.If trip mode is mated with GIS public transport network, be just determined as regular public traffic trip.If do not mated with GIS public transport network with GIS Metro Network, just forward step 6 to.
Wherein, the method for GIS gauze coupling is specially:
Step1: the space-time position (t, x, y) in single trip mode subchain step 2 obtained arranges according to ascending order according to timestamp.The starting point of trip mode subchain is decided to be O point, the terminating point of trip mode subchain is decided to be D point.
Step2: (when GIS Metro Network is mated, be track traffic website by O point, D point and the GIS track traffic website that prestores.During GIS public transport network coupling, it is regular public traffic website.) mate, see whether be in website certain limit within (distance range threshold value c
r, suggestion value is 30-50m, can modify according to actual conditions), if met simultaneously, judge that line mode subchain OD point mates with website, otherwise jump out coupling.
Step3: judge whether be in same circuit with the website of OD Point matching, if be in same circuit, and the space-time position in the middle of OD point is also in this circuit, then be judged as track traffic trip/regular public traffic trip (gauze according to GIS coupling determines) and without transfer website, export and enter website and leave website.Otherwise, redirect Step4.
Space-time position in the middle of Step4: search trip subchain OD point sees if there is the space-time position with the transfer stop Point matching met the demands, and if any existence, then judges that line mode is track traffic trip, exports and enter website, transfer website and leave website.Otherwise judge that line mode is not track traffic trip/regular public traffic trip (gauze according to GIS coupling determines), proceed to step 6 and differentiate.
Step 6: by average velocity, maximal rate and trip duration three attributes, differentiate remaining possible trip mode, the result of differentiation is electric motor car, self driving or taxi.Be specially obtain according to step 1 differentiation electric motor car, self driving and taxi prior probability, according to formula p (x|y
i) p (y
i) classification differentiated carried out calculating (attribute differentiated in x is average velocity, maximal rate and trip duration here.Y
ifor the type differentiated, be electric motor car, self driving and taxi here).Differentiate that result is chosen type and calculated p (x|y
i) p (y
i) in maximal term.
The Bayesian decision tree graph of whole differentiation process is shown in Fig. 2.Condition node represents directly used criterion to differentiate, Bayes's node represents that trip mode by differentiating needs is according to corresponding prior probability and method of discrimination, differentiates after asking for the value of f.
The present invention's threshold value used has as shown in table 3.Based on threshold value value of the present invention, those of ordinary skill in the art useless make creative work prerequisite under adopt other values of threshold value of the present invention example example, all belong to the scope of protection of the invention.
Table 3 threshold value suggestion value
| Threshold type | Suggestion value |
| Distance range threshold value c r | 30-50m |
| Threshold speed c v1 | 10-15m/s |
| Threshold speed c v2 | 4-5m/s |
| Threshold speed c v3 | 20-25m/s |
| Threshold speed c v4 | 25-30m/s |
| Time threshold c t1 | 25-20min |
| Time threshold c t2 | 20-25min |
| Distance threshold c l1 | 10-20km |
The impact of the factor such as urban land use, business activity, humane custom, public transport network layout that the value of these threshold values is related to, so different cities is different in different time values, former value for reference only recommended value.
Embodiment:
1. build prior probability by available sample.
Table 4 sample prior probability
| Prior probability | Value |
| P (walking) | 0.6 |
| P (bicycle) | 0.4 |
| P (average velocity < c v2| walking) | 0.8 |
| P (average velocity >=c v2| walking) | 0.2 |
| P (trip duration < c t1| walking) | 0.7 |
| P (trip duration >=c t1| walking) | 0.3 |
| P (trip distance < c l1| walking) | 0.8 |
| P (trip distance >=c l1| walking) | 0.2 |
| P (average velocity < c v2| bicycle) | 0.3 |
| P (average velocity >=c v2| bicycle) | 0.7 |
| P (trip duration < c t1| bicycle) | 0.3 |
| P (trip duration >=c t1| bicycle) | 0.7 |
| P (trip distance < c l1| bicycle) | 0.2 |
| P (trip distance >=c l1| bicycle) | 0.8 |
2. obtain the mobile phone signaling data of the once complete trip of 75 users be numbered, and choose single trip subchain.
The mobile phone signaling data of the complete trip of table 5 user
3. the average velocity calculating user 75 is that 6.352m/s is less than threshold speed c
v1, so judge that line mode is bicycle trip.
