CN102595323A - Method for obtaining resident travel characteristic parameter based on mobile phone positioning data - Google Patents
Method for obtaining resident travel characteristic parameter based on mobile phone positioning data Download PDFInfo
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Abstract
The invention discloses a method for obtaining a resident travel characteristic parameter based on mobile phone positioning data. The method comprises the following steps of: (1) collecting mobile phone positioning data; (2) filtering the mobile phone positioning data, matching the mobile phone positioning data to traffic zones, and obtaining the matched mobile phone positioning data; (3) using users as units, ordering the matched mobile phone positioning data in one day by time, merging the continuous data in the same one traffic zone into one piece, and obtaining mobile phone positioning pretreatment data; (4) according to the number of emerged pieces and influence duration, judging residing points, restoring a travel path between two residing points, and obtaining the travel distance and the travel speed, and then obtaining a travel log table of all users; (5) on the basis of the matched mobile phone positioning data, statistically obtaining a place of residence and workplace result table; and (6) and conjointly analyzing the travel log table and the place of residence and workplace result table, and obtaining resident travel characteristic parameters. The method has the characteristics of low cost, large sample quantity, high precision and high time validity.
Description
Technical Field
The invention relates to the technical field of traffic information acquisition and processing, in particular to a method for acquiring resident travel characteristic parameters based on mobile phone positioning data.
Background
The resident trip survey is to extract a certain proportion of citizens to comprehensively survey the trip condition in one day in a survey area so as to master the resident trip characteristics such as the total urban traffic trip amount, the main occurrence attraction source, the trip purpose, the mode structure, the space-time distribution, the trip time consumption, the trip distance and the like.
The resident travel survey is an important basic work of urban comprehensive traffic planning, is a main means for mastering urban traffic characteristics and rules, can provide important basis for scientifically making traffic development strategies, policies and technical rules, plays a key role in analyzing urban overall layout and traffic evolution rules, and has wide application value for social and economic development research.
The conventional resident trip survey implementation steps generally include that a special institution is established for unified responsibility, data preparation before survey (such as population distribution, administrative divisions, land utilization and the like) is carried out, survey plan design (such as drawing up survey areas, traffic cell division, sampling, form design and the like) is carried out, then surveyor training is carried out, and finally, comprehensive implementation of manual survey (such as home visit, telephone inquiry, postcard, employee inquiry, monthly ticket survey and the like) is carried out. Each step of the resident trip survey is critical and will directly affect the final result of the survey if carelessly done or neglected. In summary, the drawbacks of the conventional resident travel survey method mainly include:
(1) a large amount of manpower and material resources are required, and the development period is long (from planning to implementation).
(2) Due to the huge cost, the investigation interval is long (generally more than 5 years), and the data is not updated timely.
(3) The accuracy of the result is limited by multiple factors, including sample size (the sample rate is generally within 5% of urban population), sample deviation, investigator responsibility, sample user cooperation degree, input data and check data accuracy and the like, and the accuracy of the obtained result is often low.
With the development of Global Positioning System (GPS) technology, traffic researchers in recent years apply GPS devices to travel surveys, and travel feature analysis is performed on surveyed people by using accurate position information and time information provided by the GPS devices. However, the use of GPS devices for conducting traffic surveys still has several disadvantages:
(1) additional GPS equipment needs to be purchased.
(2) The data can not be collected in the indoor, under-bridge and subway environments.
(3) The sample size is small, and large-scale development cannot be realized.
In recent years, the popularity of mobile phones has been increasing day by day, and the popularity of mobile phones in China has reached 73.6% by the end of 2011 according to statistics of the ministry of national industry and informatization, thereby providing a basis for carrying out travel analysis by using mobile phone positioning data.
The existing technology for extracting part of travel characteristic parameters by using mobile phone positioning data, such as the chinese patent with application number 200910092031.3, only obtains travel origin-destination points, but cannot reflect the whole characteristic parameters of resident travel investigation in all directions. There is also a chinese invention patent application No. 200910048300.6, which focuses on detecting traffic conditions, i.e., real-time road traffic speed, and is applied to the field of traffic information services.
In view of the problems and defects of the prior art, the inventor of the present invention actively makes research and innovation to invent a method for acquiring characteristic parameters of resident travel based on mobile phone positioning data, which has the advantages of low cost, large sample size, high precision, strong timeliness and objective data result.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for acquiring resident travel characteristic parameters based on mobile phone positioning data. The method has the characteristics of low cost, large sample size, high precision and strong timeliness.
