CN116610989A

CN116610989A - Tourist group type identification method based on group following travel track data

Info

Publication number: CN116610989A
Application number: CN202310884827.2A
Authority: CN
Inventors: 杜臣昌; 李月东; 高涛; 孙黎明; 和娴; 梁其东; 王凤民; 邢秀涵; 李建
Original assignee: Rizhao Planning And Design Research Institute Group Co ltd
Current assignee: Rizhao Planning And Design Research Institute Group Co ltd
Priority date: 2023-07-19
Filing date: 2023-07-19
Publication date: 2023-08-18
Anticipated expiration: 2043-07-19
Also published as: CN116610989B

Abstract

The invention discloses a tourist group type identification method based on group following track data, which comprises the following steps: acquiring the track data of the group following and preprocessing; according to the track characteristics, analyzing whether the sequence of each tourist queue changes in the advancing process, and if the sequence of each tourist queue does not change, the type of the tourist group is a research group; calculating whether the small group proportion exceeds a preset small group proportion threshold value, if so, determining that the type of the tourist group is a loose group, otherwise, determining that the type of the tourist group is an independent group. The tourist group type identification method provided by the method can identify the type of the tourist group according to the group following track, and is convenient for scenic spots to provide proper tourist services for different types of tourist groups.

Description

Tourist group type identification method based on group following travel track data

Technical Field

The invention relates to the technical field of space-time big data application, in particular to a tourist group type identification method based on group following track data.

Background

In recent years, with the sustainable development of national economy and the continuous improvement of living standard of people, the desire of people to travel is higher and higher. Many guests travel by reporting travel groups (following the group's tour). Generally, tourist clusters are classified into a research cluster, a powder guest cluster, an independent cluster, and the like. The school group is a team formed by organizing the travel of students according to regional characteristics, age characteristics of the students and teaching content requirements of the various disciplines. For convenience of organization and management, the order of the members of the college team in the queue remains substantially unchanged during travel. The tourist group is a tourist group from a travel agency or a tourist group formed by combining different travel agencies, and the tourist group is from various industries and has different eating habits, living habits and consumption habits. During travel, members of a group of loose guests often form multiple small groups with friends as ties. Independent agglomeration refers to a unit or a group, and a travel agency is required to design a travel route for the unit or the group, provide independent vehicle packing and tour guide, reserve a hotel and form a team. The independent team members know each other, the internal members exchange frequently in the travel process, and the peers moving together may not be fixed; even if the peers moving together are fixed, the number of peers is often significantly higher than the number of teams in a loose guest group consisting of friends as ties.

For scenic spots, the scenic spot services required for different travel group types may be different. The university group needs to provide services such as education, experience, etc. to the scenic spot, while the loose guest group and independent group are generally not needed. Independent agglomerations have higher requirements on scenic spot service quality due to stronger consumption capability, while scattered guest groups have lower requirements on scenic spot service quality than independent agglomerations.

Currently, high-precision positioning service technology is mature gradually, and for example, a thousand-finding position can provide centimeter-level positioning service by adopting RTK technology. Patent document 2023106167322 discloses a tourist group type identification method based on track big data, which can identify whether tourists have staff members by analyzing track data, if the staff members have the staff members, the group type is further identified as a group following tour, but the method can not identify the tourist group type. Therefore, how to use the following group travel track data to identify the type of the tourist group, and assist in analyzing the type, quality and the like of the service required by the tourist group, so as to provide a suitable tourist service for scenic spots, and the method is a problem to be solved by those skilled in the art.

Disclosure of Invention

The invention aims to provide a tourist group type identification method based on group following track data, so as to solve the problems in the background technology.

In order to solve the technical problems, the invention provides the following technical scheme: a tourist group type identification method based on group following track data comprises the following steps:

s1: acquiring trace data of a travel group, and preprocessing to generate trace data of the travel group;

s2: analyzing whether the sequence of each tourist queue changes in the advancing process according to the track characteristics of the tourist group, and if the sequence of each tourist queue does not change, the type of the tourist group is a research group;

s3: and (3) calculating whether the small group proportion of other tourist groups exceeds a preset small group proportion threshold value or not except for the school groups, if so, the type of the tourist groups is a loose group, otherwise, the tourist groups are independent groups.

