CN113283669A - Intelligent planning travel investigation method and system combining initiative and passive - Google Patents
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Abstract
The invention discloses an intelligent planning travel investigation method and system combining active and passive modes, which comprises passive acquisition and active acquisition, wherein the passive acquisition acquires a moving track and a stop point of a user by using a GPS (global positioning system) sensor built in a smart phone; and actively acquiring, namely judging the size between the stay time of the stay point and a preset stay threshold, and popping up corresponding activity and emotion questionnaires through a human-computer interaction interface for a user to fill in if the stay time of the stay point is greater than the preset stay threshold. The invention adopts a mode of combining the initiative and the passivity to process the passively collected data and reminds the user to fill in attribute information, compared with the prior method, the travel investigation method combining the track position and the subjective feeling is perfected, the time-space precision of planning, investigating and collecting is improved, and the manpower and time cost of investigating are saved.
Description
Technical Field
The invention relates to a research method and a system, in particular to an intelligent planning travel research method and a system.
Background
(1) Traditional trip survey: paper pen survey
Travel surveys were originally in the form of paper-pen surveys filled in by interviewees independently or with the assistance of investigators through a face-to-face family interview. Travel log surveys are the main form of travel behavior surveys, and visitors need to fill in their complete activities and travel situations for a certain day or several days in chronological order, including some detailed information about the activities and travel. However, this method is time-consuming for the interviewee, and usually cannot be recorded in time, so that it is easy to miss or generate deviation during backtracking, and sometimes even deliberately delay or refuse to fill in, so that the data loss and the misactuality of the investigation result are usually serious.
(2) Computer aided investigation
The generation of computer-aided technology brings the first revolution to the technical form of travel investigation, and develops investigation forms such as computer-aided telephone access (CATI), computer-aided interview (CAPI) and the like. Such forms of surveys are typically programmed sample dialing by a computer, interviewers access standardized content, and direct interviewee responses to a computer database. On one hand, the computer-aided technology combines data entry and interview, and the database checks the data reliability in real time, so that the work flow is simplified, and the data quality is improved; meanwhile, complete address information provided by the computer system is beneficial to the interviewee to recall and input a correct activity place, and the problem that the interviewee is difficult to recall a specific address in face-to-face investigation is effectively solved. However, the sample representativeness of the network survey is poor, and the network survey is a self-filling survey, so that the data quality of the network survey is difficult to guarantee for questionnaires with flexible filling forms such as logs.
(3) Location technology assisted survey
The development of the positioning technology greatly improves the space-time precision of the behavior data. The interviewee carries the GPS equipment, and the purpose of recording the spatial position and the time information in 5-10 minutes can be realized. After recording the daily travel track, the interviewee logs in the development system to supplement information such as travel purposes, activity content and the like. The GPS provides relatively accurate geographic information and time information, meanwhile, the time range of data collection is widened, and a user can recall activities and travel through tracks recorded by the GPS, so that the quality of questionnaire filling is greatly improved. However, this method requires the user to carry the GPS device with him or her, and needs to intensively fill out activities and travel details for one or several days, so that the user has a high requirement for the matching degree, and therefore, the investigation difficulty is high.
(4) The smart phone passively collects: built-in sensor of mobile phone
Smartphones have a variety of sensors built into them, a large portion of which are location and movement related. The built-in GPS module of the smart phone can not only accurately judge the position of the user, but also update the position information at a high frequency of tens of hertz (second by second), so that the spatial information of a travel starting point, a destination, a route and the like, and the time information of the travel starting, ending, pause and the like can be accurately recorded. In addition, an acceleration sensor built in the smart phone can record the acceleration change of the movement of the user, a gyroscope can judge the movement angle deviation of the user, a gravity sensor can calculate the body swinging frequency of the user, and the results recorded by the sensors can not only reflect the movement characteristics of the user, but also judge the body movement condition (step counting) of the user. The passively collected data provide accurate space-time trajectory, activity attributes such as speed and motion intensity for research, the third-party application program records and extracts the data after reaching an agreement with a user, and researchers can obtain the data under certain conditions and use the data for travel behavior research.
(5) The smart phone actively collects: trip investigation platform
Smartphones are also used as a platform for travel behavior surveys instead of the paper questionnaires in traditional travel behavior surveys. The powerful application operating system of the smart phone provides a uniform interface and a friendly interactive interface for a user to use the smart phone, and also provides a platform for the expansion of the functions of the smart phone and the third-party software and operation. Some special questionnaire survey apps can make and release electronic version trip questionnaires, and researchers can also distribute questionnaires through internet platforms such as social software, and compared with the traditional paper questionnaire survey, a large amount of manpower and material resources are saved in such a mode. However, this approach is difficult to control the sample selection and to ensure the quality of the questionnaire compared to an offline survey.
