Impact of Trajectory Segmentation on Discovering Trajectory Sequential Patterns

Karsoum, Somayah; Gruenwald, Le; Leal, Eleazar

doi:10.1109/bigdata.2018.8622209

Cited by 4 publications

(4 citation statements)

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“…Grid-based trajectory segmentation is performed with Python version 3.10.2 on Microsoft VSCode. This study uses a grid size of 500 m × 500 m. Based on [ 15 ], which investigated the impact of grid cells on the result, a larger grid size would reduce the execution time since the number of grids is reduced. The choice of grid size should consider factors such as the characteristics of trajectory data (i.e., length and spread of trajectories) and study area size.…”

Section: Methodsmentioning

confidence: 99%

“…On the other hand, clustering methods rely on the nature of the data to determine parameters such as the number of centroids k (for k -means clustering) or minPts and epsilon (for density-based clustering methods like DBSCAN and OPTICS). Prior work by Karsoum et al compared these two approaches [ 15 ]. Results demonstrate that although the density-based method has a faster execution time, grid-based methods can discover more hidden patterns with the same parameters (i.e.,

).…”

Section: Preliminary and Problem Statementmentioning

confidence: 99%

“…An abstract method of segmenting the trajectories is essential, ensuring the fuzziness of data while reducing the size of the data and, at the same time, discretizing continuous data. Trajectory simplification methods such as grid-based or clustering-based segmentation can be applied to get discretized trajectories and remove spatial uncertainty, thereby preparing trajectory data for SPM algorithms [ 1 , 14 , 15 ]. Obtaining a smaller, less redundant set of sequential patterns can be resolved by enforcing constraints such as closed, maximal, or contiguous [ 7 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Frequent Pattern Mining with Trajectory Data

Ding

Zhang

et al. 2022

Sensors

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Sequential pattern mining (SPM) is a major class of data mining topics with a wide range of applications. The continuity and uncertain nature of trajectory data make it distinctively different from typical transactional data, which requires additional data transformation to prepare for SPM. However, little research focuses on comparing the performance of SPM algorithms and their applications in the context of trajectory data. This study selected some representative sequential pattern mining algorithms and evaluated them with various parameters to understand the effect of the involved parameters on their performances. We studied the resultant sequential patterns, runtime, and RAM consumption in the context of the taxi trajectory dataset, the T-drive dataset. It was demonstrated in this work that a method to discretize trajectory data and different SPM algorithms were performed on trajectory databases. The results were visualized on actual Beijing road maps, reflecting traffic congestion conditions. Results demonstrated contiguous constraint-based algorithms could provide a concise representation of output sequences and functions at low min_sup with balanced RAM consumption and execution time. This study can be used as a guide for academics and professionals when determining the most suitable SPM algorithm for applications that involve trajectory data.

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Section: Methodsmentioning

confidence: 99%

).…”

Section: Preliminary and Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Frequent Pattern Mining with Trajectory Data

Ding

Zhang

et al. 2022

Sensors

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“…e output of trajectory preprocessing is a set of paths R based online segments. Since the line segments in path R are not completely repeated, we use a method of grouping and partitioning to find the common movement behaviors represented by the common sub-segment (sub-segment cluster) S in all R. e boundary problem [11]in Figure 2(a) shows that directly applying the clustering algorithm to these line segments may miss some common subsegments. When the historical trajectory data are not very sufficient, this problem will be serious because we may not find enough public movement behaviors for frequent pattern mining.…”

Section: Trajectory Processing and Common Segment Extractionmentioning

confidence: 99%

Prefix-Pruning-Based Distributed Frequent Trajectory Pattern Mining Algorithm

Ding

Liu

et al. 2022

Mathematical Problems in Engineering

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An important problem to be solved in smart city construction is how to improve the efficiency of mining frequent patterns that can be used for location prediction and location-based services of massive trajectory datasets. Owing to uncertain personal trajectory and non-explicit trajectory items, the existing sequence mining algorithms cannot be used directly. To solve this problem, this study proposes a distributed trajectory frequent pattern mining algorithm (SparkTraj) based on prefix pruning. First, a grouping and partitioning technique is used to abstract the original trajectory data and convert them into a common time series.Then, the generation of a redundant trajectory pattern is avoided by using the path adjacency pruning method. Second, to improve mining efficiency, SparkTraj is designed and implemented in Spark, which employs cluster memory computing. Finally, experiments on common datasets show that the proposed algorithm can effectively extract frequent trajectory patterns, and, in particular, deal with the massive amounts of trajectory data. Compared with common trajectory pattern mining algorithms, the SparkTraj algorithm not only improves the overall performance but also has good scalability.

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