In order to solve the technical problems, the invention adopts the following technical scheme:
the resident travel characteristic parameter acquisition method based on the mobile phone positioning data comprises the following steps:
(1) collecting mobile phone positioning data;
(2) data preprocessing: filtering the mobile phone positioning data, deleting the data failed in positioning, matching the mobile phone positioning data into a corresponding traffic cell according to the longitude and the latitude of the mobile phone positioning data, and establishing the membership relationship between each piece of mobile phone positioning data and the traffic cell to obtain the matched mobile phone positioning data;
(3) based on the matched mobile phone positioning data, taking the user as a unit, and sequencing the matched mobile phone positioning data of one day according to the timestamp fields from small to large;
starting processing from the 1 st matched mobile phone positioning data, combining a plurality of continuous matched mobile phone positioning data positioned in the same traffic cell into one piece of data to obtain mobile phone positioning preprocessing data until the last 1 matched mobile phone positioning data; the mobile phone positioning preprocessing data respectively records the entering time, the leaving time, the average longitude and the average latitude of the traffic cell, the number of the combined matched mobile phone positioning data, the front influencing time and the rear influencing time of each traffic cell and the influencing duration of the traffic cell, wherein the entering time, the leaving time, the average longitude and the average latitude of the traffic cell correspond to the combined matched mobile phone positioning data; wherein,
the average longitude and the average latitude respectively take the average value of the longitude and the latitude of the plurality of matched mobile phone positioning data;
the previous influence moment is the intermediate moment between the entering moment of the mobile phone positioning preprocessing data and the leaving moment of the previous mobile phone positioning preprocessing data; assigning the front influence moment of the traffic cell where the 1 st mobile phone positioning preprocessing data is located as the moment of initially entering the traffic cell;
the later influence moment is the intermediate moment between the leaving moment of the mobile phone positioning preprocessing data and the entering moment of the next mobile phone positioning preprocessing data; assigning the post-influence moment of the traffic cell where the last 1 mobile phone positioning preprocessing data is located as the moment of finally leaving the traffic cell;
the influence duration is the difference between the rear influence moment and the front influence moment;
processing the matched mobile phone positioning data of all users according to the method to obtain a mobile phone positioning preprocessing data table;
(4) travel chain identification
Based on the mobile phone positioning preprocessing data table, taking a user as a unit, sequencing the mobile phone positioning preprocessing data of one day from small to large according to the entry time field;
starting processing from the 1 st mobile phone positioning preprocessing data of the user, and if the entry time of the 1 st mobile phone positioning preprocessing data is not less than the morning time threshold value, taking a traffic cell corresponding to the 1 st data as a residence point; continuing downward processing, and if the number of the combined matched mobile phone positioning data is larger than or equal to the residence record number threshold and the influence duration is larger than or equal to the residence duration threshold, taking a traffic cell corresponding to the data as a residence point;
regarding the last staying point to the next staying point as a trip in time sequence, recording the related information of the starting point traffic cell, the destination traffic cell, the middle passing point and the trip time consumption, wherein,
the last residence point is a starting point traffic cell, and the next residence point is an acknowledgement point traffic cell;
subtracting the departure time of the starting point traffic cell from the entry time of the destination traffic cell during travel;
based on the existing road network, calculating the shortest paths between a starting point traffic cell and each passing point and destination point traffic cell which continuously pass through a trip, wherein the starting point traffic cell and the destination point traffic cell respectively determine the geographic positions according to the average longitude and the average latitude, and the trip distance is obtained through statistics based on the continuously-passing road section end point number information of the shortest paths;
calculating the travel speed, wherein the travel speed is travel distance/travel time consumption;
processing mobile phone positioning preprocessing data of all users according to the method to obtain travel record lists of all users;
(5) judging the residence and the working place:
extracting data of a user for one continuous week based on the matched mobile phone positioning data table, and counting the occurrence frequency of each traffic cell in the residence judgment time period; the traffic cell with the largest occurrence number is the residence of the user;
extracting data of a user on a continuous working day of one week based on the matched mobile phone positioning data table, and counting the occurrence frequency of each traffic cell in a working place judgment period; the traffic cell with the largest occurrence number is the work place of the user;
obtaining a result table of a residence place and a working place;
(6) travel characteristic parameter acquisition
And (4) jointly analyzing the travel record table and the residence and workplace result table to obtain the user travel characteristic parameters.
