Trajectory similarity measurement: An enhanced maximal travel match method

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.

Section: Related Workmentioning

confidence: 99%

Mobile user identification based on road networks and multiple distance measures

Fan

Wang

et al. 2023

“…However, these methods did not consider the influence of semantic information of trajectories on the similarity measurement. Karami and Malek (2021) proposed an enhanced MTM (EMTM) method to improve the traditional MTM method by considering the location and place category simultaneously as the most basic semantic information. This method identified a set of similar place category‐location traces on each layer to calculate similar user scores.…”

Section: Related Workmentioning

confidence: 99%

“…-EMTM (Karami & Malek, 2021): An EMTM method for trajectory similarity measurement. Both location and place category are considered as basic to calculate the similarity in a category-location hierarchical framework.…”

Section: Settingsmentioning

confidence: 99%

“…Semantic enhancement of visiting locations can help better represent user trajectory features. Karami and Malek (2021) considered the location and place category together as the most basic semantic information and proposed an enhanced maximal travel match (MTM) method to match the sequence similarity between trajectories. However, this method did not fully utilize the attribute information of user visiting locations, like visiting frequency and time distribution, which can better describe the mobile behavior of users.…”

Section: Introductionmentioning

confidence: 99%

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A novel trajectory similarity measurement method based on node‐sequence hierarchical digraph

Fan,

Wang,

Wang

et al. 2023

Trajectory similarity measurement is a basic and vital task in trajectory data mining, which has attracted extensive research in the past decades. Recent works focused on the sequence and hierarchy property of trajectories to construct similarity measurements. However, these methods ignore the user information on the visiting locations, such as semantic and time distribution. In light of this, a novel trajectory similarity measurement based on Node‐Sequence Hierarchical Digraph (NSHD) framework is proposed in this article. We first propose a Time‐Weighted Stay Point Detection (TWSPD) method to extract real visiting locations of users more accurately. Then, the nodes of digraph are obtained by clustering users' stay points and the edges of digraph are sequence information that users move between these nodes. An Advanced Earth Mover's Distance (AEMD) is proposed to measure the node similarity between users, considering visiting time distribution and semantic information simultaneously. Both node and sequence similarities are used to calculate the similarity score to obtain the final trajectory similarity measurement. Experiments on Geolife and T‐Drive datasets show that our proposed method offers competitive performance with mean reciprocal rank values reaching 96.01 and 81.26%, which outperforms related trajectory similarity measurements by more than 10 and 15%.

“…In many cases, the Global Positioning System (GPS) is used to track moving objects, such as humans (Goudarzi et al, 2022), cars (Lehmann et al, 2019), airplanes (Sharif & Alesheikh, 2017), bicycles (Sharif et al, 2019), and animals (Buchin et al, 2014). Other means of collecting trajectory data include Radio Frequency Identification (RFID), Automatic Identification System (AIS) for tracking vessels (Alizadeh et al, 2021b), satellite images for tracking hurricanes (Dodge et al, 2012), video tracking to record pedestrians movement (Zaki & Sayed, 2018), and Location‐Based Social Networks (LBSNs) to record users' activities in a sequence of check‐ins (Karami & Malek, 2021). Consequently, movement data are heterogeneous and uncertain because they are collected by various devices and from sources with different accuracies in different domains (Kaffash‐Charandabi et al, 2019; Sharif & Alesheikh, 2018).…”

Section: Introductionmentioning

confidence: 99%

FLCSS: A fuzzy‐based longest common subsequence method for uncertainty management in trajectory similarity measures

Boroumand

Alesheikh

Sharif

et al. 2022

The large quantity of movement data collected from various sources can be inherently uncertain and heterogeneous. In the movement data analysis and mining spectrum, computing the similarity of trajectories while considering the uncertainty and heterogeneity has been less addressed. Generally, two factors of sampling and positioning error cause uncertainty in trajectory databases. Therefore, in this research, a method based on the longest common subsequence (LCSS), named FLCSS, is proposed that uses fuzzy theory and the bead model to consider the uncertainty of trajectories originated from positioning and sampling errors. The performance of FLCSS is evaluated by implementations on real and synthetic datasets, and compared with six important and commonly used similarity measurement methods, namely, LCSS, edit distance on real sequence (EDR), dynamic time warping (DTW), edit distance with real penalty (ERP), Hausdorff distance (HD), and Fréchet distance (FD). The results show that FLCSS has a better performance compared to other methods, in terms of sensitivity to point displacement, noise, and different sampling rates. Furthermore, the high correlation between FLCSS and LCSS (ρ = 0.91) confirms the robustness of the proposed method in considering uncertainty in the trajectory databases.