CN112579915B

CN112579915B - Analysis method and device for trip chain

Info

Publication number: CN112579915B
Application number: CN202110217722.2A
Authority: CN
Inventors: 林涛; 刘恒; 丁雪晴; 丘建栋; 周子益
Original assignee: Shenzhen Urban Transport Planning Center Co Ltd
Current assignee: Shenzhen Urban Transport Planning Center Co Ltd
Priority date: 2021-02-26
Filing date: 2021-02-26
Publication date: 2021-07-30
Anticipated expiration: 2041-02-26
Also published as: CN112579915A

Abstract

The application is applicable to the technical field of traffic planning, and provides an analysis method and an analysis device for a trip chain, wherein the analysis method comprises the following steps: acquiring historical track data of a plurality of historical trips of a user and to-be-analyzed track data of a current trip; obtaining the activity place and the living place of the user according to the distribution condition of the historical track data; determining a travel purpose and a travel mode of the current trip according to the activity place, the residence and the trajectory data to be analyzed; the trip purpose and the trip mode are used for forming a trip chain; the trip chain is an information set of the trip behavior of the user. Through the mode, the travel rule of the user is fully utilized for multiple times, the travel purpose and the travel mode are obtained, a travel chain is formed, the limitation of the traditional travel analysis mode is avoided, and the analysis accuracy is improved.

Description

Analysis method and device for trip chain

Technical Field

The application belongs to the technical field of traffic planning, and particularly relates to a method and a device for analyzing a travel chain.

Background

Along with the continuous expansion of the spatial pattern of large and medium-sized domestic cities and the continuous change of the time-space law of urban residents when going out. How to plan traffic according to the time-space law of urban resident travel becomes a problem which has to be solved.

The travel analysis of urban residents is a necessary basis for traffic planning. The urban traffic management system deeply excavates the intrinsic mechanism of urban individual traffic activities, reveals the daily travel time-space rules and activity characteristics of residents, and can provide important support for constructing a high-quality and high-efficiency urban traffic management system.

However, in a traditional travel analysis manner, travel chains of different residents are often obtained through a clustering algorithm (a travel chain refers to an information set of travel characteristics of a user and includes a large amount of time, space, manner and activity type information). Due to the fact that the traditional trip analysis mode is high in limitation, the accuracy of an analysis result is low.

Disclosure of Invention

In view of this, embodiments of the present application provide a method and an apparatus for analyzing a trip chain, a terminal device, and a computer-readable storage medium, which can solve the technical problem that the accuracy of an analysis result is low due to high limitation of a conventional trip analysis method.

A first aspect of an embodiment of the present application provides a method for analyzing a trip chain, where the method includes:

acquiring historical track data of a plurality of historical trips of a user and to-be-analyzed track data of a current trip;

obtaining the activity place and the living place of the user according to the distribution condition of the historical track data;

determining a travel purpose and a travel mode of the current trip according to the activity place, the residence and the trajectory data to be analyzed; the trip purpose and the trip mode are used for forming a trip chain; the trip chain is an information set of the trip behavior of the user.

A second aspect of embodiments of the present application provides an analysis apparatus for a trip chain, the analysis apparatus including:

the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring historical track data of a plurality of historical trips of a user and to-be-analyzed track data of a current trip;

the calculating unit is used for obtaining the activity place and the living place of the user according to the distribution condition of the historical track data;

the determining unit is used for determining the travel purpose and the travel mode of the current trip according to the activity place, the residence and the trajectory data to be analyzed; the trip purpose and the trip mode are used for forming a trip chain; the trip chain is an information set of the trip behavior of the user.

A third aspect of embodiments of the present application provides a terminal device, which includes a positioning module, a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method according to the first aspect when executing the computer program.

A fourth aspect of embodiments of the present application provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the method according to the first aspect.

