Evacuation Clearance Time Research Articles

How contraflow enhances clearance time during assisted mass evacuation – A case study exploring the Australian 2013–14 Gippsland bushfires

Evacuation during a catastrophic disaster is a crucial operation that needs to be appropriately managed and is of more importance when considering the elderly and people with disabilities. The uncertain and unpredictable nature of disasters can cause long-term repercussions, especially in traffic congestion. This study presents a mathematical model to formulate traffic balance for regular and assisted evacuation (that is, disabled and the elderly) whilst considering traffic congestion in evacuation clearance time by applying contraflow. A Branch and Price (B&P) related approach is developed to help solve the proposed model in large-size problems. The presented algorithm is applied to a case study of Australia’s 2013–14 bushfires in Gippsland, located in the eastern part of Victoria. A variation test is performed to evaluate the robustness of results generated by the developed model. Results indicate that the participation percentage of edges is different based on their location, capacity, and sustainability of blockage. The edges’ capacity influences the evacuated population most compared to route capacity and time window. The output of this approach enables authorities to improve the resilience of communities by making optimal strategic and operational decisions for enhancing an evacuation response as well as influencing appropriate policies.

A deep reinforcement learning traffic control model for Pedestrian and vehicle evacuation in the parking lot

As a necessary component of connecting the building interior and urban road network for evacuation, the evacuation of parking lots significantly impacts overall evacuation efficiency. However, existing emergency evacuation studies have ignored the control of pedestrian-vehicle mixed flow in parking lot environments, leading to underestimated evacuation time estimates. Therefore, this paper proposes a pedestrian-vehicle mixed-flow model to simulate the parking lot evacuation process at the microscopic level. Moreover, a deep reinforcement learning (DRL)-based evacuation control model is developed to control pedestrian evacuation speed. The numerical study shows that this control model can effectively reduce evacuation clearance time by 7.75 % when faced with enormous evacuation demands.

Ridesharing evacuation models of disaster response

Evacuation models based on public transportation have been shown to increase network capacity for transportation systems while improving community and societal disaster preparedness. However, due to a lack of private vehicles and inconvenient mobility, it is often difficult for vulnerable groups to arrive at shelters on time. To address this issue, this research proposes an evacuation strategy that incorporates the ridesharing concept, allowing individuals with vehicles to provide rides for carless groups. Three mixed-integer programming models are developed based on assumptions such as different capacities and pick-up principles, with the goal of maximizing the number of evacuees transported to assembly points or shelters in a limited amount of time. To evaluate the effectiveness of the proposed ridesharing evacuation models, a real-world case study in Houston is conducted. Numerical analyses are performed with five factors: evacuation scales, data generation, evacuation models, evacuation clearance times, and the number of vehicles involved in the evacuation process. The findings demonstrate that ridesharing evacuation models can provide viable alternative evacuation options to carless and public transit-dependent populations. Furthermore, the study reveals that increasing the number of vehicles to assist vulnerable groups may not be necessary in cities with high population density due to excessive traffic volume, which can hamper disaster response implementation. Additionally, the number of evacuees arriving at shelters or assembly points is unbalanced due to space–time constraints. To address this issue, relief supplies should be distributed on demand in these areas to reduce waste. The findings of this study can inform the development of more effective and efficient evacuation strategies that can better serve communities and vulnerable populations during times of crisis.

Establishing a Simple-yet-effective Approach of Early Warning System for Storm-Induced Earth-Filled Dam-Break Cases in Data-sparse Region

Historically, the occurrence of dam-break cases has been proven to cause significant loss of life and economical damage. Apart from the catastrophic nature of dam-breaks, the absence of a robust disaster prevention system exacerbates the disasters that occur. This study proposes an Early Warning System (EWS) to mitigate the impact of dam-break disasters. However, predicting the occurrence of such disasters is challenging, specifically in areas like Indonesia, where comprehensive data recording is lacking. While it may be difficult to predict the occurrence of a sunny day break, the storm-induced break is more predictable. Therefore, this study proposes a simple yet effective macro-based EWS for Earth-Filled Dam-Break Cases using a macro approach based on the Evacuation Clearance Time (ECT). By comparing the ECT value with the arrival time of the floods from the affected areas, additional evacuation time can be obtained, which will be used to determine the EWS. The proposed EWS for Cengklik Dam is given in three levels of warning indicated by the reservoir water level at +141.36 m, +141.40 m, and +141.45 m. With the proposed EWS, the results show that 100% of people are expected to reach the evacuation point safely. The case study shows that the proposed EWS can significantly reduce the risk impact of the dam-break events.

Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes

Abstract. In many flood-prone areas, it is essential for emergency responders to use advanced computer models to assess flood risk and develop informed flood evacuation plans. However, previous studies have had a limited understanding of how evacuation performance is affected by the arrangement of evacuation shelters (with respect to their number and geographical distribution) and human behaviors (with respect to the heterogeneity of household evacuation preparation times and route-searching strategies). In this study, we develop an integrated socio-hydrological modeling framework that couples (1) a hydrodynamic model for flood simulation, (2) an agent-based model for evacuation management policies and human behaviors, and (3) a transportation model for simulating household evacuation processes in a road network. We apply the model to the Xiong'an New Area and examine household evacuation outcomes for various shelter location plans and human behavior scenarios. The results show that household evacuation processes are significantly affected by the number and geographical distribution of evacuation shelters. Surprisingly, we find that establishing more shelters may not improve evacuation results if the shelters are not strategically located. We also find that low heterogeneity in evacuation preparation times can result in heavy traffic congestion and long evacuation clearance times. If each household selects their own shortest route without considering the effects of other evacuees' route choices, traffic congestion will likely occur, thereby reducing system-level evacuation performance. These results demonstrate the unique functionality of our model with respect to supporting flood risk assessment and advancing our understanding of how multiple management and behavioral factors jointly affect evacuation performance.

Modelling spatial population exposure and evacuation clearance time for the Auckland Volcanic Field, New Zealand

Auckland, New Zealand's largest city (population of ~1.6 million), is situated atop the monogenetic Auckland Volcanic Field (AVF). As in many places faced with volcanic activity, evacuation is seen as the best risk mitigation strategy for preserving lives in the event of volcanic unrest and/or an eruption. However, planning for an evacuation can be challenging. In particular, the uncertainty in vent location resulting from the monogenetic nature of the field makes identifying neighbourhoods to be evacuated impractical until well into the pre-eruption unrest period. This study uses spatial analysis methods to assess exposure for both population and private transport ownership as well as to identify those areas requiring public transport support for an evacuation. These data were overlaid on a range of possible vent locations across the AVF using a 500 × 500 m grid. At each possible vent location, a 5 km evacuation zone is modelled, following the official contingency plan for evacuation in a future AVF event. In order to simulate vent location uncertainty leading up to a future eruption, a range of buffer distances were applied around the modelled vent locations.The exposure data derived were then used to model evacuation clearance time, which considered four phases: 1) the time taken to decide to call an evacuation; 2) the public notification time; 3) the evacuee's time to prepare; and 4) evacuee's travel time to beyond the evacuation zone. The length of time involved in phases 1 to 3 are all independent of the vent location; our analysis found these phases could be completed within 36 h, with over 80% confidence. Travel times to beyond the evacuation zone were modelled using the exposure analysis for population and private transport ownership combined with road network data and vehicle carrying capacity. This revealed travel times for this phase ranging from less than 1 up to 11 h, depending on traffic congestion, when considering no vent uncertainty. By combining the times modelled for all four phases, we found that when there is high certainty in the vent location, the median total evacuation clearance time with no congestion is approximately 37 h. However, include a 10 km vent uncertainty buffer into the model, the evacuation clearance time can increase to between 38 and 55 h, dependent on traffic congestion. A vent in the densely populated inner Auckland and CBD area would result in the greatest population required to evacuate, and also the greatest need for public transport support given the low vehicle ownership in this area. Our results can be used to inform emergency management decision making, and the model can be adapted for other regions as well as for other hazards.

Effects of shadow evacuation on megaregion evacuations

Transportation systems serve important roles during emergencies, in particular for evacuations. However, efficient travel during these life-and-death scenarios can be adversely impacted by external conditions, such as unnecessary and unneeded travel. This research sought to enhance the understanding of the effects of these conditions by analyzing shadow evacuations, and their impact on regional traffic operations in megaregions, more broadly. The research was based on simulations of a range of hurricane evacuation threat scenarios in the Gulf of Mexico building upon prior study using TRANSIMS. These assessments are also targeted at what many assume could be worst case evacuation conditions and pushing the limits of current simulation modeling capability. Among the broader findings of this work was that shadow evacuation participation rates did not significantly impact the evacuation clearance times within mandatory evacuation areas of the megaregion as long as demand could be temporarily spread out. This finding does not, however, suggest that the shadow evacuations have no impact on evacuation processes. High rates of shadow evacuees can cause significant congestion if they are not able to exit critical routes before mandatory evacuees reach areas further away from the coast.

