TRANSIT: Fine-Grained Human Mobility Trajectory Inference at Scale with Mobile Network Signaling Data

International audienceCall 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

Spatio-temporal Correlations of Betweenness Centrality and Traffic Metrics

Graph-based analysis has proven to be a good approach to study topological vulnerabilities of road networks through specific metrics, such as betweenness centrality (BC). Even though BC of unweighted, undirected graphs has been widely adopted to identify critical road segments and intersections, given the very high number of potentially highly-traversed paths flowing through them, congestion and vulnerability are strongly influenced also by static and dynamic context factors, such as road capacity, speed limits, travellers' behaviors, accidents, social gatherings and maintenance operations. In this paper, we focus on the analysis of BC on dynamically weighted graphs, used as a model of a road network and associated dynamic information (e.g. travel time). The aim is to discover correlations between the centrality metric and vehicle flows, both in space and in time. The analysis proves the existence of relevant spatio-temporal correlations that provide useful information about the characteristics of road networks and the behavior of drivers. In particular, we identify the existence of anti-correlations that point out forecasting properties of BC when computed on dynamic graphs.These properties justify the usage of the metric for the implementation of next-generation proactive, data-driven urban monitoring systems. These systems are expected to empower urban planners and traffic operators with novel intelligent solutions to reduce traffic congestion and vulnerability risks, therefore contributing to implement the vision of a more resilient and sustainable city

Potential of cellular signaling data for time-of-day estimation and spatial classification of travel demand: a large-scale comparative study with travel survey and land use data

This paper proposes a framework to extract dynamic trip flows and travel demand patterns from large-scale 2 G and 3 G cellular signaling data. Novel data pre-processing techniques based on cell phone activity metrics are presented. The trip extraction method relies on the detection of stationary activities to form trip sequences related to resident users. A probabilistic solution is introduced to estimate the trip starting time, allowing to aggregate trips by time of the day and reconstruct hourly travel flows. To better characterize these flows, a spatial clustering process combined with land-use data is proposed based on the temporal demand profile of each zone. Empirical comparisons have been performed showing that the resulting dynamic travel demand patterns are consistent with those obtained from travel survey data with high correlation coefficients of about 0.9. The results prove the potential of signaling data to generate low-cost valuable information for large-scale travel demand modeling