Further, in the step S1, the following group track is formed by a series of tourist track points, and the track points include tourist group numbers, tourist numbers, tour guide marks, travelling states, time and coordinate information.

Further, the specific pretreatment step in the step S1 is as follows:

s11: deleting tour guide track points from the group following track data;

s12: and extracting tourist track points with the same tourist cluster number, and recording the tourist track points as tourist cluster track data.

Further, the specific steps in the step S2 are as follows:

s21: calculating the movement direction of the tourist clusters at each moment according to the barycenter coordinates of the tourist clusters at each moment and the corresponding previous moment in the advancing process;

s22: identifying tourists with a distance smaller than a preset distance threshold value from a certain tourist in the tourist group at each moment in the travelling process, and marking the tourists as adjacent tourists of the tourists;

s23: calculating the included angle between the connecting line of the coordinates of the tourist and the coordinates of each adjacent tourist and the moving direction of the tourist group in the advancing process, marking the included angle as a first included angle, calculating the included angle between the connecting line of the coordinates of each adjacent tourist and the moving direction of the tourist group, marking the included angle as a second included angle, and calculating the distance between the coordinates of the tourist and the coordinates of each adjacent tourist;

s24: marking adjacent tourists with a first included angle smaller than a preset travelling included angle threshold value as leading tourists of the tourists, and sorting the adjacent tourists with a second included angle smaller than the preset travelling included angle threshold value from far to near according to the distance, marking the adjacent tourists with a second included angle smaller than the preset travelling included angle threshold value as trailing tourists of the tourists, and sorting the adjacent tourists from near to far according to the distance;

s25: repeating steps S22-S24, and respectively identifying and sequencing the preceding tourists and the following tourists of the following tourists;

s26: marking the queue sequence of the tourists by taking the first tourist in the queue of the tourists as a benchmark;

s27: repeating steps S22-S26, and identifying other queue sequences;

s28: in the advancing process, comparing the queue sequence of tourists at each time and the corresponding previous time, and if the queue sequence is unchanged, the type of the tourist group is a research group.

Further, the step S3 specifically includes the steps of:

s31: identifying 4 tourists closest to the tourist at each moment according to the coordinates of the tourist at each moment in the tourist group, marking the 4 tourists as possible peers of the tourist, and summarizing the occurrence times of the possible peers;

s32: calculating the ratio of the occurrence times of the possible peers to the number of times of moments, marking the ratio as the probability of the possible peers, and marking the possible peers with the probability of the possible peers being larger than a preset threshold value of the probability of the possible peers as the peers of the tourists;

s33: repeating the steps S31-S32, and identifying the peers of the tourist peer to obtain all peers of the tourist;

s34: repeating steps S31-S33, and identifying all peers of other tourists except the tourist and all peers in the tourist group;

s35: if the number of the tourists and all the peers is 2-4, marking the tourists and all the peers as small groups;

s36: calculating the ratio of the sum of the member numbers of all small groups to the number of tourists of the tourist group, and recording the ratio as the small group ratio;

s37: if the small group proportion exceeds a preset small group proportion threshold, the tourist group type is a loose group, otherwise, the tourist group type is independent group.

Compared with the prior art, the invention has the beneficial effects that:

the tourist group type identification method based on the following group travel track data can identify the type of the tourist group as a study group, a scattered guest group or an independent group based on the following group travel track characteristics. By identifying the type of the tourist attraction, the type, quality and the like of the service required by the tourist attraction are analyzed in an auxiliary manner, so that a suitable tourist service is provided for a scenic spot.

