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This study presents a new approach to optimize the dynamic evacuation process through a dynamic traffic assignment model formulated using mixed-integer linear programming (MILP). The model approximates the travel time for evacuee groups with a piecewise linear function that accounts for variations in travel time due to load-dependent factors. Significant delays are transferred to subsequent groups to simulate delay propagation. The primary objective is to minimize the network clearance time—the total time required for the last group of evacuees to reach safety from the start of the evacuation. Given the model's computational intensity, a simplified version is introduced for comparison. Both the original and simplified models are tested on small networks and benchmarked against the Cell Transmission Model, a well-regarded method in dynamic traffic assignment literature. Additional objectives, including average travel time and average evacuation time, are explored. A sensitivity analysis is conducted to assess how varying the number of evacuee groups impacts model outcomes. [ABSTRACT FROM AUTHOR]

Wildfires pose significant threats to communities, requiring robust pre-event planning for efficient evacuations. Transportation systems are crucial for these efforts, yet global research gaps persist, especially those related to transportation assets and transportation modes beyond privately owned automobiles. This study conducts a systematic review of four under-researched areas – infrastructure, modality, networks, and planning – to build a more comprehensive understanding of wildfire evacuations. Initial research is emerging in these domains, related to post-fire debris flows, air and transit evacuations, network analysis, and shelter planning. However, systematic analyses, evidence, and recommendations remain lacking. This includes wildfire's direct impact on transportation infrastructure, multi-modal evacuations, routing strategies, and community-driven evacuation plans. We underscore the need for empirical evacuation strategies to foster resilience for wildfire-threatened communities, offering valuable context-specific insights, identifying key actions, and highlighting ongoing research gaps. [ABSTRACT FROM AUTHOR]

A transport network may face damage due to a disaster. Some roads may be wholly or partially closed, and the system cannot satisfy the whole demand. Critical considerations include transferring evacuees from dangerous zones to safe zones. This paper presents a novel optimization method that will allow a transport network to run more efficiently during a dynamic hazard that will change through the periods. The objective is to minimize the maximum time needed to evacuate the last group of people from critical and intermediate zones. Regarding the complexity class of evacuation problems, a Genetic Algorithm (GA) approach is designed to solve large-size problems. Also, the Sioux Falls network and Dublin Transportation Network case studies are defined to validate the proposed model and GA approach. This study assesses the system's resilience during a critical event by comparing the system's behavior before and during the hazard, which helps improve the recovery process. [ABSTRACT FROM AUTHOR]

We introduce a variant of the cumulative scheduling problem (CuSP) characterized by continuous modes, time windows, and a criterion that involves safety margin maximization. The study of this variant is motivated by the Geospatial based Environment for Optimisation Systems Addressing Fire Emergencies Horizon 2020 Project, which is devoted to the design of evacuation plans in the face of natural disasters and more specifically, wildfire. People and goods have to be transferred from endangered places to safe places, and evacuation planning consists of scheduling evacuee moves along precomputed paths under arc capacities and deadlines. The resulting model is relevant in other contexts, such as project or industrial process scheduling. We consider here several formulations of the continuous time-resource trade-off scheduling problem (CTRTP-TW) with a safety maximization objective. We establish a complete complexity characterization distinguishing polynomial and NP-hard special cases depending on key parameters. We show that the problem with fixed sequencing (i.e., with predetermined overlap or precedence relations between activities) is convex. We then show that the preemptive variant is polynomial, and we propose lower and upper bounds based on this relaxation. A flow-based mixed-integer linear programming formulation is presented, from which a branch-and-cut exact method and an insertion heuristic are derived. An exact dedicated branch-and-bound algorithm is also designed. Extensive computational experiments are carried out to compare the different approaches on evacuation planning instances and on general CTRTP-TW instances. The experiments also show the interest of the continuous model compared with a previously proposed discrete approximation. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This work was funded by the Horizon 2020 Marie Skłodowska-Curie Research and Innovation Staff Exchange European Project 691161 GEO-SAFE (Geospatial based Environment for Optimisation Systems Addressing Fire Emergencie). This work has also been supported by ANITI, the Artificial and Natural Intelligence Toulouse Institute. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0142) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0142). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

AbstractThis study introduces a groundbreaking application of the Bellman-Ford algorithm for optimizing evacuation routes in multi-floor academic buildings, extending its traditional use in single-source shortest-path problems to address complex multiple- source multiple-exit (MSME) problems. A comprehensive computational model was developed, reflecting real-world evacuation scenarios and incorporating key constraints and assumptions. The model was rigorously benchmarked against a Dijkstra’s algorithm-based model, revealing a 3.5% improvement in the number of evacuees evacuated after the initial 9 seconds. Detailed simulation results and extended data analysis further substantiate these findings. While the current model assumes perfect evacuee compliance and overlooks human behavior, future research could address these limitations to enhance the model’s realism. This study significantly advances the field of emergency evacuation planning, offering valuable insights for emergency response practitioners, facility managers, and policymakers.

