Design and Implementation of Trajectory Data Management and Analysis Technology Framework Based on Spatiotemporal Grid Model

Li, Jun; Liu, J. Q.; Mei, X. L.; Sun, Wanning; Huang, Qian; Zhang, Y. Y.; Qiao, Linke; Zhang, C. Y.

doi:10.5194/isprs-archives-xliii-b4-2020-471-2020

Cited by 4 publications

(7 citation statements)

References 15 publications

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“…(1) If q is a core point, add all unvisited data points within the ε-neighborhood of q to the current cluster; (2) If q is not a core point but lies within the ε-neighborhood of another cluster, mark q as a boundary point and add it to the current cluster;…”

Section: Segmentation For Trajectory Optimization Based On Greedy Alg...mentioning

confidence: 99%

“…With the widespread use of GPS intelligent terminals, the advancement of navigation and positioning technology, and the rapid development of mobile Internet, the geographic location, movement status, and related attributes of data collected by positioning devices at different moments during the movement of mobile objects in geospace, namely, trajectory data, are being generated at an unprecedented scale and speed [1,2]. As one of the world's leading transportation platforms, Didi Taxi (DDT) has more than 550 million registered users, generates more than 106 TB of vehicle trajectory data every day, and processes more than 40 billion routing requests [3].…”

Section: Introductionmentioning

confidence: 99%

“…. pn}, where p i = {x, y, t, o}, (x, y) is usually the spatial dimensional information (latitude and longitude coordinates) of the trajectory point, t is the temporal dimensional information (sampling time) of the trajectory point, and o is the descriptive information about other relevant attributes or features that may be present in addition to the spatiotemporal characteristics of the trajectory point [2,4]. Figure 1 illustrates the motion paths of each moving object in three-dimensional time-space as well as their actual motion paths in the two-dimensional space plane for different time intervals.…”

Section: Introductionmentioning

confidence: 99%

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Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data

Yang,

Zuo,

Zhao

et al. 2024

IJGI

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The presence of abundant spatio-temporal information based on the location of mobile objects in publicly accessible GPS mobile devices makes it crucial to collect, analyze, and mine such information. Therefore, it is necessary to index a large volume of trajectory data to facilitate efficient trajectory retrieval and access. It is difficult for existing indexing methods that primarily rely on data-driven indexing structures (such as R-Tree) or space-driven indexing structures (such as Quadtree) to support efficient analysis and computation of data based on spatio-temporal range queries as a service basis, especially when applied to massive trajectory data. In this study, we propose a massive GPS data storage and indexing method based on uneven spatial segmentation and trajectory optimization segmentation. Primarily, the method divides GPS trajectories in a large spatio-temporal data space into multiple MBR sequences by greedy algorithm. Then, a hybrid indexing model for segmented trajectories is constructed to form a global spatio-temporal segmentation scheme, called HHBITS index, to achieve hierarchical organization of trajectory data. Eventually, a spatio-temporal range query processing method is proposed based on this index. This paper implements and evaluates the index in MongoDB and compares it with two other spatio-temporal composite indexes for performing spatio-temporal range queries efficiently. The experimental results show that the method in this paper has high performance in responding to spatio-temporal queries on large-scale trajectory data.

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Section: Segmentation For Trajectory Optimization Based On Greedy Alg...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data

Yang,

Zuo,

Zhao

et al. 2024

IJGI

View full text Add to dashboard Cite

show abstract

“…It can be used for a real-time recommendation system [2,8,9] and urban planning scenarios [3,10]. Many works [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] are devoted to further exploration of the similarity of the trajectory.…”

Section: Related Workmentioning

confidence: 99%

PMMTss: A Parallel Multi-Way Merging-Based Trajectory Similarity Search for a Million Metro Passengers

2023

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Trajectory similarity search (TSS) is a common operation for spatiotemporal data analysis. However, the existing TSS methods are mainly focused on GPS trajectories produced by moving objects such as vehicles. Further, these corresponding optimization strategies cannot be directly applied in the metro scenario because the metro passenger trajectory is totally different from the GPS trajectory. To fill this gap, we systematically analyze the unique spatiotemporal characteristics of metro passenger trajectories and propose a similarity search solution named PMMTss for the metro system. The core idea of this solution has two key points: first, we design a multi-layer index based on the spatiotemporal feature of metro trajectories, and all points of a trajectory are stored in this index. Second, we design a parallel multi-way merging-based trajectory similar search method, in which the candidate trajectory segments are merged and filtered. We evaluate this solution on a large dataset (Shenzhen Metro data for 3 consecutive months, 6.976 million trajectories with 260 million records). When lengths of input trajectories are 16, 32, and 64, respectively, the corresponding search times are 0.004 s, 0.016 s, and 0.036 s, respectively. Compared to the baseline PPJion+, the query times are reduced by 99.7%, 98.8%, and 97.6%, respectively.

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“…Kraak and Ormling collected soil, groundwater, vegetation, animal populations, and geoheritage data in a regular grid to analyze the impact of high-speed railways on the above data and finally selected the best high-speed railway construction route from several possible routes [18]. Li et al used a spatiotemporal grid model to manage and analyze the trajectory data, which is represented by spatiotemporal grid encoding instead of vector coordinates, and then reduced the computational complexity of the algorithms [19]. The spatial environment is an important factor affecting the simulation effect of emergency evacuation of crowds [20].…”

Section: Related Workmentioning

confidence: 99%

An Occupancy Information Grid Model for Path Planning of Intelligent Robots

Zhang

Wang

et al. 2022

IJGI

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Commonly used robot map models include occupancy grid maps, topological maps, and semantic maps. Among these, an occupancy grid map is mainly represented as a quadrilateral grid. This paper proposes an occupancy information grid for intelligent robots by exploiting the advantages of the occupancy grid map and spatial information grid. In terms of geometric structure, a regular hexagonal grid is used instead of a regular quadrilateral grid. In terms of attribute structure, the single obstacle attribute is replaced by the grid terrain characteristics, grid element attributes, and grid edge attributes. Thus, the occupancy information grid model is transformed into a new data structure describing the spatial environment, and it can be effectively applied to map construction and path planning of intelligent robots. For the map construction application of intelligent robots, this paper describes the basic process of laser sensor-based grid model construction. For the path planning application of intelligent robots, this paper extends the A* algorithm based on a regular hexagonal grid. Additionally, map construction and path planning applications for intelligent robots are experimentally verified. Several experimental results were obtained. First, the experimental results confirmed the theoretical conclusion that the minimum sampling density of the hexagonal structure was 13.4% lower than that of the quadrilateral structure. Second, the regular hexagonal grid is clearly more advantageous in describing environmental scenes, which can ameliorate the "undercompleteness" phenomenon. Third, there were large differences in the planning paths based on two types of grids, as shown by the fact that the distance of the planning paths obtained by the regular hexagonal grid was reduced by at least 10.8% and at most 15.6% compared with the regular quadrilateral grid.

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Design and Implementation of Trajectory Data Management and Analysis Technology Framework Based on Spatiotemporal Grid Model

Cited by 4 publications

References 15 publications

Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data

Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data

PMMTss: A Parallel Multi-Way Merging-Based Trajectory Similarity Search for a Million Metro Passengers

An Occupancy Information Grid Model for Path Planning of Intelligent Robots

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