From cellular positioning data to trajectories: Steps towards a more accurate mobility exploration

Forghani, Mohammad Ali; Karimipour, Farid; Claramunt, Christophe

doi:10.1016/j.trc.2020.102666

Cited by 38 publications

(11 citation statements)

References 68 publications

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“…Although CDR represent the type of passive mobile phone data most widely used in the literature, other species of mobile phone data have been used for mobility related purposes. Some studies [27,28] address the low spatial granularity of CDR (or other kind of mobile phone data such as sighting data), by including precise user positioning data, obtained from the user radio signal information at multiple cellular base stations. This allows signal triangulation combined with spatial clustering [28] or the application of some probabilistic radio wave propagation models [27] for precise user position estimation.…”

Section: Related Workmentioning

confidence: 99%

“…Some studies [27,28] address the low spatial granularity of CDR (or other kind of mobile phone data such as sighting data), by including precise user positioning data, obtained from the user radio signal information at multiple cellular base stations. This allows signal triangulation combined with spatial clustering [28] or the application of some probabilistic radio wave propagation models [27] for precise user position estimation. However, radio signal level information is difficult to obtain, as it is not regularly logged by mobile network operators.…”

Section: Related Workmentioning

confidence: 99%

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TRANSIT: Fine-grained human mobility trajectory inference at scale with mobile network signaling data

Bonnetain

Furno

Faouzi

et al. 2021

Transportation Research Part C: Emerging Technologies

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Call detail records (CDR) collected by mobile phone network providers have been largely used to model and analyze human-centric mobility. Despite their potential, they are limited in terms of both spatial and temporal accuracy thus being unable to capture detailed human mobility information. Network Signaling Data (NSD) represent a much richer source of spatio-temporal information currently collected by network providers, but mostly unexploited for fine-grained reconstruction of human-centric trajectories. In this paper, we present TRANSIT, TRAjectory inference from Network SIgnaling daTa, a novel framework capable of proceessing NSD to accurately distinguish mobility phases from stationary activities for individual mobile devices, and reconstruct, at scale, fine-grained human mobility trajectories, by exploiting the inherent recurrence of human mobility and the higher sampling rate of NSD.The validation on a ground-truth dataset of GPS trajectories showcases the superior performance of TRANSIT (80% precision and 96% recall) with respect to state-of-the-art solutions in the identification of movement periods, as well as an average 190 m spatial accuracy in the estimation of the trajectories. We also leverage TRANSIT to process a unique large-scale NSD dataset of more than 10 millions of individuals and perform an exploratory analysis of city-wide transport mode shares, recurrent commuting paths, urban attractivity and analysis of mobility flows.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

TRANSIT: Fine-grained human mobility trajectory inference at scale with mobile network signaling data

Bonnetain

Furno

Faouzi

et al. 2021

Transportation Research Part C: Emerging Technologies

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“…Huang et al [4] aggregate points within a geometric circle whose radius grows over the certain time duration. Forghani et al [3] determine points as noisy if whose heading greatly deviates from the direction of the defined group of succeeding sectors. While Mohamed et al [7] filter the points that have the abnormal speeds, directions, or are far away from neighbours; afterwards, the filtered trajectories are interpolated.…”

Section: Related Workmentioning

confidence: 99%

Route Reconstruction Using Low-Quality Bluetooth Readings

Chao

et al. 2020

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

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Route reconstruction targets at recovering the actual routes of objects moving on an underlying road network from their timestamped position measurements. This fundamental pre-processing step to many location-based applications has been extensively studied for GPS data, which are object-centric and relatively densely sampled data. In this paper, we investigate the problem of route reconstruction using data collected from roadside Bluetooth scanners. In many cities, Bluetooth scanners are installed in road networks for monitoring the movement of Bluetooth-enabled devices. To address new challenges caused by such reader-centric Bluetooth data including spatial and temporal distortion, a new route reconstruction framework is proposed to transform Bluetooth readings through a family of distortion suppression strategies such that the transformed data can work well with the Hidden Markov model (HMM) map-matching approach. Extensive experiments are conducted to evaluate different transformation strategies with realworld datasets. The experimental results show that when the algorithm uses the baseline or the proposed transformation strategies, the map matching F1 score can be increased by up to 10% depending on the severity of distortion. CCS CONCEPTS • Information systems → Location based services.

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“…However, the GPS suffers from some limitations in urban areas and indoor environments. This is because, the GPS device loses substantial power in an indoor setting due to signal attenuation induced by various building materials or the GPS signals will be blocked in the deep indoors [4]. This means that the GPS cannot be used for localizing devices/items in indoor environments.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

Asaad¹,

Maghdid²

2021

Preprint

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<p>The number of connected mobile devices and Internet of Things (IoT) is growing around us, rapidly. Since, most of the people daily activities are relying on these connected things or devices. Specifically, this past year (with COVID-19) changed daily life in abroad and this is increased the use of IoT enabled technologies in health sector, work, and play. Further, the most common service via using these technologies is the localization/positioning service for different applications including: geo-tagging, billing, contact tracing, health-care system, point-of-interest recommendations, social networking, security, and more. Despite the availability of a large number of localization solutions in the literature, the precision of localization cannot meet the needs of consumers. For that reason, this paper provides an in-depth investigation of the existing technologies and techniques in the localization field, within the IoT era. Furthermore, the benefits and drawbacks of each technique with enabled technologies are illustrated and a comparison between the utilized technologies in the localization is made. The paper as a guideline is also going through all of the metrics that may be used to assess the localization solutions. Finally, the state-of-the-art solutions are examined, with challenges and perspectives regarding indoors/outdoors environments are demonstrated.</p>

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From cellular positioning data to trajectories: Steps towards a more accurate mobility exploration

Cited by 38 publications

References 68 publications

TRANSIT: Fine-grained human mobility trajectory inference at scale with mobile network signaling data

TRANSIT: Fine-grained human mobility trajectory inference at scale with mobile network signaling data

Route Reconstruction Using Low-Quality Bluetooth Readings

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

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