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The signaling traffic of a cellular network is rich of information related to the movement of devices across cell boundaries. Thus, passive monitoring of anonymized signaling traffic enables the observation of the devices' mobility patterns. This approach is intrinsically more powerful and accurate than previous studies based exclusively on Call Data Records as significantly more devices can be included for investigation, but it is also more challenging to implement due to a number of artifacts implicitly present in the network signaling. In this study we tackle the problem of estimating vehicular trajectories from 3G signaling traffic with particular focus on crucial elements of the data processing chain. The work is based on a sample set of anonymous traces from a large operational 3G network, including both the circuit-switched and packet-switched domains. We first investigate algorithms and procedures for preprocessing the raw dataset to make it suitable for mobility studies. Second, we present a preliminary analysis and characterization of the mobility signaling traffic. Finally, we present an algorithm for exploiting the refined data for road traffic monitoring, i.e., route detection. The work shows the potential of leveraging the 3G cellular network as a complementary "sensor" to existing solutions for road traffic monitoring.

Mobile users in cellular networks produce calls, initiate connections and send packets. Such events have a binary outcome -- success or failure. The term "failure" is used here in a broad sense: it can take different meanings depending on the type of event, from packet loss or late delivery to call rejection. The Mean Failure Probability (MFP) provides a simple summary indicator of network-wide performance -- i.e., a Key Performance Indicator (KPI) -- that is an important input for the network operation process. However, the robust estimation of the MFP is not trivial. The most common approach is to take the ratio of the total number of failures to the total number of requests. Such simplistic approach suffers from the presence of heavy-users, and therefore does not work well when the distribution of traffic (i.e., requests) across users is heavy-tailed -- a typical case in real networks. This motivates the exploration of more robust methods for MFP estimation. In a previous work [1] we derived a simple but robust sub-optimal estimator, called EPWR, based on the weighted average of individual (per-user) failure probabilities. In this follow-up work we tackle the problem from a different angle and formalize the problem following a Bayesian approach, deriving two variants of non-parametric optimal estimators. We apply these estimators to a real dataset collected from a real 3G network. Our results confirm the goodness of the proposed estimators and show that EPWR, despite its simplicity, yields near-optimum performance.