Smart System for Emergency Traffic Recommendations: Urban Ambulance Mobility

Abstract

With the increasing evolution of advanced technologies and techniques such as the Internet of Things, Artificial Intelligence and Big Data, the traffic management systems industry has acquired new methodologies for creating advanced and intelligent services and applications for traffic management and safety. The current contribution focuses on the implementation of a path recommendation service for paramedics in emergency situations, which is one of the most critical and complex issues in traffic management for the survival of individuals involved in emergency incidents. This work mainly focused on the response time to life-threatening incidents, which is an indicator for emergency ambulance services and for recommending a fastest ambulance route. To this end, we propose a hybrid approach consisting on a local approach using machine learning techniques to predict the congestion of different sections of a map from an origin to a destination, and a global approach to suggest the fastest path to ambulance drivers in real time as they move in OpenStreetMap.