Compared with the prior art, the embodiment of the application has the advantages that: according to the method and the system, the activity place and the living place of the user are analyzed through the historical track data, and the trip purpose and the trip mode of the current trip are analyzed according to the activity place, the living place and the track data to be analyzed, so that a trip chain of the user is formed. Because the travel of the user has a certain rule, the travel chain is analyzed according to the rule. In a traditional travel analysis mode, the obtained analysis result is limited greatly because the travel analysis mode is limited to the current travel. And this application is through above-mentioned mode, and the travel law of make full use of user a lot of obtains trip purpose and trip mode, and then forms the trip chain, has avoided traditional trip analysis mode's limitation, and then has improved the analysis accuracy.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the related technical descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic flow chart illustrating an analysis method for a trip chain provided in the present application;

FIG. 2 is a schematic diagram illustrating trajectory data provided herein;

fig. 3 shows a specific schematic flowchart of step 102 in an analysis method for a trip chain provided in the present application;

fig. 4 shows a specific schematic flowchart of step 1031 in an analysis method for a trip chain provided by the present application;

fig. 5 shows a specific schematic flowchart of step 1031 in an analysis method for a trip chain provided by the present application;

fig. 6 shows a specific schematic flowchart of step 1032 in an analysis method for a trip chain provided by the present application;

fig. 7 shows a specific schematic flowchart of step 1024 in an analysis method for a trip chain provided in the present application;

fig. 8 shows a specific schematic flowchart of step 1024 in an analysis method for a trip chain provided in the present application;

fig. 9 is a schematic diagram of an analysis apparatus for a trip chain provided in the present application;

fig. 10 is a schematic diagram of a terminal device according to an embodiment of the present invention.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

The method aims to solve the technical problem that the accuracy of an analysis result is low due to the fact that a traditional trip chain data mining mode is high in limitation. The embodiment of the application provides a method and a device for analyzing a trip chain, terminal equipment and a computer readable storage medium.

Referring to fig. 1, fig. 1 shows a schematic flow chart of an analysis method for a trip chain provided in the present application.

As shown in fig. 1, the method may include the steps of:

step 101, obtaining historical track data of a plurality of historical trips of a user and track data to be analyzed of a current trip.

Along with the popularization of communication equipment such as mobile terminals (for example, mobile phones) and the like, the acquisition of the trajectory data of the individual trip becomes very convenient, and the trajectory data can acquire richer spatio-temporal information in real time and is used for describing the trip process of the individual, so that a chance is provided for trip chain analysis based on the individual. Therefore, the trip chain of the user is analyzed based on the trajectory data collected by the positioning module in the mobile terminal.

Since users of different group types respectively have different travel characteristics, for example: the journey of office workers is mostly the round-trip commuting between the working place and the residence place, the journey of student groups is mostly the round-trip commuting between the school and the residence place, and the journey of housewives is mostly the round-trip commuting between the supermarket and the residence place. While for different users there may be travel-out, proclaimed, and shopping trips beyond the normal commute trip, most of the user's trips are on the commute trip. Therefore, historical track data of a plurality of historical trips are obtained, so that the trip rule of the user is captured, and the track data to be analyzed is analyzed according to the trip rule.

It should be noted that the "historical trajectory data" and the "trajectory data to be analyzed" mentioned in the present application may be all day trajectory data or trajectory data within a specified time period (for example, every two days, every week, etc.), and are not limited herein. Preferably, in order to analyze the travel track of the user more comprehensively and optimize the calculation amount, the all-day track data may be preferably selected.

And 102, obtaining the activity place and the living place of the user according to the distribution situation of the historical track data.

The activity place refers to a place where the user performs some active behaviors, including but not limited to a work place, a school, a shopping mall, and the like. The residential site refers to a place where the user lives daily.

In order to better explain the technical solution of the present application, the present application explains the technical solution of the present application by taking a user as a working group as an example, and it can be understood that technical solutions corresponding to other group types can be obtained by analogy with the technical solution of the present application.

Referring to fig. 2, fig. 2 is a schematic diagram illustrating track data provided by the present application. As shown in fig. 1, fig. 2 shows the distribution of the locus points of the office group between 8:00 and 12:00 (the black marks are locus points). Sites 1 and 2 are the user's place of residence and place of activity, respectively. Due to the fact that the users in the residential site and the activity site reside for a long time, the track point clusters with certain aggregation density are displayed in certain area ranges. Therefore, according to the characteristic, the activity place and the living place of the user are obtained according to the distribution situation of the historical track data. It should be emphasized that fig. 2 is only an example, and is a simple example of the trajectory data (only a part of the trajectory data in the all-day trajectory data is illustrated), and the information such as the time interval of the trajectory data, the travel trajectory of the user, and the location is not limited at all.

