50,827 research outputs found
IMPROVING EVACUATION PLANNING AND SHELTER SITE SELECTION FOR FLOOD DISASTER: THAI FLOODING CASE STUDY
Evacuation planning and shelter site selection are the most important function of disaster management for the purpose of helping at-risk persons to avoid or recover from the effect of a disaster. This study aims to propose a stochastic linear mixed-integer mathematical programming model for improving flood evacuation planning and shelter site selection under a hierarchical evacuation concept. The hierarchical evacuation concept is applied in this study that balances the preparedness and risk despite the uncertainties of flood events. This study considers the distribution of shelter sites and communities, evacuee\u27s behavior, utilization of shelter and capacity restrictions of the shelter by minimizing total population-weighted travel distance. We conduct computational experiments to illustrate how the proposed methodical model works on a real case problem in which we proposed Thai flooding case study. Also, we perform a sensitivity analysis on the parameters of the mentioned mathematical model and discuss our finding. This study will be a great significance in helping policymakers consider the spatial aspect of the strategic placement of flood shelters and evacuation planning under uncertainties of flood scenarios
Discrete Time Dynamic Traffic Assignment Models with Lane Reversals for Evacuation Planning
In an event of a natural or man-made disaster, an evacuation is likely to be called for to move residents away from potentially hazardous areas. Road congestion and traffic stalling is a common occurrence as residents evacuate towns and cities for safe refuges. Lane reversal, or contra-flow, is a remedy to increase outbound flow capacities from disaster areas which in turn will reduce evacuation time of evacuees during emergency situations. This thesis presents a discrete-time traffic assignment system with lane reversals which incorporates multiple sources and multiple destinations to predict optimal traffic flow at various times throughout the entire planning horizon. With the realization of lane reversals, naturally the threat of potential head-on collisions emerges. To avoid the occurrence of such situations, a collision prevention constraint is introduced to limit directional flow on lanes based on departure time.;This model belongs to the class of dynamic traffic assignment (DTA) problems. Initially the model was formulated as a discrete-time system optimum dynamic traffic assignment (DTA-SO) problem, which is a mixed integer nonlinear programming problem. Through various proven theorems, a linearized upper bound was derived that is able to approximate the original problem with very high precision. The result is an upper bound mixed integer linear programming problem (DTA-UB). The discrete-time DTA model is suitable for evacuation planning because the model is able to take care of dynamic demands, and temporal ow assignment. Also, simultaneous route and departure is assumed and an appropriate travel time function is used to approximate the minimum and maximum travel time on an arc.;This thesis discusses the different attributes that relates to Dynamic Traffic Assignment. DTA model properties and formulation methodology are also expounded upon. A model analysis that breaks down each output into individual entities is provided to further understand the computational results of small networks. A no reversal DTA-UB model (NRDTA-UB) is formulated and its computational results are compared to DTA-UB. Through the extensive computational results, DTA-UB is proven to obtain much better results than NRDTA-UB despite having longer solving time. This is a step toward realizing the supremacy of having lane reversals in a real-life evacuation scenario
Optimizing Emergency Transportation through Multicommodity Quickest Paths
In transportation networks with limited capacities and travel times on the arcs, a class of problems attracting a growing scientific interest is represented by the optimal routing and scheduling of given amounts of flow to be transshipped from the origin points to the specific destinations in minimum time. Such problems are of particular concern to emergency transportation where evacuation plans seek to minimize the time evacuees need to clear the affected area and reach the safe zones. Flows over time approaches are among the most suitable mathematical tools to provide a modelling representation of these problems from a macroscopic point of view. Among them, the Quickest Path Problem (QPP), requires an origin-destination flow to be routed on a single path while taking into account inflow limits on the arcs and minimizing the makespan, namely, the time instant when the last unit of flow reaches its destination. In the context of emergency transport, the QPP represents a relevant modelling tool, since its solutions are based on unsplittable dynamic flows that can support the development of evacuation plans which are very easy to be