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Mobility is a key issue for city planners. Being able to evaluate the impact of its evolution is complex and involves many factors including new technologies like electric cars, autonomous vehicles and also new social habits like vehicle sharing. We need a better understanding of different scenarios to improve the quality of long-term decisions. Computer simulations can be a tool to better understand this evolution, to discuss different solutions and to communicate the implications of different decisions. In this paper, we propose a new generic model that creates an artificial micro-world which allows the modeler to create and modify new mobility scenarios in a quick and easy way. This not only helps to better understand the impact of new mobility modes on a city, but also fosters a better-informed discussion of different futures. Our model is based on the agent-based paradigm using the GAMA Platform. It takes into account different mobility modes, people profiles, congestion and traffic patterns. In this paper, we review an application of the model of the city of Cambridge.

A fundamental aspect of well performing cities is successful public spaces. For centuries, understanding these places has been limited to sporadic observations and laborious data collection. This study proposes a novel methodology to analyze citywide, discrete urban spaces using highly accurate anonymized telecom data and machine learning algorithms. Through superposition of human dynamics and urban features, this work aims to expose clear correlations between the design of the city and the behavioral patterns of its users. Geolocated telecom data, obtained for the state of Andorra, were initially analyzed to identify "stay-points"-events in which cellular devices remain within a certain roaming distance for a given length of time. These stay-points were then further analyzed to find clusters of activity characterized in terms of their size, persistence, and diversity. Multivariate linear regression models were used to identify associations between the formation of these clusters and various urban features such as urban morphology or land-use within a 25-50 meters resolution. Some of the urban features that were found to be highly related to the creation of large, diverse and long-lasting clusters were the presence of service and entertainment amenities, natural water features, and the betweenness centrality of the road network; others, such as educational and park amenities were shown to have a negative impact. Ultimately, this study suggests a "reversed urbanism" methodology: an evidence-based approach to urban design, planning, and decision making, in which human behavioral patterns are instilled as a foundational design tool for inferring the success rates of highly performative urban places.

Abstract-The explosive data traffic demand in the context of the 5G revolution has stressed the need for network capacity increase. As the network densification has almost reached its limits, mobile network operators are motivated to share their network infrastructure and the available resources through dynamic spectrum management. Although some initial efforts have been made to this direction by concluding sharing agreements at a coarse granularity (i.e., months or years), the 5G developments require fine timescale agreements, mainly enabled by network slicing. In this article, taking into account the radical changes foreseen for next generation networks, we provide a thorough discussion on the challenges that network slicing brings in the different network parts, while introducing a new entity capable of managing the end-to-end slicing in a coherent manner. In addition, according to the paradigm shift that operators share their resources in a common centralized pool, we design a cooperative game to study the potential cooperation aspects among the participants. The experimental results highlight the performance and financial gains achievable by operators through multi-tenant slicing, providing them with the necessary incentives for network upgrade towards 5G.