Tackling the Neighboring Network Hit Problem in Cellular Data

Leiva-Araos, Andres; Allende‐Cid, Héctor; Khryashchev, Denis; Vo, Huy T.

doi:10.1109/bigdata47090.2019.9006005

Cited by 3 publications

(5 citation statements)

References 29 publications

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“…Most of the previous works make no mention of this phenomenon, or simply suggest discarding these bad records [2], rather than fixing them. As we demonstrated in our previous work [14], this practice in the preparation of the data does not affect their general distribution when we analyze large volumes (e.g., people or geographic areas), however, it introduces significant biases in studies whose scale must be reduced. As far as we know, all of the previous work done on cellular data was done by removing data deemed erroneous or by analyzing large areas, with large volumes of data that reduced the impact of outliers and incorrect data.…”

Section: Context Purpose and Significancementioning

confidence: 99%

“…Many of these adverse effects of these procedures are diluted with the use of large volumes of data. However, it is demonstrated its effect when studies have smaller spatial and temporal scales [14]. In Table 1, we include some studies that use cellular and spatio-temporal data, and the management approach of the NNH phenomenon.…”

Section: Understanding the Network And Its Limitations As A Mobility mentioning

confidence: 99%

“…There is a growing concern about security and privacy gaps in the use of information collected on a daily basis. The key point here is that we are dealing with data that do not belong to us (the researchers) but the Telco's customers [14].…”

Section: Open Challenges and Opportunities Related To The Use Of Cdrsmentioning

confidence: 99%

“…The foregoing is aggravated if there are erroneous data that are within ranges considered normal, which go unnoticed without prior in-depth analysis. The problem of NNHs was first proposed and studied in [14]. For further details, in Table 1, we include some studies that use cellular and spatio-temporal data, and the management approach of the NNH phenomenon.…”

Section: Data Qualitymentioning

confidence: 99%

“…After validating the results with the ODS survey, we used the same technique presented in [14] to generate synthetic data. Essentially, this method consists of replicating the statistical behavior of the real data in 15-minutes intervals.…”

Section: Synthetic Datamentioning

confidence: 99%

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A Hierarchical Fuzzy-Based Correction Algorithm for the Neighboring Network Hit Problem

Leiva-Araos

Allende‐Cid

2021

Mathematics

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Most humans today have mobile phones. These devices are permanently collecting and storing behavior data of human society. Nevertheless, data processing has several challenges to be solved, especially if it is obtained from obsolete technologies. Old technologies like GSM and UMTS still account for almost half of all devices globally. The main problem in the data is known as neighboring network hit (NNH). An NNH occurs when a cellular device connects to a site further away than it corresponds to by network design, introducing an error in the spatio-temporal mobility analysis. The problems presented by the data are mitigated by eliminating erroneous data or diluting them statistically based on increasing the amount of data processed and the size of the study area. None of these solutions are effective if what is sought is to study mobility in small areas (e.g., Covid-19 pandemic). Elimination of complete records or traces in the time series generates deviations in subsequent analyses; this has a special impact on reduced spatial coverage studies. The present work is an evolution of the previous approach to NNH correction (NFA) and travel inference (TCA), based on binary logic. NFA and TCA combined deliver good travel counting results compared to government surveys (2.37 vs. 2.27, respectively). However, its main contribution is given by the increase in the precision of calculating the distances traveled (37% better than previous studies). In this document, we introduce FNFA and FTCA. Both algorithms are based on fuzzy logic and deliver even better results. We observed an improvement in the trip count (2.29, which represents 2.79% better than NFA). With FNFA and FTCA combined, we observe an average distance traveled difference of 9.2 km, which is 9.8% better than the previous NFA-TCA. Compared to the naive methods (without fixing the NNHs), the improvement rises from 28.8 to 19.6 km (46.9%). We use duly anonymized data from mobile devices from three major cities in Chile. We compare our results with previous works and Government’s Origin and Destination Surveys to evaluate the performance of our solution. This new approach, while improving our previous results, provides the advantages of a model better adapted to the diffuse condition of the problem variables and shows us a way to develop new models that represent open challenges in studies of urban mobility based on cellular data (e.g., travel mode inference).

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Section: Context Purpose and Significancementioning

confidence: 99%

Section: Understanding the Network And Its Limitations As A Mobility mentioning

confidence: 99%

Section: Open Challenges and Opportunities Related To The Use Of Cdrsmentioning

confidence: 99%

Section: Data Qualitymentioning

confidence: 99%

Section: Synthetic Datamentioning

confidence: 99%

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