The effect of the emergency medical services vehicle location and response strategy on response times

Response time is currently considered to be an important performance indicator in Emergency Medical Services (EMS) systems. A number of factors may affect response times, including the location of emergency vehicles and the type of response system design used. This study aimed to assess the effects of emergency vehicle location and response system design on response time performance in a model of a large South African urban EMS system, using discrete-event simulation. Results indicated that both the emergency vehicle location and response system design factors had a significant effect on response time performance, with more decentralised vehicle location having a greater effect

UNDERSTANDING THE IMPACT OF INCIDENTS AND INCIDENT MANAGEMENT PROGRAMS ON FREEWAY MOBILITY AND SAFETY

Despite significant technological achievements over past decades, and institutional support for Intelligent Transportation System (ITS), it is not possible to prevent all traffic incidents. Numerous incidents occur every day along U.S. freeways and traffic incident management (TIM) programs have been proposed and implemented to mitigate their impact. This dissertation proposes various tools to aid in the evaluation of proposed TIM programs, contributing, thus, to the general study area of freeway incident management. In addition, moving violations specific to concurrent flow lane operations are conceived as a type of transient incident. Their impact on mobility and safety is considered. Techniques to address four key areas are proposed. First, a methodology that considers the dynamics of incident impact given a primary incident's properties and prevailing traffic conditions for identifying secondary incidents from a database is proposed. This method is computationally efficient and overcomes deficiencies of other existing techniques, with utility in any context in which the study of secondary incidents is warranted. A three-stage time-saving process is developed for conducting TIM program benefit evaluations. The process aids in sampling a relatively small set of good quality incident scenarios that can represent historical incident data and overcomes the computational burden encountered when evaluating TIM program's benefit by simulation. Modeling techniques are proposed for simulating violations associated with the operation of concurrent flow lanes. Results from a case study show significant impact to mobility that grows nonlinearly with increasing violation rate. Such illegal traffic maneuvers contribute to increased speed variation and congestion, ultimately affecting safety. Finally, diversion strategies that exploit existing capacity of managed lanes for the purpose of reducing the impact of an incident in the general purpose lanes are evaluated. Simulation modeling methodologies were developed for modeling freeway incidents and studied diversion strategy implementations. Experimental findings indicate benefits of diversion that are contrary to qualitatively developed recommendations in the literature

Cybersecurity Assessment and Mitigation Stochastic Model

With numerous cybersecurity incidents and vulnerability concerns in an increasingly contested cyber warfighting environment, the Department of Defense (DoD) has mandated cybersecurity assessment and authorization of all major weapon systems (MWS) before their use. In response to this direction, the Air Force Life Cycle Management Center (AFLCMC) created the Platform Information Technology Assessment and Authorization (PIT A&A) Process. Modeled after the NIST Risk Management Framework (RMF), this process applies a risk-based approach to cybersecurity with the goal of identifying risks and mitigating vulnerabilities in MWS. Within this work, a stochastic model of the PIT A&A Process is presented with an emphasis on understanding how the complexity of systems, accuracy of security artifacts, and workforce proficiency impacts the ability to effectively mitigate cybersecurity risks

Aeronautical life-cycle mission modelling framework for conceptual design

This thesis introduces a novel framework for life cycle mission modelling during conceptual aeronautical design. The framework supports object-oriented mission definition using Geographical Information System technology. Design concepts are defined generically, enabling simulation of most aeronautical vessels and many non-aeronautical vehicles. Moreover, the framework enables modelling of entire vessel fleets, business competitors and dynamic operational changes throughout a vessel life cycle. Vessels consist of components deteriorating over time. Vessels carry payload that operates within the vessel environment.An agent-based simulation model implements most framework features. It is the first use of an agent-based simulation utilising a Geographical Information System during conceptual aeronautical design. Two case studies for unmanned aircraft design apply the simulation. The first case study explores how the simulation supports conceptual design phase decisions. It simulates four different unmanned aircraft concepts in a search-and-rescue scenario including lifeboats. The goal is to learn which design best improves life cycle search performance. It is shown how operational and geographical impacts influence design decision making by generating novel performance information. The second case study studies the simulation optimisation capability: an existing aircraft design is modified manually based on simulation outputs. First, increasing the fuel tank capacity has a negative effect on life cycle performance due to mission constraints. Therefore, mission definition becomes an optimisation parameter. Changing mission flight speeds during specific segments leads to an overall improved design