Trade-off between signal and cross-elimination strategies during evacuation traffic management

It is well accepted in literature that the strategy of using crossing-elimination (or uninterrupted-flow) intersections has the advantage over signals in expanding network capacity during evacuation. This result holds well in a static evacuation network in which flow patterns are not explicitly modeled (e.g., queuing on network links and time-varying evacuation demand). This paper examines the problem of selecting and distributing signal control and uninterrupted flow strategies in evacuation traffic management. More specifically, the following critical issues are investigated: (1) Does an optimal distribution between signal and uninterrupted flow strategies exist in a dynamic evacuation network and what is it? and (2) How to best plan turning restriction and signal timings at those intersections with different control strategies? Results show that the best distribution of uninterrupted flow and signalized intersections exists to yield the minimum evacuation clearance time. Intersections located within the demand generating area (i.e., the impacted area) are more likely to be selected by the model as uninterrupted flow ones to facilitate fast access of evacuees to the evacuation routes; while signals tend to locate outside the impact area to reduce detours of evacuation traffic to destination. The proposed model outperforms other scenarios in terms of reducing evacuation clearance time, which demonstrates its effectiveness.

Modeling the impact of traffic incidents during hurricane evacuations using a large scale microsimulation

Traffic incidents occurring during large-scale evacuations have been suspected of causing potentially severe delays. Previous work using mesoscopic simulations suggested that while incidents may substantially extend evacuation times for those immediately impacted, overall evacuation times for all evacuees were not significantly increased. Using highly detailed, dynamic transportation micro-simulation with greater realism and accuracy, this study assessed the impact of traffic incidents on two hypothetical hurricane evacuations from a coastal city in the United States. Six evacuation scenarios were tested with up to 143,000 evacuees and 800,000 background vehicles simulated over a 24-hour period. Scenarios included from 35 to 48 traffic incidents with locations, rates, severities, and durations based on both historical values for peak period traffic and estimates based on total vehicle miles traveled per road segment. The impacts of simulated traffic incidents, assigned to match one of three severity levels, were modeled by temporary reductions in affected lane segment capacities and reductions in vehicle speeds for other lanes in the segment. Work supports findings that while traffic incidents significantly extend the travel time of those involved, the total time required for all evacuees to clear the evacuating area is only marginally impacted. The findings also show that the time incidents occur relative to current and immediately following traffic may significantly impact evacuation clearance times for comparatively short periods.

Evacuation decision-making : Building a model to account for risk-taking preference

One of the most stressful decisions a coastal emergency manager may face is the decision to order a large-scale evacuation for a hurricane. Even in a best-case scenario, evacuations are not a panacea. They are unpopular, expensive, disruptive and highly vulnerable to their own hazards. These issues become even more daunting when exacerbated by high populations in vulnerable areas, limited evacuation routes and a lack of nearby shelter space, which combine to create extremely long evacuation clearance times. Add forecast errors and uncertainty to these and one begins to understand the complexity of the decision-making process. These were the challenges faced by the authors as they tried to define and document the evacuation decision-making process in Lee County in Southwest Florida.

A Case Study on the Impacts of Connected Vehicle Technology on No-Notice Evacuation Clearance Time

No-notice evacuations of metropolitan areas can place significant demands on transportation infrastructure. Connected vehicle (CV) technology, with real-time vehicle to vehicle and vehicle to infrastructure communications, can help emergency managers to develop efficient and cost-effective traffic management plans for such events. The objectives of this research were to evaluate the impacts of CVs on no-notice evacuations using a case study of a downtown metropolitan area. The microsimulation software VISSIM was used to model the roadway network and the evacuation traffic. The model was built, calibrated, and validated for studying the performance of traffic during the evacuation. The researchers evaluated system performance with different CV penetration rates (from 0 to 30 percent CVs) and measured average speed, average delays, and total delays. The findings suggest significant reductions in total delays when CVs reached a penetration rate of 30 percent, albeit increases in delays during the beginning of the evacuation. Additionally, the benefits could be greater for evacuations that last longer and with higher proportions of CVs in the vehicle stream.