correctly implemented, assigning one single evacuation path to a whole population. This way it is possible to prevent interferences, turbulence, and congestions that may affect the transportation process, worsening the overall clearing time. Nevertheless, the current state-of-the-art presents a lack of studies on multicommodity generalizations of the QPP, where network flows refer to various populations, possibly with different origins and destinations. In this paper we provide a contribution to fill this gap, by considering the Multicommodity Quickest Path Problem (MCQPP), where multiple commodities, each with its own origin, destination and demand, must be routed on a capacitated network with travel times on the arcs, while minimizing the overall makespan and allowing the flow associated to each commodity to be routed on a single path. For this optimization problem, we provide the first mathematical formulation in the scientific literature, based on mixed integer programming and encompassing specific features aimed at empowering the suitability of the arising solutions in real emergency transportation plans. A computational experience performed on a set of benchmark instances is then presented to provide a proof-of-concept for our original model and to evaluate the quality and suitability of the provided solutions together with the required computational effort. Most of the instances are solved at the optimum by a commercial MIP solver, fed with a lower bound deriving from the optimal makespan of a splittable-flow relaxation of the MCQPP
A Co-Simulation Study to Assess the Impacts of Connected and Autonomous Vehicles on Traffic Flow Stability during Hurricane Evacuation
Hurricane evacuation has become a major problem for the coastal residents of
the United States. Devastating hurricanes have threatened the lives and
infrastructure of coastal communities and caused billions of dollars in damage.
There is a need for better traffic management strategies to improve the safety
and mobility of evacuation traffic. In this study hurricane evacuation traffic
was simulated using SUMO a microscopic traffic simulation model. The effects of
Connected and Autonomous Vehicles (CAVs) and Autonomous Vehicles (AVs) were
evaluated using two approaches. (i) Using the state-of-the-art car-following
models available in SUMO and (ii) a co-simulation study by integrating the
microscopic traffic simulation model with a separate communication simulator to
find the realistic effect of CAVs on evacuation traffic. A road network of I-75
in Florida was created to represent real-world evacuation traffic observed in
Hurricane Irma s evacuation periods. Simulation experiments were performed by
creating mixed traffic scenarios with 25, 50, 75 and 100 percentages of
different vehicle technologies including CAVs or AVs and human-driven vehicles.
HDV Simulation results suggest that the CACC car-following model, implemented
in SUMO and commonly used in the literature to represent CAVs, produces highly
unstable results On the other hand the ACC car following model, used to
represent AVs, produces better and more stable results. However, in a
co-simulation study, to evaluate the effect of CAVs in the same evacuation
traffic scenario, results indicate that with 25 percentage of CAVs the number
of potential collisions decrease up to 42.5 percentage
OPTIMIZATION MODELS AND METHODOLOGIES TO SUPPORT EMERGENCY PREPAREDNESS AND POST-DISASTER RESPONSE
This dissertation addresses three important optimization problems arising during the phases of pre-disaster emergency preparedness and post-disaster response in time-dependent, stochastic and dynamic environments.
The first problem studied is the building evacuation problem with shared information (BEPSI), which seeks a set of evacuation routes and the assignment of evacuees to these routes with the minimum total evacuation time. The BEPSI incorporates the constraints of shared information in providing on-line instructions to evacuees and ensures that evacuees departing from an intermediate or source location at a mutual point in time receive common instructions. A mixed-integer linear program is formulated for the BEPSI and an exact technique based on Benders decomposition is proposed for its solution. Numerical experiments conducted on a mid-sized real-world example demonstrate the effectiveness of the proposed algorithm.
The second problem addressed is the network resilience problem (NRP), involving
an indicator of network resilience proposed to quantify the ability of a network to recover from randomly arising disruptions resulting from a disaster event. A stochastic, mixed integer program is proposed for quantifying network resilience and identifying the optimal post-event course of action to take. A solution technique based on concepts of Benders decomposition, column generation and Monte Carlo simulation is proposed. Experiments were conducted to illustrate the resilience concept and procedure for its measurement, and to assess the role of network topology in its magnitude.