Drawings

FIG. 1 is a schematic flow chart of a tourist group type identification method based on group following track data;

FIG. 2 is a schematic diagram of the present invention for identifying the sequence of the team in the research process;

FIG. 3 is a graphical representation of the results of the identification of loose clusters of the present invention, (a) small clusters during travel, (b) small clusters in a round formation during stay, and (c) small clusters in a fan formation during stay.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

In this embodiment, a scenic spot is taken as an example to describe the implementation of the present invention, and the following specific implementation steps for identifying the type of tourist group according to the present invention will be described specifically with reference to this example:

s1: and acquiring the following group track data, and preprocessing to generate the traveling group track data. The group following track is from the processing result of the high-precision positioning track data, and is composed of a series of tourist track points, wherein the track points comprise tourist group numbers, tourist numbers, tour guide marks, travelling states, time and coordinate information, the time precision of the data is 1 second, the space precision is 0.1 meter, and the coordinate system in the embodiment is a CGCS 2000_3_Degre_GK_CM_120E projection coordinate system.

Specifically, the following-bolus trajectory data is shown in table 1.

Table 1 track data table for group travel (projection coordinates)

Sequence number	Date of day	Time	Tourist group number	Tourist number	Tour guide sign	Travel state	Abscissa of the circle	Ordinate of the ordinate
									1	20230305	09:54:01	Group C	101	Is that	Travel	459701.1	3923025.7
2	20230305	09:54:02	Group C	101	Is that	Travel	459702.1	3923026.7
									3	20230305	09:54:01	Group C	104	Whether or not	Travel	459697.8	3923022.5
4	20230305	09:54:02	Group C	104	Whether or not	Travel	459698.8	3923023.5
									5	20230305	09:54:01	Group C	105	Whether or not	Travel	459696.7	3923021.3
6	20230305	09:54:02	Group C	105	Whether or not	Travel	459697.7	3923022.3
									7	20230305	09:54:01	Group C	106	Whether or not	Travel	459695.5	3923020.1
8	20230305	09:54:02	Group C	106	Whether or not	Travel	459696.5	3923021.1
									9	20230305	09:54:01	Group C	107	Whether or not	Travel	459694.3	3923019.0
10	20230305	09:54:02	Group C	107	Whether or not	Travel	459695.3	3923020.0
									11	20230305	09:54:01	Group C	116	Whether or not	Travel	459696.3	3923019.4
12	20230305	09:54:02	Group C	116	Whether or not	Travel	459697.3	3923020.4
									13	20230305	09:58:33	Group D	203	Whether or not	Stay at	459621.6	3922946.2
14	20230305	09:58:33	Group D	204	Whether or not	Stay at	459622.7	3922946.5
									15	20230305	09:58:33	Group D	205	Whether or not	Stay at	459623.8	3922946.8
16	20230305	09:58:33	Group D	206	Whether or not	Stay at	459624.9	3922947.1
									17	20230305	09:58:33	Group D	207	Whether or not	Stay at	459626.0	3922947.4
……	……	……	……	……	……	……	……	……

The pretreatment in the step S1 comprises the following specific steps:

s11: and deleting the tour guide track points from the group following track data.

Specifically, since the types of tourist groups are mainly identified according to the mutual position relationship among tourists, and the tour guide position may affect the accuracy of the result, the tour guide related data needs to be deleted. And extracting the track point with the tour guide mark of 'yes' and deleting the track point in the group following track data.

Specifically, since the travel group type is identified in units of travel groups, it is necessary to group travel following trajectory data by travel group. And extracting tourist track points with the same tourist group number (such as C group) and recording the tourist track points as tourist group track data.

S2: and analyzing whether the sequence of each tourist queue is changed in the advancing process according to the track characteristics of the tourist group, and if the sequence is not changed, the type of the tourist group is a research group.

The specific steps in the step S2 are as follows:

s21: in the travelling process, calculating the movement direction of the tourist group at each moment according to the barycenter coordinate of the tourist group at each moment and the corresponding previous moment.

Specifically, at 09:54:01, the barycentric coordinates of the tourist C cluster are (459696.1, 3923020.7), at 09:54:02, and at 459697.1, 3923021.7, the movement direction of the C cluster at 09:54:02 is 45 degrees north-east. Similarly, the travel mass activity direction is calculated at each time.

S22: during the travelling process, identifying tourists with a distance smaller than a preset distance threshold value from a certain tourist in the tourist group at each moment, and marking the tourists as adjacent tourists of the tourists.