Step 102 can be implemented in two ways:

the method comprises the following steps: and calculating the distribution density of the track points in the plurality of equal-size areas in the track coverage area, and determining the equal-size areas with the distribution density being greater than the preset density as activity places or residence places (the activity places or the residence places can be distinguished according to the daytime and the nighttime, namely the activity places correspond to the daytime and the residence places correspond to the nighttime).

The method II comprises the following steps: as an alternative embodiment of the present application, step 102 includes the following steps 1021 to 1023. Referring to fig. 3, fig. 3 is a specific schematic flowchart illustrating step 102 in a method for analyzing a trip chain provided by the present application.

Step 1021, obtaining historical residence point clusters in the historical track data of each historical travel; the historical residing point cluster is a track point set with the user residing time exceeding a first threshold value.

Step 1021 can be obtained by analogy with step 1031, and the specific execution process can refer to step 1031, which is not described herein again.

And step 1022, regarding the historical residence point cluster with the largest number in the daytime period in all the historical trips as the activity place.

The work and rest habits of most users are 'work and rest day and night' after sunrise. Therefore, the historical residing point cluster with the largest number in the daytime period (according to the time zone information of the area, the daytime and the night are distinguished) can be confirmed as the activity place.

And 1023, using the historical residence point cluster with the largest number in the night time period in all the historical trips as the residential site.

103, determining a travel purpose and a travel mode of the current trip according to the activity place, the residence and the trajectory data to be analyzed; the trip purpose and the trip mode are used for forming a trip chain; the trip chain is an information set of the trip behavior of the user.

After the activity place and the residence place of the user are determined, whether the trajectory data to be analyzed go to and from the activity place and the residence place can be analyzed to determine the travel purpose of the current trip.

Step 103 can be implemented in two ways:

the method comprises the following steps: if the activity place and the residence place are determined to be in the trajectory data to be analyzed, the travel purpose of the user can be determined to be the commuting purpose. And if the activity place and the residence place are determined not to be in the trajectory data to be analyzed, determining that the trip purpose of the user is a non-commuting purpose. However, since there is a possibility that the user passes through the event place and the residential place when traveling for a non-commuting purpose, the analysis accuracy of the first method is low. To solve this problem, the present implementation provides a better approach.

The method II comprises the following steps: as an alternative embodiment of the present application, step 103 includes steps 1031 to 1033 as follows. Referring to fig. 4, fig. 4 shows a specific schematic flowchart of step 1031 in a method for analyzing a trip chain provided by the present application.

Step 1031, acquiring a current residence point cluster in the trajectory data to be analyzed; the current residing point cluster is a track point set with the user residing time exceeding a second threshold.

Step 1031 can be implemented in two ways:

the method comprises the following steps: and calculating the distribution density of the track points in a plurality of equal-size areas in the track coverage area, and determining the equal-size areas with the distribution density being greater than the preset density as the current resident point cluster.

The method II comprises the following steps: as an alternative embodiment of the present application, step 1031 includes the following step a1 to step a 6. Referring to fig. 5, fig. 5 shows a specific schematic flowchart of step 1031 in a method for analyzing a trip chain provided by the present application.

Step A1, according to a time sequence, taking track points in the track data to be analyzed within a preset time interval as a first track point set; the track points in each first set of track points are different.

Wherein, each trace point in this application corresponds a unique timestamp respectively.

And A2, acquiring a central track point in each first track point set.

The central track point is arranged at the middle position of the first track point set (the track points are sequenced according to the time stamp sequence). If the track points in the middle are even numbers (assuming as a first track point and a second track point), the position data of the first track point and the second track point are arithmetically summed to obtain average position data. And performing arithmetic summation on the time stamps of the first track point and the second track point to obtain an average time stamp. The average position data and the average timestamp are taken as the central trace point.

Step A3, using the track points within a preset distance range from the central track point as a second track point set; and the second track point set comprises the central track point.

And step A4, merging the second track point sets with the repeated track points to obtain a merged track point set.

Taking fig. 2 as an example, since track points of the point 1, the point 2, and the point 3 are dense, there may be a plurality of second track point sets of repeated track points at the point 1, the point 2, and the point 3.

The second track point sets with the repeated track points are merged to obtain a merged track point set. And screening the resident point clusters by calculating the time difference between each track point set.

