This thesis reports the research efforts conducted in order to develop the Dynamic Response Recovery Tool. The DRRT was developed as a decision support tool under a holistic approach considering both emergency management research and transportation studies. The proposed system was assessed by a series of case studies in order to identify its efficiency and suitability for roading organisations. Knowledge developed from two novel research approaches are comprehensively described throughout the thesis. Initially, we report on the observation of three emergency exercises and two real events in New Zealand. This set of activities indicated the complex and dynamic environment in which emergency management takes place as well as organisational settings and management structures implemented to better respond and recover from disasters events. Additionally, a secondary approach was designed to overcome limitations identified in the observation method. In this context, a game-based scenario simulation was developed and conducted with twelve participants. With a focus in resource deployment decisions during emergencies, the game simulated an earthquake scenario in which participants had to allocate physical resources to fix damage created in a road network. Simulations indicated that Naturalistic Decision-making processes were used to respond to the scenario. Thus, resource allocation followed planning priorities defined previously the simulation, which further considered individual experiences and knowledge. Taking advantage from the findings achieved and knowledge developed by the observations and game simulations, the DRRT was designed using the conceptual background identified in the literature review. The DRRT was conceptualised as a logistics sub-system as part of the broad field of Disaster Management. In particular, the DRRT was geared towards supporting decision-making by providing procedural recommendations and identifying optimum physical deployment strategies. In order to assess the proposed system, an Information Technology application was built according to the DRRT’s specifications. A series of eleven individual and three group simulations was performed in order to assess the DRRT. Data collected through the application indicated that the DRRT enhanced decision-making during extreme events. In specific, case study participants using the system at greater levels achieved better decision-making accuracy than those disregarding completely or partially the system. Case studies also indicated that emergency management knowledge was represented by the application and its logistics model provided participants with vital information to optimise resource allocation.