The smart phone has the advantages that the positioning and tracking functions of the smart phone can replace a handheld GPS device, the smart phone can become a convenient questionnaire filling platform due to the powerful interaction function, the two functions of track recording and questionnaire filling are integrated on the same device, new possibility is brought to travel behavior survey, and an intelligent planning travel investigation method and system combining initiative and passivity are developed based on the intelligent planning travel investigation system.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides an intelligent planning travel investigation method and system combining initiative and passivity.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
an active and passive combined intelligent planning travel investigation method comprises the following steps:
step 1, passive acquisition: collecting a moving track and a stop point of a user by using a GPS (global positioning system) sensor built in the smart phone;
step 2, active collection: and if the stay time of the stay point is longer than a preset stay threshold, popping up a corresponding activity and emotion questionnaire through a human-computer interaction interface for a user to fill in.
Preferably: the method for determining the stop point in the step 1 comprises the following steps:
step 101, initializing a core point, a radius R, an effective density P, a distance L, an average distance Q, an effective time M and a stop point set V;
acquiring longitude and latitude coordinate information of positioning points by using a GPS sensor, arranging the positioning points acquired in a certain time period into a temporary positioning point list pointList according to a time sequence, and storing the temporary positioning point list pointList into a database; acquiring a temporary positioning point list pointList arranged according to a time sequence from a database; acquiring A, B initial two points of a pointList of temporary positioning points, calculating the distance L of A, B two points, and comparing the distance L with the radius R; if the distance L between the A, B two points is smaller than the radius R, the point A is set as a core point, the A, B two points are placed into a stopping point set V, the average distance Q of the stopping point set V is recorded, meanwhile, the maximum effective distance from other points in the stopping point set V to the core point A is calculated, and the maximum effective distance is assigned to the radius R; if the distance L between A, B is greater than R, excluding A, and carrying out the same operation on B and the next point C in the pointList of temporary positioning points;
step 102, continuously acquiring a next point N of a pointList of temporary positioning points, comparing the distances between the point N and each point in a stop point set V, if a point Z exists in the stop point set V so that the effective density P value is maximum and the average distance Q is minimum, adding the point N into the stop point set V, and setting a core point as a point Z; if the point Z does not exist, judging that one stop point set V is finished;
103, judging whether the stop point set V meets the condition that the number of effective points is greater than the effective density P and the time from the starting point to the ending point is greater than the effective time M, if so, determining the effective set V as the effective set, and determining the core point of the effective set V, wherein the starting time and the ending time of the stop point are respectively the starting point time and the ending point time in the stop point set V; if the set is false, judging that the set V of the stop points is an illegal set, and continuously judging whether an effective set V of the stop points exists behind the pointList of the temporary positioning point after related data are eliminated;
step 104, converting the WGS84 coordinate of the dwell point in the active set into a Mars coordinate, and displaying the specific position of the dwell point on a map interface so as to judge the specific activity place of the user; and after the judgment is finished, storing the starting time, the ending time, the coordinate information and the address information of the stop point into a database.
Preferably: the method for determining the moving track in the step 1 comprises the following steps:
step 111: acquiring the position information of a user in a current period of time through a GPS sensor, if the temporary positioning point in the period of time is not compared with a previous period of time, not processing, and if the new temporary positioning point exists, updating the moving track of the user in the current day;
step 112: if a new temporary positioning point exists in the position information of a current period of time, but the track is not moved before the current period of time, directly generating a first track of the current day, wherein the first temporary positioning point in the period of time is used as the starting point of the track, and the recording time of the temporary positioning point is used as the starting time;
step 113: if a new temporary positioning point exists in the position information of a current period of time and a moving track exists before the current period of time, acquiring a latest moving track, and if the time interval between certain two points exceeds a track time threshold in all points after the starting time of the moving track, ending the previous moving track and starting the next moving track; if the track time threshold is not exceeded, updating the path and the end time of the moving track;
step 114: after the moving track is obtained, converting track coordinates based on a WGS coordinate system into Mars coordinates, matching the moving track with a map network, and performing visual display on a map interface; and storing the spatial information, the starting time, the ending time and the moving speed of each section of moving track into a database.
Preferably: updating the moving track of the user on the same day once at intervals of 8-12 minutes in step 111; the track time threshold in step 113 is 1.5-2.5 hours.
Preferably: adjusting the problems of the activity questionnaire and the emotion questionnaire according to different research problems to obtain the optimal quantitative data of the activity and the emotion; the emotion perception in the activity and emotion questionnaire content is quantitatively analyzed in the form of a Likter scale, and the activity quantitative content in the activity and emotion questionnaire content comprises activity intensity, activity purpose and activity type.
