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In the present study, a strategy to assess liquefaction risk of road infrastructures has been proposed, as combination of liquefaction hazard, infrastructures vulnerability and exposure of transportation network. The proposed methodology includes a capacity analysis of the road network performed on both pre- and post-liquefaction scenarios to evaluate the social cost in terms of delay cost suffered by the transportation system. The approach has been applied to the municipality of Terre del Reno (Italy), that in 2012 suffered a severe seismic sequence that induced extensive liquefaction evidences over the territory. A multi-layer database, on a Geographical Information Systems (GIS) platform, has been created, with the aim to overlap information about subsoil, earthquake intensity, groundwater depth and road network configuration. The Vulnerability of road has been evaluated by the settlements of embankment on liquefied soils and, according to the damage level occurred, a loss of functionality has been assigned. Finally, performing a transportation analysis, the effects on the traffic conditions have been evaluated in terms of Total Delay Cost, suffer by the road users. Preliminary results showed a redistribution of the traffic flows caused by the service loss of crucial road sections due to the liquefaction evidences on the transportation network and the related Total Delay Cost has been quantified.

Background: Since the beginning of the new millennium, sensitivity towards the environment has been spreading globally. In fact, countries are adopting measures to develop new decision support tools that can evaluate the impact of interventions to promote and encourage sustainable mobility. To reduce the levels of pollution related to road traffic, policies that favor multimodal transport alternatives have been strengthened. This involves the combined use of public transport, cycling and walking paths, as well as sharing services where available. Regardless of the type of transport, the pedestrian component remains relevant in cities, even if the infrastructures are often not adequate to accommodate it and conflicts arise that must be managed. It is, therefore, necessary to assess the exposure to risk in terms of road safety. Methods: To this end, the work proposes a forecasting model to estimate the pedestrian flows that load the network. The methodology employs a hybrid approach that appears to better capture the movements of pedestrians. Results: By comparing the results of the model with the real data collected on the study area, satisfactory estimates were obtained. Conclusions: Therefore, this can be an effective tool to help road managers to evaluate the actions to protect vulnerable users.

The pedestrian accounts for part of the road safety problem in most developed countries. Pedestrian accidents are thus an urgent issue for safety improvement, particularly in urban area. Furthermore, recent sustainable mobility oriented policies are boosting walking in urban areas. In order to cope with this increase in pedestrian flows, European municipal authorities, responsible for road safety, traffic management and mobility, need reliable engineering methods to plan urban road safety and protect vulnerable users. Road safety management systems are usually developed to identify hazardous sites and to find suitable countermeasures. Risk exposure assessment is required to identify sites with high accident potential. This assessment requires the knowledge, on one side, of the known vehicular flows and, on the other, of the pedestrian flows, which are not normally known to road operators. In this paper a methodology to develop and calibrate forecasting model aimed at evaluating pedestrian exposure is presented. The model is based on original approach that integrates the Space Syntax modelling framework with pedestrian mobility aspects and a calibration procedure was proposed that use counts on a limited number of roads. Preliminary results derived from a case study in an urban environment seem promising and confirm the model's good ability to predict pedestrian flows.