Measuring similarity of mobile phone user trajectories– a Spatio-temporal Edit Distance method

Yuan, Yihong; Raubal, Martin

doi:10.1080/13658816.2013.854369

Cited by 84 publications

(59 citation statements)

References 41 publications

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“…However, edit distance, which is the similarity measurement method of multi sub-time interval correspondence, does not require correspondence between points and the points of the two trajectories, which can reflect the structural differences between the trajectory sequences and determine the similarities of whole trajectories. At present, there are few studies on the application of edit distance to GPS trajectories [27,28]. The specific situation of edit distance will be discussed in Section 3.1.…”

Section: Related Work About Trajectory Clustering For Anomalous Trajmentioning

confidence: 99%

“…Furthermore, unlike the distance between points, in our analysis, the smaller the distance, the more similar the trajectories. The centroid of taxi trajectories is different from call data based on Yuan's [28] method. Taxi trajectory data are restricted by road networks, and the centroid of the trajectory by calculating the average position is not on the roads, which does not fully represent the trajectory and deviates from reality significance.…”

Section: Anomalous Trajectory Detection By Trajectory Clusteringmentioning

confidence: 99%

“…When the distance matrix is calculated, single linkage, complete linkage, and average linkage are relatively easy to calculate. The trajectory is composed of a series of points, and the centroid of the trajectory can be calculated as the average location of these points [28]. Ward's method calculates the sum of square errors generated by the merging of two clusters.…”

Section: Determining the Number Of Clusters In Hierarchical Clusteringmentioning

confidence: 99%

“…The classic method is the elbow point method, which is described by Ketchen [33] and presented in Yuan and Raubal [28]. The number of clusters and the merge distance are fitted to a curve.…”

Section: Determining the Number Of Clusters In Hierarchical Clusteringmentioning

confidence: 99%

“…However, the method focused on the parameters describing the dynamic characteristics of movement and did not address trajectory similarity for geospatial space. Yuan et al [28] modified the operation cost based on the effects of each operation by measuring the centroid displacement after each operation from detailed call record data. The centroid of the trajectory by calculating the average position.…”

Section: Improved Edit Distance For Gps Trajectory Datamentioning

confidence: 99%

See 4 more Smart Citations

Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data

Wang

Qin

Chen

et al. 2018

IJGI

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Anomalous taxi trajectories are those chosen by a small number of drivers that are different from the regular choices of other drivers. These anomalous driving trajectories provide us an opportunity to extract driver or passenger behaviors and monitor adverse urban traffic events. Because various trajectory clustering methods have previously proven to be an effective means to analyze similarities and anomalies within taxi GPS trajectory data, we focus on the problem of detecting anomalous taxi trajectories, and we develop our trajectory clustering method based on the edit distance and hierarchical clustering. To achieve this objective, first, we obtain all the taxi trajectories crossing the same source-destination pairs from taxi trajectories and take these trajectories as clustering objects. Second, an edit distance algorithm is modified to measure the similarity of the trajectories. Then, we distinguish regular trajectories and anomalous trajectories by applying adaptive hierarchical clustering based on an optimal number of clusters. Moreover, we further analyze these anomalous trajectories and discover four anomalous behavior patterns to speculate on the cause of an anomaly based on statistical indicators of time and length. The experimental results show that the proposed method can effectively detect anomalous trajectories and can be used to infer clearly fraudulent driving routes and the occurrence of adverse traffic events.

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Section: Related Work About Trajectory Clustering For Anomalous Trajmentioning

confidence: 99%

Section: Anomalous Trajectory Detection By Trajectory Clusteringmentioning

confidence: 99%

Section: Determining the Number Of Clusters In Hierarchical Clusteringmentioning

confidence: 99%

Section: Determining the Number Of Clusters In Hierarchical Clusteringmentioning

confidence: 99%

Section: Improved Edit Distance For Gps Trajectory Datamentioning

confidence: 99%

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Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data

Wang

Qin

Chen

et al. 2018

IJGI

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Time Geography and Space–Time Prism

Miller

2017

International Encyclopedia of Geography

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Time geography is a constraints‐oriented approach to understanding human activities. Time geography highlights the necessary (though not sufficient) spatial and temporal conditions required for human activities. It also provides an elegant framework for incorporating these conditions into individualistic and ecological analyses and models of human behavior. Two central concepts in time geography are the space–time path and prism. The path represents actual mobility (real or simulated) and the prism represents potential mobility in space with respect to time. Developed as a conceptual framework in the 1960s by Torsten Hägerstrand, analytical time geography has emerged in response to growing capabilities to collect and analyze data on mobile objects and human activities. There is a rich collection of measures and procedures for analyzing space–time paths and prisms in planar space, constrained by networks and through velocity fields. There are also methods for representing virtual interaction via information and communication technologies. Emerging are methods for mobility data mining and knowledge discovery.

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A Clustering-Based Framework for Understanding Individuals’ Travel Mode Choice Behavior

Zhao

Bucher

Henry

et al. 2019

Lecture Notes in Geoinformation and Cartography

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Measuring similarity of mobile phone user trajectories– a Spatio-temporal Edit Distance method

Cited by 84 publications

References 41 publications

Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data

Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data

Time Geography and Space–Time Prism

A Clustering-Based Framework for Understanding Individuals’ Travel Mode Choice Behavior

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