Discovering Urban Mobility Patterns and Demand for Uses of Urban Spaces from Mobile Phone Data

Butron-Revilla, Cinthya; Suarez-Lopez, Ernesto; Laura-Ochoa, Leticia

doi:10.1109/scla53004.2021.9540080

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Butron-Revilla et al [8] use mobile phone data to identify movement patterns and points of interest. Viswanathan et al use situational awareness to categorize points of interest (PoIs) as stopovers, special interest areas, or occasional stops (9). Another approach to PoI detection relies on detecting outliers.…”

Section: Existing Workmentioning

confidence: 99%

An Examination of Machine Learning-Based Outlier Identification from Mobile Phone Tracks

2024

IJFMR

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In this paper, two machine learning algorithms—local outlier factor (LOF) and density-based spatial clustering of applications with noise (DBSCAN)—that are used to identify outliers in the context of a continuous framework for point of interest (PoI) detection are analyzed. The mobile trajectories of users are continuously and almost instantaneously loaded into this system. These frameworks are still in their infancy, but they are already essential for large-scale sensing deployments, such as Smart City planning deployments, where the anonymous individual mobile user trajectories can be valuable to improve urban planning. There are two contributions made by this paper. First, the functional design of the entire PoI detection architecture is provided by the study. Second, the study evaluates the effectiveness.

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Section: Existing Workmentioning

confidence: 99%

An Examination of Machine Learning-Based Outlier Identification from Mobile Phone Tracks

2024

IJFMR

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“…However, the detection of outliers is not a core topic in this work. Butron-Revilla et al address the detection of mobility patterns (and PoIs) based on mobile phone data [8]. Viswanathan et al focus on situational awareness to define the nature of PoIs (stopovers, specific interest in an area, or occasional stop) [9].…”

Section: Related Workmentioning

confidence: 99%

An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories

Pereira

Sofia

2022

Future Internet

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This paper provides an analysis of two machine learning algorithms, density-based spatial clustering of applications with noise (DBSCAN) and the local outlier factor (LOF), applied in the detection of outliers in the context of a continuous framework for the detection of points of interest (PoI). This framework has as input mobile trajectories of users that are continuously fed to the framework in close to real time. Such frameworks are today still in their infancy and highly required in large-scale sensing deployments, e.g., Smart City planning deployments, where individual anonymous trajectories of mobile users can be useful to better develop urban planning. The paper’s contributions are twofold. Firstly, the paper provides the functional design for the overall PoI detection framework. Secondly, the paper analyses the performance of DBSCAN and LOF for outlier detection considering two different datasets, a dense and large dataset with over 170 mobile phone-based trajectories and a smaller and sparser dataset, involving 3 users and 36 trajectories. Results achieved show that LOF exhibits the best performance across the different datasets, thus showing better suitability for outlier detection in the context of frameworks that perform PoI detection in close to real time.

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Socially Acceptable Route Planning and Trajectory Behavior Analysis of Personal Mobility Device for Mobility Management with Improved Sensing

Mishra¹,

Rajendran²,

Har³

2022

Robot Intelligence Technology and Applications 6

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Discovering Urban Mobility Patterns and Demand for Uses of Urban Spaces from Mobile Phone Data

Cited by 3 publications

References 23 publications

An Examination of Machine Learning-Based Outlier Identification from Mobile Phone Tracks

An Examination of Machine Learning-Based Outlier Identification from Mobile Phone Tracks

An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories

Socially Acceptable Route Planning and Trajectory Behavior Analysis of Personal Mobility Device for Mobility Management with Improved Sensing

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