Pedestrian Leadership and Egress Assistance Simulation Environment (PLEASE)

Over the past decade, researchers have been developing new ways to model pedestrian egress especially in emergency situations. The traditional methods of modeling pedestrian egress, including ow-based modeling and cellular automata, have been shown to be poor models of human behavior at an individual level, as well as failing to capture many important group social behaviors of pedestrians. This has led to the exploration of agent-based modeling for crowd simulations including those involving pedestrian egress. Using this model, we evaluate different heuristic functions for predicting good egress routes for a variety of real building layouts. We also introduce reinforcement learning as a means to represent individualized pedestrian route knowledge. Finally, we implement a group formation technique, which allows pedestrians in a group to share route knowledge and reach a consensus in route selection. Using the group formation technique, we consider the effects such knowledge sharing and consensus mechanisms have on pedestrian egress times

Agent-Based Simulation and Analysis of Human Behavior towards Evacuation Time Reduction

Human factors play a significant part in the time taken to evacuate following an emergency. An agent-based simulation, using the Prometheus methodology (SEEP 1.5), has been developed to study the complex behavior of human (the ‘agents’) in high-rise buildings evacuations. In the case of hostel evacuations, simulation results show that pre-evacuation phase takes 60.4% of Total Evacuation Time (TET). The movement phase (including queuing time) only takes 39.6% of TET. From sensitivity analysis, it can be shown that a reduction in TET by 41.2% can be achieved by improving the recognition phase. Exit signs have been used as smart agents. Expanded Ant Colony Optimization (ACO) was used to determine the feasible evacuation routes. Both the ‘familiarity of environment’ wayfinding method, which is the most natural method, and the ACO wayfinding, have been simulated and comparisons made. In scenario 1, where there were no obstacles, both methods achieved the same TET. However, in scenario 2, where an obstacle was present, the TET for the ACO wayfinding method was 21.6% shorter than that for the ‘familiarity’ wayfinding method

Understanding and Managing Wildfire Risks to Residential Communities and Supply Chain Networks

Wildfire has become an increasing threat to humans, the built environment, and ecosystems in the United States. Several factors contribute to such an increase in wildfire risk, including climate change, rapid population growth and infrastructure development at the wildland-urban interface, and accumulated fuels from past wildfire management practices. Increases in wildfire activity have resulted in substantial human and economic losses in the past decade. For example, the 2023 Hawaii wildfires razed more than 2,200 homes and businesses while tragically claiming the lives of at least 115 individuals. A series of California wildfires in 2015, 2017, and 2018 resulted in direct economic losses of $4.8 billion,$18 billion, and $26.3 billion, respectively. In addition to the direct economic losses, the 2018 California wildfires disrupted many supply chain systems, which propagated across the regional economy and induced$88.6 billion in direct losses. These recent wildfires have underscored the urgent need for understanding, assessing, and managing wildfire risks to residential communities and supply chains. To this end, this dissertation aims at understanding and managing wildfire risks to humans, properties, and the regional economy, with a particular focus on residential communities and supply chain networks. To advance our understanding of various proactive and emergency activities, this dissertation begins by examining homeowners’ decisions on wildfire-related proactive actions, such as home hardening, vegetation treatment, and homeowners insurance, through an online survey and subsequently assesses the effect of these actions on the process of housing recovery. Next, this dissertation shifts its focus towards individual behaviors during wildfire events, encompassing their preferences and decisions made during wildfire evacuations. This entails the study of factors like evacuation triggers and timing, as well as a series of en-route decisions made by residents in wildfire-prone areas, all gathered through an online survey. Based on the survey results, data-driven models are developed for predicting evacuees’ behaviors during wildfires. Furthermore, this dissertation integrates these data-driven predictive models with wildfire simulations, vulnerability assessment, and traffic simulation to construct a comprehensive agent-based modeling (ABM) framework for wildfire evacuations under damaged transportation settings. The framework is designed to simulate traffic conditions during a wildfire evacuation and identifies the critical parts of the transportation network for pre-fire risk mitigation actions aimed at improving mobility during a wildfire evacuation.To assess wildfire risk to a supply chain network, this dissertation also proposes a probabilistic wildfire risk assessment framework. It provides rigorous probabilistic descriptions of wildfire ignition likelihood and growth, interaction between supply chain components and wildfire, consequent component damage, and network-level performance reduction. Then, a hypothetical forest-residuals-to-sustainable-aviation-fuel supply chain network is utilized as an illustrative example to demonstrate the capability and applicability of the proposed framework. The proposed framework can be used as a planning tool to evaluate network performance subject to a set of what-if scenarios and the effect of pre- and post-wildfire risk mitigation measures.Overall, this dissertation provides valuable insights for understanding the inherent drivers of individual’s preference on both wildfire proactive actions and evacuation decisions. This information can serve as a foundation for increasing community resilience by helping policymakers and stakeholders to increase participation rates in proactive actions and the responsiveness to evacuation orders. Moreover, the simulation tools and quantitative frameworks developed in this dissertation provide valuable support for stakeholders and policymakers in forecasting post-wildfire performance and implementing more effective pre-event mitigation strategies. These adaptable tools and frameworks show potential for broader applications across various domains, including water distribution networks, transportation systems, and electric power grids, making them valuable assets in addressing the complex challenges posed by dynamic and interconnected systems

Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders

The NASA commitment to human space flight includes continuing to fly astronauts on the ISS until it is decommissioned as well as possibly returning astronauts to the moon or having astronauts venture to an asteroid or Mars. As missions leave low Earth orbit and explore deeper space, BHP supports and conducts research to enable a risk posture that considers the risk of adverse cognitive or behavioral conditions and psychiatric disorders acceptable given mitigations, for pre-, in, and post-flight.The Human System Risk Board (HSRB) determines the risk of various mission scenarios using a likelihood (per person per year) by consequences matrix examining those risks across two categorieslong term health and operational (within mission). Colors from a stoplight signal are used by HSRB and quickly provide a means of assessing overall perceived risk for a particular mission scenario. Risk associated with the current six month missions on the ISS are classified as accepted with monitoring while planetary missions, such as a mission to Mars, are recognized to be a red risk that requires mitigation to ensure mission success.Currently, the HSRB deems that the risk of adverse cognitive or behavioral conditions and psychiatric outcomes requires mitigation for planetary missions owing to long duration isolation and radiation exposure (see Table 1). While limited research evidence exists from spaceflight, it is well known anecdotally that the shift from the two week shuttle missions to the six month ISS missions renders the psychological stressors of space as more salient over longer duration missions. Shuttle astronauts were expected just to tolerate any stressors that arose during their mission and were successful at doing so (Whitmire et al, 2013). While it is possible to deal with stressors such as social isolation and to live with incompatible crewmembers for two weeks on shuttle, ignoring it is much less likely to be a successful coping mechanism on station. For the longer missions of the ISS, astronauts require a larger, more robust set of coping skills and more psychological support. Evidence of this are the number of BHPs Operational Psychology (Op Psy) staff who have been awarded silver Snoopys by long duration astronauts, in the statements of praise for the Op Psy and Family Support Office teams, and in the written and oral statements from flown astronauts regarding difficulty of longer missions and how much Op Psy helped

Heat stress and a countermeasure in the Shuttle rescueman's suit

Rescue of the astronaut flight crew from a contingency landing may risk exposure of the rescue crew to toxic propellants spilling from potentially ruptured tanks in the crew module area. An Aquala dry diver's suit has been in service by the rescue team to preclude exposure, especially in the water rescue scenario. Heat stress has become a factor of concern in recent years when older and less physically-fit team members work in this suit. Methods: Field testing was initiated using fully instrumented rescue men in a simulated scenario to determine the extent of heat stress. Two tests were accomplished, one in the normal (N) configuration and one with a proposed cooling countermeasure, the Steele vest (S). Results: Heat stress was high as indicated by average rectal temperatures (Tre) of 38.28 degrees C(100.9 degrees F) after the 45 minute protocol. Slopes of the regression equations describing the increase in Tre with time were greater (P less than 0.05) with N (0.073 plus or minus .008) compared to S (0.060 plus or minus .007). Projection of time to the 38.89 degree C (102 degree F) limit was increased by 15.3 percent with the vest. Mean skin temperature (Tsk) was higher (P less than 0.05) in N (38.33 plus or minus .11 degrees C) compared to S (34.33 plus or minus .39 degrees C). Average heart rate was higher (P less than 0.05 in N than S. Sweat loss, as measured by weight loss, was more (P less than 0.05) for N (1.09 plus or minus .09 kg versus 0.77 plus or minus .06 kg). Air usage, while slightly less for S, was not statistically different. Conclusion: The use of the cool vest provided significant relief from thermal stress in spite of the addition of 3.4 kg (7.5 pounds) weight and some loss in mobility