Preferably: the user's stop point and movement track are visually displayed on the Gade map interface; filling in activity and travel attribute information by clicking a stop point and a movement track mark on an interface; and selecting dates on the interface through a calendar, checking the historical stop points and the movement tracks of the user, and modifying and supplementing the historical activity and attribute information.
Preferably: after the stop points and the moving tracks are identified, the user uploads photos related to activities or trips, and the uploaded photos are deleted or added for historical activities or trips.
The utility model provides an intelligence planning trip investigation system that initiative and passivity combined together, includes front end and rear end, wherein:
the front end comprises a GPS sensor, a problem module and an information display module, wherein the GPS sensor is used for acquiring longitude and latitude coordinate information of a temporary positioning point of a user and uploading the acquired longitude and latitude coordinate information of the temporary positioning point to the rear end; the problem module is used for prompting a user to fill in activity and travel information after each activity and travel are finished, and uploading the filled activity and travel information to a back end; the information display module is used for displaying according to information issued by the rear end;
the back end comprises a positioning point and stop point determining module, a moving track determining module and an activity and emotion questionnaire starting module, wherein the positioning point and stop point determining module is used for determining a positioning point and a stop point according to longitude and latitude coordinate information of the temporary positioning point and transmitting the determined positioning point and stop point to the front end for displaying; the mobile track determining module is used for determining a mobile track according to longitude and latitude coordinate information of the temporary positioning point and transmitting the mobile track to the front end for displaying; and the activity and emotion questionnaire starting module is used for judging whether the staying time of the staying point is greater than a preset staying threshold value or not, and popping up the corresponding activity and emotion questionnaire through a front-end human-computer interaction interface for a user to fill in if the staying time of the staying point is greater than the preset staying threshold value.
Preferably: the system comprises a photo uploading module, wherein the photo uploading module is used for uploading photos related to activities or traveling by a user, and is used for deleting the uploaded photos or adding the photos for historical activities or traveling by the user.
Preferably: and a reverse proxy mechanism is adopted between the back end and the front end to provide services.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, through the determination of the positioning point, the stopping point and the moving track, the activity and trip data of the user can be accurately recorded in real time, more detailed and accurate individual activity and trip information can be obtained, and a large amount of labor and time cost can be saved.
2. The person to be investigated only needs to use the smart phone, and does not need to carry or use additional equipment, so that the burden of the user is reduced.
3. By adopting a mode of combining the initiative and the passivity, the system processes the passively collected data to determine the stop point and the moving track and reminds the user to fill in the attribute information, and compared with the previous method, the method greatly reduces the information input amount of the user.
4. The user's stopping point and moving track are displayed visually on the user interface's Gade map, providing more intuitive and convenient access and read-write functions for the user.
5. The front-end operating system is directly associated with the rear-end management system, data collected by the smart phone are directly synchronized into the database, the complex step of questionnaire entry in the previous survey is omitted, and the data can be managed and inquired more flexibly.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a flow of stop point determination.
Fig. 3 is a flow of determining a movement trajectory.
Fig. 4 shows the operating principle of nginnx.
Fig. 5 is a flow chart of data collection, management, and analysis techniques.
Detailed Description
The present invention is further illustrated by the following description in conjunction with the accompanying drawings and the specific embodiments, it is to be understood that these examples are given solely for the purpose of illustration and are not intended as a definition of the limits of the invention, since various equivalent modifications will occur to those skilled in the art upon reading the present invention and fall within the limits of the appended claims.
An intelligent planning travel investigation method combining active and passive modes is shown in fig. 1, and includes the following steps:
step 1, passive acquisition: collecting a moving track and a stop point of a user by using a GPS (global positioning system) sensor built in the smart phone;
the positioning technology of the invention is mainly based on a GPS sensor built in a mobile phone, the GPS is a global positioning system which utilizes a satellite to carry out real-time positioning and navigation in the global range, the basic principle of the GPS positioning is that the instantaneous position of the satellite moving at high speed is used as known calculation data, and the position of a point to be measured is determined by adopting a space distance rear intersection method. The GPS sensor collects longitude and latitude coordinate information of a temporary positioning point based on a WGS84 coordinate system, and updates the position information every 8 seconds, and specifically comprises the following steps:
step 10, locating point and stopping point acquisition
Arranging positioning points acquired in a certain time period into a list according to a time sequence, determining stop points according to the positioning points, judging whether each stop point can be put into a set based on a specific distance threshold value according to the sequence, judging whether the set is an effective set according to the number of effective points and the effective time in the set, judging that core points of the set are the stop points if the set is the effective set, and clearing relevant data if the set is an illegal set, continuously forming a next set and judging whether the set is the effective set, and so on, as shown in fig. 2, the specific method comprises the following steps:
basic data: core point corePoint, radius R (the maximum effective distance from other points to the core point), effective density P (the number of effective points), distance L (the distance between two points), average distance Q (the average value of the distances from the effective points to the core point), effective time M, and dwell point set V.
