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SENSOR-BASED COMPUTING TECHNIQUES FOR REAL-TIME TRAFFIC EVACUATION MANAGEMENT
Title
SENSOR-BASED COMPUTING TECHNIQUES FOR REAL-TIME TRAFFIC EVACUATION MANAGEMENT.
Creator
Hamza-Lup, Georgiana, Hua, Kien, University of Central Florida
Abstract / Description
The threat of terrorist incidents is higher than ever before and devastating acts, such as the terrorist attacks on the World Trade Center and the Pentagon, have left many concerns about the possibility of future incidents and their potential impact. Unlike some natural disasters that can be anticipated, terrorist attacks are sudden and unexpected. Even if sometimes we do have partial information about a possible attack, it is generally not known exactly where, when, or how an attack will...
Show moreThe threat of terrorist incidents is higher than ever before and devastating acts, such as the terrorist attacks on the World Trade Center and the Pentagon, have left many concerns about the possibility of future incidents and their potential impact. Unlike some natural disasters that can be anticipated, terrorist attacks are sudden and unexpected. Even if sometimes we do have partial information about a possible attack, it is generally not known exactly where, when, or how an attack will occur. This lack of information posses great challenges on those responsible for security, specifically, on their ability to respond fast, whenever necessary with flexibility and coordination. The surface transportation system plays a critical role in responding to terrorist attacks or other unpredictable human-caused disasters. In particular, existing Intelligent Transportation Systems (ITS) can be enhanced to improve the ability of the surface transportation system to efficiently respond to emergencies and recover from disasters. This research proposes the development of new information technologies to enhance today's ITS with capabilities to improve the crisis response capabilities of the surface transportation system. The objective of this research is to develop a Smart Traffic Evacuation Management System (STEMS) that responds rapidly and effectively to terrorist threats or other unpredictable disasters, by creating dynamic evacuation plans adaptable to continuously changing traffic conditions based on real-time information. The intellectual merit of this research is that the proposed STEMS will possess capabilities to support both the unexpected and unpredictable aspects of a terrorist attack and the dynamic aspect of the traffic network environment. Studies of related work indicate that STEMS is the first system that automatically generates evacuation plans, given the location and scope of an incident and the current traffic network conditions, and dynamically adjusts the plans based on real-time information received from sensors and other surveillance technologies. Refining the plans to keep them consistent with the current conditions significantly improves evacuation effectiveness. The changes that STEMS can handle range from slow, steady variations in traffic conditions, to more sudden variations caused by secondary accidents or other stochastic factors (e.g., high visibility events that determine a sudden increase in the density of the traffic). Being especially designed to handle evacuation in case of terrorist-caused disasters, STEMS can also handle multiple coordinated attacks targeting some strategic area over a short time frame. These are frequently encountered in terrorist acts as they are intended to create panic and terror. Due to the nature of the proposed work, an important component of this project is the development of a simulation environment to support the design and test of STEMS. Developing analytical patterns for modeling traffic dynamics has been explored in the literature at different levels of resolution and realism. Most of the proposed approaches are either too limited in representing reality, or too complex for handling large networks. The contribution of this work consists of investigating and developing traffic models and evacuation algorithms that overcome both of the above limitations. Two of the greatest impacts of this research in terms of science are as follows. First, the new simulation environment developed for this project provides a test bed to facilitate future work on traffic evacuation systems. Secondly, although the models and algorithms developed for STEMS are targeted towards traffic environments and evacuation, their applicability can be extended to other environments (e.g., building evacuation) and other traffic related problems (e.g., real-time route diversion in case of accidents). One of the broader impacts of this research would be the deployment of STEMS in a real environment. This research provides a fundamental tool for handling emergency evacuation for a full range of unpredictable incidents, regardless of cause, origin and scope. Wider and swifter deployment of STEMS will support Homeland Security in general, and will also enhance the surface transportation system on which so many Homeland Security stakeholders depend.
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Date Issued
2006
Identifier
CFE0001248, ucf:46919
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001248
Understanding Crisis Communication and Mobility Resilience during Disasters from Social Media
Title
Understanding Crisis Communication and Mobility Resilience during Disasters from Social Media.
Creator
Roy, Kamol, Hasan, Samiul, Eluru, Naveen, Wu, Yina, University of Central Florida
Abstract / Description
Rapid communication during extreme events is one of the critical aspects of successful disaster management strategies. Due to their ubiquitous nature, social media platforms offer a unique opportunity for crisis communication. Moreover, social media usage on GPS enabled devices such as smartphones allow us to collect human movement data which can help understanding mobility during a disaster. This study leverages social media (Twitter) data to understand the effectiveness of social media...
Show moreRapid communication during extreme events is one of the critical aspects of successful disaster management strategies. Due to their ubiquitous nature, social media platforms offer a unique opportunity for crisis communication. Moreover, social media usage on GPS enabled devices such as smartphones allow us to collect human movement data which can help understanding mobility during a disaster. This study leverages social media (Twitter) data to understand the effectiveness of social media-based communication and the resilience of human mobility during a disaster. This thesis has two major contributions. First, about 52.5 million tweets related to hurricane Sandy are analyzed to assess the effectiveness of social media communication during disasters and identify the contributing factors leading to effective crisis communication strategies. Effectiveness of a social media user is defined as the ratio of attention gained over the number of tweets posted. A model is developed to explain more effective users based on several relevant features. Results indicate that during a disaster event, only few social media users become highly effective in gaining attention. In addition, effectiveness does not depend on the frequency of tweeting activity only; instead it depends on the number of followers and friends, user category, bot score (controlled by a human or a machine), and activity patterns (predictability of activity frequency). Second, to quantify the impacts of an extreme event to human movements, we introduce the concept of mobility resilience which is defined as the ability of a mobility infrastructure system to manage shocks and return to a steady state in response to an extreme event. We present a method to detect extreme events from geo-located movement data and to measure mobility resilience and loss of resilience due to those events. Applying this method, we measure resilience metrics from geo-located social media data for multiple types of disasters occurred all over the world. Quantifying mobility resilience may help us to assess the higher-order socio-economic impacts of extreme events and guide policies towards developing resilient infrastructures as well as a nation's overall disaster resilience.
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Date Issued
2018
Identifier
CFE0007362, ucf:52090
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007362