Specifically, 104 guests, 105 guests, 107 guests, 116 guests, etc. in the group C are marked as 106 guests' neighbors with a distance 106 guests less than a preset distance threshold (e.g., 4 meters) at 09:54:02. Similarly, adjacent guests to each guest at each time 106 are identified separately.

S23: in the advancing process, calculating the included angle between the connecting line of the tourist coordinates and the coordinates of each adjacent tourist and the moving direction of the tourist group, marking the included angle as a first included angle, calculating the included angle between the connecting line of the coordinates of each adjacent tourist and the moving direction of the tourist group, marking the included angle as a second included angle, and calculating the distance between the coordinates of each adjacent tourist and the coordinates of each adjacent tourist.

Specifically, in 09:54:02, the direction of the line connecting the 106 tourist coordinates (459696.5,3923021.1) and the 104 tourist coordinates (459698.8,3923023.5) in the tourist group C is 43.8 degrees north and the included angle between the line and the activity direction of the tourist group C is 1.2 degrees (the first included angle). The included angle between the connecting line of the coordinates of the tourist 104 and the coordinates of the tourist 106 and the moving direction of the tourist group is 178.8 degrees (second included angle). The distance between the 104 guest coordinates and the 106 guest coordinates is 3.3 meters. Similarly, the first included angle, the second included angle, and the distance of other adjacent guests of the group C106 are calculated, respectively, and the results are shown in Table 2.

Table 2 table of positional relationship with other guests

Date of day	Time	Tourist group number	Tourist number	Travel state	First included angle (degree)	Second included angle (degree)	Distance (Rice)
								20230305	09:54:02	Group C	104	Travel	1.2	178.8	3.3
20230305	09:54:02	Group C	105	Travel	0.0	180.0	1.7
								20230305	09:54:02	Group C	107	Travel	177.5	2.5	1.6
20230305	09:54:02	Group C	116	Travel	86.2	93.8	1.1
								……	……	……	……	……	……	……	……

S24: and marking adjacent tourists with the first included angle smaller than a preset travelling included angle threshold value as leading tourists of the tourists, sorting the adjacent tourists with the second included angle smaller than the preset travelling included angle threshold value from far to near according to the distance, marking the adjacent tourists with the second included angle smaller than the preset travelling included angle threshold value as trailing tourists of the tourists, and sorting the adjacent tourists from near to far according to the distance.

Specifically, at 09:54:02, the first included angle of the tourist group C, 104, 105, and the second included angle of the 107, are less than the predetermined travel included angle threshold (e.g., 15 degrees), so that the three tourists and 106 tourists belong to a queue, and 104, 105 are the preceding tourists of 106 tourists, and 107 are the following tourists of 106 (FIG. 2). Since 104 guests are more distant than 105 guests, 104 guests are ranked ahead of 105 guests. 116 guests are not in a queue with 106 guests because the first angle and the second angle are both greater than the predetermined travel angle threshold.

S25: repeating steps S22-S24, and respectively identifying and sequencing the preceding tourists and the following tourists of the following tourists.

Specifically, repeating steps S22-S24 identifies and sorts the leading guests of the group C104 guests, the following guests of the 107 guests. If 106 all the first included angles of a guest in the queue of guests are greater than the preset travel included angle threshold, the guest is the first guest in the queue. Similarly, if all the second included angles of a guest are greater than the preset travel included angle threshold, it is the queue tail guest (fig. 2).

S26: and marking the queue sequence of the tourists by taking the first tourist in the queue of the tourists as a benchmark.

Specifically, at 09:54:02, the queue order of the first tourist of the queue where the tourist group C106 tourist is located is marked as 1, and the queue order of the tourist group C106 is marked as 4. Similarly, the queue order of other guests in the queue in which the guest is located is marked 106.

S27: steps S22-S26 are repeated, identifying other queue orders.

Specifically, the tourists in the queue of the tourists are removed 106, and the steps are repeated to identify other queue sequences of the tourist group C.

Specifically, the queue order of tourists in the tourist group C106 is 4 in 09:54:02, and the queue order of tourists in 09:54:01 and 106 is also 4, so that the queue order of tourists in 09:54:02 and 106 is unchanged. Similarly, comparing the queue order of tourists at each time and the previous time, finding that the order of all tourists of the tourist group C is unchanged, and the group C is a research group.

