With increasing emphasis on new soft mobility in urban areas, it becomes more and more important to provide effective speed control measures for vehicular traffic. Among those, the ones based on vehicle vertical deflection, namely vertical traffic calming measures, are historically the most widespread. However, since the basic operating principle of these devices is related to the vertical dynamic response due to the interaction between the moving vehicle and the road profile, different vehicles may exhibit different speed behaviors when traveling on a specific road profile shape. As a matter of fact, recent research provided evidence of this connection, and therefore it has been worth investigating the dynamics underlying the phenomenon in order to develop a new approach to the design of vertical traffic calming devices. In this paper, following an initial state-of-the-art review, an in-depth study on the dynamic interaction between vehicle and road profile has been presented by means of an ad hoc-developed mathematical model. The proposed simulation model has been used to evaluate the root mean square acceleration value associated with each vehicle/traffic calming device/crossing speed. Following the outcomes provided by numerical simulations, an experimental investigation has been designed and carried out on a vertical traffic calming device. Speed profiles of different vehicles have been acquired, and preliminary results seem to provide evidence of a different dynamic response for each vehicle type, yielding the basis to reconsider the design approach of such devices in order to control urban traffic speed.

Several determinants affect the reason to cycle or not, and some of them are described in a detailed way in the current technical literature review. The recent spread of new modes of active mobility brings up questions for urban transport planners on how to foresee future demand and assess safety conditions; from this comes the need to explore the relationships among several determinants. In this paper, after the collection of the main data required, three Regression Models are proposed, which demonstrate evidence for the role of safety and energy expenditure issues as important predictors. The method is applied to a dataset of 90 Italian cities selected according to their class of dimensionality and geographical position. The three models for each class of dimensionality (50,000–100,000 no. of inhabitants, 10,000–50,000 no. of inhabitants, and 0–10,000 no. of inhabitants) show a good accuracy (in terms of adj-R² values of 0.6991, 0.7111, and 0.6619, respectively). The results show that energy expenditure, which is related to the terrain characteristics of an urban area and individual aerobic abilities, and safety perception, which is related to cycle network extensions, appear to be significant determinants in predicting bicycle modal share. The aim is to provide a useful and simplified tool, when only aggregated-type data are available, to help urban road designers and city planners in identifying and forecasting bike-sharing.

Transportation networks are one of the most vulnerable civil infrastructures during an earthquake and an estimation of traffic impacts in the post-earthquake scenario is a crucial aspect in the context of risk assessment and evaluation of remedial measures. In this paper, a methodology is presented, combining GIS tools, probabilistic seismic risk analysis and traffic simulation models, which is able to assess the direct and indirect (social) costs: bridge repairs, increase in travel time and a lack of accessibility. Operating issues related to the development and calibration of traffic models applicable to a damaged road network are carefully analysed and reviewed and an innovative approach to evaluate the social cost due to the lack of accessibility is also proposed. The developed modelling framework has been applied on a realistic bridge stock within a road transportation network in central Italy where local land-use data have been collected, extensive traffic surveys have been performed and a traffic model has been calibrated. A probabilistic risk analysis employing a ShakeMap derived from a historical real seismic event has been carried out.