CN105101092A - Mobile phone user travel mode recognition method based on C4.5 decision tree - Google Patents
Mobile phone user travel mode recognition method based on C4.5 decision tree Download PDFInfo
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- CN105101092A CN105101092A CN201510549482.0A CN201510549482A CN105101092A CN 105101092 A CN105101092 A CN 105101092A CN 201510549482 A CN201510549482 A CN 201510549482A CN 105101092 A CN105101092 A CN 105101092A
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Abstract
The invention discloses a mobile phone user travel mode recognition method based on a C4.5 decision tree. The mobile phone user travel pattern recognition method comprises: collecting mobile phone signaling data of a volunteer, and processing the mobile phone signaling data into a travel track sequence with reference to time information and position information of the data; excavating a relationship of a travel characteristic attribute and a corresponding travel traffic mode from the travel track data of the volunteer; dividing the collected mobile phone signaling data of the volunteer into two parts according to a supervised machine learning method, which are respectively a training data set and a test data set; extracting a variety of characteristic attributes of travel behaviors from mobile phone data corresponding to the travel process of the volunteer according to the communication principle and the traffic theory; training a travel mode recognition model in combination with a real travel mode condition fed back by the volunteer via an artificial intelligence algorithm, C4.5 algorithm with supervision and learning ability to obtain a high-precision mobile phone user travel mode recognition model based on the mobile phone signaling data.
Description
Technical field
The present invention relates to traffic programme and management method technical field, specifically a kind of cellphone subscriber's trip mode recognition methods based on C4.5 decision tree.
Background technology
The trip proportion, public transportation mode share etc. of various mode of transportation are the significant datas that traffic programme and vehicle supervision department pay close attention to always.In traffic analysis, conventional traffic information collection means comprise coil, microwave, video etc.Different information gathering means have its advantage and the scope of application.
Because the usage space scope of the traffic acquisition means such as coil, microwave, video is less, cannot carry out continuing and effectively following the trail of to people's travel behaviour, more be difficult to the OD information obtaining people's trip.These traffic information collection equipment general costs are higher, due to the restriction of cost and the scope of application, cannot cover on a large scale whole city.
In modern society, because cellphone subscriber measures huge, operator is in order to provide the communication service of high-quality, by increasing base station construction, with make quorum sensing inhibitor wider, mobile phone signal wide coverage, therefore mobile phone signal data are well suited for the behavior for analyzing people's trip, thus the traffic programme of Optimizing City and management.
At present, mobile phone signal data are used to carry out the primary application such as the analysis of trip terminus, section mobile phone guest flow statistics maturation gradually.Such as, but using mobile phone signal data as data source, the analysis carrying out becoming more meticulous is also few, how to identify that the trip mode of transportation of cellphone subscriber is an industry difficult problem always.Although this is because mobile phone signal can extensively cover, the precision of architecture is lower, belong to fuzzy location, because as analysis gps data, by the velocity amplitude of trip process, the trip mode of transportation of user cannot can be judged.But GPS user's ratio is well below cellphone subscriber's ratio, and gps data can only illustrate the trip characteristics of specific crowd, truly cannot reflect the trip situation of general masses sieve.
Although mobile phone signal data Shortcomings in positioning precision, people go on a journey the identification of mode of transportation, all have great significance, and cellphone subscriber colony expand to urban planning and traffic programme, close to bulk sample statistically this.
Summary of the invention
The object of the present invention is to provide the cellphone subscriber's trip mode recognition methods based on C4.5 decision tree that a kind of accuracy is high, easy to use, to solve the problem proposed in above-mentioned background technology.