Step 101, initializing a core point, a radius R, an effective density P, a distance L, an average distance Q, an effective time M and a stop point set V.
The GPS sensor is used for acquiring the longitude and latitude coordinate information of the positioning points, the positioning points acquired in a certain time period are arranged into a temporary positioning point list pointList according to the time sequence and are stored into a database. And acquiring a temporary positioning point list pointList which is arranged according to the time sequence from a database. The first two points A, B of the pointList of temporary positioning points are obtained, the distance L between the two points A, B is calculated, and the distance L is compared with the radius R. If the distance L between the A, B two points is smaller than the radius R, the point A is set as a core point, the A, B two points are placed into the stop point set V, the average distance Q of the stop point set V is recorded, meanwhile, the maximum effective distance from other points in the stop point set V to the core point A is calculated, and the maximum effective distance is assigned to the radius R. If the distance L between A, B is greater than R then a is excluded and the same is done for B and the next point C in the temporary anchor point list pointList.
And 102, continuously acquiring the next point N of the pointList of the temporary positioning points, comparing the distances between the point N and each point in the stop point set V, if the point Z in the stop point set V enables the effective density P value to be maximum and the average distance Q to be minimum, adding the point N into the stop point set V, and setting the core point as the point Z. If such a point Z does not exist, a stop point set V is judged to be completed.
And 103, judging whether the stop point set V meets the condition that the number of the effective points is greater than the effective density P and the time from the starting point to the ending point is greater than the effective time M, if so, determining the effective set V as the effective set, and determining the core point of the effective set V, wherein the starting time and the ending time of the stop point are respectively the starting point time and the ending point time in the stop point set V. If the set is false, the set V of the stop points is judged to be an illegal set, and after relevant data is cleared, whether an effective set V of the stop points exists behind the pointList of the temporary positioning points is continuously judged.
Step 104, converting the WGS84 coordinate of the stop point in the active set into a mars coordinate, and displaying the specific position of the stop point on a Gagde map interface, so as to determine the specific activity place of the user. And after the judgment is finished, storing the starting time, the ending time, the coordinate information and the address information of the stop point into a database.
Step 11, recording and displaying the moving track
And recording the movement track of the user according to a preset movement track determination method based on the GPS positioning point information. There are two main ways for GPS trajectory acquisition, acquiring according to a certain time interval or acquiring according to a certain distance interval, and the system sets a more accurate determination method for the movement trajectory based on a certain time interval, as shown in fig. 3:
step 111: and acquiring the position information of the user in a current period of time through a GPS sensor, if the temporary positioning point in the period of time is not compared with the previous period of time, not processing, and if the new temporary positioning point exists, updating the moving track of the user in the current day. At 10 minute intervals.
Step 112: if a new temporary positioning point exists in the position information of a current period of time, but the track is not moved before the current period of time, a first track of the current day is directly generated, the first temporary positioning point in the period of time is used as the starting point of the track, and the recording time of the temporary positioning point is used as the starting time.
Step 113: if a new temporary positioning point exists in the position information of a current period of time and a moving track exists before the current period of time, a latest moving track is obtained, and if the time interval between certain two points exceeds a track time threshold value in all points after the starting time of the moving track, the previous moving track is ended and the next moving track is started. If none of the trajectory time thresholds have been exceeded, then the path and end time of this movement trajectory are updated, and so on. Wherein the track time threshold is 1.5-2.5 hours
Step 114: and after the moving track is obtained, converting the track coordinate based on the WGS coordinate system into a Mars coordinate, matching the moving track with a map network, and performing visual display on a Gagde map interface. Based on the function, the user can check the latest movement track of the user at any time and can observe and recall the previous travel condition of the user. And storing the spatial information, the starting time, the ending time and the moving speed of each section of moving track into a database.
Step 12, activity and travel information collection
Based on the two functions, the stay points and the moving tracks of the user are passively collected by a built-in GPS of the smart phone, and the user also needs to actively fill in attribute information related to the stay points and the moving tracks according to system prompts. After the system identifies the stop points and the moving tracks, the system actively prompts the user to fill in corresponding attribute information, such as activity types, activity perceptions, travel modes, travel perceptions and the like. The user can check the previous stop points and the movement tracks of the user and supplement or modify the attribute information, and the stop points and the movement tracks which are visually displayed on the high-grade map can help the user to recall the activity and the travel condition of the user. The user can also actively upload photos related to activities or travel according to own wishes, and can delete the uploaded photos or add photos for historical activities or travel. The background management end of the system can modify the problem module, so that a researcher or a planner can set problems according to research or actual needs and investigate the problems in a more targeted manner.