Further, the judgment proportion of the residence, namely the ratio of the occurrence frequency of the residential traffic cell to the total frequency in the judgment time period, is recorded. And recording the judgment proportion of the working place, namely the ratio of the occurrence times of the traffic cells of the working place to the total times in the judgment moment. The residential area discrimination ratio and the working area discrimination ratio are used as indexes for representing the reliability of the discrimination result.
Compared with the prior art, the invention has the beneficial effects that:
the data source of the method for acquiring the resident travel characteristic parameters based on the mobile phone positioning data is based on the mobile phone positioning data, and the acquired resident travel characteristic parameters can comprise urban traffic travel total amount, space-time distribution, travel time consumption, travel distance, travel path selection and the like. The method has the advantages of low cost, large sample size, high precision, strong timeliness and the like. In the face of a rapid urbanization process, the travel characteristics of residents need to be mastered in real time so as to meet the demand of urban traffic planning. The method provides a brand-new technical means for resident trip investigation, makes full use of massive large-sample mobile phone positioning data, and continuously tracks the evolution of resident trip laws by establishing a continuous observation mechanism.
The resident characteristic acquisition method based on the mobile phone positioning data provided by the invention is based on the mobile phone positioning data (generated and recorded in a mobile communication system) and combines with the traffic basic geographic information to extract the resident trip characteristics, thereby providing a new technical means for resident trip investigation. The method can reflect the moving condition of the artificial detection unit, has high sample rate, and can provide a panoramic image of user distribution and traffic travel in the investigation region.
According to the resident characteristic parameter acquisition method based on the mobile phone positioning data, disclosed by the invention, based on the urban mobile phone positioning data and the traffic basic geographic information, resident travel characteristics such as urban traffic travel total amount, time-space distribution, travel time consumption, travel distance and travel path selection can be obtained through processing.
The resident characteristic parameter acquisition method based on the mobile phone positioning data provided by the invention has the following remarkable advantages:
(1) no extra equipment needs to be purchased, the mobile communication infrastructure is fully utilized, and the comprehensive survey cost is relatively low.
(2) And the automatic implementation mode needs less manpower.
(3) The method belongs to passive data acquisition, basic signals of an investigation region are fully covered, and an analysis result is more objective and accurate.
(4) The sample size is large, and basically every person in a city investigation area can be covered.
(5) The data updating period is short, dynamic traffic planning, organization and management are supported more flexibly, and more humanized urban service is realized.
The method of the invention systematically solves the problem of extracting the resident trip characteristic parameters by using the mobile phone positioning data. The method has the remarkable advantages of strong practicability, high algorithm efficiency, high result precision and the like, and can promote the development of resident trip investigation towards a direction of being more objective, lower in cost and short in updating period. Is suitable for popularization and application in various cities and has wide prospect.
Drawings
FIG. 1 is a flow chart of the method for acquiring the resident travel characteristic parameters based on the mobile phone positioning data according to the present invention;
FIG. 2 is a schematic diagram of the distribution of the original positioning locations of user A using the method of the present invention;
fig. 3 is a schematic diagram of a trip chain identification result of the user a by using the method of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the examples, but without limiting the invention.
The mobile phone positioning data in the invention means that the real-time position of the mobile phone is acquired by using a cellular base station mode in the mobile phone communication process, and the mobile phone positioning data is usually recorded in a longitude and latitude mode. The mobile phone location data acquisition usually adopts an event trigger mechanism, such as making or receiving a call, receiving and sending a short message, crossing an LAC area, forcibly positioning for a specific time length, and the like, so that the time interval cannot be guaranteed to be standard. Fig. 1 is a flow chart of the method for acquiring the resident travel characteristic parameters based on the mobile phone positioning data according to the present invention. As shown in fig. 1, the method for acquiring the resident trip characteristic parameters based on the mobile phone positioning data includes the following steps:
(1) and collecting mobile phone positioning data.
The data field typically includes a handset pseudo code, a timestamp, longitude, latitude, status, etc., where a status field of 1 indicates a successful location and a status field of 0 indicates a failed location. In the invention, one mobile phone corresponds to one user, and whether the mobile phone positioning data belongs to the same user is determined according to the mobile phone pseudo code. Table 1 below is a portion of the handset positioning data for user a.