For achieving the above object, the invention provides following technical scheme:
Based on cellphone subscriber's trip mode recognition methods of C4.5 decision tree, concrete steps are as follows:
(1) according to the trip vehicles that survey region people commonly use, raise the volunteer of corresponding trip mode, obtain the trip situation information of volunteer and corresponding mobile phone signaling data;
(2) gather the mobile phone signaling data of volunteer, based on Customs Assigned Number, the mobile phone signaling data collected is divided into different groups, then carry out group internal sort according to the time, form the mobile phone trip track sets of different volunteer;
(3) according to the time of staying, identify the Origin And Destination of each trip in each mobile phone trip track sets, the stroke between often pair of Origin And Destination is exactly the once trip process in current phone trip track;
(4) corresponding according to each trip process data in mobile phone, extracts the trip process feature of current trip process, obtains all trip process features of all volunteers, thus forms sample data collection;
(5) data of A% sample data concentrated are as training dataset, sample data concentrated the data of remaining B% as test data set, use C4.5 decision Tree algorithms, concentrate at training data, pass through model training, obtain the rule of trip process feature when adopting current vehicle trip, thus for finding the rule of different data in mobile phone features when going on a journey with the different vehicles, generate decision-tree model, wherein, A+B=100, and A>B;
(6) again the decision-tree model that step (5) generates is applied to test data to concentrate, judge whether the recognition accuracy of decision-tree model reaches expection requirement, expection requirement is reached if fail, then or after accumulating more volunteer's data return step (2), or return step (5) algorithm parameter is adjusted; If reach expection requirement, then decision-tree model is disposed.
As the present invention's further scheme: the trip process feature in described step (4) at least comprises theoretical, trip duration, average speed, tracing point cumulative distance, whether has record in subway station, subway trip duration, subway trip distance, subway trip distance account for always than, subway travel time account for always than and Path complexity.
Compared with prior art, the invention has the beneficial effects as follows:
The present invention can according to the base station construction situation of different cities, mobile phone signal feature, by the method for artificial intelligence, automatic learning also obtains the trip mode recognition methods being applicable to this city and area, for traffic administration and planning provide high-quality data results.
Embodiment
Be described in more detail below in conjunction with the technical scheme of embodiment to this patent.
Based on cellphone subscriber's trip mode recognition methods of C4.5 decision tree, concrete steps are as follows:
(1) according to the trip vehicles that survey region people commonly use, as: province, city, district etc., raise the volunteer of corresponding trip mode, obtain the trip situation information of volunteer and corresponding mobile phone signaling data;
(2) gather the mobile phone signaling data of volunteer, based on Customs Assigned Number, the mobile phone signaling data collected is divided into different groups, then carry out group internal sort according to the time, form the mobile phone trip track sets of different volunteer;
(3) according to the time of staying, identify the Origin And Destination of each trip in each mobile phone trip track sets, the stroke between often pair of Origin And Destination is exactly the once trip process in current phone trip track;
(4) corresponding according to each trip process data in mobile phone, extracts the trip process feature of current trip process, obtains all trip process features of all volunteers, thus forms sample data collection;
(5) data of sample data concentrated 70% are as training dataset, sample data concentrated the data of remaining 30% as test data set, use C4.5 decision Tree algorithms, concentrate at training data, pass through model training, obtain the rule of trip process feature when adopting current vehicle trip, thus for finding the rule of different data in mobile phone features when going on a journey with the different vehicles, generate decision-tree model;
(6) again the decision-tree model that step (5) generates is applied to test data to concentrate, judge whether the recognition accuracy of decision-tree model reaches expection requirement, expection requirement is reached if fail, then or after accumulating more volunteer's data return step (2), or return step (5) algorithm parameter is adjusted; If reach expection requirement, then decision-tree model is disposed.
Trip process feature in described step (4) at least comprises theoretical, trip duration, average speed, tracing point cumulative distance, whether has record in subway station, subway trip duration, subway trip distance, subway trip distance account for always than, subway travel time account for always than and Path complexity, being calculated as follows of process feature of going on a journey:
Theoretical
d: namely theoretical is the air line distance of the terminus that cellphone subscriber goes on a journey, i.e. OD air line distance, concrete account form be starting point longitude and latitude according to cellphone subscriber (
lon o,
lat o) and terminal longitude and latitude (
lon d,
lat d) being calculated to be theoretical, formula is as follows:
d=6371*acos{ [cos (
lat o* 3.14/180) * cos (
lat d* 3.14/180) * cos [(
lon o-
on d) * 3.14/180]+sin (
lat o* 3.14/180) * sin (
lat d* 3.14/180)] } (formula 1).
