Planning for Evacuation: Insights from an Efficient Network Design Model

This article presents a reliability-based evacuation route planning model that seeks to find the relationship between the clearance time, number of evacuation paths, and congestion probability during an evacuation. Most of the existing models for network evacuation assume deterministic capacity estimates for road links without taking into account the uncertainty in capacities induced by myriad external conditions. Only a handful of models exist in the literature that account for capacity uncertainty of road links. A dynamic network–based evacuation model is extended by incorporating probabilistic arc capacity constraints and a minimum-cost network flow problem is formulated that finds a lower bound on the clearance time within the framework of a chance-constrained programming technique. Network breakdown minimization principles for traffic flow in evacuation planning problem are applied and a path-based evacuation routing and scheduling model is formulated. Given the horizon time for evacuation, the model selects the evacuation paths and finds flows on the selected paths that result in the minimum congestion in the network along with the reliability of the evacuation plan. Numerical examples are presented and the effectiveness of the stochastic models in evacuation planning is discussed. It is shown that the reliability-based evacuation plan is conservative compared with plans made using a deterministic model. Stochastic models guarantee that congestion can be avoided with a higher confidence level at the cost of an increased clearance time.

Evacuation is an important activity for reducing the number of casualties and amount of damage in disaster management. Evacuation planning is tackled as a spatial optimization problem. The decision-making process for evacuation involves high uncertainty, conflicting objectives, and spatial constraints. This study presents a Multi-Objective Artificial Bee Colony (MOABC) algorithm, modified to provide a better solution to the evacuation problem. The new approach combines random swap and random insertion methods for neighborhood search, the two-point crossover operator, and the Pareto-based method. For evacuation planning, two objective functions were considered to minimize the total traveling distance from an affected area to shelters and to minimize the overload capacity of shelters. The developed model was tested on real data from the city of Kigali, Rwanda. From computational results, the proposed model obtained a minimum fitness value of 5.80 for capacity function and 8.72 × 108 for distance function, within 161 s of execution time. Additionally, in this research we compare the proposed algorithm with Non-Dominated Sorting Genetic Algorithm II and the existing Multi-Objective Artificial Bee Colony algorithm. The experimental results show that the proposed MOABC outperforms the current methods both in terms of computational time and better solutions with minimum fitness values. Therefore, developing MOABC is recommended for applications such as evacuation planning, where a fast-running and efficient model is needed.

Effective incident management and traffic control measurements require a full understanding of the characteristics of incidents to accurately estimate incident durations and to help make more efficient decisions to reduce the impacts of non recurring congestion due to these accidents. The Incident duration consist of four elements: detection time, response time, clearance time and recovery time. This paper develops a recovery model and incident delay model based on CTM, which has analytical simplicity and the ability to reproduce the traffic behavioral. Impact analysis of recovery time and incident delay have been done in quantity to demonstrate recovery time and delay during recovery time can not be neglected because it often accounts for larger proportion of the duration time and total delay especially in the city freeways. Accurate estimation of traffic incident delay is helpful to effective incident management and traffic control.

Trajectories of community fire adaptation: Social diversity, social fragmentation and the temporal evolution of wildfire action.

Network redesign for efficient crowd flow and evacuation

This article presents a linear programming formulation to solve the network design problem using the link transmission model (LTM) as the underlying traffic flow model. The original LTM was adapted by incorporating link-sending and receiving flows using linear inequalities. Furthermore, route choice was relaxed, and transfer flow variables were used to model vehicles’ routing decisions within the network. The objective function of the linear program aimed to minimize the total difference between the cumulative vehicle numbers (CVN) at the upstream and at the downstream boundaries of each link subject to flow-conservation and budget constraints. CVN were represented using transfer flows from connected links. The resulting formulation is a linear program that represents a dynamic system optimum traffic flow pattern, embedding the LTM’s network loading procedure. In contrast to the single-destination system optimum dynamic traffic assignment, based on the cell transmission model, the proposed formulation requires considerably fewer decision variables, thus potentially providing a more scalable approach. The proposed formulation was implemented on an example network to illustrate the behavior of the model, and was compared with the cell-based formulation. We show that the model describes free-flow and congested traffic flow states accurately in terms of shock-wave propagation, queuing of vehicles, and optimal total system travel time.

In many emergency management operations, an efficient evacuation strategy is of great importance because if it is successful, it has the ability to significantly reduce the loss of property and human life. This paper develops a routing and scheduling optimization framework for large-scale vehicular evacuation. To guarantee high optimization efficiency, we consider the routing and scheduling optimization as a two- stage problem instead of optimizing them as a whole (i.e. using time-space network). In the first phase, a multiple-objective mixed integer programming (MIP) model, with the objectives of minimizing the total in-network time and network clearance time is proposed to find an optimal routing plan. In the second phase, a simulation-based scheduling Heuristic is proposed to dynamically generate the time-dependent departure rates. A real-world evacuation scenario in Eastern Shore of Maryland is studied by using the proposed optimization model. The calculation results indicate a good optimization capability and flexibility of the proposed model. Keywords: Vehicular Evacuation, Routing, Scheduling, Optimization, Heuristic Copyright c 2016 SERSC Language: en

Determination of the optimal normal water level of reservoirs (RNWL) was investigated, incorporating environmental ecology as a primary consideration. RNWL constitutes a relatively significant eigenvalue of any water conservancy project. In the present study, a four-step method based on a mathematical programming model and suitable for RNWL decision making was developed and applied to the water conservancy project of the Songyuan backwater dam in China. System analysis, correlation analysis, significance testing, principal component analysis, sensitivity analysis, and system optimisation theory are used in the solution process. In this study, various factors that impact the economic viability, engineering characteristics, environmental and urban ecology are considered for holistic optimisation. The study shows that the proposed four-step method may provide a feasible quantitative form of support for RNWL decision making.