Step 13, reverse proxy and load
As shown in fig. 4, the service center provides services using a reverse proxy mechanism: realizing load balance and service authorization management control and carrying out operation and maintenance management and statistics on service states through the service agent address and the IP related to the user information; the mechanism can enable a host on the Internet to access different intranet host resources through different domain names, so that the intranet host is prevented from receiving supply of an external network host. The load balance provided by the reverse proxy mechanism can dynamically adjust the load condition in the service center system on the existing network structure, and the data processing capacity is improved.
The reverse Proxy mechanism of the service center is implemented based on the Nginx Proxy. The Nginx Proxy is a high-performance reverse Proxy server, and can compile application scripts through an embedded scripting language Lua to realize load balancing and reverse Proxy, thereby greatly reducing the burden of the Web server and improving the access speed. Common load balancing strategies are: firstly, distributing each request to different back-end servers one by one according to a time sequence, and if the back-end servers are down, automatically eliminating the requests, namely polling; appointing polling probability which is in direct proportion to the access ratio and is used for the condition that the performance of the back-end server is uneven, and the polling probability is called Weight; distributing each request according to the hash result of the access IP. Thus, each visitor fixedly accesses a back-end server, and the problem of session can be solved, which is called as IP _ hash; fourthly, the request is distributed according to the response time of the back-end server, and the priority distribution with short response time is called Fair.
Step 2, active collection: and if the stay time of the stay point is longer than a preset stay threshold, popping up a corresponding activity and emotion questionnaire through a human-computer interaction interface for a user to fill in.
The system has a powerful data processing function, can judge the stop point and the movement track in real time and high precision after the sensor passively collects the position, movement and other original data, and immediately reminds the user to actively fill in the latest activity or trip event information in a message pushing mode after the judgment is finished. The emotion perception and related activity quantification of the user can be adjusted according to different research problems, the emotion perception can be quantitatively analyzed in the form of a Likter scale, and the activity quantification content comprises different dimensions such as activity intensity, activity purpose and activity type.
An intelligent planning travel investigation system combining active and passive functions, as shown in fig. 5, comprises a front end and a back end, wherein:
the front end comprises a GPS sensor, a problem module, an information display module and a photo uploading module, wherein the GPS sensor is used for acquiring longitude and latitude coordinate information of a temporary positioning point of a user and uploading the acquired longitude and latitude coordinate information of the temporary positioning point to the rear end. The problem module is used for prompting a user to fill in activity and travel information after each activity and travel are finished, and uploading the filled-in activity and travel information to the back end. The information display module is used for displaying according to the information issued by the back end. The photo uploading module is used for uploading photos related to activities or trips by a user, and is used for deleting the uploaded photos or adding the photos for historical activities or trips by the user.
The problem module comprises an activity and trip perception problem module besides an activity information module such as the most basic activity type and a trip information problem module such as a trip mode. The satisfaction and the emotional experience are two aspects of activity, trip evaluation and cognitive measurement of people, and the trip satisfaction and the emotional experience scale are introduced into the problem module, so that the contents such as trip quality and space quality can be evaluated in an auxiliary manner. The activity perception measurement in the problem module adopts a two-dimensional model of emotion titer and arousal degree, the trip perception measures the trip Satisfaction scale (STS) of Ettema, and researchers can modify the problem module according to requirements.
The back end comprises a positioning point and stop point determining module, a moving track determining module and an activity and emotion questionnaire starting module, wherein the positioning point and stop point determining module is used for determining a positioning point and a stop point according to longitude and latitude coordinate information of the temporary positioning point and transmitting the determined positioning point and stop point to the front end for displaying. The mobile track determining module is used for determining a mobile track according to the longitude and latitude coordinate information of the temporary positioning point and sending the mobile track to the front end for displaying. And the activity and emotion questionnaire starting module is used for judging whether the staying time of the staying point is greater than a preset staying threshold value or not, and popping up the corresponding activity and emotion questionnaire through a front-end human-computer interaction interface for a user to fill in if the staying time of the staying point is greater than the preset staying threshold value.
And a reverse proxy mechanism is adopted between the back end and the front end to provide services.
The system integrates the functions of data acquisition, data management and data analysis, the front end mainly undertakes the function of data acquisition, and the rear end mainly takes charge of the functions of data management and data analysis. The smart phone collects space-time information such as a stop point, a moving track and the like, and a user records detailed information related to activities and traveling in an operating system of the smart phone.