Trip duration
time total: by data in mobile phone, calculate the departure time of cellphone subscriber
time oand the time of advent
time d, that is:
time total=
time d-
time o(formula 2).
In order to ensure the precision calculated, general to use minute or second as unit of account.
Average speed
v avg: average speed is the ratio of theoretical and travel time, that is:
v avg= d/
time total(formula 3).
Tracing point cumulative distance
d acc: tracing point cumulative distance is different from the theoretical D in formula (1), and under normal circumstances, the trip track of cellphone subscriber's trip is not often the form in straight line, but forms complicated trip route curve according to condition of road surface.Therefore the air line distance of terminus can be far smaller than the actual distance of cellphone subscriber's trip usually.So, need, from trip data in mobile phone, to extract tracing point cumulative distance attribute, obtain real trip distance as much as possible at this.
Suppose that the starting point of setting out of cellphone subscriber is
d 0, terminal is
d n, from
d 0arrive
d nprocess in create mobile phone signal data successively
i 1,
i 2...,
i n-1, the location point information corresponding at a little signal data is respectively
d 1,
d 2...,
d n-1.Suppose to use
d irepresent point
d i-1to point
d iair line distance, so according to the chronological order of trip, carry out tracing point obtaining straight line successively and connect distance and accumulation calculating, obtain tracing point cumulative distance
d acc.
(formula 4).
Whether there is record in subway station: owing to being used in different base station, ground in subway station, therefore according to base station information, can judge whether cellphone subscriber once occurred in trip process in subway station.
Subway trip duration
time metro: because mobile phone is from ground moving to underground subway station, or move on to ground from underground subway station, all can produce corresponding switching information of base station, these two information enter the station information and outbound information exactly.Suppose that the time of entering the station is
time in, the departures time is
time out , so subway trip duration
time metrofor:
time metro=
time out -
time in(formula 5).
Subway trip distance
d metro: suppose in cellphone subscriber's trip process, from trip to end trip, create positional information and be followed successively by
d 0,
d 2...,
d n.When taking subway in cellphone subscriber's trip process, supposed that the location point entering subway station was
d i, the location point leaving subway station is
d j, so subway trip distance
d metrofor:
(formula 6),
Wherein, 0≤i < j≤n.
Subway trip distance account for always than
r distance: in the relatively flourishing city of track traffic as Shanghai, people can be partial to selectively descend rail friendship (subway) as the main vehicles or the vehicles of plugging into.Obtaining subway trip distance to account for always than this attributive character, is to judge cellphone subscriber in trip process except taking subway, whether also using other the vehicles.Subway trip distance accounts for always than lower, and cellphone subscriber more likely selects subway to mix with the vehicles to go on a journey, iron trip distance account for always than
r distancefor:
r distance=
d metro/
d acc(formula 7).
The subway travel time account for always than
r time: the effect comparing class of " subway travel time always account for than " and " subway trip distance always account for than " seemingly, is all for judging that cellphone subscriber is except subway, other the vehicles that whether also may use, the subway travel time account for always than
r timefor:
r time=
time metro/
time total(formula 8).
Path complexity
c: except characteristic attributes such as distance, speed, Path complexity is also the important evidence identifying different mode of transportation.For bus and private car, bus is in order to serve the more common people, and therefore driving path complexity is relatively high, and operating range is longer; Private car generally can be more convenient than the driving path of bus.Therefore, need in the data in mobile phone of cellphone subscriber, to extract Path complexity feature, as important recognition feature.
Path complexity
c=
d acc/
d(formula 9),
Namely Path complexity C is tracing point cumulative distance and the ratio of theoretical.
The present invention for data source, in conjunction with artificial intelligence, data mining, machine learning and GSM technology principle, builds a kind of user's trip mode recognition methods that can be widely used in different cities actual state with mobile phone signal data.The method can identify walking, bicycle, track traffic, bus, power assist vehicle, car (private car, taxi) etc., and mixing trip mode of transportation.By this method, the trip proportion of various mode of transportation and travel amount roughly can be separated out by auxiliary separating, for traffic study, demand analysis, planning and promote operation management service and provide important Data support.