TABLE 1
| Mobile phone pseudo code | Time stamp | Longitude (G) | Latitude | Status of state |
| A | 13:35:21 | 116.5322 | 39.77763 | 1 |
| A | 13:39:49 | 116.5322 | 39.77769 | 1 |
| A | 13:45:26 | 116.5322 | 39.77762 | 1 |
| A | 13:50:27 | 116.5322 | 39.77760 | 1 |
| A | 13:54:46 | 116.5271 | 39.77463 | 1 |
| A | 14:00:15 | 116.5309 | 39.77815 | 1 |
| A | 14:04:46 | 116.5270 | 39.77463 | 1 |
| A | 14:10:25 | 116.5288 | 39.77422 | 1 |
| A | 14:14:46 | 116.5271 | 39.77467 | 1 |
| A | 14:19:43 | 0 | 0 | 0 |
| A | 14:25:36 | 116.5288 | 39.77426 | 1 |
| A | 14:30:22 | 116.5309 | 39.77808 | 1 |
| A | 14:35:13 | 116.5288 | 39.77426 | 1 |
| A | 14:40:33 | 116.5288 | 39.77427 | 1 |
| A | 14:45:13 | 116.5288 | 39.77426 | 1 |
| A | 14:49:50 | 116.5288 | 39.77424 | 1 |
| A | 14:54:46 | 116.5288 | 39.77426 | 1 |
| A | 14:59:49 | 116.5270 | 39.77457 | 1 |
| A | 15:05:19 | 116.5288 | 39.77426 | 1 |
| A | 15:09:43 | 0 | 0 | 0 |
| A | 15:15:27 | 116.5254 | 39.79493 | 1 |
| A | 15:19:46 | 116.5254 | 39.79491 | 1 |
(2) And (5) arranging traffic cell data and road network data.
The division of the traffic district is determined according to the land scale, the land utilization property and the characteristics of the planning layout of the planning area, and generally administrative divisions, artificial structures and natural boundaries are taken as the boundaries of the traffic district.
The road network data is a hierarchical road network with topological relation, and is usually divided into express roads, main roads, secondary roads and branch roads in a GIS form. Traffic cell data and road network data are typically available from residential trip research units.
(3) And (4) preprocessing data.
And (3.1) filtering the data. The record of the positioning failure (the status field is 0) is deleted.
And (3.2) matching the traffic cells. And establishing the membership of each mobile phone positioning data and the traffic cell according to the inclusion relation between the geographical position determined by the longitude and the latitude of the mobile phone positioning data and the traffic cell. A matched handset positioning data table as shown in table 2 below was obtained.
TABLE 2
| Mobile phone pseudo code | Time stamp | Longitude (G) | Latitude | Status of state | Traffic community |
| A | 13:35:21 | 116.5322 | 39.77763 | 1 | 219823 |
| A | 13:39:49 | 116.5322 | 39.77769 | 1 | 219823 |
| A | 13:45:26 | 116.5322 | 39.77762 | 1 | 219823 |
| A | 13:50:27 | 116.5322 | 39.77760 | 1 | 219823 |
| A | 13:54:46 | 116.5271 | 39.77463 | 1 | 240625 |
| A | 14:00:15 | 116.5309 | 39.77815 | 1 | 240625 |
| A | 14:04:46 | 116.5270 | 39.77463 | 1 | 240625 |
| A | 14:10:25 | 116.5288 | 39.77422 | 1 | 240625 |
| A | 14:14:46 | 116.5271 | 39.77467 | 1 | 240625 |
| A | 14:25:36 | 116.5288 | 39.77426 | 1 | 240625 |
| A | 14:30:22 | 116.5309 | 39.77808 | 1 | 240625 |
| A | 14:35:13 | 116.5288 | 39.77426 | 1 | 240625 |
| A | 14:40:33 | 116.5288 | 39.77427 | 1 | 240625 |
| A | 14:45:13 | 116.5288 | 39.77426 | 1 | 240625 |
| A | 14:49:50 | 116.5288 | 39.77424 | 1 | 240625 |
| A | 14:54:46 | 116.5288 | 39.77426 | 1 | 240625 |
| A | 14:59:49 | 116.5270 | 39.77457 | 1 | 240625 |
| A | 15:05:19 | 116.5288 | 39.77426 | 1 | 240625 |
| A | 15:15:27 | 116.5254 | 39.79493 | 1 | 240621 |
| A | 15:19:46 | 116.5254 | 39.79491 | 1 | 240621 |
Fig. 2 is a schematic diagram of the distribution of the original positioning positions of the user a by using the method of the present invention.
