Abstract
Even though safety systems are present in every transportation hub, there are still crucial outstanding issues to be addressed and resolved. Public infrastructures such as tunnels and metro stations are paradigms, where travellers are frequently jammed at the exits, even under normal evacuation. Existing safety systems fail to guide effectively the most vulnerable travellers and to take into consideration the mobility impairment of each individual. In this context, current work, within SAVE ME project, proposes a robust evacuation mechanism from disaster areas in public transport terminals and infrastructures, which is incorporated in our Decision Support System (DSS). The DSS provides group-wised and personalized optimal routes to the exits, taking into consideration the personal preferences and capabilities of the travellers, along with guidelines to the rescue teams, given that rescuers must be sent to prioritized targets with trapped travellers. Furthermore, an innovative contribution of this work is the service personalization platform that was implemented in order to ensure the personalized communication of each traveller with the DSS. Service personalization is based on an intelligent agent framework. The paper provides a thorough analysis of existing evacuation models and mechanisms, presents the DSS architecture, its embedded algorithms, and the service personalization platform. Finally it discusses its performance based on the two SAVE ME project pilot trials in Colle Capretto tunnel in Umbria, Italy and in Monument metro station in Newcastle, UK. (6 pages)