The method of merging is as follows: the point or the midpoint of the second set of overlapping trace points at the center position may be taken as the center point of both. And taking all the track points of the two track points as the track points of the combined track point set, or taking the track point within a preset distance from the central point in all the track points of the two track points as the track point of the combined track point set.

Step A5, calculating the time difference between the initial track point and the termination track point in each third track point set; the third track point set refers to the merged track point set and the second track point set which is not merged.

And step A6, taking the third trace point set with the time difference larger than a third threshold value as the current resident point cluster.

The third threshold may be determined according to an actual application scenario to screen the third set of trace points for clusters of resident points.

Step 1032, determining the travel purpose of the current trip according to the position distribution relationship among the current resident point cluster, the activity place and the living place of the current trip.

Since the current resident point cluster may be an activity place or a residence place, the travel purpose of the current trip needs to be determined according to the position distribution relationship among the three. Travel purposes include, but are not limited to commuting purposes, non-commuting purposes, and the like.

Step 1032 may be implemented in two ways:

it should be noted that, in order to better explain the technical solution of the present application, the present application explains the technical solution of the present application by taking the current residing point cluster as the first current residing point cluster and the second current residing point cluster as an example. It can be understood that the number of the current residing point clusters may be more, and when the number of the current residing point clusters is greater than two, the technical solution when the number of the current residing point clusters is greater than two can be obtained by analogy with the technical solution of this embodiment, and details are not described herein again.

As an alternative embodiment of the present application, step 1032 includes the following step B1 to step B4. Referring to fig. 6, fig. 6 shows a specific schematic flowchart of step 1032 in an analysis method for a trip chain provided by the present application.

Step B1, if the first current residing point cluster and the second current residing point cluster both meet preset conditions, and the time points corresponding to the first current residing point cluster and the second current residing point cluster are within a commuting time period, determining that the probability that the trip purpose of the current trip is commuting is a first probability; the preset condition is that the distance between the first current resident point cluster and the activity site is smaller than a fourth threshold, or the distance between the second current resident point cluster and the living site is smaller than a fourth threshold.

It can be understood that, if the current residing point cluster is an activity site or a residence site, the distance between the current residing point cluster and the activity site is within a certain error range, and the distance between the current residing point cluster and the residence site is within a certain error range.

Therefore, the present application presets two conditions: the distance between the first current resident point cluster and the activity site is smaller than a fourth threshold value, and the distance between the second current resident point cluster and the living site is smaller than the fourth threshold value. And judging the trip purpose of the user according to the two conditions.

If the two preset conditions are met and the time points corresponding to the first current residing point cluster and the second current residing point cluster are in the commuting time period, the trip purpose of the user is most likely to be the commuting purpose.

Because the probability that the trip purpose of the current trip meeting different conditions is commuting is different, different commuting probabilities can be preset under different conditions. So as to accurately distinguish the travel purpose of the user through the commuting probability. For example: setting the commute probability corresponding to the step B1 as 1, setting the commute probability of the step B3 as 0.7, setting the commute probability of the step B4 as 0.5, and so on to obtain a more accurate travel chain.

And step B2, if the first current residing point cluster meets the preset condition and the second current residing point cluster does not meet the preset condition, judging whether the second current residing point cluster is between the activity place and the living place.

If only one of the preset conditions is satisfied, it is necessary to perform step B3 or step B4 according to the position of the second current resident point cluster.

Step B2 can be implemented in two ways:

the method comprises the following steps: it is determined whether the second current cluster of resident points is in the commute trajectory. If the second current cluster of stay points is in the commute trajectory, it is determined that the second current cluster of stay points is between the activity site and the residence site.

The method II comprises the following steps: as an alternative embodiment of the present application, step B2 includes the following steps B21 through B24. Referring to fig. 7, fig. 7 is a schematic flowchart illustrating a step 1024 in a method for analyzing a trip chain according to the present application.

And step B21, calculating a first straight line formed by the current resident point cluster and the activity place, and calculating a second straight line formed by the current resident point cluster and the living place.

When the current resident point cluster which does not meet the preset condition is positioned between the activity site and the residence site, the current resident point cluster, the activity site and the residence site form two straight lines which tend to be parallel. I.e. the angle between the first line and the second line is around 180 degrees.