And (3.3) based on the matched mobile phone positioning data table, sequencing the matched mobile phone positioning data of one day from small to large according to the timestamp fields by taking the user as a unit.
(3.4) starting processing from the 1 st data, combining a plurality of continuous matched mobile phone positioning data in the same traffic cell into one piece of data to obtain mobile phone positioning preprocessing data until the last 1 matched mobile phone positioning data; the mobile phone positioning preprocessing data respectively records the entering time (start _ time) of entering the traffic cell, the leaving time (end _ time) of leaving the traffic cell, the average longitude (lon), the average latitude (lat) and the number (num) of the combined matched mobile phone positioning data corresponding to the combined matched mobile phone positioning data; wherein the average longitude (lon) and average latitude (lat) are respectively the average value of the longitude and latitude of the plurality of matched mobile phone positioning data, 
up to the last 1 data.
(3.5) respectively recording the front influence time (in _ time) and the rear influence time (out _ time) of the traffic cell and the influence duration (interval) of the traffic cell in the mobile phone positioning preprocessing data.
The front influence time (in _ time) is the middle time between the entering time (start _ time) of the mobile phone positioning pre-processing data and the leaving time (end _ time) of the previous mobile phone positioning pre-processing data; the after-influence time (out _ time) is the middle time between the leaving time (end _ time) of the mobile phone positioning pre-processing data and the entering time (start _ time) of the next mobile phone positioning pre-processing data. For special case handling:
assigning the front influence moment (in _ time) of the 1 st mobile phone positioning preprocessing data as the moment of entering the traffic cell at first;
the last post-impact time (out _ time) of the 1 handset positioning pre-processed data is assigned as the time of finally leaving the traffic cell.
The impact duration (interval) is taken as the difference between the impact time (out _ time) after and the impact time (in _ time) before.
And (3.6) processing the data of all users according to the method to obtain a mobile phone positioning preprocessing data table shown in the following table 3.
(4) And identifying a trip chain.
And (4.1) based on the mobile phone positioning preprocessing data table, sequencing the mobile phone positioning preprocessing data of one day from small to large according to the entry time field by taking the user as a unit.
And (4.2) judging the residence point. Starting processing from the 1 st mobile phone positioning preprocessing data of the user, and if the entry time of the 1 st mobile phone positioning preprocessing data is greater than the morning time threshold (set to be 6:00), taking a traffic cell corresponding to the 1 st data as a residence point; continuing downward processing, and if the number (num) > (the number of resident records) of the combined matched mobile phone positioning data of the mobile phone positioning preprocessing data is a threshold (set to be 2) and the influence duration (interval) > (the number of resident duration) is a threshold (set to be 2700 seconds), taking the traffic cell corresponding to the data as a resident point.
The above threshold value can be set reasonably based on the actual condition of the data.
And (4.3) travel identification. Regarding the last staying point (starting point) to the next staying point (destination point) as a trip in time sequence, recording related information (including the traffic cell numbers of the starting point and the destination point, the front influence time, the rear influence time, the average longitude, the average latitude and the like) and the middle passing point.
The travel time consumption (time _ trip) is taken to the entry time of the origin traffic cell minus the departure time of the origin traffic cell.
And (4.4) path reduction. Based on the existing road network, calculating the shortest path between a starting point traffic cell and each passing point and destination point traffic cell which continuously pass through a trip, wherein the starting point traffic cell and the destination point traffic cell respectively determine the geographic position according to the average longitude and the average latitude, and the trip distance (length _ trip) is obtained through statistics based on the continuously-passing road section end point number information of the shortest path.
(4.5) calculating a travel speed, wherein the travel speed (speed _ trip) is travel distance (length _ trip)/travel time consumption (time _ trip).
(4.6) processing the data of all users according to the method to obtain a travel record table shown in the following table 4.
Fig. 3 is a schematic diagram of a travel chain identification result formed by three trips of the user a between a residence, a working place and a residence point by using the method of the present invention.