The present invention can according to the base station construction situation of different cities, mobile phone signal feature, by the method for artificial intelligence, automatic learning also obtains the trip mode recognition methods being applicable to this city and area, for traffic administration and planning provide high-quality data results.
Above the better embodiment of this patent is explained in detail, but this patent is not limited to above-mentioned execution mode, in the ken that one skilled in the relevant art possesses, various change can also be made under the prerequisite not departing from this patent aim.
Claims (2)
1., based on cellphone subscriber's trip mode recognition methods of C4.5 decision tree, it is characterized in that, concrete steps are as follows:
(1) according to the trip vehicles that survey region people commonly use, raise the volunteer of corresponding trip mode, obtain the trip situation information of volunteer and corresponding mobile phone signaling data;
(2) gather the mobile phone signaling data of volunteer, based on Customs Assigned Number, the mobile phone signaling data collected is divided into different groups, then carry out group internal sort according to the time, form the mobile phone trip track sets of different volunteer;
(3) according to the time of staying, identify the Origin And Destination of each trip in each mobile phone trip track sets, the stroke between often pair of Origin And Destination is exactly the once trip process in current phone trip track;
(4) corresponding according to each trip process data in mobile phone, extracts the trip process feature of current trip process, obtains all trip process features of all volunteers, thus forms sample data collection;
(5) data of A% sample data concentrated are as training dataset, sample data concentrated the data of remaining B% as test data set, use C4.5 decision Tree algorithms, concentrate at training data, pass through model training, obtain the rule of trip process feature when adopting current vehicle trip, thus for finding the rule of different data in mobile phone features when going on a journey with the different vehicles, generate decision-tree model, wherein, A+B=100, and A>B;
(6) again the decision-tree model that step (5) generates is applied to test data to concentrate, judge whether the recognition accuracy of decision-tree model reaches expection requirement, expection requirement is reached if fail, then or after accumulating more volunteer's data return step (2), or return step (5) algorithm parameter is adjusted; If reach expection requirement, then decision-tree model is disposed.
2. the cellphone subscriber's trip mode recognition methods based on C4.5 decision tree according to claim 1, it is characterized in that, the trip process feature in described step (4) at least comprises theoretical, trip duration, average speed, tracing point cumulative distance, whether has record in subway station, subway trip duration, subway trip distance, subway trip distance account for always than, subway travel time account for always than and Path complexity.
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Cited By (12)
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| CN106448173A (en) * | 2016-11-28 | 2017-02-22 | 东南大学 | Method for classifying long-distance travel transportation types based on data of mobile phones |
| CN106778652A (en) * | 2016-12-26 | 2017-05-31 | 东软集团股份有限公司 | Physical activity recognition methods and device |
| CN107402397A (en) * | 2017-06-30 | 2017-11-28 | 北京奇虎科技有限公司 | User Activity state based on mobile terminal determines method, device and mobile terminal |
| CN107845260A (en) * | 2017-10-26 | 2018-03-27 | 杭州东信北邮信息技术有限公司 | A kind of recognition methods of user's bus trip mode |
| CN110519690A (en) * | 2019-09-05 | 2019-11-29 | 浙江大华技术股份有限公司 | The determination method and device in candidate search region, storage medium, electronic device |
| CN111222381A (en) * | 2018-11-27 | 2020-06-02 | 中国移动通信集团上海有限公司 | User travel mode identification method and device, electronic equipment and storage medium |
