User Identification across Asynchronous Mobility Trajectories

Qi, Mengjun; Wang, Zhongyuan; He, Zheng; Shao, Zhenfeng

doi:10.3390/s19092102

Cited by 19 publications

(19 citation statements)

References 28 publications

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“…The main feature of wandering behavior is to walk around a place repeatedly. The existing wandering behavior research mainly focuses on the abnormal behavior of patients with Alzheimer’s disease [ 7 , 32 ].…”

Section: Research Methodsmentioning

confidence: 99%

Uncovering Abnormal Behavior Patterns from Mobility Trajectories

Tang

Wang

et al. 2021

Sensors

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Using personal trajectory information to grasp the spatiotemporal laws of dangerous activities to curb the occurrence of criminal acts is a new opportunity and method for security prevention and control. This paper proposes a novel method to discover abnormal behaviors and judge abnormal behavior patterns using mobility trajectory data. Abnormal behavior trajectory refers to the behavior trajectory whose temporal and spatial characteristics are different from normal behavior, and it is an important clue to discover dangerous behavior. Abnormal patterns are the behavior patterns summarized based on the regular characteristics of criminals’ activities, including wandering, scouting, random walk, and trailing. This paper examines the abnormal behavior patterns based on mobility trajectories. A Long Short-Term Memory Network (LSTM)-based method is used to extract personal trajectory features, and the K-means clustering method is applied to extract abnormal trajectories from the trajectory dataset. Based on the characteristics of different abnormal behaviors, the spatio-temporal feature matching method is used to identify the abnormal patterns based on the filtered abnormal trajectories. Experimental results showed that the trajectory-based abnormal behavior discovery method can realize a rapid discovery of abnormal trajectories and effective judgment of abnormal behavior patterns.

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Section: Research Methodsmentioning

confidence: 99%

Uncovering Abnormal Behavior Patterns from Mobility Trajectories

Tang

Wang

et al. 2021

Sensors

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“…The Geolife trajectory dataset has been used in different research fields such as in privacy preserving location data [26], measuring trajectory stops and moves [27], user identification [28], trajectory completion [29], and transport mode detection [30].…”

Section: Gps Trajectoriesmentioning

confidence: 99%

Modelling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories From Beijing

Otim¹,

Dörfer²,

Ahmed³

et al. 2022

Preprint

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Over the years, researchers have been studying the effect of weather and context data on the transport mode choice. The majority of these works are based on survey data, however the accuracy of their findings relies on how respondents give accurate and honest answers. In this paper, the potential of using GPS trajectories as an alternative to travel surveys in studying the impact of weather and context data on transport mode choices is investigated in Beijing city. In the analysis, we apply both descriptive and statistical models such as the MNL and MNP models. Our findings indicate that temperature has the most prominent effect among weather conditions. For instance, for temperatures greater than 25 °C, the walking share increases by 27% and the bike share reduces by 21%, which is line with the results from several survey studies. In addition, the evidence of government policy on transport regulation is revealed when the air quality becomes hazardous as people are encouraged to use environmentally friendly travel mode choices such as the bike instead of the bus and car, which are known CO2 emitters. Moreover, due to a series of traffic restrictions introduced by the Beijing government during the 2008 summer Olympics, a decrease of 17.5% in the car share and an increase of 13% and 10% in the walking and bus shares, respectively are observed. These findings provide a scientific basis for effective transport regulation and planning purposes.

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“…Roedler et al [47] combined user-generated timestamp information with location information for use in constructing a personalized social behavior pattern designed to address user identification issues. Qi et al [48] designed an identification scheme based on user trajectory, which analyzes the TOP-N region with the most frequent distribution of user trajectories and uses different similarity calculation methods to measure the similarity between two trajectories. This achieves better identification performance.…”

Section: User Behavior Information-based User Identificationmentioning

confidence: 99%

Exploiting Two‐Level Information Entropy across Social Networks for User Identification

Xing

Deng

et al. 2021

Wireless Communications and Mobile Computing

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As the popularity of online social networks has grown, more and more users now hold multiple virtual accounts at the same time. Under these circumstances, identifying multiple social accounts belonging to the same user across different social networks is of great importance for many applications, such as user recommendation, personalized services, and information fusion. In this paper, we mainly aggregate user profile information and user behavior information, then measures and analyzes the attributes contained in these two types of information to implement across social networks user identification. Moreover, as different user attributes have different effects on user identification, this paper therefore proposes a two-level information entropy-based weight assignment method (TIW) to weigh each attribute. Finally, we combine the scoring formula with the bidirectional stable marriage matching algorithm to achieve optimal user account matching and thereby obtain the final matching pairs. Experimental results demonstrate that the proposed two-level information entropy method yields excellent performance in terms of precision rate, recall rate, F -measure ( F 1 ), and area under curve (AUC).

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User Identification across Asynchronous Mobility Trajectories

Cited by 19 publications

References 28 publications

Uncovering Abnormal Behavior Patterns from Mobility Trajectories

Uncovering Abnormal Behavior Patterns from Mobility Trajectories

Modelling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories From Beijing

Exploiting Two‐Level Information Entropy across Social Networks for User Identification

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