Urban Emergency Management using Intelligent Traffic Systems: Challenges and Future Directions

Emergency management systems' performance relies on effective utilisation of available resources and timely delivery of designated services. Intelligent transportation systems are key enablers of such systems in urban areas, where both resource utilisation and service delivery are hugely impacted by the accessibility of the road network. Using machine learning techniques, and accessing big data and the Internet of Things has already made tremendous advancements in the field of intelligent transportation systems and emergency management. However, the assumption of having access to historical data and predictive modelling is not always practical as emergency situations may occur unanticipated. In this paper, we briefly review the most recent related work, discuss the existing challenges and highlight future directions. We also present some preliminary results wherein the absence of historical data, ontological knowledge, and normative systems are used to improve the emergency service delivery and avoid new accidents while keeping the overall system performance. In this paper, we briefly review the most recent related work, discuss the existing challenges and highlight future directions. We also present some preliminary results wherein the absence of historical data, ontological knowledge, and normative systems are used to improve the emergency service delivery and avoid new accidents while keeping the overall system performance.