This thesis sought to understand reasons for noncompliant respondents and ineffective leadership in the National Incident Management System (NIMS) by Texas public safety training officers. Research has been conducted on the policies and their implications for NIMS, organizational culture and its impact on NIMS, and the hierarchy network of the Incident Command System (ICS). However, research evaluating the attitudes and perceptions regarding the effectiveness of NIMS is scarce. Training officers from rural fire departments, emergency medical services, and law enforcement agencies were the population for this study (n=33). The results of this study have implications for combined fire department, emergency medical services, and law enforcement training (interoperability), simplification of the management structure, and a mentoring program. This study should be explored further in an urban setting, based on this model. This study showed that respondents agreed that rural emergency responders tend to be noncompliant with NIMS. Respondents mentioned that rural emergency responders disagree with the effectiveness of NIMS. This study showed that a correlation occurred between the effectiveness of NIMS and the number of times a respondent was involved in a formal NIMS incident command. The following recommendations were made based on the findings and conclusions of this study. Researchers should continue to look at what public safety training officers believe affects the adaptability of NIMS. Training officers should consider contributing to the future NIMS curriculum. Training officers should focus on interoperability issues through increased field exercises. Research should be conducted to determine what improvements to curriculum effect future NIMS compliance. Further research should be conducted on the effectiveness of individual compliance, and achievement.

Intelligent Transportation Systems (ITS) play an important role in supporting motorists and first responders to handle major incidents, hazmat spills, security measures and other emergency operations. Increasingly, technology advancements and applications are providing invaluable integration opportunities to interconnect safety, maintenance, ITS, traffic operations, facilities, and transportation equipment programs to maximize available resources and achieve efficiency in transportation operations. This research focuses on one of the prominent ITS devices: Dynamic Message Signs (DMS) operations. Using real time incident management operations data and ITS operations data, an attempt is made to develop ITS Operations Evaluations Models (ITSOEMs) that are capable of quantifying the operations value of DMS and their complementing ITS surveillance and detection systems, such as CCTV, Safety Service Patrol (SSP) and Computer Aided Dispatch (CAD) systems. The I-95 corridor in the state of Virginia is selected as the Study Corridor. Operations data from the Virginia Department of Transportation (VDOT) on DMS and ITS detection systems and incident management is analyzed to accomplish four key research objectives: a) To collect, extract and evaluate real time operations data from VDOT ITS and incident management operations programs and identify the factors contributing to the successful operation of DMS for the duration of incidents and events occurring on the I-95 Study Corridor; b) To develop analytical models to determine measurable influence of incident detections in successfully disseminating messages on DMS; c) To develop ITS Operations evaluation models to determine the effectiveness of DMS messages during incidents by quantifying the influence of DMS messages in the deterrence of secondary incidents on the I-95 Study Corridor; and d) To develop guidelines on the application of these quantitative ITS evaluation models so that transportation engineers can effectively use these models to perform ITS evaluations at other ITS locations. The ITS evaluation framework developed in this research work is expected to assist transportation engineers in the prioritization, selection and implementation of operations and technology integration strategies.

With perpetual strains on resources, road agencies need to develop network-level decision-making frameworks to ensure optimum resource allocation. This is especially true for incident management services and in particular variable message signs (VMS), which are relatively immature disciplines compared to traditional road engineering. The objective of incident management and VMS is to minimise the safety, efficiency, reliability and environmental impacts of incidents on the operations of the transport system. This may be achieved by informing travellers of the incidents so they can adapt their behaviour in a manner that reduces community impacts, such as lateness and the associated vehicle emissions, unreliability of travel times, as well as secondary accidents due to incidents. Generally, road authorities do carry out needs assessments, but qualitatively in many cases. Therefore, this masters research presents a framework that is systematic, quantitative and relatively easy to implement. In order to prioritise VMS infrastructure deployment, a risk management approach was taken that focuses on minimising the impacts on, and costs to the community. In the framework and case study conducted, safety, efficiency and reliability, and environmental impacts are quantified using an economic risk management approach to determine an overall risk score. This score can be used to rank road sections within the network, indicating the roads with the highest risk of incident network impacts and therefore the roads with the highest need for intervention. A cost-effectiveness based risk-reduction ranking can then be determined for each incident management treatment type, comparing the net risk with treatment to that without treatment, and dividing by the net present value of deployment. The two types of ranking, pure risk and cost-effectiveness based risk reduction, will help to minimise the network impacts on the community and optimise resource allocation.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1995. / Vita. Abstract. Includes bibliographical references (leaves 137-140). Also available via the Internet.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1996. / Vita. Abstract. Includes bibliographical references (leaves 161-166). Also available via the Internet.

This diploma thesis is focused on incident and problem management on computer network at University of Economics, Prague. Its goal is to study available models used in network management, analyze current status of network management support tools used at University of Economics and design and implement an application for effective diagnosis of network incidents based on this analysis. Information about network management models will be acquired from available sources -- primarily ITIL v3 and also online information published on the Web. The final part of this thesis is a fully functional web-based application which will be used by helpdesk operators to support them in finding event information in historical log files. This application will then be deployed on servers in University of Economics, Prague.

