Trajectory Similarity Assessment On Road Networks Via Embedding Learning

Zhang, Rui; Rong, Yacheng; Wu, Zilong; Zhuo, Yifan

doi:10.1109/bigmm50055.2020.00012

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Yuan and Guoliang (2019) proposed a similarity function based on road‐network‐aware to measure the similarity between trajectories. Zhang et al (2020) proposed a trajectory similarity assessment approach based on road network embedding to capture both topology and the spatiality of road networks for embedding learning. These works have proved that road information is useful for user feature construction, and we can consider road information together with location information to enrich the trajectory features of users.…”

Section: Related Workmentioning

confidence: 99%

“…Yuan and Guoliang (2019) defined a roadnetwork-aware function to measure trajectory similarity. Zhang et al (2020) extraction of this method is based on equally sized grid cells with a given side-length in latitude and longitude (Ren et al, 2020), which cannot make full use of the geographic information of user trajectories.…”

Section: Introductionmentioning

confidence: 99%

“…Yuan and Guoliang (2019) defined a road‐network‐aware function to measure trajectory similarity. Zhang et al (2020) evaluated the trajectory similarity based on road network embedding. However, these works generally simplified the road network to a weighted directed graph, which neglects the significance of visiting locations.…”

Section: Introductionmentioning

confidence: 99%

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Mobile user identification based on road networks and multiple distance measures

Fan

Wang

et al. 2023

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Mobile user identification aims at matching different mobile devices of the same user using trajectory data, which has attracted extensive research in recent years. Most of the previous work extracted trajectory features based on regular grids, which will lead to incorrect feature representation due to lack of geographic information. Besides, most trajectory similarity models only considered one single distance measure to calculate the similarity between users, which ignore the connection between different distance measures and may lead to some false matches. In light of this, we present a novel user identification method based on road networks and multiple distance measures in this article. The proposed method segments a city map into several grids and road segments based on road networks. Then it extracts location and road information of trajectories to jointly construct user features. Multiple distance measures are fused by a discriminant model to improve the effect of user identification. Experiments on real GPS trajectory datasets show that our proposed method outperforms related similarity measure methods and is stable for mobile user identification. Meanwhile, our method can also achieve good identification results even on sparse trajectory datasets.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mobile user identification based on road networks and multiple distance measures

Fan

Wang

et al. 2023

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“…Various forms of movement impact people's lives, necessitating comprehensive investigation and analysis. The advent of science and technology, particularly the proliferation of data-gathering instruments like GPS, has resulted in a substantial accumulation of movement-related information [1]. Within this context, trajectories provide valuable insights into the movement of point objects, such as vehicles and bicycles, over time.…”

Section: Introductionmentioning

confidence: 99%

Measuring Trajectory Similarity Based on the Spatio-Temporal Properties of Moving Objects in Road Networks

Dorosti,

Alesheikh,

Sharif

2024

Information

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Advancements in navigation and tracking technologies have resulted in a significant increase in movement data within road networks. Analyzing the trajectories of network-constrained moving objects makes a profound contribution to transportation and urban planning. In this context, the trajectory similarity measure enables the discovery of inherent patterns in moving object data. Existing methods for measuring trajectory similarity in network space are relatively slow and neglect the temporal characteristics of trajectories. Moreover, these methods focus on relatively small volumes of data. This study proposes a method that maps trajectories onto a network-based space to overcome these limitations. This mapping considers geographical coordinates, travel time, and the temporal order of trajectory segments in the similarity measure. Spatial similarity is measured using the Jaccard coefficient, quantifying the overlap between trajectory segments in space. Temporal similarity, on the other hand, incorporates time differences, including common trajectory segments, start time variation and trajectory duration. The method is evaluated using real-world taxi trajectory data. The processing time is one-quarter of that required by existing methods in the literature. This improvement allows for spatio-temporal analyses of a large number of trajectories, revealing the underlying behavior of moving objects in network space.

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X-FIST: Extended flood index for efficient similarity search in massive trajectory dataset

Ramadhan

Kwon

2022

Information Sciences

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Trajectory Similarity Assessment On Road Networks Via Embedding Learning

Cited by 5 publications

References 21 publications

Mobile user identification based on road networks and multiple distance measures

Mobile user identification based on road networks and multiple distance measures

Measuring Trajectory Similarity Based on the Spatio-Temporal Properties of Moving Objects in Road Networks

X-FIST: Extended flood index for efficient similarity search in massive trajectory dataset

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