A-RESCUE: An Agent based Regional Evacuation Simulator Coupled with User Enriched Behavior

Household behavior and dynamic traffic flows are the two most important aspects of hurricane evacuations. However, current evacuation models largely overlook the complexity of household behavior leading to oversimplified traffic assignments and, as a result, inaccurate evacuation clearance times in the network. In this paper, we present a high fidelity multi-agent simulation model called A-RESCUE (Agent-based Regional Evacuation Simulator Coupled with User Enriched behavior) that integrates the rich activity behavior of the evacuating households with the network level assignment to predict and evaluate evacuation clearance times. The simulator can generate evacuation demand on the fly, truly capturing the dynamic nature of a hurricane evacuation. The simulator consists of two major components: household decision-making module and traffic flow module. In the simulation, each household is an agent making various evacuation related decisions based on advanced behavioral models. From household decisions, a number of vehicles are generated and entered in the evacuation transportation network at different time intervals. An adaptive routing strategy that can achieve efficient network-wide traffic measurements is proposed. Computational results are presented based on simulations over the Miami-Dade network with detailed representation of the road network geometry. The simulation results demonstrate the evolution of traffic congestion as a function of the household decision-making, the variance of the congestion across different areas relative to the storm path and the most congested O-D pairs in the network. The simulation tool can be used as a planning tool to make decisions related to how traffic information should be communicated and in the design of traffic management policies such as contra-flow strategies during evacuations.

Panic That Spreads Sociobehavioral Contagion in Pedestrian Evacuations

Crowds are a part of everyday public life, from stadiums and arenas to school hallways. Occasionally, pushing within the crowd spontaneously escalates to crushing behavior, resulting in injuries and even death. The rarity and unpredictability of these incidents provides few options to collect data for research on the prediction and prevention of hazardous emergent behaviors in crowds. This study takes a close look at the way states of agitation, such as panic, can spread through crowds. Group composition—mainly family groups composed of members with differing mobility levels—plays an important role in the spread of agitation through the crowd, ultimately affecting the exit density and evacuation clearance time of a simulated venue. This study used an agent-based model of pedestrian movement during the egress of a hypothetical room and adopted an emotional, cognitive, and social framework to explore the transference and dissipation of agitation through a crowd. The preliminary results reveal that average group size in a crowd is a primary contributor to the exit density and evacuation clearance time. The study provides the groundwork on which to build more elaborate models that incorporate sociobehavioral aspects to simulate human movement during panic situations and account for the potential for dangerous behavior to emerge in crowds.

K-Shortest-Path-Based Evacuation Routing with Police Resource Allocation in City Transportation Networks.

Emergency evacuation aims to transport people from dangerous places to safe shelters as quickly as possible. Police play an important role in the evacuation process, as they can handle traffic accidents immediately and help people move smoothly on roads. This paper investigates an evacuation routing problem that involves police resource allocation. We propose a novel k-th-shortest-path-based technique that uses explicit congestion control to optimize evacuation routing and police resource allocation. A nonlinear mixed-integer programming model is presented to formulate the problem. The model’s objective is to minimize the overall evacuation clearance time. Two algorithms are given to solve the problem. The first one linearizes the original model and solves the linearized problem with CPLEX. The second one is a heuristic algorithm that uses a police resource utilization efficiency index to directly solve the original model. This police resource utilization efficiency index significantly aids in the evaluation of road links from an evacuation throughput perspective. The proposed algorithms are tested with a number of examples based on real data from cities of different sizes. The computational results show that the police resource utilization efficiency index is very helpful in finding near-optimal solutions. Additionally, comparing the performance of the heuristic algorithm and the linearization method by using randomly generated examples indicates that the efficiency of the heuristic algorithm is superior.

A Game Theory Based on Monte Carlo Analysis for Optimizing Evacuation Routing in Complex Scenes

With more complex structures and denser populations, congestion is a crucial factor in estimating evacuation clearance time. This paper presents a novel evacuation model that implements a game theory combining the greatest entropy optimization criterion with stochastic Monte Carlo methods to optimize the congestion problem and other features of emergency evacuation planning. We introduce the greatest entropy criterion for convergence to Nash equilibrium in then-person noncooperative game. The process of managing the conflict problem is divided into two steps. In the first step, we utilize Monte Carlo methods to evaluate the risk degree of each route. In the second step, we propose an improved method based on game theory, which obtains an optimal solution to guide the evacuation of all agents from the building.