Impact of Connected Vehicles on Mitigating Secondary Crash Risk

Reducing the risk of secondary crashes is a key goal for effective traffic incident management. However, only few countermeasures have been established in practices to achieve the goal. This is mainly due to the stochastic nature of both primary and secondary crashes. Given the emerging connected vehicle (CV) technologies, it is highly likely that CVs will soon be able to communicate with each other through the ad-hoc wireless vehicular network. Information sharing among vehicles is deemed to change traffic operations and allow motorists for more proactive actions. Motorists who receive safety messages can be motivated to approach queues and incident sites with more caution. As a result of the improved situational awareness, the risk of secondary crashes is expected to be reduced. To examine whether this expectation is achievable or not, this study aims to assess the impact of connectivity on the risk of secondary crashes. A simulation-based modeling framework that enables vehicle-to-vehicle communication module was developed. Since crashes cannot be directly simulated in micro-simulation, the use of surrogate safety measures was proposed to capture vehicular conflicts as a proxy for secondary crash risk upstream of a primary crash site. An experimental study was conducted based on the developed simulation modeling framework. The results show that the use of connected vehicles can be a viable way to reduce the risk of secondary crashes. Their impact is expected to change with an increasing market penetration of connected vehicles. © 2017 Tongji University and Tongji University Press

Incident duration time prediction using a supervised topic modeling method

Precisely predicting the duration time of an incident is one of the most prominent components to implement proactive management strategies for traffic congestions caused by an incident. This thesis presents a novel method to predict incident duration time in a timely manner by using an emerging supervised topic modeling method. Based on Natural Language Processing (NLP) techniques, this thesis performs semantic text analyses with text-based incident dataset to train the model. The model is trained with actual 1,466 incident records collected by Korea Expressway Corporation from 2016-2019 by applying a Labeled Latent Dirichlet Allocation(L-LDA) approach. For the training, this thesis divides the incident duration times into two groups: shorter than 2-hour and longer than 2-hour, based on the MUTCD incident management guideline. The model is tested with randomly selected incident records that have not been used for the training. The results demonstrate that the overall prediction accuracies are approximately 74% and 82% for the incidents shorter and longer than 2-hour, respectively

Container and VM Visualization for Rapid Forensic Analysis

Cloud-hosted software such as virtual machines and containers are notoriously difficult to access, observe, and inspect during ongoing security events. This research describes a new, out-of-band forensic tool for rapidly analyzing cloud based software. The proposed tool renders two-dimensional visualizations of container contents and virtual machine disk images. The visualizations can be used to identify container / VM contents, pinpoint instances of embedded malware, and find modified code. The proposed new forensic tool is compared against other forensic tools in a double-blind experiment. The results confirm the utility of the proposed tool. Implications and future research directions are also described

Best-subset Selection for Complex Systems using Agent-based Simulation

It is difficult to analyze and determine strategies to control complex systems due to their inherent complexity. The complex interactions among elements make it difficult to develop and test decision makers' intuition of how the system will behave under different policies. Computer models are often used to simulate the system and to observe both direct and indirect effects of alternative interventions. However, many decision makers are unwilling to concede complete control to a computer model because of the abstractions in the model, and the other factors that cannot be modeled, such as physical, human, social and organizational relationship constraints. This dissertation develops an agent-based simulation (ABS) model to analyze a complex system and its policy alternatives, and contributes a best-subset selection (BSS) procedure that provides a group of good performing alternatives to which decision makers can then apply their subject and context knowledge in making a final decision for implementation. As a specific example of a complex system, a mass casualty incident (MCI) response system was simulated using an ABS model consisting of three interrelated sub-systems. The model was then validated by a series of sensitivity analysis experiments. The model provides a good test bed to evaluate various evacuation policies. In order to find the best policy that minimizes the overall mortality, two ranking-and-selection (R&S) procedures from the literature (Rinott (1978) and Kim and Nelson (2001)) were implemented and compared. Then a new best-subset selection (BSS) procedure was developed to efficiently select a statistically guaranteed best-subset containing all alternatives that are close enough to the best one for a pre-specified probability. Extensive numerical experiments were organized to prove the effectiveness and demonstrate the performance of the BSS procedure. The BSS procedure was then implemented in conjunction with the MCI ABS model to select the best evacuation policies. The experimental results demonstrate the feasibility and effectiveness of our agent-based optimization methodology for complex system policy evaluation and selection