On a clustering-based mining approach with labeled semantics for significant place discovery

Niu, Xinjian; Wang, Shimin; Wu, Chase Q.; Li, Yuran; Wu, Peng; Zhu, Jiahui

doi:10.1016/j.ins.2021.07.050

Cited by 11 publications

(3 citation statements)

References 23 publications

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“…CB-SMoT was originally proposed for discovering interesting places in trajectories. A place has the potential to be interesting [29] or significant [30], if someone spends a certain amount of time (i.e., over a time limit) in it. This means that by analyzing the trajectory of a moving object (e.g., a pedestrian, vehicle, animal, etc.…”

Section: Methodsmentioning

confidence: 99%

Recognition of Intersection Traffic Regulations From Crowdsourced Data

Zourlidou¹,

Sester²,

Hu³

2022

Preprint

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In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules of regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which in turn affects vehicle idling time at intersections, fuel consumption, CO_2 emissions and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable and inexpensive way to identify the type of intersection control (e.g. traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicles crossing intersections is proposed. A modification of a well-known clustering algorithm for detecting stopping and decelerating events is presented. These detected events are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per junction arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the junction arms according to their traffic control type dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single junction arm are used (one-arm model) and another where features also from neighbouring junction arms of the same junction are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 94\% to 97\%.

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Section: Methodsmentioning

confidence: 99%

Recognition of Intersection Traffic Regulations From Crowdsourced Data

Zourlidou¹,

Sester²,

Hu³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…CB-SMoT was originally proposed for discovering interesting places in trajectories. A place has the potential to be interesting [38] or significant [39], if someone spends a certain amount of time (i.e., over a time limit) in it. This means that by analyzing the trajectory of a moving object (e.g., a pedestrian, vehicle, animal, etc.…”

Section: Clustering-based Stop and Deceleration Event Detection In Tr...mentioning

confidence: 99%

Recognition of Intersection Traffic Regulations from Crowdsourced Data

Zourlidou

Sester

2022

IJGI

View full text Add to dashboard Cite

In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules for regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which, in turn, affects vehicle idling time at intersections, fuel consumption, CO2 emissions, and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable, and inexpensive way to identify the type of intersection control (e.g., traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicle crossing intersections is proposed. A modification of a well-known clustering algorithm is used to detect stopping and deceleration episodes. These episodes are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per intersection arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the intersection arms according to their traffic control type (dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single intersection arm are used (one-arm model) and another where features also from neighboring intersection arms of the same intersection are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 95% to 97%.

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“…For example, Do and Gatica-Perez [53] presented a framework for predicting future user's location and which mobile applications she will use by exploiting different contextual information from smartphone sensors, including the user's locations history. On the other hand, Niu et al [54] have recently proposed a clustering-based approach to discover significant places for the mobile user by analyzing her spatial and temporal trajectories. Even though current solutions are effective in mining meaningful information from raw GPS traces, they are ultimately based on centralized approaches, where a big amount of data are processed by using offline techniques.…”

Section: Related Workmentioning

confidence: 99%

On-device modeling of user's social context and familiar places from smartphone-embedded sensor data

Campana¹,

Delmastro²

2022

Preprint

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Context modeling and recognition represent complex tasks that allow mobile and ubiquitous computing applications to adapt to the user's situation. The real advantage of context-awareness in mobile environments mainly relies on the prompt system's and applications' reaction to context changes. Current solutions mainly focus on limited context information generally processed on centralized architectures, potentially exposing users' personal data to privacy leakage, and missing personalization features. For these reasons on-device context modeling and recognition represent the current research trend in this area. Among the different information characterizing the user's context in mobile environments, social interactions and visited locations remarkably contribute to the characterization of daily life scenarios. In this paper we propose a novel, unsupervised and lightweight approach to model the user's social context and her locations based on ego networks directly on the user mobile device. Relying on this model, the system is able to extract high-level and semantic-rich context features from smartphone-embedded sensors data. Specifically, for the social context it exploits data related to both physical and cyber social interactions among users and their devices. As far as location context is concerned, we assume that it is more relevant to model the familiarity degree of a specific location for the user's context than the raw location data, both in terms of GPS coordinates and proximity devices. We demonstrate the effectiveness of the proposed approach with 3 different sets of experiments by using 5 real-world datasets collected from a total of 956 personal mobile devices. Specifically, we assess the structure of the social and location ego networks, we provide a semantic evaluation of the proposed models and a complexity evaluation in terms of mobile computing performance. Finally, we demonstrate the relevance of the extracted features by showing the performance of 3 different machine learning algorithms to recognize daily-life situations, obtaining an improvement of 3% of AUROC, 9% of Precision, and 5% in terms of Recall with respect to use only features related to physical context.

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On a clustering-based mining approach with labeled semantics for significant place discovery

Cited by 11 publications

References 23 publications

Recognition of Intersection Traffic Regulations From Crowdsourced Data

Recognition of Intersection Traffic Regulations From Crowdsourced Data

Recognition of Intersection Traffic Regulations from Crowdsourced Data

On-device modeling of user's social context and familiar places from smartphone-embedded sensor data

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