In this paper some results obtained in the SICURO research project carried out by the Laboratory for Transport Systems Analysis (LAST) of Mediterranea University of Reggio Calabria (Italy) are presented. Microscopic models able to simulate supply and demand-supply interaction of a road transportation system in emergency conditions are described. A microscopic link model (car-following) is specified and calibrated. Parameters are calibrated from data observed during a real simulation of evacuation executed in the test site of Melito Porto Salvo (Italy). A computer application is performed in order to reproduce the evacuation phases observed. Some indicators for testing the performance of a road transportation network in emergency conditions are defined and estimated.

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Advancements regarding Dynamic Traffic Assignment (DTA) microscopic models for the simulation of supply and demand-supply interaction of a road transportation system in emergency conditions are presented. They are related to link and node models specified in the research project SICURO, carried out by the Laboratory for Transport Systems Analysis (LAST) of the Mediterranea University of Reggio Calabria (Italy). Microscopic link (car-following) and gap-acceptance (rejection) models for non-signalized intersections are calibrated from data observed during a real simulation of evacuation. An application is performed in order to reproduce the observed evacuation phases through a set of performance indicators.

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.

In the last two decades several Decision Support Systems (DSS) implementing transport modelling have been developed to support transport planning in ordinary conditions. However, especially since 9/11, great efforts have been made to adapt the existing DSS, on the one hand, and develop dedicated DSS, on the other, to simulate transportation systems in emergency conditions in order to support evacuation planning and/or operative stages. Nowadays, several DSS are available on the market, or have been developed as research prototypes, for the above purpose.In this paper, based upon a comprehensive literature review, a selection of DSS are analysed and compared according to their ability to support evacuation planning activities. An application is set up to test transport system simulation through a DSS implementing a microscopic DTA model in order to support evacuation planning. The objective is to test the response capabilities of a DSS in supporting the validation of procedures to be undertaken in the event of emergency evacuation. The aim of the work is to provide planners, technicians and agencies with detailed understanding of the potential and shortcomings of modelling and DSS currently available both on the market and in research.