(5) The residence and the working place are distinguished.
And (5.1) extracting data of a user in one continuous week based on the mobile phone positioning data table after the traffic cells are matched, and counting the occurrence frequency of each traffic cell in a residence judgment time period (set to be 23:00 to 6:00 of the next day). And recording the number of the traffic cell with the largest occurrence number and the ratio (ratio _ home) of the occurrence number to the total number in the judgment period. The traffic cell is identified as the residence of the user, and the ratio is used as an index for representing the credibility of the discrimination result.
And (5.2) extracting data of a user on a working day of one week (generally Monday to Friday) continuously based on the mobile phone positioning data table after the traffic cells are matched, and counting the frequency of occurrence of each traffic cell in a working place judgment time period (set to be 9:00 to 17: 00). And recording the number of the traffic cell with the largest occurrence frequency and the ratio of the occurrence frequency to the total frequency in the judgment time period. (ratio word). The traffic district is identified as the work place of the user, and the ratio is used as an index for representing the credibility of the discrimination result.
And (5.3) processing the data of all the users according to the method. A residence and workplace results table is obtained as shown in table 5 below.
TABLE 5
(6) And extracting and comprehensively analyzing the characteristic parameters of the user trip.
And (6.1) carrying out combined analysis by combining the travel record table and the result table of the residence and the working place to obtain detailed travel characteristic parameters of different types of users.
The travel characteristic parameters mainly comprise a travel starting point, a travel destination, a travel distance, travel time consumption, travel time (including departure time, on-trip time and arrival time), and the like.
A certain amount of mobile phone users are collected, and through joint analysis with user attribute information such as residence and workplace, trip characteristics of users with different attributes can be obtained, such as users whose workplace is a certain administrative district, trip rate (i.e. daily average trip times), average trip distance, average trip time consumption, departure time distribution and the like of the users whose working day is the city, and trip rate (i.e. daily average trip times), average trip distance, average trip time and departure time distribution and the like of the users whose residence is the city center city are obtained.
In practical application, the total travel demands of the whole city domain in an analysis period, such as the number of travel times per day, are obtained by combining the total number of the users in the whole city domain based on the travel characteristic parameters of the users, and the method is very important for developing urban comprehensive traffic planning matched with traffic demands. The travel characteristic parameters play an important role in urban traffic operation and management, for example, by mastering travel starting point-Destination point (Origin-Destination) distribution and departure time distribution of users, the transportation volume and departure interval of buses, subways and the like can be reasonably configured, so that urban traffic transportation resources are optimally utilized.
If the mobile phone positioning data and the land utilization data are further combined, a travel mode (such as a car, a bus, a subway and the like) and a travel purpose (such as working, going to school, shopping, going home and the like) and the like can be obtained. Such as:
and (6.2) combining a travel record table, taking a traffic district, a street and an administrative area as objects, and carrying out population living and employment distribution, travel characteristic analysis and the like. Or,
(6.3) summarizing the results. And (5) performing comprehensive analysis by combining other statistical results.
1000 volunteers in Beijing are collected and tested by mobile phone positioning data, and the method is compared with other methods, including the aspects of result precision, operation speed and the like. Experimental results show that the accuracy of residential area and workplace identification of the resident characteristic parameter acquisition method based on mobile phone positioning data provided by the invention reaches 99%, and the average accuracy of travel identification reaches more than 95%, which are better than the existing similar methods; the method provided by the invention has the advantage that the operation speed is improved by more than 30% compared with the existing algorithm, and the level of practical application is achieved. In addition, based on the method provided by the invention, the obtained data types are richer, and various comprehensive applications of resident trip investigation are supported.
In addition, by utilizing the processing result of the mobile phone positioning data, a dynamic OD data source can be provided for a traffic dynamic model, the traffic running condition (such as the formation and dissipation of congestion nodes) can be better described, the practical problem can be guided to be solved, and the method has a wide market prospect.
The above embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and the scope of the present invention is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present invention, and such modifications and equivalents should also be considered as falling within the scope of the present invention.