At the time of evacuation, placement of the facilities for the support of evacuees is an important task. The proper allocation of the facilities in such a way that the reduction in the flow value due to the placement of facilities on the arcs is minimal, is another important aspect of the problem. In this paper, we introduce an evacuation planning problem with facility allocation by using bi-level formulation. The upper level problem identifies the best possible location and lower level problem finds the optimal solution in the network with facility allocation. We solve the problem with a naive approach of combinatorial optimization and the Karush-Kuhn-Tucker (KKT) transformation. [ABSTRACT FROM AUTHOR]

The paper deals with transportation system models (TSMs) on behalf of methods for risk reduction in urban areas. As far as urban transport networks are concerned, further development of models is necessary in order to capture the potentialities of emerging information and communication technologies (e-ICTs) in providing valuable information about the evolution of a transportation network during an evacuation. A new learning process structure has been proposed to take into account how the path costs (disutilities) in ordinary and emergency conditions will be perceived and updated by the users. The data provided by emerging ICT allow for the incorporation of dynamics inside the network model, concerning the update of information provided by the internet of things and big data. [ABSTRACT FROM AUTHOR]

Floods are a significant issue across Southeast Asia, with Thailand, particularly the Nakhon Si Thammarat Municipality, being heavily affected due to its unique conditions such as heavy rainfall, rapid urbanization, and low-lying coastal position. This study utilizes Geographic Information Systems (GIS) to analyze the flood-prone areas of Nakhon Si Thammarat Municipality, assess its road networks, and identify optimal evacuation locations and routes. Various data layers such as slope angle, elevation, distance from roads, rainfall, TWI, NDVI, land use, soil texture, distance from rivers, stream density, and road density are integrated using the frequency ratio method. The findings reveal areas with very high flood susceptibility, which span 198.78 sq. km (56.74%) at the high frequency level, 299.43 sq. km (37.63%) at the low frequency level, and 284.62 sq. km (53.88%) at the moderate frequency level. During flood scenarios, travel times saw an average increase to 21.4 min, which is a fourfold surge compared to regular conditions, highlighting the impact of floods on evacuation strategies. Upon evaluating the road network under flood conditions and applying network analysis techniques, efficient and safe evacuation routes were determined. The results, which underscore a fourfold increase in travel time during flood scenarios, present valuable insights for emergency management authorities across Thailand and Southeast Asia to devise comprehensive evacuation plans, thereby enhancing regional resilience against future flood events. Furthermore, the methodologies and findings are adaptable and can be applied to other flood-prone regions within Southeast Asia, contributing to improved disaster preparedness and response.

Natural and anthropic disasters produce relevant economic and environmental losses at a global level. Many countries have adopted a risk management cycle to limit these losses. Agenda 2030 defined specific Sustainable Development Goals (SDGs) and relative targets dedicated to disaster risk reduction. Planned actions can pertain to the context "before" or "after" a disastrous event. The United Nations recommend a risk-informed approach for achieving the SDGs by working on anticipation and prevention. Planning with training and exercises, referring to transport systems in emergency conditions, can limit disaster impacts and strongly support the pursuit of sustainability. This research focuses on actions and methodologies for increasing preparedness levels "before" a disastrous event, to improve the capabilities of managers and people by increasing awareness of the effects of planned actions. Transport system models (TSMs) play an important role in simulating the evacuation of people to reduce theoretical risk, thereby reducing theoretical exposure. In this work, a method is presented that allows us to investigate how this theoretical reduction can become effective, through training and exercises. The paper proposes a general framework of training and exercises for risk reduction based on a given transportation planning model. The framework supports ex ante evaluations of exposure reduction produced by an evacuation plan. The obtained results show that the effectiveness of the planned actions increases with different levels of exercise implementation. The progressive implementation of exercises contributes to achieving the risk reductions estimated at the evacuation planning stage. The TSM in the mitigation phase is the basis for the development of specific quantitative evacuation plans, that must be implemented in the preparedness phase by means of training and exercises to test the planned actions in terms of reducing the exposure risk component. Some exercises implemented worldwide verify the proposed framework by means of some empirical evidence. The results and discussions reported in this paper can be useful for researchers, decisionmakers, and society by offering a contribution to the growing knowledge about risk and the potential actions and their relative effects on reducing it. [ABSTRACT FROM AUTHOR]