4. utilize the sample prior probability previously built in conjunction with formula p (x|y
i) p (y
i) trip mode of user 75 is differentiated.The average velocity of user 75 is 6.352m/s, and trip duration is 36.3min, and trip distance is 7.2km.
P (walking) p (average velocity <c
v2| walking) p (trip duration>=c
t1| walking) p (trip distance <c
l1| walking)=0.6*0.8*0.3*0.8=0.1152
P (bicycle) p (average velocity <c
v2| bicycle) p (trip duration>=c
t1| bicycle) p (trip distance <c
l1| bicycle)=0.4*0.3*0.7*0.2=0.0168
Differentiate that result is chosen type and calculated p (x|y
i) p (y
i) in maximal term, the trip mode of all users 75 is walking.
Those of ordinary skill in the art will appreciate that, embodiment described here is to help reader understanding's implementation method of the present invention, should be understood to that protection scope of the present invention is not limited to so special statement and embodiment.Those of ordinary skill in the art can make various other various concrete distortion and combination of not departing from essence of the present invention according to these technology enlightenment disclosed by the invention, and these distortion and combination are still in protection scope of the present invention.
Claims (6)
1., based on a method for the resident trip mode comprehensive distinguishing of mobile phone signaling data, it is characterized in that, comprise the following steps:
Step 1: by existing travel modal situation, extract line mode feature, obtain the prior probability of trip mode according to available sample;
Step 2: the mobile phone signaling data obtaining certain user once complete trip, and by the identification with dwell point of portraying in Mobile Space-time path, extract the single trip mode subchain of user;
Step 3: by the average velocity of trip mode subchain, trip mode is differentiated for the first time, the result of differentiation is Vehicle emission or bicycle trip;
Step 4: by average velocity, trip duration and trip distance three attributes, differentiate bicycle trip, the result of differentiation is walking or cycling trip;
Step 5: the trip mode subchain belonging to Vehicle emission mode mated with GIS gauze, if trip mode is mated with GIS Metro Network, is just determined as track traffic trip; If trip mode is mated with GIS public transport network, be just determined as regular public traffic trip; If do not mated with GIS public transport network with GIS Metro Network, just forward step 6 to;
Step 6: by average velocity, maximal rate and trip duration three attributes, differentiate remaining possible trip mode, the result of differentiation is electric motor car, self driving or taxi.
2. the method for a kind of resident trip mode comprehensive distinguishing based on mobile phone signaling data according to claim 1, it is characterized in that, step 1 comprises:
1) the extraction feature of travel modal;
2) the choosing of the discrimination properties that differentiates of walking and bicycle;
3) walking and bicycle prior probability build;
4) choosing of the discrimination properties that electric motor car, self driving and taxi differentiate;
5) electric motor car, self driving and taxi prior probability build.
3. the method for a kind of resident trip mode comprehensive distinguishing based on mobile phone signaling data according to claim 1 and 2, it is characterized in that, step 2 also comprises:
By the segmentation of dwell point, each trip mode subchain is made only to comprise a kind of trip mode.
4. the method for a kind of resident trip mode comprehensive distinguishing based on mobile phone signaling data according to claim 3, it is characterized in that, step 3 is specially:
If average velocity is less than threshold speed c
v1, then bicycle trip is determined as; If average velocity is more than or equal to threshold speed c
v1, then Vehicle emission is determined as.
5. the method for a kind of resident trip mode comprehensive distinguishing based on mobile phone signaling data according to claim 4, it is characterized in that, step 4 specifically comprises:
The differentiation walking obtained according to step 1 and the prior probability of bicycle, according to formula p (x|y
i) p (y
i) classification differentiated is calculated, and choose type calculating p (x|y
i) p (y
i) in maximal term as differentiate trip mode; Wherein, the attribute differentiated in x is average velocity, trip duration and trip distance here, y
ifor the type differentiated, be walking and bicycle here.
6. the method for a kind of resident trip mode comprehensive distinguishing based on mobile phone signaling data according to claim 4 or 5, it is characterized in that, step 6 comprises:
In conjunction with the prior probability of electric motor car, self driving and trip of taxi, utilize formula p (x|y
i) p (y
i) classification differentiated is calculated, and choose type calculating p (x|y
i) p (y
i) in maximal term as differentiate trip mode; Wherein, the attribute differentiated in x is average velocity, maximal rate and trip duration here, y
ifor the type differentiated, be electric motor car, self driving and taxi here.
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