Abstract
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).
Highlights
In order to ensure a better understanding, we will use the following terminology throughout the paper: Definition 1 (Cellular Site)
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
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, it introduces significant biases in studies whose scale must be reduced
Summary
In order to ensure a better understanding, we will use the following terminology throughout the paper: Definition 1 (Cellular Site). A set S of cellular serving nodes consisting in one or several Base. Transceiver Station (BTS), that aims to optimize the signaling in a specific coverage area [1,2]. There is one BTS per technology (GSM, UMTS, LTE, 5G, etc.) and several antennas per technology. Sn } is the set off all n sites (with n = 201) in our study, see Figure 1. A physical object m that has an IP stack, enabling two-way communication over a network interface (e.g., mobile phones, tablets) that generates user plane events (CDR) and control plane events (XDR) data
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