Evacuation plans are traditionally based on empirical knowledge and local experience. Many studies are underway to improve and optimize the process of evacuation planning and operations. Therefore, a commonly accepted model for evacuation is necessary. During the process of an evacuation, the fact of risk of exposure(e.g. airborne toxins and radiation) may be neglected. In order to solve the Evacuation planning problem, an evacuation objective formulation is established based on total evacuation time and evacuation distance optimization, and these two parts were combined with disaster risk function which considers time-dependent disaster risk. The authors use the model to calculate the evacuation routing assignment extension, experimental results show that evacuation plan with system optimal policy can achieve no blocking and minimum network clearance time; therefore it ensures greater safety in the evacuation process.

The contribution of tsunami evacuation analysis to evacuation planning in Chile: Applying a multi-perspective research design

Robust scheduling for large scale evacuation planning

Computational modeling of organic interface formation on semiconductors poses a challenge to a density functional theory-based description due to structural and chemical complexity. A hierarchical approach is presented, where parts of the interface are successively removed in order to increase computational efficiency while maintaining the necessary accuracy. First, a benchmark is performed to probe the validity of this approach for three model reactions and five dispersion corrected density functionals. Reaction energies are generally well reproduced by generalized gradient approximation-type functionals but accurate reaction barriers require the use of hybrid functionals. Best performance is found for the model system that does not explicitly consider the substrate but includes its templating effects. Finally, this efficient model is used to provide coverage dependent reaction energies and suggest synthetic principles for the prevention of unwanted growth termination reactions for organic layers on semiconductor surfaces.

Traffic evacuation is one of the most important tasks in emergency management, and it is challenging for governments to plan an efficient and safe evacuation before a huge disaster strikes. This paper presents a traffic evacuation simulation system that generates agent's driving behavior based on multi-level driving decision models. The agent's driving behavior combines multiple widely used behavior models from each decision level. The agent-based traffic evacuation system is proposed and a prototype system implements each agent's multi-level modular driving decision models. The simulation experiment studies show varied clearance time, evacuation rates per shelter, and the variety and number of traffic jams to support traffic evacuation planning decisions in a crowded city liked Beijing, China. The simulation studies compare the existing evacuation plan with other simulated plans and evaluate it for designing a better and more realistic traffic evacuation plan.

Traffic evacuation is one of the most important tasks in emergency management, and it is challenging for governments to plan an efficient and safe evacuation before a huge disaster strikes. This paper presents a traffic evacuation simulation system that generates agent's driving behavior based on multi-level driving decision models. The agent's driving behavior combines multiple widely used behavior models from each decision level. The agent-based traffic evacuation system is proposed and a prototype system implements each agent's multi-level modular driving decision models. The simulation experiment studies show varied clearance time, evacuation rates per shelter, and the variety and number of traffic jams to support traffic evacuation planning decisions in a crowded city liked Beijing, China. The simulation studies compare the existing evacuation plan with other simulated plans and evaluate it for designing a better and more realistic traffic evacuation plan.

There have been two streams in the 3D detection from point clouds: single-stage methods and two-stage methods. While the former is more computationally efficient, the latter usually provides better detection accuracy. By carefully examining the two-stage approaches, we have found that if appropriately designed, the first stage can produce accurate box regression. In this scenario, the second stage mainly rescores the boxes such that the boxes with better localization get selected. From this observation, we have devised a single-stage anchor-free network that can fulfill these requirements. This network, named AFDetV2, extends the previous work by incorporating a self-calibrated convolution block in the backbone, a keypoint auxiliary supervision, and an IoU prediction branch in the multi-task head. We take a simple product of the predicted IoU score with the classification heatmap to form the final classification confidence. The enhanced backbone strengthens the box localization capability, and the rescoring approach effectively joins the object presence confidence and the box regression accuracy. As a result, the detection accuracy is drastically boosted in the single-stage. To evaluate our approach, we have conducted extensive experiments on the Waymo Open Dataset and the nuScenes Dataset. We have observed that our AFDetV2 achieves the state-of-the-art results on these two datasets, superior to all the prior arts, including both the single-stage and the two-stage 3D detectors. AFDetV2 won the 1st place in the Real-Time 3D Detection of the Waymo Open Dataset Challenge 2021. In addition, a variant of our model AFDetV2-Base was entitled the "Most Efficient Model" by the Challenge Sponsor, showing a superior computational efficiency. To demonstrate the generality of this single-stage method, we have also applied it to the first stage of the two-stage networks. Without exception, the results show that with the strengthened backbone and the rescoring approach, the second stage refinement is no longer needed.