| CN111341135A (en) * | 2019-12-24 | 2020-06-26 | 中山大学 | Mobile phone signaling data travel mode identification method based on interest points and navigation data |
| CN111653093A (en) * | 2020-05-29 | 2020-09-11 | 南京瑞栖智能交通技术产业研究院有限公司 | Urban trip mode comprehensive identification method based on mobile phone signaling data |
| CN111723835A (en) * | 2019-03-21 | 2020-09-29 | 北京嘀嘀无限科技发展有限公司 | Vehicle movement track distinguishing method and device and electronic equipment |
| CN111951926A (en) * | 2020-08-11 | 2020-11-17 | 北京易康医疗科技有限公司 | Method for detecting sensitivity of different types of tumors to radiotherapy rays based on machine learning technology |
| WO2021237812A1 (en) * | 2020-05-29 | 2021-12-02 | 南京瑞栖智能交通技术产业研究院有限公司 | Urban travel mode comprehensive identification method based on mobile phone signaling data and including personal attribute correction |
| CN116206452A (en) * | 2023-05-04 | 2023-06-02 | 北京城建交通设计研究院有限公司 | Sparse data characteristic analysis method and system for urban traffic travel |
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Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106448173A (en) * | 2016-11-28 | 2017-02-22 | 东南大学 | Method for classifying long-distance travel transportation types based on data of mobile phones |
| CN106778652A (en) * | 2016-12-26 | 2017-05-31 | 东软集团股份有限公司 | Physical activity recognition methods and device |
| CN107402397A (en) * | 2017-06-30 | 2017-11-28 | 北京奇虎科技有限公司 | User Activity state based on mobile terminal determines method, device and mobile terminal |
| CN107402397B (en) * | 2017-06-30 | 2020-06-05 | 北京奇虎科技有限公司 | User activity state determination method and device based on mobile terminal and mobile terminal |
| CN107845260A (en) * | 2017-10-26 | 2018-03-27 | 杭州东信北邮信息技术有限公司 | A kind of recognition methods of user's bus trip mode |
| CN107845260B (en) * | 2017-10-26 | 2020-02-14 | 杭州东信北邮信息技术有限公司 | Method for identifying public transport trip mode of user |
| CN111222381A (en) * | 2018-11-27 | 2020-06-02 | 中国移动通信集团上海有限公司 | User travel mode identification method and device, electronic equipment and storage medium |
| CN111723835A (en) * | 2019-03-21 | 2020-09-29 | 北京嘀嘀无限科技发展有限公司 | Vehicle movement track distinguishing method and device and electronic equipment |
| CN110519690A (en) * | 2019-09-05 | 2019-11-29 | 浙江大华技术股份有限公司 | The determination method and device in candidate search region, storage medium, electronic device |
| CN111341135A (en) * | 2019-12-24 | 2020-06-26 | 中山大学 | Mobile phone signaling data travel mode identification method based on interest points and navigation data |
| CN111341135B (en) * | 2019-12-24 | 2021-05-14 | 中山大学 | Mobile phone signaling data travel mode identification method based on interest points and navigation data |
| CN111653093A (en) * | 2020-05-29 | 2020-09-11 | 南京瑞栖智能交通技术产业研究院有限公司 | Urban trip mode comprehensive identification method based on mobile phone signaling data |
| WO2021237812A1 (en) * | 2020-05-29 | 2021-12-02 | 南京瑞栖智能交通技术产业研究院有限公司 | Urban travel mode comprehensive identification method based on mobile phone signaling data and including personal attribute correction |
| CN111653093B (en) * | 2020-05-29 | 2022-06-17 | 南京瑞栖智能交通技术产业研究院有限公司 | Urban trip mode comprehensive identification method based on mobile phone signaling data |
| CN111951926A (en) * | 2020-08-11 | 2020-11-17 | 北京易康医疗科技有限公司 | Method for detecting sensitivity of different types of tumors to radiotherapy rays based on machine learning technology |
| CN111951926B (en) * | 2020-08-11 | 2021-07-30 | 山东省肿瘤防治研究院（山东省肿瘤医院） | Method for detecting sensitivity of different types of tumors to radiotherapy rays based on machine learning technology |
| CN116206452A (en) * | 2023-05-04 | 2023-06-02 | 北京城建交通设计研究院有限公司 | Sparse data characteristic analysis method and system for urban traffic travel |
| CN116206452B (en) * | 2023-05-04 | 2023-08-15 | 北京城建交通设计研究院有限公司 | Sparse data characteristic analysis method and system for urban traffic travel |
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Application publication date: 20151125 |