This study introduces a groundbreaking application of the Bellman-Ford algorithm for optimizing evacuation routes in multi-floor academic buildings, extending its traditional use in single-source shortest-path problems to address complex multiple- source multiple-exit (MSME) problems. A comprehensive computational model was developed, reflecting real-world evacuation scenarios and incorporating key constraints and assumptions. The model was rigorously benchmarked against a Dijkstra's algorithm-based model, revealing a 3.5% improvement in the number of evacuees evacuated after the initial 9 seconds. Detailed simulation results and extended data analysis further substantiate these findings. While the current model assumes perfect evacuee compliance and overlooks human behavior, future research could address these limitations to enhance the model's realism. This study significantly advances the field of emergency evacuation planning, offering valuable insights for emergency response practitioners, facility managers, and policymakers. [ABSTRACT FROM AUTHOR]

In order to reduce huge losses from natural and human caused disasters, an appropriate strategy of evacuation planning, preparedness, response and recovery is very essential. The placement of facilities in appropriate arcs of a network plays an important role for the optimal evacuation planning. Such an efficient assignment should respect the least decreases on the arc capacity and hence should minimize the reduction of the optimal flow value. On the other hand, due to the bottleneck capacity, not all flows out from the sources may reach to the sinks. To maximize the flow value, the resulting excess flow can be stored at intermediate nodes that are comparatively safer than the sources.By integrating both concepts, we introduce the single-source single-sink maximum static and dynamic flow location (FlowLoc) problems with excess flow storage allowed. Our objective here is to minimize the reduction of maximum flow in an evacuation network where given facilities are placed on arcs and excess flow is stored at intermediate nodes. We provide their mathematical models and also present polynomial time algorithms to solve them. For the maximum static and dynamic multi-FlowLoc problems where multiple facilities are located, we give their mathematical models and present polynomial time heuristics. These solutions here are practically important, especially in evacuation planning, to maximize the flow value out of the disastrous places by locating the facilities. [ABSTRACT FROM AUTHOR]

The frequency of natural and man-made disasters has increased over the past few decades, which has doubled the significance of evacuation planning because it directly affects people's lives and properties. How evacuees behave during a disaster and the methodologies to assess their behaviour are vital factors in managing any emergency scenario. For example, during the evacuation of a transportation network, users' amount of information and how they react are imperative to achieve a resilient response to a disaster. Even though there are numerous approaches for assessing evacuee behaviour, further research is needed to determine how and when each methodology should be employed. This paper intends to evaluate and classify the methodologies that have been presented up to date to create a more consistent approach to interpreting human behaviour during an evacuation process. The majority of studies focus on how people behave during an evacuation of a building, with less attention paid to how they behave during a transport evacuation. Among the proposed methodologies in this paper, the virtual reality approach, in individual behaviour, and agent-based models, in crowd behaviour, have more advantages than other approaches. Overall, a comparison between the proposed approaches is made in the discussion part. The output of this study provides the classifications and suggestions for researchers to pick an appropriate approach based on the types of problems, and some direction for future studies are introduced. To reach the research goal, 177 papers have been reviewed between 1954 and 2022.

Bangladesh has successfully reduced the number of deaths caused by tropical cyclones associated with storm surges in its territory. The factors responsible for this success include significant improvements in satellite-based cyclone detection, early warning systems, and emergency evacuation. However, during such events, some residents prefer to not evacuate the region. Based on the first-hand data generated through in-depth interviews, participant observations, focus group discussions with residents aged over 59 years, field visits, and investigations, in this study, we provide information on the shelter-seeking places in Bangladesh during the latest cyclones and analyse the reasons why some residents do not evacuate the affected regions post cyclone warning. The findings suggest that in Bangladesh, community trust in warnings increased from 56% during the 1991 Cyclone Gorky to 96% during the 2020 Cyclone Amphan. We identified 33 reasons for the preference of the population in Bangladesh living along the coast to not move to a cyclone shelter during emergencies; these reasons can be subdivided into six categories: 1) resilient housing, or the residents are living adjacent to strong buildings; 2) reasons associated with public cyclone shelters; 3) to protect livelihoods, properties, and personal belongings; 4) transportation problems and practical issues during the warning period; 5) interrelated community beliefs and perceptions; and 6) reasons directly associated with cyclone warnings. We identified the foundational reasons for the non-evacuation behaviour of the residents and analysed why they preferred to remain home or take shelter in adjacent strong building infrastructures. Notably, our study provides valuable insights into the factors that affect effective evacuation planning and cyclone disaster risk management along the Bangladesh coast.

