The number of portable and wearable devices has been increasing in the population of most developed countries. Meanwhile, the capacity to monitor and register not only data about people's habits and locations but also more complex data such as intensity and strength of movements has created an opportunity to their contribution to the general wealth and sustainability of environments. Ambient Intelligence and Intelligent Decision Making processes can benefit from the knowledge gathered by these devices to improve decisions on everyday tasks such as planning navigation routes by car, bicycle or other means of transportation and avoiding route perils. Current applications in this area demonstrate the usefulness of real time system that inform the user of conditions in the surrounding area. Nevertheless, the approach in this work aims to describe models and approaches to automatically identify current states of traffic inside cities and relate such information with knowledge obtained from historical data recovered by ubiquitous and pervasive devices. Such objective is delivered by analysing real time contributions from those devices and identifying hazardous situations and problematic sites under defined criteria that has significant influence towards user well-being, economic and environmental aspects, as defined is the sustainability definition.