The data collected by the front end is stored in the back end for grouping management. The system adopts a MySQL database to store and manage data, and the idle information such as stop points, tracks and the like collected by the smart phone and the related activity and trip information are stored in the database for group management. Different packet data are correlated with each other, and screening, query and other operations can be performed after linkage.
At the back end, the spatio-temporal data such as the stop point, the moving trajectory and the like can be displayed and analyzed in space based on a GIS method. The GIS technology provides firstly the electronic map based query and browse functions for the project and secondly powerful analysis and decision-making assistance functions. The dwell point and the spatio-temporal trajectory data are visually displayed on a high-grade map interface, and a manager can observe the spatio-temporal characteristics of user activities and travel. The manager can also perform spatial analysis and statistical analysis on the time and space data through the back-end management system, for example, the spatial distribution of the stop points, the spatial preference of the user, and other related information can be analyzed.
The front end is a trip investigation App combining the functions of passive collection and active collection of the smart phone, a GPS sensor arranged in the smart phone is used for collecting a stop point and a moving track of a user, and a problem module is arranged to prompt the user to fill in activities and trip details after each activity and trip are finished. Therefore, the travel investigation system can collect the time-space track of the movement of the interviewee, synchronously carry out log questionnaire investigation and photo collection, and visually display the travel track of the user and the related information on the map. With the increasing popularization of smart phones, most people carry the smart phones with them in daily trips, so that the passive and active acquisition combination mode can record the activity tracks of users as completely as possible, and enable the users to fill in trip information in time or in spare time. Compared with the prior trip investigation method, the method and the system greatly improve timeliness, interactivity and the like of data collection.
The back end is mainly used for data storage, management and analysis. The data collected by the front end is stored in a database, the space-time information of the staying points and the tracks, the activity and trip detail information, the user attribute information and the like are subjected to partition management, and researchers can perform operations such as linking, screening and inquiring on the data. The user's dwell point and movement trajectory will be visually displayed on the Gade map interface so that the researcher can directly observe and analyze the spatiotemporal characteristics of the user's activities and travel on the platform. The platform also provides some space analysis functions based on a GIS method, and researchers can directly analyze the distribution condition of user stop points, the coincidence condition of track routes and the like on the platform
Compared with the prior travel investigation method, the method adopted by the system not only can obtain more detailed and accurate individual activity and travel information, but also can save a large amount of labor and time cost. The front end is directly associated with the rear end, data collected by the smart phone are directly synchronized to the database, the complex step of questionnaire entry in the previous survey is omitted, and the data can be managed and inquired more flexibly. The back end can also check the use and operation conditions of the interviewee in real time, so that abnormal conditions can be found and corrected in time, and the effective rate of the sample is improved.
The travel investigation system provides an efficient, convenient and low-cost method for collecting more accurate and detailed activity and travel data, can help to analyze the space-time behavior characteristics of urban residents, specifically analyze some urban problems related to the activities and travel of the residents, and can also assist in the planning and management of smart cities. Several application scenarios are listed below:
scene one: research on space-time behavior characteristics of urban residents
The system combines the passive acquisition function and the active acquisition function of the smart phone, not only can collect more accurate space-time behavior data, but also can manage and analyze the data by a more efficient method, so that the system can replace the traditional activity log research method, be applied to the space-time behavior characteristic research of urban residents, analyze the space preference of the daily activities of the residents, establish the influence of the environment on the travel and the activities of the residents and the like.
Scene two: research on related problems of individual health, social differentiation and the like
The travel investigation method supports the problem module in the App to be modified by the back-end management system, so that researchers can modify questionnaires according to own research content and research purpose. The function widens the application field and research dimension of the travel investigation system, and can be used for the relevant research of individual health, social diversity and the like related to travel behaviors.
Scene three: planning management aid decision
The smart city emphasizes that public participation is enhanced in the process of city planning and management, and differentiation requirements of different crowds are fully considered. The system can be applied to urban planning management decision making, and by letting residents in different communities, different sexes and different age groups participate in the investigation, the using conditions of different groups on urban space, public facilities and the like and the perception and evaluation of the urban environment by the residents can be analyzed, so that the urban space and various facilities can be planned and managed more pertinently.
The method has the core innovation point that the combination of user track identification and extraction and subjective perception quantitative measurement is realized, position data collected by a GPS sensor arranged in a mobile phone comprises contents such as a timestamp latitude, a longitude, a speed, a precision and an orientation, whether a user performs corresponding activities at a parking point is judged by setting a parking point duration threshold, once the user is identified as performing the activities, the app pops corresponding activities and emotion questionnaires on a human-computer interaction interface for the user to fill in, the activities and emotion questionnaires are adjusted to obtain the optimal quantitative data of the activities and emotions, and the optimal quantitative data are uploaded and stored in a background database for data analysis and visualization of corresponding problems.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (10)
1. An active and passive combined intelligent planning travel investigation method is characterized by comprising the following steps:
step 1, passive acquisition: collecting a moving track and a stop point of a user by using a GPS (global positioning system) sensor built in the smart phone;
step 2, active collection: and if the stay time of the stay point is longer than a preset stay threshold, popping up a corresponding activity and emotion questionnaire through a human-computer interaction interface for a user to fill in.
