A new approach for mobile positioning using the CDR data of cellular networks

Lind, Artjom; Hadachi, Amnir; Batrashev, Oleg

doi:10.1109/mtits.2017.8005687

Cited by 14 publications

(3 citation statements)

References 20 publications

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“…In the following section, we provide the achieved results and compare them with our baseline methods [ 36 ]. The method is designed to process the CDR logs assuming that a corresponding cellular coverage map is available and that the cell ids from CDR events are present in the coverage map.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

Application of Gaussian Mixtures in a Multimodal Kalman Filter to Estimate the State of a Nonlinearly Moving System Using Sparse Inaccurate Measurements in a Cellular Radio Network

Lind

Hadachi

2023

Sensors

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Kalman filter is a well-established accuracy correction method in control, guidance, and navigation. With the popularity of mobile communication and ICT, Kalman Filter has been used in many new applications related to positioning based on spatiotemporal data from the cellular network. Despite the low accuracy compared to Global Positioning System, the method is an excellent supplement to other positioning technologies. It is often used in sensor fusion setups as a complementary source. One of the reasons for the Kalman Filter’s inaccuracy lies in naive radio coverage approximation techniques based on multivariate normal distributions assumed by previous studies. Therefore, in this paper, we evaluated those disadvantages and proposed a Gaussian mixtures model to address the non-arbitrary shape of the radio cells’ coverage area. Having incorporated the Gaussian mixtures model into Switching Kalman Filter, we achieved better accuracy in positioning within the cellular network.

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Section: Evaluation and Resultsmentioning

confidence: 99%

Application of Gaussian Mixtures in a Multimodal Kalman Filter to Estimate the State of a Nonlinearly Moving System Using Sparse Inaccurate Measurements in a Cellular Radio Network

Lind

Hadachi

2023

Sensors

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“…The 4irst option to obtain trip data from mobile phones is through the telephone operator's signal. Zhang et al 2010 Lind et al (2017) determined user trajectories through data detected from information provided by the operators. Typical data consisted of the time a device was registered on an antenna, the connection time, the time that a message was received, and the time that internet by 3G or 4G was accessed.…”

Section: Detection Of Mobile Devices From Wi-fi Signals 31 Detec8onmentioning

confidence: 99%

Bus passenger counts using Wi-Fi signals: some cautionary findings

2019

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Analisa-se a viabilidade de pesquisas sobre usuários de ônibus com base na detecção de endereços MAC WiFi de dispositivos portáteis. A motivação para o estudo decorre da aparente contradição entre casos de sucesso publicados na literatura e resultados de experimentos de campo que realizamos. Requisitos para identificação adequada de passageiros de ônibus são usados como base para avaliar as capacidades do hardware e software de detecção comumente disponíveis. Mais especificamente, os intervalos de tempo decorridos entre as detecções do mesmo dispositivo são tomados como requisito para a determinação do estado do portador do dispositivo e, portanto, a identificação deste como passageiro. Por exemplo, ao realizar pesquisas de embarque e desembarque com equipamentos de detecção instalados a bordo, é necessário que várias detecções ocorram logo após o embarque do passageiro e antes do desembarque, permitindo assim uma estimativa precisa da origem e destino da viagem. Resultados experimentais em ensaios controlados e não controlados indicam que os componentes disponíveis no mercado com software de código aberto podem não fornecer detecções bem-sucedidas. No experimento controlado, encontramos tempos de 40 s para a primeira detecção de 86% dos dispositivos e uma média de 80 s para a segunda detecção de dispositivos. Para o experimento não controlado de viagens em ônibus com carregamento médio, foram encontradas diferenças significativas entre as contagens manuais e os dispositivos detectados. Como resultado dessas observações empíricas, recomenda-se uma avaliação cuidadosa dos esquemas de detecção existentes usados nas pesquisas de número de passageiros.

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“…While previous research has highlighted several important aspects of mobility [5,6], there remains a lack of comprehensive parameters that can exhaustively describe citywide movement and the daily routines of its inhabitants. Moreover, most existing studies are limited to smaller geographic areas and do not encompass diverse populations or varied user profiles.…”

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confidence: 99%

Comprehensive Analysis of Key Spatial Parameters for Characterizing Human Mobility Patterns

Matloub,

Kostanic

2023

IEEE Access

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This paper conducts a thorough analysis of human mobility patterns using commercially available cellular location data from October 2020. The study focuses on six fundamental spatial mobility parameters. These parameters were chosen based on their prominence in existing literature and their importance for understanding human mobility. Our analysis spans daily, weekly, and monthly time scales and draws upon data from the New York-Newark, NJ City Statistical Area. Our findings highlight the predictability of human mobility, suggesting that accurate predictive models could be developed through broader studies across various time frames and geographic regions. We also demonstrate that the choice of appropriate sampling thresholds is not arbitrary but depends on the mobility parameters being investigated, the dataset size, and available computational resources. The research emphasizes the complex relationship between spatial resolution, temporal granularity, and human mobility patterns. Even though the mean and standard deviation values vary based on different sampling threshold combinations, the distribution patterns of mobility parameters remain remarkably consistent. The insights gained from this study lay the groundwork for more comprehensive future research on human mobility.

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A new approach for mobile positioning using the CDR data of cellular networks

Cited by 14 publications

References 20 publications

Application of Gaussian Mixtures in a Multimodal Kalman Filter to Estimate the State of a Nonlinearly Moving System Using Sparse Inaccurate Measurements in a Cellular Radio Network

Application of Gaussian Mixtures in a Multimodal Kalman Filter to Estimate the State of a Nonlinearly Moving System Using Sparse Inaccurate Measurements in a Cellular Radio Network

Bus passenger counts using Wi-Fi signals: some cautionary findings

Comprehensive Analysis of Key Spatial Parameters for Characterizing Human Mobility Patterns

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