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Natural and anthropic disasters produce relevant economic and environmental losses at a global level. Many countries have adopted a risk management cycle to limit these losses. Agenda 2030 defined specific Sustainable Development Goals (SDGs) and relative targets dedicated to disaster risk reduction. Planned actions can pertain to the context "before" or "after" a disastrous event. The United Nations recommend a risk-informed approach for achieving the SDGs by working on anticipation and prevention. Planning with training and exercises, referring to transport systems in emergency conditions, can limit disaster impacts and strongly support the pursuit of sustainability. This research focuses on actions and methodologies for increasing preparedness levels "before" a disastrous event, to improve the capabilities of managers and people by increasing awareness of the effects of planned actions. Transport system models (TSMs) play an important role in simulating the evacuation of people to reduce theoretical risk, thereby reducing theoretical exposure. In this work, a method is presented that allows us to investigate how this theoretical reduction can become effective, through training and exercises. The paper proposes a general framework of training and exercises for risk reduction based on a given transportation planning model. The framework supports ex ante evaluations of exposure reduction produced by an evacuation plan. The obtained results show that the effectiveness of the planned actions increases with different levels of exercise implementation. The progressive implementation of exercises contributes to achieving the risk reductions estimated at the evacuation planning stage. The TSM in the mitigation phase is the basis for the development of specific quantitative evacuation plans, that must be implemented in the preparedness phase by means of training and exercises to test the planned actions in terms of reducing the exposure risk component. Some exercises implemented worldwide verify the proposed framework by means of some empirical evidence. The results and discussions reported in this paper can be useful for researchers, decisionmakers, and society by offering a contribution to the growing knowledge about risk and the potential actions and their relative effects on reducing it. [ABSTRACT FROM AUTHOR]

This research concerns the topic of Land Use and Transport Interaction (LUTI) models. In particular, the patterns between residential households' location and mobility choices are analyzed and simulated. The attributes that influence household residential location choices belong to four categories: socioeconomic and mobility attributes of households and/or of their components; land use; real-estate market; transport system. The paper presents the results of a pilot study on households' location and mobility patterns in the metropolitan area of Reggio Calabria (Southern Italy). The pilot study is divided into two stages. In the first stage, a survey allowed to collect information and identify existing patterns about residential and mobility choices of a sample of households. In the second stage, a residential location model is proposed and some preliminary calibrations are presented in a prototypal way. The pilot study could be extended and improved in terms of spatial extension and sample dimension in order to allow a complete specification-calibration-validation process of the model. The model development can support the land use-transport planning process in the Metropolitan City of Reggio Calabria., 1. Introduction Land Use and Transport Interaction (LUTI) models simulate the two-way relationship between land use and transport systems in urban areas. The LU-to-T relationship influences transport, in the component [...]

This paper focuses on the estimation of energy consumption of Electric Vehicles (EVs) by means of models derived from traffic flow theory and vehicle locomotion laws. In particular, it proposes a bi-level procedure with the aim to calibrate (or update) the whole parameters of traffic flow models and energy consumption laws by means of Floating Car Data (FCD) and probe vehicle data. The reported models may be part of a procedure for designing and planning transport and energy systems. This aim is to verify if, and in what amount, the existing parameters of the resistances/energy consumptions model calibrated in the literature for Internal Combustion Engines Vehicles (ICEVs) change for EVs, considering the above circular dependency between supply, demand, and supply–demand interaction. The final results concern updated parameters to be used for eco-driving and eco-routing applications for design and a planning transport system adopting a multidisciplinary approach. The focus of this manuscript is on the transport area. Experimental data concern vehicular data extracted from traffic (floating car data and probe vehicle data) and energy consumption data measured for equipped EVs performing trips inside a sub-regional area, located in the Città Metropolitana of Reggio Calabria (Italy). The results of the calibration process are encouraging, as they allow for updating parameters related to energy consumption and energy recovered in terms of EVs obtained from data observed in real conditions. The latter term is relevant in EVs, particularly on urban routes where drivers experience unstable traffic conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]