The last problem addressed is the urban search and rescue team deployment problem (USAR-TDP). The USAR-TDP seeks an optimal deployment of USAR teams to disaster sites, including the order of site visits, with the ultimate goal of maximizing the expected number of saved lives over the search and rescue period. A multistage stochastic program is proposed to capture problem uncertainty and dynamics. The solution technique involves the solution of a sequence of interrelated two-stage stochastic programs with recourse. A column generation-based technique is proposed for the solution of each problem instance arising as the start of each decision epoch over a time horizon. Numerical experiments conducted on an example of the 2010 Haiti earthquake are presented to illustrate the effectiveness of the proposed approach
The Capability of Spatial Analysis in Planning the Accessibility for Hazard Community from Debris-Flow Events
Debris flow is a destructive disaster causing tragic loss and damages to vulnerable people and their
properties in many regions around the world. According an impact of this disaster, hazard areas are
submerged in mud and debris causing enormous difficulties to all relevant organisations and affected
people to access over the hazard community. Although an inaccessibility is one of the major problems
considered to be solved in an urgent stage, the lack of a comprehensive study in activities of involved
people through time line since the disaster occurrence causes a difficulty to plan the feasible solution to
overcome those problems effectively.
Therefore, this paper presents the existing knowledge in several activities related to accessibilities in
hazard areas. Additionally, the initial findings derived from interviews conducted as a part of a doctoral
research are determined showing real activities related to accessibilities in a study area of Thailand where
was attacked by a major debris-flow event in 2001. Regarding the explored acitivities, this study aims to
introduce a potential solution to overcome the inaccessibility problems in hazard areas by applying spatial
analysis techniques. This solution presents a new method of an optimum balance between the explored
problems from the interviews of affected people and the practices conducted by the local government to
solve the inaccessibility in the hazard area. Some suggestions are addressed at the end of the paper to
propose some additional practices with some considered factors for the spatial database design
Invisible control of self-organizing agents leaving unknown environments
In this paper we are concerned with multiscale modeling, control, and
simulation of self-organizing agents leaving an unknown area under limited
visibility, with special emphasis on crowds. We first introduce a new
microscopic model characterized by an exploration phase and an evacuation
phase. The main ingredients of the model are an alignment term, accounting for
the herding effect typical of uncertain behavior, and a random walk, accounting
for the need to explore the environment under limited visibility. We consider
both metrical and topological interactions. Moreover, a few special agents, the
leaders, not recognized as such by the crowd, are "hidden" in the crowd with a
special controlled dynamics. Next, relying on a Boltzmann approach, we derive a
mesoscopic model for a continuum density of followers, coupled with a
microscopic description for the leaders' dynamics. Finally, optimal control of
the crowd is studied. It is assumed that leaders exploit the herding effect in
order to steer the crowd towards the exits and reduce clogging. Locally-optimal
behavior of leaders is computed. Numerical simulations show the efficiency of
the optimization methods in both microscopic and mesoscopic settings. We also
perform a real experiment with people to study the feasibility of the proposed
bottom-up crowd control technique.Comment: in SIAM J. Appl. Math, 201
"Last-Mile" preparation for a potential disaster
Extreme natural events, like e.g. tsunamis or earthquakes, regularly lead to catastrophes with dramatic consequences. In recent years natural disasters caused hundreds of thousands of deaths, destruction of infrastructure, disruption of economic activity and loss of billions of dollars worth of property and thus revealed considerable deficits hindering their effective management: Needs for stakeholders, decision-makers as well as for persons concerned include systematic risk identification and evaluation, a way to assess countermeasures, awareness raising and decision support systems to be employed before, during and after crisis situations. The overall goal of this study focuses on interdisciplinary integration of various scientific disciplines to contribute to a tsunami early warning information system. In comparison to most studies our focus is on high-end geometric and thematic analysis to meet the requirements of small-scale, heterogeneous and complex coastal urban systems. Data, methods and results from engineering, remote sensing and social sciences are interlinked and provide comprehensive information for disaster risk assessment, management and reduction. In detail, we combine inundation modeling, urban morphology analysis, population assessment, socio-economic analysis of the population and evacuation modeling. The interdisciplinary results eventually lead to recommendations for mitigation strategies in the fields of spatial planning or coping capacity
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