With the trend of global warming and destructive human activities, the frequent occurrences
of catastrophes have posed devastating threats to human life and social stability worldwide …

Given the dynamic and cascading nature of urban emergencies, decision-makers are often
faced with challenging emergency decisions. In this study, a dynamic emergency decision …

Abstract Many problems in Reinforcement Learning (RL) seek an optimal policy with large
discrete multidimensional yet unordered action spaces; these include problems in …

Literature about global emergencies and their impact on people's health underlines the
need to improve the social cohesion of human community and the availability of tools to …

The optimization of Emergency Medical Services is a central issue in modern healthcare
systems. With this in focus, we study a data set containing medical emergencies for the …

Background The frequent occurrence of major public health emergencies globally poses a
threat to people's life, health, and safety, and the convergence development of digital …

A classical problem in city-scale cyber-physical systems (CPS) is resource allocation under
uncertainty. Typically, such problems are modeled as Markov (or semi-Markov) decision …

Modern smart cities are focusing on smart transportation solutions to detect and mitigate the
effects of various traffic incidents in the city. To materialize this, roadside units and ambient …

Recent wildfires in the United States have resulted in loss of life and billions of dollars,
destroying countless structures and forests. Fighting wildfires is extremely complex. It is …

Developing a high-performing traffic incident-duration prediction model is considered a key
component for evaluating the impact of these incidents on the roadway network. Various …