2. The intelligent planning travel investigation method combining the active mode and the passive mode according to claim 1, wherein: the method for determining the stop point in the step 1 comprises the following steps:
step 101, initializing a core point, a radius R, an effective density P, a distance L, an average distance Q, an effective time M and a stop point set V;
acquiring longitude and latitude coordinate information of positioning points by using a GPS sensor, arranging the positioning points acquired in a certain time period into a temporary positioning point list pointList according to a time sequence, and storing the temporary positioning point list pointList into a database; acquiring a temporary positioning point list pointList arranged according to a time sequence from a database; acquiring A, B initial two points of a pointList of temporary positioning points, calculating the distance L of A, B two points, and comparing the distance L with the radius R; if the distance L between the A, B two points is smaller than the radius R, the point A is set as a core point, the A, B two points are placed into a stopping point set V, the average distance Q of the stopping point set V is recorded, meanwhile, the maximum effective distance from other points in the stopping point set V to the core point A is calculated, and the maximum effective distance is assigned to the radius R; if the distance L between A, B is greater than R, excluding A, and carrying out the same operation on B and the next point C in the pointList of temporary positioning points;
step 102, continuously acquiring a next point N of a pointList of temporary positioning points, comparing the distances between the point N and each point in a stop point set V, if a point Z exists in the stop point set V so that the effective density P value is maximum and the average distance Q is minimum, adding the point N into the stop point set V, and setting a core point as a point Z; if the point Z does not exist, judging that one stop point set V is finished;
103, judging whether the stop point set V meets the condition that the number of effective points is greater than the effective density P and the time from the starting point to the ending point is greater than the effective time M, if so, determining the effective set V as the effective set, and determining the core point of the effective set V, wherein the starting time and the ending time of the stop point are respectively the starting point time and the ending point time in the stop point set V; if the set is false, judging that the set V of the stop points is an illegal set, and continuously judging whether an effective set V of the stop points exists behind the pointList of the temporary positioning point after related data are eliminated;
step 104, converting the WGS84 coordinate of the dwell point in the active set into a Mars coordinate, and displaying the specific position of the dwell point on a map interface so as to judge the specific activity place of the user; and after the judgment is finished, storing the starting time, the ending time, the coordinate information and the address information of the stop point into a database.
3. The intelligent planning travel investigation method combining the active and passive according to claim 2, wherein: the method for determining the moving track in the step 1 comprises the following steps:
step 111: acquiring the position information of a user in a current period of time through a GPS sensor, if the temporary positioning point in the period of time is not compared with a previous period of time, not processing, and if the new temporary positioning point exists, updating the moving track of the user in the current day;
step 112: if a new temporary positioning point exists in the position information of a current period of time, but the track is not moved before the current period of time, directly generating a first track of the current day, wherein the first temporary positioning point in the period of time is used as the starting point of the track, and the recording time of the temporary positioning point is used as the starting time;
step 113: if a new temporary positioning point exists in the position information of a current period of time and a moving track exists before the current period of time, acquiring a latest moving track, and if the time interval between certain two points exceeds a track time threshold in all points after the starting time of the moving track, ending the previous moving track and starting the next moving track; if the track time threshold is not exceeded, updating the path and the end time of the moving track;
step 114: after the moving track is obtained, converting track coordinates based on a WGS coordinate system into Mars coordinates, matching the moving track with a map network, and performing visual display on a map interface; and storing the spatial information, the starting time, the ending time and the moving speed of each section of moving track into a database.
4. The intelligent planning travel investigation method combining the active and passive according to claim 3, wherein: updating the moving track of the user on the same day once at intervals of 8-12 minutes in step 111; the track time threshold in step 113 is 1.5-2.5 hours.
5. The intelligent planning travel investigation method combining the active and passive according to claim 3, wherein: adjusting the problems of the activity questionnaire and the emotion questionnaire according to different research problems to obtain the optimal quantitative data of the activity and the emotion; the emotion perception in the activity and emotion questionnaire content is quantitatively analyzed in the form of a Likter scale, and the activity quantitative content in the activity and emotion questionnaire content comprises activity intensity, activity purpose and activity type.
6. The intelligent planning travel investigation method combining the active and passive according to claim 5, wherein: the user's stop point and movement track are visually displayed on the Gade map interface; filling in activity and travel attribute information by clicking a stop point and a movement track mark on an interface; and selecting dates on the interface through a calendar, checking the historical stop points and the movement tracks of the user, and modifying and supplementing the historical activity and attribute information.
