Predicting Occupant Locations Using Association Rule Mining

Ryan, Conor; Brown, Kenneth N.

doi:10.1007/978-3-319-02621-3_5

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Moreover, the hidden Markov model has found application in predicting trip destinations, as exemplified in [18] and [19], where location characteristics or user activity transitions are considered as latent parameters. Another approach, articulated in [20]- [22], adopts a rule-based methodology to discover associations from movement transaction databases. In the domain of location prediction within cellular communication networks, Neural Networks (NN) have been extensively employed, aiming to mitigate traffic loads through the automatic updating of mobile user location information [23]- [27].…”

Section: Fig 1 Induce Of Distance and Friendship On User Check-in Beh...mentioning

confidence: 99%

STA-STF based Recurrent Neural Network for Next Location Prediction

et al. 2024

IJFMR

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In the evolving landscape of technology and smart devices, the focus on modeling spatial correlations, temporal dynamics, and friendship influence in point-of-interest (POI) check-ins has intensified. Existing works center on capturing user check-in behavior, emphasizing spatial and temporal dependencies of POIs. Markov chain-based methods address instance-level interactions, while recurrent neural network (RNN) approaches excel in handling variable-length check-in sequences. However, the former struggles with high-order POI transition dependency, and the latter cannot discern individual POI contributions in a historical check-in sequence. Additionally, RNNs propagate local and global information through a single bottleneck—hidden states. To address these limitations, a novel model is presented, enforcing contextual constraints on sequential data. The design incorporates spatial and temporal attention mechanisms over an RNN, highlighting the significance of POIs visited by users within specific time intervals and geographical distances during successive check-ins. The attention mechanism aids in identifying crucial POIs based on time difference and spatial distance in user check-in history for predicting the next POI. Periodicity and friendship influence are also considered in the model design. Experimental results on the BrightKite location-based social network demonstrate the proposed method's outperformance over existing state-of-the-art deep neural network methods for the next POI prediction and understanding of user transition behavior. Sensitivity analyses of parameters, including context windows for capturing sequential effects and estimating temporal and spatial attention, further validate the model's effectiveness. In parallel, this paper introduces a Space-Time Features-based Recurrent Neural Network (STF-RNN) for predicting individuals' next movements using mobility patterns from GPS device logs. Unlike traditional approaches, the STF-RNN automatically extracts internal representations of space and time features, enhancing efficiency in uncovering valuable insights into human behavior. Leveraging the sequence-representing ability of RNN structures, the model keeps track of user movement history, enabling the discovery of more meaningful dependencies and, consequently, improving performance. Collectively, these contributions advance the understanding and prediction capabilities in the realms of spatial dynamics, temporal dependencies, and human mobility patterns.

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Section: Fig 1 Induce Of Distance and Friendship On User Check-in Beh...mentioning

confidence: 99%

STA-STF based Recurrent Neural Network for Next Location Prediction

et al. 2024

IJFMR

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“…Mixed Markov Model Asahara, Maruyama, Sato, and Seto (2011) Shopping and selling services Association rule mining Ryan and Brown (2012) Energy efficiency in buildings Movement rules extraction Monreale et al (2009)); Morzy (2007) Mobile applications Bayesian networks Lee, Lee, and Cho (2010); Petzold, Pietzowski, Bagci, Trumler, and Ungerer (2005) Mobile applications, smart office environment Neural networks Vintan, Gellert, Petzold, and Ungerer (2004) Mobile applications…”

Section: Next Locationmentioning

confidence: 99%

“…Another group of approaches is based on sequential rules or frequent pattern mining. Ryan and Brown (2012) investigate how association rule mining, an unsupervised technique to find patterns in large datasets, can be used for location prediction. They make use of the Apriori algorithm because of its simplicity and adaptability.…”

Section: Next Location Predictionmentioning

confidence: 99%

Efficient Parcel Delivery by Predicting Customers’ Locations*

Praet

Martens

2019

Decision Sciences

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An important aspect of the growing e-commerce sector involves the delivery of tangible goods to the end customer, the so-called last mile. This final stage of the logistics chain remains highly inefficient due to the problem of failed deliveries. To address this problem, delivery service providers can apply data science to determine the optimal, customercentered location and time window for handover. In this article, we present a three-step approach for location prediction, based on mobile location data, in order to support delivery planning. The first step is identifying a user's locations of interest through density-based clustering. Next, the semantics (home or work) of the user's locations of interest are discovered, based on temporal assumptions. Finally, we predict future locations with a decision tree model that is trained on each user's historical location data. Though the problem of location prediction is not new, this work is the first to apply it to the field of parcel delivery with its corresponding implications. Moreover, we provide a novel and detailed evaluation on real-world data from a parcel delivery service. The promising results indicate that our approach has the potential to help delivery service providers to gain insights into their customers' optimal delivery time and location in order to support delivery planning. Eventually this will decrease last-mile delivery costs and boost customer satisfaction. [

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A p-Persistent Frequent Itemsets with 1-RHS Based Correction Algorithm for Improving the Performance of WiFi-Based Occupant Detection Method

Wang

Cao

et al. 2019

Advances in Intelligent Systems and Computing

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Predicting Occupant Locations Using Association Rule Mining

Cited by 3 publications

References 8 publications

STA-STF based Recurrent Neural Network for Next Location Prediction

STA-STF based Recurrent Neural Network for Next Location Prediction

Efficient Parcel Delivery by Predicting Customers’ Locations*

A p-Persistent Frequent Itemsets with 1-RHS Based Correction Algorithm for Improving the Performance of WiFi-Based Occupant Detection Method

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