Therefore, the present embodiment calculates the included angle between the first straight line and the second straight line to determine whether the current residing point cluster is located between the activity site and the living site according to the included angle.

And B22, calculating an included angle between the first straight line and the second straight line.

And step B23, if the included angle is within a preset angle range, determining that the current resident point cluster which does not meet the preset condition is located between the activity place and the living place.

And step B24, if the included angle is not in the preset angle range, determining that the current resident point cluster which does not meet the preset condition is not between the activity place and the living place.

Step B3, if the second current residing point cluster is located between the activity place and the living place, and the time point corresponding to the first current residing point cluster and the second current residing point cluster is within the commuting time period, determining that the probability that the travel purpose of the current trip is commuting is a second probability.

As an optional embodiment of the present application, after determining that the current resident point cluster which does not satisfy the preset condition is located between the activity place and the residence place, the missing track point may be supplemented according to the distance from the current resident point cluster to the activity place and the residence place, so as to enrich the information in the travel chain.

Step B4, if the second current residing point cluster is not located between the activity site and the living site, and the time interval corresponding to the second current residing point cluster is located in the commuting time interval, determining that the probability that the travel item of the current trip is commuted is a third probability; wherein the first probability is greater than the second probability, the second probability is greater than the third probability, and the first probability is greater than the third probability.

If the first current residing point cluster and the second current residing point cluster do not satisfy steps B1 and B3, then the travel purpose is confirmed to be a non-commuting purpose.

Step 1033, determining the travel mode according to the trajectory data to be analyzed.

Step 1033 can be implemented in two ways:

the method comprises the following steps: and calculating the average travelling speed of the user according to the trajectory data to be analyzed. And if the average travelling speed is lower than the preset speed, determining that the travelling mode is walking travelling. And if the average travelling speed is not lower than the preset speed, determining that the travelling mode is the riding travelling. However, the judgment method of the first method is rough, and it cannot be further determined which type of automobile is adopted for the "sitting trip". Therefore, the present embodiment provides a better way.

The method II comprises the following steps: as an alternative embodiment of the present application, step 1033 includes the following steps C1 through C9. Referring to fig. 8, fig. 8 is a schematic flowchart illustrating a step 1024 in a method for analyzing a trip chain according to the present application.

Step C1, cutting out travel track data corresponding to each travel from the track data to be analyzed; the travel is the travel process between different resident point clusters.

Because a plurality of trip trips may exist in the trajectory data to be analyzed, the trip modes adopted by each trip may be different. Therefore, the travel track data corresponding to each travel is segmented from the track data to be analyzed.

The method for segmenting the trajectory data to be analyzed comprises the following steps: and segmenting the trajectory data to be analyzed by taking one or more resident point clusters as a starting point or an end point. For example: and taking the residential site and the work site as a starting point or an end point, taking a journey when the user travels from the residential site to the work site, taking a journey when the user travels from the work site to the residential site at the moment, and the like. If the starting point or the ending point is of other location types, it can be obtained by analogy with the above example, and will not be described herein again.

After the travel track data corresponding to each travel is obtained, the steps C2 to C10 are sequentially executed for each travel track data.

And step C2, acquiring the sampling frequency of the travel track data.

The sampling frequency of the positioning module is at a lower level when the user is not using the navigation class software. When the user uses the navigation software, the sampling frequency of the positioning module is at a higher level because the real-time performance of the positioning needs to be ensured.

It will be appreciated that navigation-like software is often not used during the user's walking or riding in public transportation (including buses, subways, trains, etc.), whereas navigation-like software is often used to the user's habits during driving.

Therefore, the implementation utilizes the rule to acquire the sampling frequency of the travel track data so as to distinguish different travel modes.

And step C3, if the sampling frequency is greater than a fifth threshold, calculating to obtain a nonlinear coefficient through a preset formula.

And if the sampling frequency is greater than the fifth threshold value, the user is represented to adopt the automobile for traveling, and the automobile traveling is divided into a taxi taking traveling and a self-driving traveling. Therefore, the nonlinear coefficient needs to be calculated through a preset formula, and the travel mode is further subdivided according to the nonlinear coefficient.

Wherein, the preset formula is as follows:

wherein the content of the first and second substances,

representing the non-linear coefficients of the said non-linear coefficients,nrepresenting the trip times of the track data to be analyzed after segmentation is completed,drepresenting the straight-line distance between the starting track point and the ending track point in the track data to be analyzed,

and representing the sum of the distances of all track points in the track data to be analyzed.