To meet evacuation needs from carless populations who need personalized assistance to evacuate safely, in this article we propose a ridesharing-based evacuation program that recruits volunteer drivers before a disaster strikes, and then matches volunteer drivers with evacuees once demand is realized. We optimize resource planning and evacuation operations under uncertain spatiotemporal demand, and construct a two-stage stochastic mixed-integer program to ensure high demand fulfillment rates. We consider three formulations to improve the number of evacuees served, by minimizing an expected penalty cost, imposing a probabilistic constraint, and enforcing a constraint on the conditional value at risk of the total number of unserved evacuees, respectively. We discuss the benefits and disadvantages of the different risk measures used in the three formulations, given certain carless population sizes and the variety of evacuation modes available. We also develop a heuristic approach to provide quick, dynamic and conservative solutions. We demonstrate the performance of our approaches using five different networks of varying sizes based on regions of Charleston County, South Carolina, an area that experienced a mandatory evacuation order during Hurricane Florence, and utilize real demographic data and hourly traffic count data to estimate the demand distribution. [ABSTRACT FROM AUTHOR]

Floods are a significant issue across Southeast Asia, with Thailand, particularly the Nakhon Si Thammarat Municipality, being heavily affected due to its unique conditions such as heavy rainfall, rapid urbanization, and low-lying coastal position. This study utilizes Geographic Information Systems (GIS) to analyze the flood-prone areas of Nakhon Si Thammarat Municipality, assess its road networks, and identify optimal evacuation locations and routes. Various data layers such as slope angle, elevation, distance from roads, rainfall, TWI, NDVI, land use, soil texture, distance from rivers, stream density, and road density are integrated using the frequency ratio method. The findings reveal areas with very high flood susceptibility, which span 198.78 sq. km (56.74%) at the high frequency level, 299.43 sq. km (37.63%) at the low frequency level, and 284.62 sq. km (53.88%) at the moderate frequency level. During flood scenarios, travel times saw an average increase to 21.4 min, which is a fourfold surge compared to regular conditions, highlighting the impact of floods on evacuation strategies. Upon evaluating the road network under flood conditions and applying network analysis techniques, efficient and safe evacuation routes were determined. The results, which underscore a fourfold increase in travel time during flood scenarios, present valuable insights for emergency management authorities across Thailand and Southeast Asia to devise comprehensive evacuation plans, thereby enhancing regional resilience against future flood events. Furthermore, the methodologies and findings are adaptable and can be applied to other flood-prone regions within Southeast Asia, contributing to improved disaster preparedness and response. • Utilization of GIS-based methods to identify evacuation areas and routes in flood-prone zones. • Identification of elevation, slope angle, and land use as significant flood triggering factors for effective flood risk mitigation. • Road network analysis considering normal and flood scenarios to establish optimal evacuation routes for the safe evacuation of residents. [ABSTRACT FROM AUTHOR]

In spite of, the importance of sustainability, and its concept and protection begun in the 1970s. Yet, with framework for action 2005-2025, it integrated risk prevention strategies into sustainable development activities. Whilst, Al-Hillah is the city center of important location near the holy governorate is surrounded by archaeological sites back to more than 4 000 years ago. These features make it perfect city as the case study. Parctically, this article compares multiple planning to obtain an effective scenario that manages events of operations before, during, and after the disaster. By creating a proactive scenario can based on indicators inspired from population density, easy access of traffic movement, and the shortest time at road intersections, barriers, and land uses. The article process represented by spatial analysis a12 planned based on the parameters, to choose the preferred evocation scenarios. Firstly, combined between rout and service network analysis to evaluate roads of the city by the shortest time, number of barriers, intersection, construction of shrouding zone and density of population. That result scenario (B) is selected, which protection for (19.08%) of the city's population within a radius of two zones (0-4000), (4000-7000) m, followed by scenario (D) within a radius (0-4000) m zone, which serves 12.9% of the city's population, then scenario (C) within a radius (4000-7000) m zone, can protect the city center of the city. Finally, scenario (A) within a radius (0-4000) m zone, can protect 4.9% city's population towards the university. In conclusion, an integrated scenario that includes the entire city to obtain goal of the article can be achieved by protecting the civilian people to protect and directed chaos directed away from holy sites and farther from the heritage sites to achieve urban sustainability. [ABSTRACT FROM AUTHOR]