A multilevel analysis of private-vehicle evacuation clearance times along the US Gulf Coast

This study uses multilevel regression analysis to examine the effect of social characteristics and the built environment on clearance time under an evacuation scenario. The primary unit of analysis is the US Census tract (N = 1660), nested within 31 incorporated places spanning five US states. The dependent variable is an estimate of clearance time in hours derived using network analysis techniques within a geographic information system. We find that tracts with a more peripheral location, more female residents, a higher proportion of Hispanic residents, and higher median household incomes are associated with higher clearance times, on average. Our research suggests the relationship between suburbanization and clearance time is complex and evolving, mediated by past investments in the built environment and shifting social conditions. In addition to facilitating the evacuation of areas with low access to personal vehicles, urban planners and emergency management officials should also consider how the degree of connectivity in the street network impacts congestion and clearance time.

Clearance Time Estimation for Incorporating Evacuation Risk in Routing Strategies for Evacuation Operations

This study seeks to develop an approximate but efficient approach for estimating evacuation clearance time which is defined as the time required to evacuate the population of a location to areas of safety. Elsewhere, this estimate of the clearance time is used as input to infer evacuation risk for a location which reflects whether the population of that location can be safely evacuated before the disaster impacts it. As the computed evacuation risk is used in a real-time stage-based framework for evacuation operations, the approach for clearance time estimation needs to be computationally efficient while being capable of approximating traffic flow dynamics reasonably. To address these needs, a dynamic routing policy labeled the location-priority routing is proposed, and the associated clearance time estimation problem is formulated as a dynamic network flow problem. The location-priority is based on the lead time that a location has until it is impacted by the disaster, and designates that the population at a location with a shorter lead time has higher priority in using roadway capacity for evacuation. By combining the location-priority routing with the consideration of a super sink, the routing problem for the evacuation network is transformed into sequential single-origin single-destination dynamic routing problems. It avoids expensive iterative search processes, enabling computational efficiency for real-time evacuation operations. The solution approach approximately captures the evolution of traffic dynamics across the stages of the operational framework. Results from numerical experiments illustrate that the proposed approach can address the aforementioned needs related to clearance time estimation.

Application of Transit Signal Priority for No-Notice Urban Evacuation

During an urban evacuation, is it advisable for regional planners to allow transit buses signal priority in cases where police-assisted traffic controls are not an option? With only a finite number of available units, buses will be required to make multiple trips in and out of evacuation zones. Therefore, it is within reason that some regional municipalities would want to allow transit priority to hasten trips made by buses. However, studies in the past have shown that, during times of high roadway demand, transit priority causes major delays for vehicular traffic. The goal of this paper is to examine and quantify the benefit of transit signal priority will have on transit vehicles and any hindrance this priority has on nontransit evacuees. By applying state-of-the-art tools in evacuation modeling and microscopic traffic simulation, the aspects of a multimodel evacuation and transit signal priority impact study are merged within a single study of Washington, DC. On the basis of simulation results, transit signal priority has the potential to save lives by reducing bus travel time by 26%. This signifies that three prioritized buses can handle the workload of four nonprioritized buses. Furthermore, this priority has no adverse effect on the evacuation clearance time of nontransit evacuees. On the basis of this study, transit priority in a multimodal evacuation should be used.

Critical Intersection Signal Optimization During Urban Evacuation Utilizing Dynamic Programming

Despite advancements in the field of traffic planning and operations, many major cities still rely on pretimed signal settings. With only pretimed signal control strategies, the secondary effects of a terror attack are magnified by slow evacuation times resulting in further loss of life. However, the cost of implementing new infrastructure based solely on the chance of a no-notice evacuation is not something that city planners are willing to do. The purpose of the research is to define a cost-effective methodology to develop pretimed signal control strategies to assist evacuations in urban areas. To that end, a dynamic programming methodology was developed to assist critical intersections in urban corridors. To test this methodology, a microscopic traffic-simulation environment was created for a case study of a 10 intersection evacuation corridor in Washington, DC. Using the proposed methodology to optimizing signal splits of a critical intersection within an evacuation corridor, evacuation clearance time was reduced by approximately 1 h. Furthermore, this formulation can be used to develop pretimed signal control settings for evacuation scenarios. Results showed that peak-hour signal timings are not sufficient in the case of an emergency, and signal timing plans must be tailored for emergency evacuation.

Examining the Level of Service Consequence of Transit Signal Priority During Urban Evacuation

This research answers the question, during an urban evacuation should regional planners allow transit units signal priority when police assisted traffic controls are not an option. A case study of Washington D.C. shows allowing transit signal priority (TSP) during an urban evacuation has little to no effect on evacuation clearance time. Furthermore, four non-prioritized units are required to accomplish the task of three prioritized vehicles. © 2011 Published by Elsevier Ltd.