A mixed autoregressive hidden-markov-chain model applied to people's movements

Asahara, Akinori; Maruyama, Kishiko; Shibasaki, Ryosuke

doi:10.1145/2424321.2424378

Cited by 12 publications

(7 citation statements)

References 4 publications

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“…An analysis of tracks of moving points is called "trajectory analysis", namely trajectory smoothing [13] (for reducing errors in positioning), trajectory clustering [14][15][16][17] (for retrieving similar trajectories from a database), prediction of movement [18,19], representative path extraction [15,17,[20][21][22] and an algorithm for labeling positioning data [23].…”

Section: Trajectory Analysis and Density Estimatorsmentioning

confidence: 99%

Moving Point Density Estimation Algorithm Based on a Generated Bayesian Prior

Asahara¹,

Hayashi²,

Kai³

2015

IJGI

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To improve decision making, real-time population density must be known. However, calculating the point density of a huge dataset in real time is impractical in terms of processing time. Accordingly, a fast algorithm for estimating the distribution of the density of moving points is proposed. The algorithm, which is based on variational Bayesian estimation, takes a parametric approach to speed up the estimation process. Although the parametric approach has a drawback, that is the processes to be carried out on the server are very slow, the proposed algorithm overcomes the drawback by using the result of an estimation of an adjacent past density distribution.

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Section: Trajectory Analysis and Density Estimatorsmentioning

confidence: 99%

Moving Point Density Estimation Algorithm Based on a Generated Bayesian Prior

Asahara¹,

Hayashi²,

Kai³

2015

IJGI

Self Cite

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“…In [20], a model is proposed based on hidden Markov models for modeling movements from one stay-point to another, while authors of [29] used mixed Markov model for the same purpose. A mixed autoregressive hidden Markov model is proposed in [30] on stay-points. The main drawback of these methods is that they do not completely consider the temporal variability in the mobility data.…”

Section: Related Workmentioning

confidence: 99%

A hierarchical hidden semi-Markov model for modeling mobility data

Baratchi

Meratnia

Havinga

et al. 2014

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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Ubiquity of portable location-aware devices and popularity of online location-based services, have recently given rise to the collection of datasets with high spatial and temporal resolution. The subject of analyzing such data has consequently gained popularity due to numerous opportunities enabled by understanding objects' (people and animals, among others) mobility patterns. In this paper, we propose a hidden semi-Markov-based model to understand the behavior of mobile entities. The hierarchical state structure in our model allows capturing spatiotemporal associations in the locational history both at staypoints and on the paths connecting them. We compare the accuracy of our model with a number of other spatiotemporal models using two real datasets. Furthermore, we perform sensitivity analysis on our model to evaluate its robustness in presence of common issues in mobility datasets such as existence of noise and missing values. Results of our experiments show superiority of the proposed scheme compared with the other models.

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“…In [180], a model based on hidden Markov models is proposed for modeling movements from one stay-point to another, while authors of [199] used mixed Markov model for the same purpose. A mixed autoregressive hidden Markov model is proposed in [179] on stay-points.…”

Section: Probabilistic Trajectory Modelingmentioning

confidence: 99%

“…Firstly, trajectories are formed by components with different speeds (stay-points and transitions) being repeated with different frequencies. A model, which only captures frequency of visit to places, turns out to be biased to stay-points [179,180]. On the other hand, preprocessing trajectories to take out segments with similar speed is time and energy consuming.…”

Section: Introductionmentioning

confidence: 99%

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Lattitude, Longitude, and Beyond : Mining Mobile Object's Behavior

Baratchi¹

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A mixed autoregressive hidden-markov-chain model applied to people's movements

Cited by 12 publications

References 4 publications

Moving Point Density Estimation Algorithm Based on a Generated Bayesian Prior

Moving Point Density Estimation Algorithm Based on a Generated Bayesian Prior

A hierarchical hidden semi-Markov model for modeling mobility data

Lattitude, Longitude, and Beyond : Mining Mobile Object's Behavior

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