Human mobility: Models and applications

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordRecent years have witnessed an explosion of extensive geolocated datasets related to human movement, enabling scientists to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. The study of human mobility is especially important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling. In this survey, we review the approaches developed to reproduce various mobility patterns, with the main focus on recent developments. This review can be used both as an introduction to the fundamental modeling principles of human mobility, and as a collection of technical methods applicable to specific mobility-related problems. The review organizes the subject by differentiating between individual and population mobility and also between short-range and long-range mobility. Throughout the text the description of the theory is intertwined with real-world applications.US Army Research Offic

Immigrant community integration in world cities

As a consequence of the accelerated globalization process, today major cities all over the world are characterized by an increasing multiculturalism. The integration of immigrant communities may be affected by social polarization and spatial segregation. How are these dynamics evolving over time? To what extent the different policies launched to tackle these problems are working? These are critical questions traditionally addressed by studies based on surveys and census data. Such sources are safe to avoid spurious biases, but the data collection becomes an intensive and rather expensive work. Here, we conduct a comprehensive study on immigrant integration in 53 world cities by introducing an innovative approach: an analysis of the spatio-temporal communication patterns of immigrant and local communities based on language detection in Twitter and on novel metrics of spatial integration. We quantify the "Power of Integration" of cities --their capacity to spatially integrate diverse cultures-- and characterize the relations between different cultures when acting as hosts or immigrants.Comment: 13 pages, 5 figures + Appendi

A deep learning approach for intelligent cockpits: learning drivers routines

Nowadays an increasing number of vehicles are being equipped with powerful cockpit systems capable of collecting drivers’ footprints over time. The collection of this valuable data opens effective opportunities for routine prediction. With the growing ability of vehicles to collect spatial and temporal information solving the routine prediction problem becomes crucial and feasible. It is then extremely important to advance and take advantage of the capabilities of these cockpit systems. A vehicle that is capable of predicting the next destination of the driver and when the driver intends to leave to that destination can prepare the journey in advance. Previous studies tackling the next location prediction problem have made use of Traditional Markov models, Neural Networks, Dynamic models, among others. In this work, a framework based on the hierarchical density-based clustering algorithm followed by a Long Short-Term Memory (LSTM) recurrent neural network is proposed for spatial-temporal prediction of drivers’ routines. Based on real-life driving scenarios of three different users, the proposed approach achieved a test set accuracy of 96.20%, 90.23%, and 86.40% when predicting the next destination and a R2 Score of 93.69, 79.21, and 28.81 when predicting the departure time, respectively. The results indicate that the proposed architecture can be implemented on the vehicle cockpit for the assistance of the management of future trips.Programme (COMPETE 2020) and national funds, through the ADI Project Bosch & UMinho “Easy Ride: Experience is everything” , ref POCI-01-0247 FEDER-039334FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and UIDB/00013/2020

Mobile Phone Data for Children on the Move: Challenges and Opportunities

Today, 95% of the global population has 2G mobile phone coverage and the number of individuals who own a mobile phone is at an all time high. Mobile phones generate rich data on billions of people across different societal contexts and have in the last decade helped redefine how we do research and build tools to understand society. As such, mobile phone data has the potential to revolutionize how we tackle humanitarian problems, such as the many suffered by refugees all over the world. While promising, mobile phone data and the new computational approaches bring both opportunities and challenges. Mobile phone traces contain detailed information regarding people's whereabouts, social life, and even financial standing. Therefore, developing and adopting strategies that open data up to the wider humanitarian and international development community for analysis and research while simultaneously protecting the privacy of individuals is of paramount importance. Here we outline the challenging situation of children on the move and actions UNICEF is pushing in helping displaced children and youth globally, and discuss opportunities where mobile phone data can be used. We identify three key challenges: data access, data and algorithmic bias, and operationalization of research, which need to be addressed if mobile phone data is to be successfully applied in humanitarian contexts.Comment: 13 pages, book chapte

Unveiling patterns of international communities in a global city using mobile phone data

n/

Peut-on se fier à l'information spatiale extraite des données TIC ?

International audienceWhile an increasing number of human activities are studied using data produced by individuals' ICT devices, there have been relatively few contributions investigating the robustness of results against fluctuations of data characteristics. In particular, when ICT data contain spatial information, they represent an invaluable new source for analyzing urban phenomena. Here, we present a stability analysis of higher-level information extracted from mobile phone metadata passively produced during an entire year by 9 million individuals in Senegal. We focus on two specific information-retrieval tasks: (a) the identification of land use in the region of Dakar by analyzing the temporal rhythms of the communication activity; (b) the identification of home and work locations of anonymized individuals, allowing for the construction of the Origin-Destination (OD) matrices for commuting flows. Our analysis reveals that the spatial distributions of land use computed for different samples are remarkably robust, with on average 80% of shared surface area between the different spatial partitions. The OD matrix is less stable with a share of about 70% of commuters in common when considering all types of flows. Better results can be obtained at larger levels of aggregation. These different results confirm that ICT data are mostly a very useful source for the spatial analysis of urban systems, but that their reliability should be tested more thoroughly

AccesSim: simulation of accessibility in urban settings and experiments in teaching applications

International audienceno abstrac