7. The intelligent planning travel investigation method combining the active and passive according to claim 6, wherein: after the stop points and the moving tracks are identified, the user uploads photos related to activities or trips, and the uploaded photos are deleted or added for historical activities or trips.
8. A system for intelligently planning a trip investigation method based on the combination of active and passive according to claim 3, wherein: including front end and rear end, wherein:
the front end comprises a GPS sensor, a problem module and an information display module, wherein the GPS sensor is used for acquiring longitude and latitude coordinate information of a temporary positioning point of a user and uploading the acquired longitude and latitude coordinate information of the temporary positioning point to the rear end; the problem module is used for prompting a user to fill in activity and travel information after each activity and travel are finished, and uploading the filled activity and travel information to a back end; the information display module is used for displaying according to information issued by the rear end;
the back end comprises a positioning point and stop point determining module, a moving track determining module and an activity and emotion questionnaire starting module, wherein the positioning point and stop point determining module is used for determining a positioning point and a stop point according to longitude and latitude coordinate information of the temporary positioning point and transmitting the determined positioning point and stop point to the front end for displaying; the mobile track determining module is used for determining a mobile track according to longitude and latitude coordinate information of the temporary positioning point and transmitting the mobile track to the front end for displaying; and the activity and emotion questionnaire starting module is used for judging whether the staying time of the staying point is greater than a preset staying threshold value or not, and popping up the corresponding activity and emotion questionnaire through a front-end human-computer interaction interface for a user to fill in if the staying time of the staying point is greater than the preset staying threshold value.
9. The system of claim 8, wherein: the system comprises a photo uploading module, wherein the photo uploading module is used for uploading photos related to activities or traveling by a user, and is used for deleting the uploaded photos or adding the photos for historical activities or traveling by the user.
10. The system of claim 8, wherein: and a reverse proxy mechanism is adopted between the back end and the front end to provide services.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117739995A (en) * | 2024-02-20 | 2024-03-22 | 厦门精图信息技术有限公司 | System and method for realizing navigation and space-time backtracking based on shooting and live-action map |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103218442A (en) * | 2013-04-22 | 2013-07-24 | 中山大学 | Method and system for life mode analysis based on mobile device sensor data |
| CN104159189A (en) * | 2013-05-15 | 2014-11-19 | 同济大学 | Resident trip information obtaining method based on intelligent mobile phone |
| US20180283882A1 (en) * | 2017-04-04 | 2018-10-04 | Appropolis Inc. | Location-based services system and method therefor |
| CN110909788A (en) * | 2019-11-19 | 2020-03-24 | 湖南博通信息股份有限公司 | Statistical clustering-based road intersection position identification method in track data |
| WO2020227435A1 (en) * | 2019-05-07 | 2020-11-12 | Intel Corporation | V2x services for providing journey-specific qos predictions |
-
2021
- 2021-06-18 CN CN202110675610.1A patent/CN113283669B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103218442A (en) * | 2013-04-22 | 2013-07-24 | 中山大学 | Method and system for life mode analysis based on mobile device sensor data |
| CN104159189A (en) * | 2013-05-15 | 2014-11-19 | 同济大学 | Resident trip information obtaining method based on intelligent mobile phone |
| US20180283882A1 (en) * | 2017-04-04 | 2018-10-04 | Appropolis Inc. | Location-based services system and method therefor |
| WO2020227435A1 (en) * | 2019-05-07 | 2020-11-12 | Intel Corporation | V2x services for providing journey-specific qos predictions |
| CN110909788A (en) * | 2019-11-19 | 2020-03-24 | 湖南博通信息股份有限公司 | Statistical clustering-based road intersection position identification method in track data |
Non-Patent Citations (3)
| Title |
|---|
| ZHANG SHANQI等: "Exploring Temporal Activity Patterns of Urban Areas Using Aggregated Network-driven Mobile Phone Data: A Case Study of Wuhu, China", 《CHIN. GEOGRA. SCI.》, vol. 30, no. 4, pages 695, XP037192409, DOI: 10.1007/s11769-020-1130-3 * |
| 张姗琪等: "面向移动位置数据的移动性指标计算工具：设计实现与实践应用", 《地理信息世界》, vol. 27, no. 5, pages 83 - 89 * |
| 蒋金亮等: "时空间行为分析支撑的乡村规划设计方法", 《现代城市研究》, vol. 11, pages 61 - 67 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117739995A (en) * | 2024-02-20 | 2024-03-22 | 厦门精图信息技术有限公司 | System and method for realizing navigation and space-time backtracking based on shooting and live-action map |
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