The step S3 comprises the following specific steps:

s31: and identifying 4 tourists closest to the tourist at each moment according to the coordinates of the tourist at each moment in the tourist group, marking the 4 tourists as possible peers of the tourist, and summarizing the occurrence times of the possible peers.

Specifically, 4 guests closest to the 205 guests in the group D are 203 guests, 204 guests, 206 guests and 207 guests, respectively, the four guests are marked as possible peers of the 205 guests, and the number of occurrences of the possible peers is recorded as 1. At 09:58:34, 4 guests closest to 205 guests in the group D are 204 guests, 206 guests, 208 guests and 209 guests, respectively, and the number of occurrences of the possible peers is 2 for the 204 guests and 206 guests and 1 for the 203 guests, 207 guests, 208 guests and 209 guests. And by analogy, counting the occurrence times of each possible companion of tourist group D205 tourist at all times.

S32: calculating the ratio of the occurrence times of the possible peers to the number of times of moments, marking the possible peers with the probability larger than a preset peer probability threshold as the peers of the tourist.

Specifically, in the tourist group D, 205 tourists are in the scenic spot (09:58:33-10:28:32, total time 1800), the number of occurrences of the 205 tourist, which is marked as 205 possible peers, is 640 times, and the 206 tourist is 1650 times, and the 204 tourists are possible peers probability of 205 tourists: 640/1800×100% = 35.56%, the probability of a possible companion for 206 guests is 91.67%. Since the probability of possible peers of 206 guests is greater than a preset probability of peers threshold (e.g., 90%), the probability of possible peers of 204 guests is less than the preset probability of peers threshold, and thus 206 guests are peers of 205 guests, 204 guests are not peers of 205 guests.

S33: repeating steps S31-S32, identifying the companion of the guest companion, and obtaining all the peers of the guest.

Specifically, steps S31-S32 are repeated to identify 206 the peer of the guest, and if the peer of the guest 206 is obtained as 205 guests, then all peers of the guest 205 have 1, i.e. 206 guests.

S34: repeating steps S31-S33, identifying all peers of the guest and other guests outside of the guest and all peers in the travel group.

Specifically, tourist group D removes 205 and 206 tourists, and repeats the above steps to identify all the peers of other tourists.

S35: if the number of tourists and all peers is 2-4, the tourists and all peers are marked as small groups.

Specifically, the number of 205 guests and all peers (206 guests) is 2, and the 205 guests and 206 guests are marked as small groups. Similarly, it is determined whether a guest and all peers thereof are small groups based on the number of remaining guests and all peers thereof.

In general, small groups are shown in fig. 3 (a) during traveling, in round formations are shown in fig. 3 (b), and in fan formations are shown in fig. 3 (c).

S36: and calculating the ratio of the sum of the member numbers of each small group to the number of tourists of the tourist group, and recording the ratio as the small group ratio.

Specifically, the number of the tourist group D groups is 4, the sum of the member numbers of all the small groups is 9, the number of the tourist group tourists is 12, and the proportion of the small groups is as follows: 9/12×100% =75%.

Specifically, if the small group proportion of the group D exceeds a preset small group proportion threshold (e.g., 65%), the group D is a loose group. Similarly, other travel group types are identified, respectively.

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims

1. A tourist group type identification method based on group following track data is characterized by comprising the following steps:

2. The method for identifying a tourist group based on data of a tourist group track according to claim 1, wherein in the step S1, the tourist group track is formed by a series of tourist track points including tourist group number, tourist number, tour guide mark, travelling state, time and coordinate information from the processing result of high-precision positioning track data.

3. The tourist group type identification method based on the group following track data according to claim 1, wherein the preprocessing in step S1 specifically comprises the following steps:

s11: deleting tour guide track points from the group following track data;

4. The tourist group type identification method based on the group following track data according to claim 1, wherein the specific steps in the step S2 are as follows:

s27: repeating steps S22-S26, and identifying other queue sequences;

5. The tourist group type identification method based on the group following track data according to claim 1, wherein the specific steps of the step S3 are as follows: