Movement group classification and behavior pattern recognition method based on trajectory semantic similarity

Abstract

The present invention discloses a method for classifying sports groups and identifying behavioral patterns based on trajectory semantic similarity. Sports group classification method: Step 1, acquiring cycling motion data to obtain cycling trajectory data; Step 2, performing road network matching to obtain the road type of each cycling trajectory point; dividing each cycling trajectory according to the road type to obtain multiple cycling trajectory segments; determining behavioral attributes and motion attributes, performing attribute calculations and assigning semantics to the cycling trajectory segments to obtain semantic trajectory segments; Step 3, calculating the semantic trajectory similarity to obtain a similarity matrix; Step 4, constructing a similarity network based on the similarity matrix, and using the similarity network to perform community detection to obtain multiple communities, each community being a sports group category. The method of the present invention can effectively classify sports groups and identify complex cycling motion behavior patterns, and provide a scientific basis for urban planning and construction such as greenways, and personalized services and management for different sports groups.

Description

Technical Field

[0001] The present invention belongs to the field of data mining and motion behavior pattern data analysis, and relates to a motion group classification and behavior pattern recognition method based on trajectory semantic similarity. Background Art

[0002] With rising health awareness and a growing emphasis on sustainable tourism, cycling tourism has become an emerging trend in the modern tourism industry. As a healthy, environmentally friendly, flexible, and locally exploratory form of travel, cycling tourism has not only experienced significant growth but has also become a key driver of sustainable development in addressing local environmental, social, and economic challenges. However, due to the complexity of cycling tourism behavior and the diverse experiences generated by individuals due to their varying cycling purposes, methods, and needs, a comprehensive understanding of cycling tourism presents numerous challenges and difficulties. Therefore, in-depth research on cycling tourism behavior is crucial for promoting destination planning, providing personalized services and management, and promoting sustainable tourism development.

[0003] Behavioral complexity requires big data, yet research on bicycle tourism has yet to fully develop this data. Data collection in the cycling tourism field primarily relies on traditional questionnaires, revealing tourists' preferences through pre-prepared survey content, rather than big data. Questionnaires and travel diaries rely on respondents' subjective feedback and recollections of their travel activities, requiring careful and diligent recording, resulting in a relatively coarse-grained measurement method. Furthermore, traditional data collection methods rely on the cooperation of respondents, a high level of cooperation that often results in limited sample sizes. With the development and widespread application of big data technologies, new data sources, such as GPS trajectory data, are increasingly being used in tourism behavior research. GPS data objectively and accurately records information such as traveler time, location, distance, and speed, enabling fine-grained tracking of travel paths. User-generated GPS trajectory data not only boasts a large sample size, long sampling period, and high resolution, but is also applicable to a variety of complex environments (such as urban and natural environments), accurately describing cyclists' routes, speeds, and distances. This provides data support for revealing the complexity of cyclists' behavior from a fine-grained perspective.

[0004] Time, space, and activity are three key areas of tourism behavior research. While spatiotemporal patterns can reveal the "when," "where," and "how" of tourists' movements, they cannot fully shed light on the purpose and preferences of their behavior. In terms of space, spatiotemporal patterns simplify tourist behavior into a topological structure, focusing solely on the trajectory of travelers. While topological structures and spatiotemporal patterns can explain the structure of tourism activities, they largely overlook the content of the activities themselves and the quality of the tourist experience. Cycling tourism models extend beyond spatiotemporal patterns; the more crucial aspects are the traveler's style and experience.

[0005] As individual behavior becomes increasingly complex and diverse, semantic trajectory similarity calculation has become crucial for trajectory similarity mining and application. Trajectory similarity can identify travelers exhibiting similar movement patterns and, in turn, categorize the movement behaviors of individuals with similar interests and preferences according to homogeneous travel groups. Existing research on tourist behavior focuses on exploring the spatiotemporal characteristics of tourists. For example, considering both the spatial and temporal dimensions of tourist travel flows, the spatiotemporal analysis of individual travelers' trajectory similarity has been conducted. A heuristic method combining dynamic time warping (DTW) and the earth mover's distance (EMD) has been proposed to measure tourist trajectory similarity from a temporal and spatial perspective. However, these methods only explore the flow of tourist movement using temporal and spatial information, without further exploring the complexity of tourist behavior. Semantic trajectories can incorporate multiple aspects of information, providing rich semantic knowledge, such as contextual and geographic background information, for understanding the complexity of tourist behavior. Semantic trajectory similarity also has several representative algorithms, such as the multidimensional similarity measure (MSM), the multi-aspect semantic trajectory similarity (MUITAS), the maximum-minimum trajectory distance, and the sum of minimum trajectory distances (MMTD and SUMTD). Therefore, semantic trajectories are an effective way to deeply reveal the behavior of cycling tourists. Summary of the Invention

[0006] In view of the defects or shortcomings of the above-mentioned existing technologies, the purpose of the present invention is to provide a method for sports group classification and behavior pattern recognition based on trajectory semantic similarity, so as to solve the problem that the existing recognition methods have traditional data collection methods, only consider topological structures and spatiotemporal patterns, and fail to consider rider preferences and styles.

[0007] In order to achieve the above tasks, the present invention adopts the following technical solutions:

[0008] In one aspect, the present invention provides a method for classifying a group of moving objects based on trajectory semantic similarity, which specifically includes the following steps:

[0009] Step 1: Acquire cycling motion data and perform data preprocessing to obtain cycling trajectory data; the cycling motion data includes cycling GPS trajectory data and road network data, and the cycling GPS trajectory data is composed of relevant data of all cycling trajectory points;

[0010] Step 2: Use the GPS cycling trajectory data to perform road network matching to obtain the road type of each cycling trajectory point on each cycling trajectory; use the road type to segment each cycling trajectory into multiple cycling trajectory segments; then determine the behavioral attribute and motion attribute information, perform attribute calculation, and use the attribute calculation results to perform semantic assignment on each segmented cycling trajectory segment to obtain a semantic trajectory segment for each cycling trajectory segment. The semantic trajectory segments of all the cycling trajectory segments constitute the semantic trajectory corresponding to the cycling trajectory;

[0011] Step 3: Using the semantic track corresponding to each riding track obtained in step 2, calculate the semantic track similarity between each riding track, and obtain a similarity matrix consisting of the semantic track similarities of all riding tracks;

[0012] Step 4: Based on the similarity matrix obtained in step 3, a similarity network is constructed using the KNN algorithm. The nodes in the similarity network represent semantic trajectories, and the edges represent the similarity between two semantic trajectories. Community detection is performed using the similarity network to obtain multiple communities, each of which represents a movement group.

[0013] Furthermore, in step 1, the data preprocessing includes: (1) eliminating trajectory data outside the geographical scope of the study area; (2) deleting trajectory points with missing attribute information from the data, and identifying and deleting duplicate trajectory data through all field attribute information; (3) eliminating trajectory data above the highest altitude; and (4) removing abnormal data based on riding speed.

[0014] Furthermore, in step 2, the road network matching is performed based on the KDTree nearest neighbor search method, which includes the following process:

[0015] Step 1), road position representation: abstract the roads in the road network into several inflection points, and the spatial position of each road can be represented by the coordinates of these inflection points;

[0016] Step 2), KDTree construction: construct a KDTree for the coordinates of all the turning points of all the roads in the road network to obtain a KDTree;

[0017] Step 3), neighbor search: perform neighbor search on the cycling trajectory points in step 1 and the KDTree obtained in step 2), obtain the road breakpoint closest to each cycling trajectory point, and use the road type of the road breakpoint as the road type of its corresponding cycling trajectory point.

[0018] Furthermore, in step 2, the behavior attributes include riding time, road altitude, and road type, and the motion attributes include average speed and riding distance; the attribute calculation is divided into behavior attribute calculation and motion attribute calculation.

[0019] Furthermore, in step 3, the semantic trajectory similarity between the two riding trajectories is obtained by the following formula:

[0020]

[0021] in:

[0022] sim_ST(ep i ,ep k ) represents any two cycling trajectories ep i ,ep k The similarity of the semantic trajectory;

[0023] and represents the i-th and k-th semantic tracks, m and n refer to the number of semantic track segments in these two semantic tracks respectively; Represents the semantic trajectory ST i Semantic track fragments in and semantic track ST k Semantic track fragments in similarity;

[0024] Represents the semantic trajectory ST i Each semantic trajectory segment Instead of being in the semantic track ST k The most similar semantic trajectory segments The sum of similarities;

[0025] Represents the semantic trajectory ST k Each semantic trajectory segment Instead of being in the semantic track ST i The most similar semantic trajectory segments The sum of similarities;

[0026] From two semantic tracks ST i 、ST k The similarity of two semantic trajectory segments is obtained by the following formula:

[0027]

[0028] Among them, w P For each attribute's weight, p=5; matching function match p(A p ) are the corresponding attributes A of any two semantic trajectory segments P The similarity between the behavior attributes of the two semantic trajectory segments is calculated by the semantic matching function, and the similarity between the motion attributes of the two semantic trajectory segments is calculated by the distance matching function.

[0029] Furthermore, the weights of the attributes are set to w1 = 0.063, w2 = 0.098, w3 = 0.372, w4 = 0.103, and w5 = 0.364.

[0030] Furthermore, the semantic matching function is obtained by the following formula:

[0031]

[0032] in, Is to get attribute A P Functions of the category to which they belong, The equal sign in the text represents the same attribute A in any two semantic track segments. P Belong to the same category.

[0033] Furthermore, the distance matching function is obtained by the following formula:

[0034]

[0035] in, and Attribute A P The distance function and distance threshold are as follows: the distance function adopts d(X,Y)=|XY|, where X and Y represent the motion attribute values ​​corresponding to any two semantic trajectory segments.

[0036] Furthermore, in step 4, in the similarity network construction, K=10 in the KNN algorithm.

[0037] On the other hand, the present invention provides a method for identifying motion behavior patterns based on trajectory semantic similarity, the method comprising: merging groups with the same cycling tourism pattern among multiple cycling sports groups to obtain pattern classification results of the cycling sports groups, the cycling sports groups being obtained by the above-mentioned motion group classification method based on trajectory semantic similarity of the present invention.

[0038] Compared with the prior art, the present invention has the following technical effects:

[0039] (1) By introducing multi-dimensional semantic information such as riding time, road elevation, riding road type, average speed, and riding distance, this paper integrates and fragments the multi-dimensional features of the original riding GPS trajectory, thereby converting it into a more expressive riding semantic trajectory. This improvement not only enhances the interpretability of the trajectory representation but also overcomes the limitations of traditional trajectory representation methods, laying the foundation for in-depth data analysis.

[0040] (2) The present invention calculates the semantic similarity of multiple attributes (such as road type, speed, etc.) of semantic trajectory segments and comprehensively evaluates the similarity between semantic trajectories from multiple dimensions. It can comprehensively and accurately reflect the actual similarity between riding trajectories. It is superior to the traditional GPS-based trajectory similarity calculation method, which mainly considers the similarity of spatial position and time, but does not fully consider other complex conditions of the riding environment.

[0041] (3) The present invention integrates road types into coarse-grained categories (e.g., freeways and freeway connectors are unified as freeways). This avoids excessive subdivision of road types, which results in an excessive number of semantic trajectory segment categories for a single semantic trajectory. This prevents frequent changes in the road types matched by cycling trajectory points, while also reducing the complexity of semantic trajectory similarity calculation. This simplified classification approach allows for better aggregation of trajectories with similar semantic features, thereby improving the accuracy and efficiency of cycling trajectory group classification.

[0042] (4) This paper uses a community detection algorithm to identify groups with intrinsic connections by analyzing the complex network relationships between semantic tracks. Unlike traditional clustering algorithms that rely solely on spatial similarity, community detection algorithms can reveal the underlying structure in semantic track data and consider the topological relationships and interactions between semantic tracks, thereby providing more accurate group classification. This method effectively captures complex group characteristics and relationships, improving the accuracy of group classification.

[0043] In summary, the method of the present invention can effectively classify sports groups and identify complex cycling sports behavior patterns, thereby promoting destination planning, providing personalized services and management, and promoting sustainable tourism development. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is the research framework of the method of the present invention;

[0045] Figure 2 It is the process of semantic trajectory construction;

[0046] Figure 3 It is the relationship between K value of KNN and Silhouette coefficient;

[0047] Figure 4 is the community detection algorithm result in an embodiment of the present invention;

[0048] Figure 5 It is the cycling tourism mode and cycling tourism activity behavior;

[0049] Figure 6 The distribution of leisure and sightseeing cycling modes and typical cycling routes in the embodiment of the present invention;

[0050] Figure 7 The distribution of sports challenging riding modes and typical riding routes in the embodiment of the present invention;

[0051] Figure 8 It is the distribution of fitness exercise cycling patterns and typical cycling routes in an embodiment of the present invention.

[0052] The present invention is further explained in detail below with reference to the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0053] Example 1

[0054] See also Figure 1 、 Figure 2 This embodiment provides a method for classifying a group of moving objects based on trajectory semantic similarity, which specifically includes the following steps:

[0055] Step 1: Obtain cycling motion data, perform data preprocessing, and obtain cycling trajectory data.

[0056] Specifically, cycling data includes GPS track data and road network data (including road types). The GPS track data is uploaded by cycling enthusiasts in Xi'an and crawled from the 2bulu outdoor travel website; the road network data is urban road network data obtained from OpenStreetMap (OSM).

[0057] Cycling GPS trajectory data consists of relevant data of all cycling trajectory points. For example, the cycling GPS trajectory data shown in Table 1 records in detail the participant's user ID, trajectory longitude, latitude, date and time, speed, altitude and other multi-dimensional motion information.

[0058] Table 1 Cycling GPS trajectory data sample

[0059] User ID longitude Dimensions time Speed ​​(m / s) Altitude (m) 1024***67 108.*** 34.*** 2019-11-8 14:11:43 0.4853 411.2028 1024***67 108.*** 34.*** 2019-11-8 14:11:47 1.6229 411.7378 1024***67 108.*** 34.*** 2019-11-8 14:11:41 3.1387 412.2087 1024***67 108.*** 34.*** 2019-11-8 14:11:55 3.1514 412.4787 1024***67 108.*** 34.*** 2019-11-8 14:11:59 3.0886 412.7186

[0060] Specifically, in this embodiment, due to factors such as GPS signal loss, equipment errors, and weather conditions, the open GPS trajectory data has missing information and inaccurate information. In order to eliminate the impact of data errors on the research results, this embodiment adopts the following data preprocessing: (1) Trajectory data outside the geographical scope of the study area are eliminated, and the administrative area of ​​Xi'an is selected as the study area of ​​this embodiment. (2) Trajectory points with missing attribute information are deleted from the data, and duplicate trajectory data are identified and deleted through all field attribute information. (3) With reference to the highest altitude of Xi'an, trajectory data beyond 4000m are eliminated. (4) Abnormal data is removed according to riding speed. The riding data collected in this embodiment are distributed in urban areas and mountainous areas. Considering that the instantaneous speed during downhill mountain riding can reach very high, this embodiment sets the speed threshold to a relatively large 30m / s. Through the data cleaning and screening steps, 3532 valid riding trajectories were finally obtained, which contain a total of 6079556 trajectory points.

[0061] Step 2: Use the cycling GPS trajectory data obtained in step 1 to perform road network matching to obtain the road type of each cycling trajectory point on each cycling trajectory; use the road type to segment each cycling trajectory into multiple cycling trajectory segments; then determine the information of behavioral attributes and motion attributes, perform attribute calculations, and use the attribute calculation results to perform semantic assignment on each segmented cycling trajectory segment to obtain a semantic trajectory segment for each cycling trajectory segment. The semantic trajectory segments of all cycling trajectory segments constitute the semantic trajectory corresponding to the cycling trajectory.

[0062] In this embodiment, a KDTree (K-Dimensional Tree)-based neighbor search method is used to match a road network, including the following process: Step 1), road position representation: abstract the roads in the road network into a number of inflection points (also called turning points or intersections), and the spatial position of each road can be represented by the coordinates of these inflection points. Step 2), KDTree construction: construct a KDTree based on the coordinates of all inflection points of all roads in the road network to obtain a KDTree. Step 3), neighbor search: perform a neighbor search on the riding trajectory points in step 1 and the KDTree obtained in step 2) to obtain the road inflection point closest to each riding trajectory point, and use the road type of the road inflection point as the road type of the corresponding riding trajectory point; this method can quickly find the closest road inflection point between the riding trajectory point and the road network, thereby achieving road network matching.

[0063] Specifically, cycling track segmentation involves breaking a given cycling track into a series of disjoint sub-tracks with similar road types. Since cycling tourism is constrained by the road network, different road types directly reflect travelers' cycling preferences. Therefore, the present invention merges track points with consistent road types within a cycling track based on their road type information, achieving segmentation of the cycling track and generating multiple cycling track segments.

[0064] Specifically, attributes represent the cycling behavior and movement characteristics of a cycling community across five dimensions: riding time, road altitude, road type, average speed, and riding distance. Behavioral attributes include riding time, road altitude, and road type, while movement attributes include average speed and riding distance. Attribute calculation is divided into behavioral attribute calculation and movement attribute calculation.

[0065] Among them, behavioral attribute calculation is based on the trajectory segmentation based on road type information. The riding time of each cycling trajectory segment is represented by the time of the first GPS point, that is, the time when the user arrives at the road segment of this road type; the road altitude is the difference between the maximum and minimum altitudes of the GPS points in each cycling trajectory segment.

[0066] In motion attribute calculations, average speed refers to the average speed of all GPS track points in each cycling track segment, and cycling distance refers to the sum of the Euclidean distances between all GPS track points in each cycling track segment.

[0067] Step 3: Using the semantic track corresponding to each riding track obtained in step 2, the semantic track similarity between each riding track is calculated to obtain a similarity matrix consisting of the semantic track similarities of all riding tracks.

[0068] Preferably, the semantic trajectory similarity is obtained by formula 1:

[0069]

[0070] in:

[0071] sim_ST(ep i ,ep k ) represents any two cycling trajectories ep i ,ep k The similarity of the semantic trajectory;

[0072] and represents the i-th and k-th semantic tracks, m and n refer to the number of semantic track segments in these two semantic tracks respectively; Represents the semantic trajectory ST i Semantic track fragments in and semantic track ST k Semantic track fragments in similarity;

[0073] Represents the semantic trajectory ST i Each semantic trajectory segment Instead of being in the semantic track ST k The most similar semantic trajectory segments The sum of similarities;

[0074] Represents the semantic trajectory ST k Each semantic trajectory segment Instead of being in the semantic track ST i The most similar semantic trajectory segments The sum of similarities;

[0075] Preferably, from two semantic tracks ST i 、ST k The similarity of two semantic trajectory segments is obtained by formula 2:

[0076]

[0077] Among them, w P For each attribute's weight, p=5; matching function match p (A p ) are the corresponding attributes A of any two semantic trajectory segments P Each semantic segment contains P attributes, including 3 behavioral attributes and 2 motion attributes. The similarity between the behavioral attributes of any two semantic trajectory segments is calculated using the semantic matching function, while the similarity between the motion attributes of any two semantic trajectory segments is calculated using the distance matching function.

[0078] The weight w of each attribute P This determination is based on information entropy, a commonly used statistical tool for measuring the uncertainty or information content of a variable. It can effectively assess the dispersion and importance of each attribute in the overall dataset. Specifically, in this embodiment, the weights of each attribute are set to w1 = 0.063, w2 = 0.098, w3 = 0.372, w4 = 0.103, and w5 = 0.364.

[0079] Preferably, the semantic matching function is obtained by Formula 3:

[0080]

[0081] in, Is to get attribute A P Functions of the category to which they belong, The equal sign in the text represents the same attribute A in any two semantic track segments. P In this embodiment, the cycling road types in the behavior attributes are classified into highways, urban roads, bicycle lanes, residential roads, sightseeing roads, and suburban roads (Appendix 2); the cycling time is classified into morning (6:00-12:00), afternoon (12:00-18:00), and evening (18:00-6:00); and the road altitude is classified into flat (road altitude difference ≤ 100) and challenging (road altitude difference > 100).

[0082] Table 2 Road type classification

[0083]

[0084]

[0085]

[0086] Preferably, the distance matching function is obtained by formula 4:

[0087]

[0088] in, and Attribute A P Distance function and distance threshold. In the present invention, the distance function is: d(X,Y) = |XY|, where X and Y represent the motion attribute values ​​corresponding to any two semantic trajectory segments. In this embodiment, based on the application context of cycling tourism activities and the distribution of the cycling dataset of the present invention, the distance threshold for average speed is set to 1.4m / s, and the distance threshold for cycling distance is set to 3038m.

[0089] Step 4: Based on the similarity matrix obtained in step 3, a similarity network is constructed using the KNN algorithm. The nodes in the similarity network represent semantic trajectories, and the edges represent the similarity between two semantic trajectories. The similarity network is used to perform community detection to obtain multiple communities. Each community is a sports group. These groups exhibit similar cycling tourism activities, such as Figure 5 shown.

[0090] Specifically, in the actual construction of the similarity network, the setting of the K value in the KNN algorithm is closely related to the result of community classification. The present invention selects the Silhouette coefficient as the evaluation index of the clustering effect to determine the appropriate K value for setting KNN. Figure 3It can be seen that when K = 7, 10, and 15, the Silhouette coefficient values ​​are relatively high, and the corresponding number of communities is 2, 9, and 7. Through comparative analysis, the clustering effect is most significant when K = 10, so the present invention selects 9 communities under this parameter as the classification result of Xi'an's cycling behavior pattern.

[0091] Specifically, the community detection algorithm (Infomap algorithm) detects multiple communities by analyzing the similarity network.

[0092] In this example, the cycling preferences of different communities are sorted out from three aspects: cycling behavior characteristics (i.e., behavior attributes), cycling movement characteristics (i.e., movement attributes), and other characteristics. Each community is sorted into cycling tourism activities with differentiated characteristics (see Figure 5 In this embodiment, 9 communities with significant cycling behavior characteristics were successfully detected. Each community collected a large number of cycling tracks, fully demonstrating different cycling tourism activities (see Figure 4 Among these communities, Comm1 is the largest with 664 cycling tracks (19% of the total), followed by Comm2 with 607 cycling tracks (17%), while the smallest community is Comm9, which contains 143 cycling tracks, accounting for 4% of the total sample.

[0093] Example 2:

[0094] This embodiment provides a motion behavior pattern recognition method based on trajectory semantic similarity, which includes the following steps: merging groups with the same cycling travel pattern among multiple cycling groups to obtain pattern classification results of the cycling groups.

[0095] Specifically, the cycling sports group is obtained by the sports group classification method based on trajectory semantic similarity of the present invention.

[0096] In order to discover and summarize the intrinsic connections between behaviors with the same functional characteristics and simplify the complexity caused by excessive subdivision, in this embodiment, activities with similar functions are combined into one pattern (see Figure 5 This approach helps to identify and meet the needs of different tourists and promotes the diversification and personalization of cycling tourism activities. Figure 5 In the riding time Represents morning, afternoon and evening respectively; and Represents gentle riding and challenging riding respectively; the background color of the cell under average speed indicates the speed rating: light green - low speed, light yellow - medium speed, light red - high speed; They represent distance ratings: short ride, medium ride, and long ride.

[0097] Finally, this embodiment divides Xi'an cycling behavior into three cycling tourism modes:

[0098] (1) Leisure and sightseeing riding mode.

[0099] This mode covers two types of cycling tourism activities: sightseeing and exploration cycling (comm6) and night leisure cycling (comm7). It is a cycling tourism mode that pursues leisure and entertainment, and enjoys the city's daytime and nighttime scenery. This type of cycling tourists focuses on the leisure function of cycling rather than pure mobility efficiency, which is mainly manifested in slow cycling speed, short cycling distance, and larger groups cycling in the afternoon and evening. Cycling paths are distributed in both urban and suburban areas, and mainly tend to surround or pass through roads and areas with sightseeing value (sightseeing roads and bicycle roads), such as those around the city wall, the Bahe Greenway, the Weihe Greenway, the Qujiang Lake, the Daming Palace, the Kunlun Lake, and other riverside, greenways and parks ( Figure 6 It is worth noting that, with West Street and East Street inside the city wall, and West Youyi Road and East Youyi Road south of the city wall as the main sections, specific trajectories outline the route shapes of "2021" and "2024" in the urban space, further demonstrating the creativity and fun of the leisure and sightseeing cycling mode in cycling tourism.

[0100] (2)Sports challenge riding mode.

[0101] This mode combines the two cycling tourism activities of short-distance challenge cycling (comm2) and extreme speed challenge cycling (comm8). It is a cycling mode that highlights short-distance mountain challenges, pursues high-speed experience and skill challenges. This mode has flexible cycling time and simple and direct cycling routes. It is distributed in cities, suburbs and mountainous areas, covering multiple terrain areas. This cycling group shows a strong preference for mountainous terrain. It not only requires high cycling skills in terms of cycling speed, but also requires them to be able to flexibly cope with changing terrain and road conditions. Figure 7 As shown, Fengyu, Lishan, Dayu, Taipingyu, Daiyu, and Wangyu are popular mountain biking areas. Popular mountain biking routes include Fengyu → Dabagou → Jiwozi, Lishan National Forest Park → Longwangmiaogou → Hongqingshan National Forest Park, Dayu → Xianyukou → Xigou, and Taipingyu → Hongyuhe → Qinling Alpine Camp → Taiping National Forest Park. Some cyclists also prefer routes that start in the city, cross the suburbs, and then explore the mountains, reflecting this group's pursuit of speed, technical challenges, and endurance.

[0102] (3) Fitness training cycling mode.

[0103] This mode mainly consists of three types of cycling tourism activities: long-distance exercise cycling (comm1), urban exercise cycling (comm3, 4, 5) and commuting exercise cycling (comm9). It is a cycling mode that focuses on long-distance fitness cycling, daily cycling in urban areas and morning commuting fitness (see Figure 8 This type of cycling group focuses on the convenience of cycling, and mainly chooses to cycle in urban areas close to residential areas, using convenient daily commuting methods. This type of cycling pattern is usually manifested as cycling around the city, cycling in the suburbs of the city, and the main cycling routes cover the city, suburbs and Mount Lishan area, and mainly chooses direct and efficient urban roads and residential roads. In addition, the study found that although participants generally showed a preference for bicycle lanes, their cycling starting points and destinations were often significantly distanced from the bicycle lanes, which made them difficult to use in practice.

Claims

1. A motion group classification method based on trajectory semantic similarity, characterized in that: The specific steps include: Step 1: Acquire cycling motion data and perform data preprocessing to obtain cycling trajectory data; the cycling motion data includes cycling GPS trajectory data and road network data, and the cycling GPS trajectory data is composed of relevant data of all cycling trajectory points; Step 2: Use the GPS cycling trajectory data to perform road network matching to obtain the road type of each cycling trajectory point on each cycling trajectory; use the road type to segment each cycling trajectory into multiple cycling trajectory segments; then determine the behavioral attribute and motion attribute information, perform attribute calculation, and use the attribute calculation results to perform semantic assignment on each segmented cycling trajectory segment to obtain a semantic trajectory segment for each cycling trajectory segment. The semantic trajectory segments of all the cycling trajectory segments constitute the semantic trajectory corresponding to the cycling trajectory; Step 3: Using the semantic track corresponding to each riding track obtained in step 2, calculate the semantic track similarity between each riding track, and obtain a similarity matrix consisting of the semantic track similarities of all riding tracks; Step 4: Based on the similarity matrix obtained in step 3, a similarity network is constructed using the KNN algorithm. The nodes in the similarity network represent semantic trajectories, and the edges represent the similarity between two semantic trajectories. Community detection is performed using the similarity network to obtain multiple communities, each of which represents a movement group.

2. The method for classifying sports groups based on trajectory semantic similarity according to claim 1, wherein: In step 1, the data preprocessing includes: (1) eliminating trajectory data outside the geographical scope of the study area; (2) deleting trajectory points with missing attribute information from the data, and identifying and deleting duplicate trajectory data through all field attribute information; (3) eliminating trajectory data above the highest altitude; and (4) removing abnormal data based on riding speed.

3. The method for classifying sports groups based on trajectory semantic similarity according to claim 1, wherein: In step 2, the road network matching is performed based on the KDTree nearest neighbor search method, which includes the following process: Step 1), road position representation: abstract the roads in the road network into several inflection points, and the spatial position of each road can be represented by the coordinates of these inflection points; Step 2), KDTree construction: construct a KDTree for the coordinates of all the turning points of all the roads in the road network to obtain a KDTree; Step 3), neighbor search: perform neighbor search on the cycling trajectory points in step 1 and the KDTree obtained in step 2), obtain the road breakpoint closest to each cycling trajectory point, and use the road type of the road breakpoint as the road type of its corresponding cycling trajectory point.

4. The method for classifying sports groups based on trajectory semantic similarity according to claim 1, wherein: In step 2, the behavioral attributes include riding time, road altitude, and road type, and the motion attributes include average speed and riding distance; attribute calculation is divided into behavioral attribute calculation and motion attribute calculation.

5. The method for classifying sports groups based on trajectory semantic similarity according to claim 1, wherein: In step 3, the semantic trajectory similarity between the two riding trajectories is obtained by the following formula: in: sim_ST(ep i ,ep k ) represents any two cycling trajectories ep i ,ep k The similarity of the semantic trajectory; and represents the i-th and k-th semantic tracks, m and n refer to the number of semantic track segments in these two semantic tracks respectively; Represents the semantic trajectory ST i Semantic track fragments in and semantic track ST k Semantic track fragments in similarity; Represents the semantic trajectory ST i Each semantic trajectory segment Instead of being in the semantic track ST k The most similar semantic trajectory segments The sum of similarities; Represents the semantic trajectory ST k Each semantic trajectory segment Instead of being in the semantic track ST i The most similar semantic trajectory segments The sum of similarities; From two semantic tracks ST i 、ST k The similarity of two semantic trajectory segments is obtained by the following formula: Among them, w P For each attribute's weight, p=5; matching function match p (A p ) are the corresponding attributes A of any two semantic trajectory segments P The similarity between the behavior attributes of the two semantic trajectory segments is calculated by the semantic matching function, and the similarity between the motion attributes of the two semantic trajectory segments is calculated by the distance matching function.

6. The method for classifying sports groups based on trajectory semantic similarity according to claim 5, wherein: The weights of each attribute are set to w1 = 0.063, w2 = 0.098, w3 = 0.372, w4 = 0.103, and w5 = 0.

364.

7. The method for classifying sports groups based on trajectory semantic similarity according to claim 5, wherein: The semantic matching function is obtained by the following formula: in, Is to get attribute A P Functions of the category to which they belong, The equal sign in the text represents the same attribute A in any two semantic track segments. P Belong to the same category.

8. The method for classifying sports groups based on trajectory semantic similarity according to claim 5, wherein: The distance matching function is obtained by the following formula: in, and Attribute A P The distance function and distance threshold are as follows: the distance function adopts d(X,Y)=|XY|, where X and Y represent the motion attribute values ​​corresponding to any two semantic trajectory segments.

9. The method for classifying sports groups based on trajectory semantic similarity according to claim 5, wherein: In step 4, in the similarity network construction, K=10 in the KNN algorithm.

10. A motion behavior pattern recognition method based on trajectory semantic similarity, characterized in that: The method comprises: merging groups with the same cycling tourism mode among multiple cycling sports groups to obtain a pattern classification result of the cycling sports groups, wherein the cycling sports groups are obtained by the sports group classification method based on trajectory semantic similarity according to any one of claims 1 to 8.

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