An Aircraft Evacuation Simulation Baseline Using DES for Passenger Path Planning

This paper introduced a Discrete Event Simulation (DES) model that simulates passengers’ evacuation paths and decision-making processes during aircraft certification. The model was built using ARENA® 14, which is a DES simulation tool. This model used A380 cabin configuration with capacity of 538 passengers. Each passenger was considered as an independent human being with variations in walking speed, decision-making processes, and evacuation path. This model generated total evacuation time and presented total congestion conditions of each gate. Federal Regulation has suggested that all passengers in the airplane should finish the evacuation within 90 seconds. The model was validated with the A380 certification evacuation, which was 78.2 sec. This model was tested and statistically validated for aircraft evacuation. However, the validation model has limitations in passengers’ freedom of choosing a gate. To advance the simulation, an experiment was conducted based on the modification of the validation model to simulate the effect on total evacuation time of passengers switching gates while waiting to exit. At the end of this paper, future study directions were suggested to innovate the baseline by adding human interactions and advanced methods in dynamic simulation technology

Intelligent evacuation management systems: A review

Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios

Comparing Single-Objective Optimization Protocols for Calibrating the Birds Nest Aquifer Model—A Problem Having Multiple Local Optima

To best represent reality, simulation models of environmental and health-related systems might be very nonlinear. Model calibration ideally identifies globally optimal sets of parameters to use for subsequent prediction. For a nonlinear system having multiple local optima, calibration can be tedious. For such a system, we contrast calibration results from PEST, a commonly used automated parameter estimation program versus several meta-heuristic global optimizers available as external packages for the Python computer language—the Gray Wolf Optimization (GWO) algorithm; the DYCORS optimizer framework with a Radial Basis Function surrogate simulator (DRB); and particle swarm optimization (PSO). We ran each optimizer 15 times, with nearly 10,000 MODFLOW simulations per run for the global optimizers, to calibrate a steady-state, groundwater flow simulation model of the complex Birds Nest aquifer, a three-layer system having 8 horizontal hydraulic conductivity zones and 25 head observation locations. In calibrating the eight hydraulic conductivity values, GWO averaged the best root mean squared error (RMSE) between observed and simulated heads—20 percent better (lower) than the next lowest optimizer, DRB. The best PEST run matched the best GWO RMSE, but both the average PEST RMSE and the range of PEST RMSE results were an order of magnitude larger than any of the global optimizers

Chemical Plume Tracing by Discrete Fourier Analysis and Particle Swarm Optimization

A novel methodology for solving the chemical plume tracing problem that utilizes data from a network of stationary sensors has been developed in this study. During a toxic chemical release and dispersion incident, the imperative need of first responders is to determine the physical location of the source of chemical release in the shortest possible time. However, the chemical plume that develops from the source of release may evolve into a highly complex distribution over the entire contaminated region, making chemical plume tracing one of the most challenging problems known to date. In this study, the discrete Fourier series method was applied for re-construction of the contour map representing the concentration distribution of chemical over the contaminated region based on point measurements by sensors in a pre-installed network. Particle Swarm Optimization was then applied to the re-constructed contour map to locate the position of maximal concentration. Such a methodology was found to be highly successful in solving the chemical plume tracing problem via the sensor network approach and thus closes a long-standing gap in the literature. Furthermore, the nature of the methodology is such that a visual of the entire chemical dispersion process is made available during the solution process and this can be beneficial for warning purposes and evacuation planning. In the context of such chemical release scenarios, the algorithm developed in this study is believed to be able to play an instrumental role towards national defense for any country in the world that is subjected to such threats

Metaheuristic Algorithms for Spatial Multi-Objective Decision Making

Spatial decision making is an everyday activity, common to individuals and organizations. However, recently there is an increasing interest in the importance of spatial decision-making systems, as more decision-makers with concerns about sustainability, social, economic, environmental, land use planning, and transportation issues discover the benefits of geographical information. Many spatial decision problems are regarded as optimization problems, which involve a large set of feasible alternatives, multiple conflicting objectives that are difficult and complex to solve. Hence, Multi-Objective Optimization methods (MOO)—metaheuristic algorithms integrated with Geographical Information Systems (GIS) are appealing to be powerful tools in these regards, yet their implementation in spatial context is still challenging. In this thesis, various metaheuristic algorithms are adopted and improved to solve complex spatial problems. Disaster management and urban planning are used as case studies of this thesis.These case studies are explored in the four papers that are part of this thesis. In paper I, four metaheuristic algorithms have been implemented on the same spatial multi-objective problem—evacuation planning, to investigate their performance and potential. The findings show that all tested algorithms were effective in solving the problem, although in general, some had higher performance, while others showed the potential of being flexible to be modified to fit better to the problem. In the same context, paper II identified the effectiveness of the Multi-objective Artificial Bee Colony (MOABC) algorithm when improved to solve the evacuation problem. In paper III, we proposed a multi-objective optimization approach for urban evacuation planning that considered three spatial objectives which were optimized using an improved Multi-Objective Cuckoo Search algorithm (MOCS). Both improved algorithms (MOABC and MOCS) proved to be efficient in solving evacuation planning when compared to their standard version and other algorithms. Moreover, Paper IV proposed an urban land-use allocation model that involved three spatial objectives and proposed an improved Non-dominated Sorting Biogeography-based Optimization algorithm (NSBBO) to solve the problem efficiently and effectively.Overall, the work in this thesis demonstrates that different metaheuristic algorithms have the potential to change the way spatial decision problems are structured and can improve the transparency and facilitate decision-makers to map solutions and interactively modify decision preferences through trade-offs between multiple objectives. Moreover, the obtained results can be used in a systematic way to develop policy recommendations. From the perspective of GIS - Multi-Criteria Decision Making (MCDM) research, the thesis contributes to spatial optimization modelling and extended knowledge on the application of metaheuristic algorithms. The insights from this thesis could also benefit the development and practical implementation of other Artificial Intelligence (AI) techniques to enhance the capabilities of GIS for tackling complex spatial multi-objective decision problems in the future

Optimizing the Placement of Multiple UAV--LiDAR Units Under Road Priority and Resolution Requirements

Real-time road traffic information is crucial for intelligent transportation systems (ITS) applications, like traffic navigation or emergency response management, but acquiring such data is tremendously challenging in practice because of the high costs and inefficient placement of sensors. Some modern ITS applications contribute to this problem by equipping vehicles with multiple light detection and ranging (LiDAR) sensors, which are expensive and gather data inefficiently; one solution that avoids vehicle-mounted LiDAR acquisition has been to install elevated LiDAR instruments along roadways, but this approach remains unrefined. The eventual development of sixth-generation (6G) wireless communication will enable new, creative solutions to solve these challenges. One new solution is to deploy multiple multirotor unmanned aerial vehicles (UAVs) outfitted with LiDAR sensors (ULiDs) to acquire data remotely. These ULiDs can capture accurate and real-time road traffic information for ITS applications while maximizing the capabilities of LiDAR sensors, which in turn reduces the number of sensors required. Accordingly, this thesis aims to find the optimal 3D placement of multiple ULiDs to maximize road coverage efficiency for ITS purposes. The formulated optimization problem is constrained by unique ULiD specifications, including field-of-view (FoV), point cloud resolution, geographic information system location, and road segment coverage priorities. A computational intelligent algorithm based on particle swarm optimization is proposed to solve the designed optimization problem. Furthermore, this thesis illustrates the benefits of using the proposed algorithm over existing baselines --Abstract, p. ii