And step C4, if the nonlinear coefficient is larger than a sixth threshold value, determining that the trip mode is a taxi taking trip.

When a user is in self-driving travel, the destination is often clear, so that the self-driving travel is close to a straight line. The destination of the net car reservation or the taxi which is taken for travel is measured in a magic way, so the running distance of the net car reservation or the taxi is not fixed. Therefore, the present embodiment utilizes this rule and uses the non-linear coefficient as the criterion. And determining the trip mode with the nonlinear coefficient larger than the sixth threshold value as the taxi taking trip.

And step C5, if the nonlinear coefficient is not greater than a sixth threshold, determining that the travel mode is a self-driving travel.

And step C6, if the sampling frequency is not greater than a fifth threshold, calculating the row distance according to the starting track point and the ending track point in the travel track data.

And if the sampling frequency is greater than the fifth threshold value, the user is represented to adopt a walking trip or a public transport means.

In particular, when walking is used, the distance traveled is often short. While public transportation vehicles tend to travel longer distances. Therefore, the implementation utilizes the rule to calculate the travel distance so as to further judge the travel mode.

Step C7, if the travel distance is smaller than a seventh threshold, calculating a first similarity between the travel trajectory data and preset walking trajectory data.

The preset walking track data can be pre-stored standard track data or a walking route provided by pulling navigation software.

And step C8, if the first similarity is greater than the eighth threshold, determining that the travel mode is walking travel.

And step C9, if the travel distance is not less than a seventh threshold value, calculating a second similarity between the travel trajectory data and preset automobile trajectory data.

The preset automobile track data can be pre-stored standard track data or a driving route provided by pulling navigation software.

And step C10, if the second similarity is greater than the eighth threshold, determining that the travel mode is public transportation travel.

As an optional embodiment of the present application, different probabilities may be set for different trip modes according to information such as similarity or sampling frequency, so as to further enrich information in the trip chain.

Thus, the travel purpose and the travel mode of the user are obtained. The user's trip chain can be formed through the trip purpose and the trip mode.

The travel purpose and the travel mode can be directly used as a travel chain. Or the travel purpose, the travel mode and other information are taken as a travel chain. Other information includes, but is not limited to, all trace point data, timestamps for the trace points, start point information, and end point information, etc. The type of other information may be determined according to the actual application scenario, and is not limited herein.

In this embodiment, the activity place and the residence place of the user are analyzed according to the historical trajectory data, and the trip purpose and the trip mode of the current trip are analyzed according to the activity place, the residence place and the trajectory data to be analyzed, so as to form a trip chain of the user. Because the travel of the user has a certain rule, the travel chain is analyzed according to the rule. In a traditional travel analysis mode, the obtained analysis result is limited greatly because the travel analysis mode is limited to the current travel. And this application is through above-mentioned mode, and the travel law of make full use of user a lot of obtains trip purpose and trip mode, and then forms the trip chain, has avoided traditional trip analysis mode's limitation, and then has improved the analysis accuracy.

Fig. 9 shows a schematic diagram of an analysis apparatus for a trip chain, where fig. 9 shows a schematic diagram of an analysis apparatus for a trip chain, and the analysis apparatus for a trip chain shown in fig. 9 includes:

an obtaining unit 91, configured to obtain historical trajectory data of a plurality of historical trips of a user and trajectory data to be analyzed of a current trip;

the calculating unit 92 is configured to obtain an activity place and a living place of the user according to the distribution condition of the historical trajectory data;

a determining unit 93, configured to determine a travel purpose and a travel mode of the current trip according to the activity place, the residence and the trajectory data to be analyzed; the trip purpose and the trip mode are used for forming a trip chain; the trip chain is an information set of the trip behavior of the user.

The application provides an analytical equipment of trip chain, through historical orbit data analysis user's activity place and place of residence to according to activity place, place of residence and wait to analyze the trip purpose and the trip mode of orbit data analysis current trip, and then form user's trip chain. Because the travel of the user has a certain rule, the travel chain is analyzed according to the rule. In a traditional travel analysis mode, the obtained analysis result is limited greatly because the travel analysis mode is limited to the current travel. And this application is through above-mentioned mode, and the travel law of make full use of user a lot of obtains trip purpose and trip mode, and then forms the trip chain, has avoided traditional trip analysis mode's limitation, and then has improved the analysis accuracy.

Fig. 10 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 10, a terminal device 100 of this embodiment includes: a positioning module 1000, a processor 1001, a memory 1002 and a computer program 1003, such as a trip chain analysis program, stored in said memory 1002 and executable on said processor 1001. When the processor 1001 executes the computer program 1003, the steps in each embodiment of the analysis method for a trip chain described above are implemented, for example, steps 101 to 103 shown in fig. 1. Alternatively, the processor 1001, when executing the computer program 1003, implements the functions of the units in the above-described device embodiments, for example, the functions of the units 91 to 93 shown in fig. 9.

Illustratively, the computer program 1003 may be divided into one or more units, which are stored in the memory 1002 and executed by the processor 1001 to implement the present invention. The one or more units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program 1003 in the terminal device 100. For example, the computer program 1003 may be divided into an acquisition unit and a calculation unit, and the specific functions of the units are as follows:

The terminal device may include, but is not limited to, a positioning module 1000, a processor 1001, and a memory 1002. Those skilled in the art will appreciate that fig. 10 is merely an example of one type of terminal device 100 and is not intended to limit one type of terminal device 100 and may include more or fewer components than shown, or some components may be combined, or different components, for example, the one type of terminal device may also include input-output devices, network access devices, buses, etc.

The Positioning module 1000 includes, but is not limited to, a Global Positioning System module (GPS), a BeiDou Navigation Satellite System module (BDS), and a GLONASS Satellite Navigation System module (GLONASS), and combinations of multiple modules.

The Processor 1001 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The storage 1002 may be an internal storage unit of the terminal device 100, such as a hard disk or a memory of the terminal device 100. The memory 1002 may also be an external storage device of the terminal device 100, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the terminal device 100. Further, the memory 1002 may also include both an internal storage unit and an external storage device of the terminal device 100. The memory 1002 is used for storing the computer programs and other programs and data required by the kind of terminal equipment. The memory 1002 may also be used to temporarily store data that has been output or is to be output.

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

The embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the above-mentioned method embodiments.

The embodiments of the present application provide a computer program product, which when running on a mobile terminal, enables the mobile terminal to implement the steps in the above method embodiments when executed.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunication signal, and software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other ways. For example, the above-described apparatus/network device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to monitoring ". Similarly, the phrase "if it is determined" or "if [ a described condition or event ] is monitored" may be interpreted depending on the context to mean "upon determining" or "in response to determining" or "upon monitoring [ a described condition or event ]" or "in response to monitoring [ a described condition or event ]".

Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.

Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims

1. An analysis method for a trip chain, the analysis method comprising:

acquiring historical track data of a plurality of historical trips of a user and to-be-analyzed track data of a current trip; the time span of the trajectory data to be analyzed is all day;

determining the travel purpose of the current trip according to the activity place, the residence and the trajectory data to be analyzed;

cutting out travel track data corresponding to each travel from the track data to be analyzed; each trip refers to a traveling process between different resident point clusters;

acquiring the sampling frequency of the travel track data;

if the sampling frequency is greater than a fifth threshold value, calculating by a preset formula to obtain a nonlinear coefficient;

if the nonlinear coefficient is larger than a sixth threshold value, determining that the trip mode is a taxi taking trip;

if the nonlinear coefficient is not larger than a sixth threshold, determining that the travel mode is a self-driving travel;

if the sampling frequency is not greater than a fifth threshold, calculating a travel distance according to an initial track point and a final track point in travel track data;

if the travel distance is smaller than a seventh threshold value, calculating a first similarity between the travel track data and preset walking track data;

if the first similarity is larger than the eighth threshold, determining that the travel mode is walking travel;

if the travel distance is not smaller than a seventh threshold value, calculating a second similarity between the travel track data and preset automobile track data;

if the second similarity is larger than the eighth threshold, determining that the travel mode is public transport travel; the trip purpose and the trip mode are used for forming a trip chain; the trip chain is an information set of the trip behavior of the user.

2. The analysis method of claim 1, wherein the obtaining the activity site and the residence site of the user according to the distribution of the historical track data comprises:

acquiring a history resident point cluster in the history track data of each history travel; the historical residing point cluster is a track point set with user residing time exceeding a first threshold;

taking the historical residence point cluster with the largest number in the daytime period in all the historical trips as the activity place;

and taking the historical residence point cluster with the largest number in the night period in all the historical trips as the residential site.

3. The analysis method according to claim 1, wherein the determining the travel purpose of the current trip according to the activity place, the residence and the trajectory data to be analyzed comprises:

acquiring a current resident point cluster in the track data to be analyzed; the current resident point cluster is a track point set with the resident time of the user exceeding a second threshold;

and determining the travel purpose of the current journey according to the position distribution relationship among the current resident point cluster, the activity place and the living place of the current journey.

4. The analysis method as claimed in claim 3, wherein said obtaining the current cluster of residing points in the trajectory data to be analyzed comprises:

according to the time sequence, taking track points in the track data to be analyzed within a preset time interval as a first track point set; the track points in each first track point set are different;

acquiring a central track point in each first track point set;

taking the track points within a preset distance range from the central track point as a second track point set; the second track point set comprises the central track point;

merging the second track point sets with the repeated track points to obtain a merged track point set;

calculating the time difference between the initial track point and the termination track point in each third track point set; the third track point set refers to the combined track point set and the second track point set which is not combined;

and taking the third trace point set with the time difference larger than a third threshold value as the current resident point cluster.

5. The analysis method of claim 3, wherein the current cluster of resident points comprises a first current cluster of resident points and a second current cluster of resident points;

the determining the travel purpose of the current trip according to the position distribution relationship among the current resident point cluster, the activity place and the living place of the current trip comprises:

if the first current resident point cluster and the second current resident point cluster both accord with preset conditions, and the time points corresponding to the first current resident point cluster and the second current resident point cluster are in a commuting time period, determining that the probability that the trip purpose of the current trip is commuting is a first probability; the preset condition is that the distance between the first current resident point cluster and the activity site is smaller than a fourth threshold, or the distance between the second current resident point cluster and the living site is smaller than a fourth threshold;

if the first current resident point cluster meets the preset condition and the second current resident point cluster does not meet the preset condition, judging whether the second current resident point cluster is between the activity place and the living place;

if the second current resident point cluster is located between the activity place and the living place, and the time point corresponding to the first current resident point cluster and the second current resident point cluster is located in the commuting time period, determining that the probability that the trip purpose of the current trip is commuting is a second probability;

if the second current resident point cluster is not located between the activity place and the living place and the time period corresponding to the second current resident point cluster is located in the commuting time period, determining that the probability that the travel item of the current trip is commuted is a third probability; wherein the first probability is greater than the second probability, the second probability is greater than the third probability, and the first probability is greater than the third probability.

6. The analysis method of claim 5, wherein if the first current residing point cluster meets the predetermined condition and the second current residing point cluster does not meet the predetermined condition, determining whether the second current residing point cluster is located between the activity site and the residence site comprises:

calculating a first straight line formed by the second current resident point cluster and the activity site, and calculating a second straight line formed by the second current resident point cluster and the living site;

calculating an included angle between the first straight line and the second straight line;

if the included angle is within a preset angle range, determining that a second current resident point cluster is located between the activity place and the living place;

and if the included angle is not in the preset angle range, the second current resident point cluster is not positioned between the activity place and the living place.

7. The analytical method of claim 6, wherein the predetermined formula is as follows:

wherein the content of the first and second substances,

representing the non-linear coefficients of the said non-linear coefficients,nrepresenting the travel times of the trajectory data to be analyzed,dand expressing the straight-line distance between the initial track point and the final track point in the track data to be analyzed, and expressing the sum of the distances of all track points in the track data to be analyzed by S.

8. An analysis apparatus for a trip chain, the analysis apparatus comprising:

the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring historical track data of a plurality of historical trips of a user and to-be-analyzed track data of a current trip; the time span of the trajectory data to be analyzed is all day;

the determining unit is used for determining the travel purpose of the current trip according to the activity place, the residence and the trajectory data to be analyzed; cutting out travel track data corresponding to each travel from the track data to be analyzed; each trip refers to a traveling process between different resident point clusters;

acquiring the sampling frequency of the travel track data;

9. A terminal device comprising a positioning module, a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.