Dynamic emergency route planning for major chemical accidents: Models and application

Combining scenario construction with the characteristics of individual emergencybehavior is necessary for the emergency route planning of major chemical accidents. Weinvestigated this challenging decision problem and constructed a multi-indicator emergencyrisk assessment method that considers the evacuation speed of different population types andhealth consequences caused by various risk components. We also designed a modified Dijkstraalgorithm to solve this dynamic multi-objective route planning problem. The comparativeexperiment results demonstrated that the proposed algorithm performs relatively better thanthe traditional Dijkstra algorithm. Finally, we performed extensive case studies where oursimulation results demonstrate that the proposed model provides reliable and practicalemergency route planning services for various personnel types under different accidentscenarios. Compared with the commonly used single-dimensional assessment method, thiscomprehensive and informative assessment of the emergency risks faced by the population indifferent regions could serve as a useful reference for the formulation and implementation ofemergency plans in case of major chemical accidents

Software agents & human behavior

People make important decisions in emergencies. Often these decisions involve high stakes in terms of lives and property. Bhopal disaster (1984), Piper Alpha disaster (1988), Montara blowout (2009), and explosion on Deepwater Horizon (2010) are a few examples among many industrial incidents. In these incidents, those who were in-charge took critical decisions under various ental stressors such as time, fatigue, and panic. This thesis presents an application of naturalistic decision-making (NDM), which is a recent decision-making theory inspired by experts making decisions in real emergencies. This study develops an intelligent agent model that can be programed to make human-like decisions in emergencies. The agent model has three major components: (1) A spatial learning module, which the agent uses to learn escape routes that are designated routes in a facility for emergency evacuation, (2) a situation recognition module, which is used to recognize or distinguish among evolving emergency situations, and (3) a decision-support module, which exploits modules in (1) and (2), and implements an NDM based decision-logic for producing human-like decisions in emergencies. The spatial learning module comprises a generalized stochastic Petri net-based model of spatial learning. The model classifies routes into five classes based on landmarks, which are objects with salient spatial features. These classes deal with the question of how difficult a landmark turns out to be when an agent observes it the first time during a route traversal. An extension to the spatial learning model is also proposed where the question of how successive route traversals may impact retention of a route in the agent’s memory is investigated. The situation awareness module uses Markov logic network (MLN) to define different offshore emergency situations using First-order Logic (FOL) rules. The purpose of this module is to give the agent the necessary experience of dealing with emergencies. The potential of this module lies in the fact that different training samples can be used to produce agents having different experience or capability to deal with an emergency situation. To demonstrate this fact, two agents were developed and trained using two different sets of empirical observations. The two are found to be different in recognizing the prepare-to-abandon-platform alarm (PAPA ), and similar to each other in recognition of an emergency using other cues. Finally, the decision-support module is proposed as a union of spatial-learning module, situation awareness module, and NDM based decision-logic. The NDM-based decision-logic is inspired by Klein’s (1998) recognition primed decision-making (RPDM) model. The agent’s attitudes related to decision-making as per the RPDM are represented in the form of belief, desire, and intention (BDI). The decision-logic involves recognition of situations based on experience (as proposed in situation-recognition module), and recognition of situations based on classification, where ontological classification is used to guide the agent in cases where the agent’s experience about confronting a situation is inadequate. At the planning stage, the decision-logic exploits the agent’s spatial knowledge (as proposed in spatial-learning module) about the layout of the environment to make adjustments in the course of actions relevant to a decision that has already been made as a by-product of situation recognition. The proposed agent model has potential to be used to improve virtual training environment’s fidelity by adding agents that exhibit human-like intelligence in performing tasks related to emergency evacuation. Notwithstanding, the potential to exploit the basis provided here, in the form of an agent representing human fallibility, should not be ignored for fields like human reliability analysis

Operational risk assessment for shipping in Arctic waters

Arctic navigation has many complexities due to its particular features such as ice, severe weather conditions, remoteness, low temperatures, lack of crew experience, and extended period of darkness or daylight. For these reasons, vessels, such as oil tankers, dry cargo ships, offshore supply vessels, research vessels, and passenger ships operating in the Arctic waters may pose a high risk of collision with ice and other ships causing human casualties, environmental pollution and the loss of assets. This thesis presents a conceptual framework that is focused on collision modelling. In order to understand the process of risk escalation and to attempt a proactive approach in constituting the collision models for Arctic navigation, the present thesis identifies various risk factors that are involved in a collision. Furthermore, the thesis proposes the probabilistic framework tools that are based on the identified risk factors to estimate the risks of collision in the Arctic. The proposed frameworks are used to model the collision based risk scenarios in the region. They are developed with the use of Bayesian Networks, the Nagel-Schreckenberg (NaSch), and Human Factor Analysis and Classification (HFACS) models. In the present thesis, the proposed models are theoretical in nature, but they can be useful in developing a collision monitoring system that provides a real time-estimate of collision probability that could help avoid collisions in the Arctic. Further, the estimated probabilities are also useful in decision making concerning safe independent and convoy operations in the region. The proposed frameworks simplifies maritime accident modeling by developing a practical understanding of the role of physical environment, navigational and operational related aspects of ships, and human errors, such as individual lapses, management failures, organizational failures, and economic factors in the collision related accidents in the Arctic. This research also identifies the macroscopic properties of maritime traffic flow and demonstrates how these properties influence collision properties. The thesis also presents an innovative accident model for ice-covered waters that estimates the collision probability and establishes the relationship between the macroscopic properties of the traffic flow with the contributory accidental risk factors in the region. The main focus of the present thesis is, to better understand, communicate, and incorporate specific risk factors into the maritime risk assessment processes, involve shipping organizations to agree on best practice methodologies and make the data sources easily available, and modify the Arctic risk management processes by implementing effective risk assessment techniques and appropriate risk treatment

Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains

The idea of remote controlling ships for operational and commercial uses has developed beyond concepts. Controlling and monitoring vessels from a distant location requires updating the concept and requirements of shore control centers (SCCs), where human operators control the fleet via cameras, GPS, and many other types of sensors. While remote ship operation promises to reduce operational and maintenance costs, while increasing loading capacity and safety, it also brings significant uncertainty related to both the human-machine and human-human interactions which will affect operations. Achieving safe, reliable, and efficient remote ship operations requires consideration of both technological, cultural, social and human factor aspects of the system. Indeed, operators will act as captain and crew remotely, from the SCC, introducing new types of hardware and software interactions. This paper provides an overview of human factor issues that may affect human-machine and human-human interactions in the course of remote ship operations. In doing so, the literature related to remote operations in the domains of shipping, aerial vehicles, cranes, train transportation, automobiles, and mining is reviewed. Findings revealed that human factor issues are likely to fall into 13 distinct groups based on the type of human interactions that take place in SCCs