Dynamic approach to update utility and choice by emerging technologies to reduce risk in urban road transportation systems

International research attention on evacuation issues has increased significantly following the human and natural disasters at the turn of the century, such as 9/11, Hurricane Katrina, Cyclones Idai and Kenneth, the Black Saturday forest fires and tsunamis in Japan. The main problem concerning when a disaster can occur involves studying the risk reduction. Risk, following all the theoretical and experimental studies, is determined by the product of three components: occurrence, vulnerability and exposure. Vulnerability can be improved over time through major infrastructure actions, but absolute security cannot be achieved. When the event will occur with certainty, only exposure remains to reduce the risk to people before the effect hits them. Exposure can be improved, under fixed conditions of occurrence and vulnerability, by improving evacuation. The main problem in terms of evacuating the population from an area is the available transport system, which must be used to its fullest. So, if the system is well managed, the evacuation improves (shorter times), meaning the exposure is reduced, and therefore, the risk is reduced. A key factor in the analysis of transport systems under emergency conditions is the behavior of the user, and therefore, the study of demand. This work identifies the main research lines that are useful for studying demand under exposure-related risk conditions. The classification of demand models that simulate evacuation conditions in relation to the effect on the transportation system is summarized. The contribution proposes a model for updating choice in relation to emergency conditions and utility. The contribution of emerging ICTs to actualization is formally introduced into the models. Intelligent technologies make it possible to improve user decisions, reducing exposure and therefore risk. The proposed model moves within the two approaches of the literature: it is an inter-period dynamic model with the probability expressed within the discrete choice theory; furthermore, it is a sequential dynamic model with the probability dependent on the previous choices. The contribution presents an example of application of the model, developing a transition matrix considering the case of choice updating under two extreme conditions.