This diploma thesis deals with implementation of JIRA in a selected company. The aim of the thesis is to propose solution design for implementation of JIRA in order to support the processes of the company and also eliminate the problems occuring at present. The thesis is divided into three parts. The introduction of section provides a theoretical basis for the thesis, which consists of a description of the essential characteristics ADLM tools, analysis of the current competitors of the company Atlassian, which develops JIRA, and also description of the functionality of JIRA and plugins integrable with JIRA. The beginning of the second part consists of basic information about selected company, the defined processes and problems which new solution should eliminate. The core of practical part includes solution design for implementation of JIRA within the defined processes. The last part deals with the evaluation of implementation, proposal of possible improvements and generalization of experience gained in the implementation.

Modern IT companies manage security of their customers'networks following particular models, processes and procedures. In this thesis are presented the most important and widespread guidelines on Security Incident Response Plans as well as the implementation of a software for an IT danish company called NNIT. In particular, this software aims to improve NNIT's Security Incident Management Process generating automatic reports of vulnerabilities found in NNIT clients networks. Enhancing this process reducing its execution time is directly translated into a proactive response where vulnerabilities are tackled and patched before an attacker could exploit them. The software developed and described in this thesis is called VRC and thanks to a particular algorithm analyzes the database of vulnerabilities found by the vulnerability scanner and produces customizable reports. In the reports, the list of vulnerabilities is ordered by severity and number of machines a ected in order to present the most urgent vulnerabilities that should be xed. Finally, an evaluation of the VRC performance and usefulness is also included.

The early observation and elimination of non-recurring incidents is a crucial task in trafficmanagement. The performance of the conventional incident detection methods (trafficcameras and other sensory technologies) is limited and there are still challenges inobtaining an accurate picture of the traffic conditions in real time. During the last decade,the technical development of mobile platforms and the growing online connectivity made itpossible to obtain traffic information from social media and applications based on spatialcrowdsourcing. Utilizing the benefits of crowdsourcing, traffic authorities can receiveinformation about a more comprehensive number of incidents and can monitor areaswhich are not covered by the conventional incident detection systems. The crowdsourcedtraffic data can provide supplementary information for incidents already reported throughother sources and it can contribute to earlier detection of incidents, which can lead tofaster response and clearance time. Furthermore, spatial crowdsourcing can help to detectincident types, which are not collected systematically yet (e.g. potholes, traffic light faults,missing road signs). However, before exploiting crowdsourced traffic data in trafficmanagement, numerous challenges need to be resolved, such as verification of the incidentreports, predicting the severity of the crowdsourced incidents and integration with trafficdata obtained from other sources.During this thesis, the possibilities and challenges of utilizing spatial crowdsourcingtechnologies to detect non-recurring incidents were examined in form of a case study.Traffic incident alerts obtained from Waze, a navigation application using the concept ofcrowdsourcing, were analyzed and compared with officially verified incident reports inStockholm. The thesis provides insight into the spatial and temporal characteristics of theWaze data. Moreover, a method to identify related Waze alerts and to determine matchingincident reports from different sources is presented. The results showed that the number ofreported incidents in Waze is 4,5 times higher than the number of registered incidents bythe Swedish authorities. Furthermore, 27,5 % of the incidents could have been detectedfaster by using the traffic alerts from Waze. In addition, the severity of Waze alerts isexamined depending on the attributes of the alerts.

The early observation and elimination of non-recurring incidents is a crucial task in traffic management. The performance of the conventional incident detection methods (traffic cameras and other sensory technologies) is limited and there are still challenges in obtaining an accurate picture of the traffic conditions in real time. During the last decade, the technical development of mobile platforms and the growing online connectivity made it possible to obtain traffic information from social media and applications based on spatial crowdsourcing. Utilizing the benefits of crowdsourcing, traffic authorities can receive information about a more comprehensive number of incidents and can monitor areas which are not covered by the conventional incident detection systems. The crowdsourced traffic data can provide supplementary information for incidents already reported through other sources and it can contribute to earlier detection of incidents, which can lead to faster response and clearance time. Furthermore, spatial crowdsourcing can help to detect incident types, which are not collected systematically yet (e.g. potholes, traffic light faults, missing road signs). However, before exploiting crowdsourced traffic data in traffic management, numerous challenges need to be resolved, such as verification of the incident reports, predicting the severity of the crowdsourced incidents and integration with traffic data obtained from other sources. During this thesis, the possibilities and challenges of utilizing spatial crowdsourcing technologies to detect non-recurring incidents were examined in form of a case study. Traffic incident alerts obtained from Waze, a navigation application using the concept of crowdsourcing, were analyzed and compared with officially verified incident reports in Stockholm. The thesis provides insight into the spatial and temporal characteristics of the Waze data. Moreover, a method to identify related Waze alerts and to determine matching incident reports from different sources is presented. The results showed that the number of reported incidents in Waze is 4,5 times higher than the number of registered incidents by the Swedish authorities. Furthermore, 27,5 % of the incidents could have been detected faster by using the traffic alerts from Waze. In addition, the severity of Waze alerts is examined depending on the attributes of the alerts.