The frequency of natural and man-made disasters has increased over the past few decades, which has doubled the significance of evacuation planning because it directly affects people's lives and properties. How evacuees behave during a disaster and the methodologies to assess their behaviour are vital factors in managing any emergency scenario. For example, during the evacuation of a transportation network, users' amount of information and how they react are imperative to achieve a resilient response to a disaster. Even though there are numerous approaches for assessing evacuee behaviour, further research is needed to determine how and when each methodology should be employed. This paper intends to evaluate and classify the methodologies that have been presented up to date to create a more consistent approach to interpreting human behaviour during an evacuation process. The majority of studies focus on how people behave during an evacuation of a building, with less attention paid to how they behave during a transport evacuation. Among the proposed methodologies in this paper, the virtual reality approach, in individual behaviour, and agent-based models, in crowd behaviour, have more advantages than other approaches. Overall, a comparison between the proposed approaches is made in the discussion part. The output of this study provides the classifications and suggestions for researchers to pick an appropriate approach based on the types of problems, and some direction for future studies are introduced. To reach the research goal, 177 papers have been reviewed between 1954 and 2022. [ABSTRACT FROM AUTHOR]

Evacuation is a way to reduce disaster risks. Evacuation destination choice is essential in modeling and planning for logistical arrangements in future evacuations. In this study, exogenous variables that determine the destination choice of households were identified. Households in selected areas in Quezon City, Philippines were selected since they are highly vulnerable in the event of urban flooding. Gender-based discrete choice models for the whole evacuating households were developed. The resulting exogenous variables include income, source of warning, distance traveled from their house to their chosen destination, and length of stay in the selected destination. For male evacuees, determinants are the number of house floors, source of warning, and duration of stay in the choice of destination, while for female evacuees, factors are the type of work and house materials. This study provides valuable insights for government to plan for more effective evacuations. At the individual and household level, it gives insights to understand their decisions and increase self or household evacuation compliance. This can be a basis for more effective evacuation logistical arrangements for future flood evacuations. [ABSTRACT FROM AUTHOR]

Using the case of the Acropolis of Athens, this paper aims to broaden current knowledge on risk and emergency management in archaeological complexes of high visitation. More specifically, it focuses on the protection of visitors and staff and intends to provide guidelines towards an emergency response plan for geodynamic and meteorological hazards in the Athens Acropolis archaeological site, along with a risk reduction and preparedness strategy. To this end, the paper first analyzes the main challenges arising from the everyday use of the archaeological site and the high visitor flows, mainly during summer. Secondly, it sets out the main parameters for drawing up an emergency evacuation plan for staff and visitors. Finally, it proposes preparedness guidelines, including training and information for all involved, together with a roadmap towards reducing existing risk and the implementation of necessary infrastructure interventions against residual risk. To finish, we conclude that challenges in emergency planning for the Acropolis of Athens do not arise solely from the unique conditions of the place and restrictions associated with the universal value of the monument but point towards reducing high residual risk and improving risk governance. [ABSTRACT FROM AUTHOR]

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Emergency management is a fundamental issue in building management. Therefore, this study was established to simulate the required times and movement conditions for user evacuation from Campus Infrastructure Building in a 3D environment. Eight simulations are carried out by a multi-agent evacuation simulator, Pathfinder, considering 100% and 70% users capacity with a combination of random, divide, zone 1 and zone 2 access selection. The users evacuated were handled by the A* algorithm in the steering mode of 3D evacuation simulation. The evacuation flow was investigated with the user density heat map and Fruin’s Level of Service (LOS). This study found the best path planning obtained from the divide access factor and the impact of the user's capacity is relying on total evacuation time. The critical space is caused by the access width fewer than 1.5 m for every floor, and by widening some spaces helped to improve the evacuation flow.

