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In this paper a structured process to plan an urban system in emergency conditions is presented. The internal planning process is described with reference to planning dimensions and to a generic product-plan component. Guidelines for evacuation planning resulting from the SICURO project are presented, with a view to developing and testing an evacuation plan for an urban system in emergency conditions. Quantitative evaluations play an essential role in the guidelines.

Diffusion and availability of new technologies has influenced the evolution and organization of cities. New technologies and services, in particular in the areas of transport, energy and ICT, are requirements to transform a city into a smart city contributing towards reaching a high level of urban sustainable development.The purpose of this paper is to analyse the process of smart city definition following the European approach. In the context of the European Union's ten-year growth and jobs strategy (Europe 2020 strategy), the role of smart cities is defined. The main European objective is to tackle the major societal challenges at an urban level, adopting smart cities' solutions. The European Commission proposes an integrated approach to connect policies and resources at EU, national, regional and local levels to promote smart cities' solutions.

In this paper an advancement on analysis of the planning process in urban systems in emergency conditions is presented. The internal planning process is analysed according to the Logical Framework Approach. Methods and models resulting from the SICURO project are applied to evaluate a local evacuation plan. The results from experimentations of an evacuation plan for an urban system in emergency conditions are presented.

The paper deals with the integration of data provided from traditional transport surveys (small data) with big data, provided from Information and Communication Technology (ICT), in building Transport System Models (TSMs). Big data are used to observe historical mobility patterns and transport facilities and services, but they are not able to assess ex-ante effects of planned interventions and policies. To overcome these limitations, TSMs can be specified, calibrated and validated with small data, but they are expensive to obtain. The paper proposes a procedure to increase the benefits of TSMs’ building in forecasting capabilities, on one side; and limiting the costs connected to traditional surveys thanks to the availability of big data, on the other side. Small data (e.g., census data) are enriched with Floating Car Data (FCD). At the current stage, the procedure focuses on two specific elements of TSMs: zoning and graph building. These processes are both executed considering the estimated values of an intensity function of FCDs, consistently with traditional methods based on small data. The data-fusion of small and big data, operated with a Geographic Information System (GIS) tool, in a real extra-urban context is presented in order to validate the proposed procedure.