Pervasive presence of location-sharing services made it possible for researchers to gain an unprecedented access to the direct records of human activity in space and time. This article analyses geo-located Twitter messages in order to uncover global patterns of human mobility. Based on a dataset of almost a billion tweets recorded in 2012, we estimate the volume of international travelers by country of residence. Mobility profiles of different nations were examined based on such characteristics as mobility rate, radius of gyration, diversity of destinations, and inflow–outflow balance. Temporal patterns disclose the universally valid seasons of increased international mobility and the particular character of international travels of different nations. Our analysis of the community structure of the Twitter mobility network reveals spatially cohesive regions that follow the regional division of the world. We validate our result using global tourism statistics and mobility models provided by other authors and argue that Twitter is exceptionally useful for understanding and quantifying global mobility patterns.

Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network, which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting toward a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.carpooling | human mobility | urban computing | maximum matching

We propose a framework for tightly-coupled lidarvisual-inertial odometry via smoothing and mapping, LVI-SAM, that achieves real-time state estimation and map-building with high accuracy and robustness. LVI-SAM is built atop a factor graph and is composed of two sub-systems: a visual-inertial system (VIS) and a lidar-inertial system (LIS). The two subsystems are designed in a tightly-coupled manner, in which the VIS leverages LIS estimation to facilitate initialization. The accuracy of the VIS is improved by extracting depth information for visual features using lidar measurements. In turn, the LIS utilizes VIS estimation for initial guesses to support scan-matching. Loop closures are first identified by the VIS and further refined by the LIS. LVI-SAM can also function when one of the two sub-systems fails, which increases its robustness in both texture-less and feature-less environments. LVI-SAM is extensively evaluated on datasets gathered from several platforms over a variety of scales and environments. Our implementation is available at https://git.io/lvi-sam.

Introductioǹ`I n today's Dublin, you wouldn't need a novelist's omniscience to follow Leopold Bloom, Stephen Dedalus, and Buck Mulligan around the city; you could just track their cell phone usage. And if Leopold could get access to the logs, he could figure out precisely what Molly was up to.'' Mitchell (2003, page 116) Whether you are a techno-enthusiast or not, Mitchell's (2003) e-topia has certainly become a reality in the field of mobile communications. Just look at data from the booming mobile communications industry. According to the European Information Technology Observatory (EITO, 2004), cell phone subscriptions in Western Europe reached 350 million in 2003 (157 million in the USA). In Italy, where the case studies presented in this paper are located, the number of users is approximately 54 million (EITO , 2004)öthat is, the second largest market in Europe after Germany. Furthermore, with a total population of 57 million, Italy has one of the highest penetrations of mobile devices in the world. Why should the urban planning community be interested in the aforementioned data? First, the widespread deployment of mobile communications, supported by personal handheld electronics, is having a significant impact on urban life. People are changing their social and working habits because of the new technology (Rheingold, 2002). Activities that once required a fixed location and connection can now be achieved with higher flexibility, which results in the users' ability to act and move more freely [for an analysis in the corporate working domain, see Duffy (1997)]. As a consequence, urban dynamics are becoming more complex and require new analysis techniques. Second, and more importantly in this context, data based on the location

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