Natural disasters can cause widespread damage to buildings and infrastructures and kill thousands of living beings. These events are difficult to be overcome both by the populations and by government authorities. Two challenging issues require in particular to be addressed: find an effective way to evacuate people first, and later to rebuild houses and other infrastructures. An adequate recovery strategy to evacuate people and start reconstructing damaged areas on a priority basis can then be a game changer allowing to overcome effectively those terrible circumstances. In this perspective, we here present DiReCT, an approach based on i) a dynamic optimization model designed to timely formulate an evacuation plan of an area struck by an earthquake, and ii) a decision support system, based on double deep Q Network, able to guide efficiently the reconstruction the affected areas. The latter works by considering both the resources available and the needs of the various stakeholders involved (e.g., residents social benefits and political priorities). The ground on which both the above solutions stand was a dedicated geographical data extraction algorithm, called “GisToGraph”, especially developed for this purpose. To check applicability of the whole approach, we dovetailed it on the real use-case of the historical city center of L’Aquila (Italy) using detailed GIS data and information on urban land structure and buildings vulnerability. Several simulations were run on the underlining network generated. First, we ran experiments to safely evacuate in the shortest possible time as many people as possible from an endangered area towards a set of safe places. Then, using DDQN, we generated different reconstruction plans and selected the best ones considering both social benefits and political priorities of the building units. The described approaches are comprised in a more general data science framework delved to produce an effective response to natural disasters.

To take advantage of enormous evacuation data for the efficiency of emergency evacuation, we in this paper develop an improved framework for evacuation planning, which is composed of a modified network-based model and a corresponding optimization structure. Under the proposed framework, we describe pedestrians' features in a computationally efficient way according to their movement pattern and design a model where a typical optimization problem can be well integrated. At the optimization stage, we present a novel algorithm that is more suitable for the allocation of network nodes. For validation, the framework is applied based on the data from experiments and a specific scenario to simulate emergency evacuation. The preliminary numerical results show that a satisfactory evacuation plan can be developed within an acceptable calculation time. Combining with other supporting data, the framework can be further extended to investigate other evacuation strategies in large-scale and crowded pedestrian facilities. [ABSTRACT FROM AUTHOR]

Abstract Almost every year, First Nations are evacuated in Canada because of wildfire proximity and smoke. Dynamics of wildfires, and remote locations, unique sociocultural characteristics, and limited emergency management resources present challenges for evacuation organizers and residents. This study explores how Dene Tha’ First Nation evacuated their Taché community in July 2012 due to wildfire smoke and how the evacuation process affected evacuees. Interviews were completed with 31 evacuation organizers and residents to examine the factors that helped and hindered the evacuation process. Lack of information about the nearby wildfire, smoke, and evacuation of the nearby small community of Zama City, combined with a generic evacuation plan, delayed and posed challenges during the evacuation of this Dene Tha’ community. Strong leadership and its role in community organizing, keeping families together, providing the social support they needed, and using familiar host communities, demonstrated and contributed to the community’s resilience during the evacuation. Measures to improve evacuations and emergency management in the community and other First Nations in Canada are identified and discussed.

Evacuation drills are critical to evaluate emergency preparedness and infrastructure capacity. Before conducting drills, it is necessary to design the evacuation routes that people are likely to follow in a real evacuation. In this paper, we present a path-oriented optimization model for evacuation planning. To solve this model, we propose a column-oriented approach in which a master problem assigns people to evacuation paths, while an auxiliary problem generates feasible evacuation paths. To recreate possible evacuation drills, we embed the column generation method in an optimization-based simulation procedure that mimics the evacuation dynamics and unveils critical evacuation routes that are likely to become congested. To illustrate the applicability of our method, we recreated a real evacuation drill conducted in a university campus. Our results support evacuation planners that often make infrastructure decisions with the goal of enhancing evacuation dynamics with the commendable goal of saving lives.

An important aspect of evacuation planning is to save human population by sending the people in dangerous areas (sources) to the safe places (sinks). Optimization models for evacuation planning focus on regulating the traffic ow in urban road networks so as to maximize the number of evacuees reaching the safe places or to minimize the evacuation time of evacuees. Recent studies show that reversing the direction of the usual traffic ow in necessary road segments increases the traffic ow and decreases the evacuation time significantly. However, this may block the ow of some necessary support towards the source. Based on network ow models, we consider the problem of saving a path from a given node to the source, in a single-source-single-sink network, as a bilevel program. With an objective to minimize a function depending on ow rate and the path chosen, the upper level selects a path from a given node to the source, and the lower level maximizes the dynamic ow allowing arc reversals in the resulting network within a given time horizon. We discuss a solution strategy based on replacing the lower level problem by the corresponding KKT conditions. [ABSTRACT FROM AUTHOR]