Claims (2)
1. The method for acquiring the resident travel characteristic parameters based on the mobile phone positioning data is characterized by comprising the following steps of:
(1) collecting mobile phone positioning data;
(2) data preprocessing: filtering the mobile phone positioning data, deleting the data failed in positioning, matching the mobile phone positioning data into a corresponding traffic cell according to the longitude and the latitude of the mobile phone positioning data, and establishing the membership relationship between each piece of mobile phone positioning data and the traffic cell to obtain the matched mobile phone positioning data;
(3) based on the matched mobile phone positioning data, taking the user as a unit, and sequencing the matched mobile phone positioning data of one day according to the timestamp fields from small to large;
starting processing from the 1 st matched mobile phone positioning data, combining a plurality of continuous matched mobile phone positioning data positioned in the same traffic cell into one piece of data to obtain mobile phone positioning preprocessing data until the last 1 matched mobile phone positioning data; the mobile phone positioning preprocessing data respectively records the entering time, the leaving time, the average longitude and the average latitude of the traffic cell, the number of the combined matched mobile phone positioning data, the front influencing time and the rear influencing time of each traffic cell and the influencing duration of the traffic cell, wherein the entering time, the leaving time, the average longitude and the average latitude of the traffic cell correspond to the combined matched mobile phone positioning data; wherein,
the average longitude and the average latitude respectively take the average value of the longitude and the latitude of the plurality of matched mobile phone positioning data;
the previous influence moment is the intermediate moment between the entering moment of the mobile phone positioning preprocessing data and the leaving moment of the previous mobile phone positioning preprocessing data; assigning the front influence moment of the traffic cell where the 1 st mobile phone positioning preprocessing data is located as the moment of initially entering the traffic cell;
the later influence moment is the intermediate moment between the leaving moment of the mobile phone positioning preprocessing data and the entering moment of the next mobile phone positioning preprocessing data; assigning the post-influence moment of the traffic cell where the last 1 mobile phone positioning preprocessing data is located as the moment of finally leaving the traffic cell;
the influence duration is the difference between the rear influence moment and the front influence moment;
processing the matched mobile phone positioning data of all users according to the method to obtain a mobile phone positioning preprocessing data table;
(4) travel chain identification
Based on the mobile phone positioning preprocessing data table, taking a user as a unit, sequencing the mobile phone positioning preprocessing data of one day from small to large according to the entry time field;
starting processing from the 1 st mobile phone positioning preprocessing data of the user, and if the entry time of the 1 st mobile phone positioning preprocessing data is not less than the morning time threshold value, taking a traffic cell corresponding to the 1 st data as a residence point; continuing downward processing, and if the number of the combined matched mobile phone positioning data is larger than or equal to the residence record number threshold and the influence duration is larger than or equal to the residence duration threshold, taking a traffic cell corresponding to the data as a residence point;
regarding the last staying point to the next staying point as a trip in time sequence, recording the related information of the starting point traffic cell, the destination traffic cell, the middle passing point and the trip time consumption, wherein,
the last residence point is a starting point traffic cell, and the next residence point is an acknowledgement point traffic cell;
subtracting the departure time of the starting point traffic cell from the entry time of the destination traffic cell during travel;
based on the existing road network, calculating the shortest paths between a starting point traffic cell and each passing point and destination point traffic cell which continuously pass through a trip, wherein the starting point traffic cell and the destination point traffic cell respectively determine the geographic positions according to the average longitude and the average latitude, and the trip distance is obtained through statistics based on the continuously-passing road section end point number information of the shortest paths;
calculating the travel speed, wherein the travel speed is travel distance/travel time consumption;
processing mobile phone positioning preprocessing data of all users according to the method to obtain travel record lists of all users;
(5) judging the residence and the working place:
extracting data of a user for one continuous week based on the matched mobile phone positioning data table, and counting the occurrence frequency of each traffic cell in the residence judgment time period; the traffic cell with the largest occurrence number is the residence of the user;
extracting data of a user on a continuous working day of one week based on the matched mobile phone positioning data table, and counting the occurrence frequency of each traffic cell in a working place judgment period; the traffic cell with the largest occurrence number is the work place of the user;
obtaining a result table of a residence place and a working place;
(6) travel characteristic parameter acquisition
And (4) jointly analyzing the travel record table and the residence and workplace result table to obtain the user travel characteristic parameters.
2. The method for acquiring resident travel characteristic parameters based on mobile phone positioning data according to claim 1, characterized in that the ratio of the occurrence frequency of the traffic cells as the residential area and the working area to the total frequency of the traffic cells is recorded, and the ratio is used as an index for representing the reliability of the discrimination result.
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