On the way of integrating evacuation approaches

With the growing pressure on available urban space and the construction of more and more complex building infrastructures, the navigational task for building users is getting increasingly difficult. As people react more impulsive under stressful situations, emergencies can exacerbate way finding problems. Additionally, leadership and familiarity with the (topological structure of a) building can influence the ease of finding appropriate evacuation routes. In research, two separate and distinctive techniques for modelling evacuation paths have been developed: evacuation simulation modelling through complex computer simulations and 3D network modelling based on graph theory. Taking into account a global user perspective, the 3D network modelling approach has the advantage to preserve a close connection with the semantic building structure. Following this approach, existing three dimensional evacuation routing algorithms tend to use Dijkstra's shortest path algorithm. However, as more factors, compared to path length, influence evacuation situations, literature acknowledges a void in representing more realistic, complete and accurate emergency situations. This paper presents a first step in creating such integral algorithm by implementing capacity constraints based on user flow control on a 3D geometric network model. In the future additional topics such as zonal partitioning can be added to the algorithm, moving to an integration of both evacuation approaches

Fitting the Pieces Together Improving Transportation Security Planning in the Delaware Valley

Transportation security planning is essential for the Delaware Valley to prevent, prepare for, expedite response to, and aid in the recovery from major events. The all-hazards approach prepares for any of a range of major natural or manmade events. This report provides an overview of transportation security planning in the region to facilitate communication and coordination across disciplines. It is relevant for a wide range of professionals in transportation security, operations, and planning; emergency management; emergency response; land use planning and development, and other fields at a variety of geographic levels. This report focuses on how different disciplines can better cooperate, and on the role of DVRPC in this field. Appendices include a summary of grants available and reference list

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event

A Modelling Approach To Human Navigation in Constrained Spaces

In this thesis, we consider algorithms and systems which dynamically guide evacuees towards exits during an emergency to minimise building evacuation time. We observe that the "shortest safe path" routing approach is inadequate when congestion is a predominant factor, and therefore focus on systems which manage congestion. We first implement a "Reactive" metric which compares paths based on real-time transit times. We find that regular route corrections must be issued to address the constant changes in path delays, and that routes oscillate. We also implement a model-based "Proactive" metric which forecasts the increase in future congestion that results from every routing decision, allowing the routing algorithm to operate offline. We combine both metrics with the Cognitive Packet Network (CPN), a distributed self-aware routing algorithm which uses neural networks to efficiently explore the building graph. We also present the first thorough sensitivity analysis on CPN's parameters, and use this to tune CPN for optimal performance. We then compare the proactive and reactive approaches through simulation and find both approaches reduce building evacuation times -- especially when evacuees are not evenly distributed in the building. We also find major differences between the Proactive and Reactive approach, in terms of stability, flexibility, sensory requirements, etc. Finally, we consider guiding evacuees using dynamic exit signs, whose pointing direction can be controlled. Dynamic signs can readily be used with Reactive routing, but since Proactive routing issues routes on an individual basis, one display is required for each evacuee. This is incompatible with dynamic signs; therefore we propose a novel algorithm which controls the dynamic signs according to the Proactive algorithm's output. We simulate both systems, compare their performance, and review their practical limitations. For both approaches, we find that updating the sign's display more often improves performance, but this may reduce evacuee compliance and make the system inefficient in real-life conditions.Open Acces

A Proposed Framework for Simultaneous Optimization of Evacuation Traffic Distribution and Assignment

In the conventional evacuation planning process, evacuees are assigned to fixed destinations based mainly on the criterion of geographical proximity. However, such pre-specified destinations (OD table) almost always lead to sub-optimal evacuation efficiencies due to uncertain road conditions such as congestion, road blockage, and other hazards associated with the emergency. By relaxing the constraint of assigning evacuees to pre-specified destinations, a one-destination evacuation (ODE) concept has the potential of greatly improving the evacuation efficiency. A framework for simultaneous optimization of evacuation traffic distribution and assignment is therefore proposed in this study. Based on the concept of ODE, the optimal destination and route assignment can be determined by solving a one-destination (1D) traffic assignment problem on a modified network representation. When tested on real-world networks for evacuation studies, the proposed 1D model presents substantial improvement over the conventional multiple-destination (nD) model. For instance, for a hypothetical county-wide evacuation, a nearly 80% reduction in the overall evacuation time can be achieved when modeling of traffic routing with en route information in the 1D framework, and the 1D optimization results can also be used to improve the planning OD tables, resulting in an up to 60% reduction in the overall evacuation time. More importantly, this framework can be actually implemented, and its efficiency enhancement can be realized simply by instructing evacuees to head for more efficient destinations determined from the 1D optimization performed beforehand

A DECISION PROCESS FOR SURFACE MEDICAL EVACUATION ROUTING UNDER ADVERSARY THREAT AND UNCERTAIN DEMAND USING ONLINE OPTIMIZATION

Emerging threats have focused U.S. Navy operating concepts on agile, distributed tactical forces in the littoral and maritime zones. Given the nature of the threat and its location, medical evacuation via air may be infeasible due to hostile conditions or distance, requiring a shift to a surface or subsurface strategy. Medical demand and adversary actions are unpredictable in warfare, therefore a decision process for routing that accounts for uncertainty is required. Using the principles of the U.S. Marine Corps Rapid Response Planning Process and online optimization, we propose a decision process for surface medical evacuation routing against an adversary given uncertain demand that can be applied to manned and autonomous transport operations. We showcase the computational tractability of the decision process by developing an algorithm to route a medical transport through a network then implement the algorithm as a simulation model in Python. The base case of the model is compared to two modified cases under perfect information to discuss the risks of modeling with inappropriate assumptions. Multiple runs of the simulation model are then used to propose a process to develop a distance multiplier to estimate the impact of adversary presence in existing simulation models without a complete re-design.Outstanding ThesisLieutenant, United States NavyApproved for public release. Distribution is unlimited