An Optimization Model for Guiding Pedestrian–Vehicle Mixed Flows During an Emergency Evacuation

In most metropolitan areas, an emergency evacuation may require a potentially large number of pedestrians to walk some distance to access their passenger cars or resort to transit systems. In this process, the massive number of pedestrians may place a tremendous burden on vehicles in the roadway network, especially at critical intersections. Thus, the effective road enforcement of the vehicle and pedestrian flows and the proper coordination between these two flows at critical intersections during a multimodal evacuation process is a critical issue in evacuation planning. This article presents an integrated linear model for the design of optimized flow plans for massive mixed pedestrian–vehicle flows within an evacuation zone. The optimized flow can also be used to generate signal timing plans at critical intersections. In addition, the linear nature of the model can circumvent the computational burden to apply in large-scale networks. An illustrating example of the evacuation around the M&T Bank Stadium in downtown Baltimore, MD, is presented and used to demonstrate the model's capability to address the complex interactions between vehicle and pedestrian flows within an evacuation zone. Results of simulation experiments verify the applicability of our model to a real-world scenario and further indicate that accounting for such conflicting movements will yield more reliable estimation of an evacuation's required clearance time.     

Behavior-consistent information-based network traffic control for evacuation operations

The evacuation operations problem aims to avoid or mitigate the potential loss of life in a region threatened or affected by a disaster. It is shaped to a large extent by the evolution of evacuation traffic resulting from the demand–supply interactions of the associated transportation network. Information-based control is a strategic tool for evacuation traffic operations as it can enable greater access to the affected population and more effective response. However, comparatively few studies have focused on the implementation of information-based control in evacuation operations. This study develops a control module for evacuation operations centered on addressing the demand–supply interactions by using behavior-consistent information strategies. These strategies incorporate the likely responses of evacuees to the information provided in the determination of route guidance information. The control module works as an iterative computational process involving an evacuee route choice model and a control model of information strategies to determine the route guidance information to direct evacuation traffic so as to approach a desired network traffic flow pattern. The problem is formulated as a fuzzy logic based optimization framework to explicitly incorporate practical concerns related to information dissemination characteristics and social equity in evacuation operations. Numerical experiments highlight the importance of accounting for the demand–supply interactions, as the use of behavior-consistent information strategies can lead evacuee route choices to approach the operator-desired proportions corresponding to the desired traffic pattern. The results also indicate that while a behavior-consistent information strategy can be effective, gaps with the desired route proportions can exist due to the discrete nature of the linguistic messages and the real-world difficulty in accurately modeling evacuees’ actual route choice behavior.     

从消防角度探讨地铁隧道疏散平台的设计问题

疏散平台是地铁隧道消防安全的必要保障,但疏散平台的主要作用是中转,道床面才是疏散的主要途径。从消防疏散的角度出发,探讨地铁隧道内设置疏散平台的重要性,并对疏散平台和道床面的主要作用进行了对比说明,重点分析疏散平台设置步梯的必要性和步梯给区间管线安装带来的问题,最后提出相关技术主张。     

紧急疏散情况下地铁车站动态瓶颈识别

随着城市轨道交通系统逐渐成网,地铁车站日渐拥挤,为了制定科学有效的紧急疏散方案,有必要预先找到车站瓶颈点。基于时间可靠度函数模拟乘客在紧急情况下路径选择行为,在此基础上引入M//G/C/C排队模型对车站排队系统各个服务台进行排队性能指标计算,找出潜在瓶颈点。以广州地铁3号线岗顶站为例,结合车站硬件设施尺寸数量,模拟计算最恶劣情况下乘客疏散过程,找到3处潜在瓶颈点,测算结果与实际观测情况相符合,验证了该方法的有效性。     

Location planning for transit-based evacuation under the risk of service disruptions

The effectiveness of transit-based emergency evacuation highly depends on the location of pick-up facilities, resource allocation, and management. These facilities themselves are often subject to service disruptions during or after the emergency. This paper proposes a reliable emergency facility location model that determines both pre-emergency facility location planning and the evacuation operations afterwards, while facilities are subject to the risk of disruptions. We analyze how evacuation resource availability leverages individual evacuees’ response to service disruptions, and show how equilibrium of the evacuee arrival process could be reached at a functioning pick-up facility. Based on this equilibrium, an optimal resource allocation strategy is found to balance the tradeoff between the evacuees’ risks and the evacuation agency’s operation costs. This leads to the development of a compact polynomial-size linear integer programming formulation that minimizes the total expected system cost from both pre-emergency planning (e.g., facility set-up) and the evacuation operations (e.g., fleet management, transportation, and exposure to hazardous surroundings) across an exponential number of possible disruption scenarios. We also show how the model can be flexibly used to plan not only pre-disaster evacuation but also post-disaster rescue actions. Numerical experiments and an empirical case study for three coastal cities in the State of Mississippi (Biloxi, Gulfport, and D’lberville) are conducted to study the performance of the proposed models and to draw managerial insights.     

A mixed logit model for predicting exit choice during building evacuations

Knowledge on human behaviour in emergency is crucial to increase the safety of buildings and transportation systems. Decision making during evacuations implies different choices, of which one of the most important concerns is the escape route. The choice of a route may involve local decisions on alternative exits from an enclosed environment. This study investigates the effect of environmental (presence of smoke, emergency lighting and distance of exit) and social factors (interaction with evacuees close to the exits and with those near the decision-maker) on local exit choice. This goal is pursued using an online stated preference survey carried out making use of non-immersive virtual reality. A sample of 1503 participants is obtained and a mixed logit model is calibrated using these data. The model shows that the presence of smoke, emergency lighting, distance of exit, number of evacuees near the exits and the decision-maker and flow of evacuees through the exits significantly affect local exit choice. Moreover, the model indicates that decision making is affected by a high degree of behavioural uncertainty. Our findings support the improvement of evacuation models and the accuracy of their results, which can assist in designing and managing building and transportation systems. The main aim of this study is to enrich the understanding of how local exit choices are made and how behavioural uncertainty affects these choices.     

Unraveling the evacuation behavior of the medically fragile population: Findings from hurricane Irene

Despite the widely recognized importance of evacuation planning for residents with special needs – in this paper referred to as the medically fragile population – there is virtually no research available to guide such planning, as opposed to the numerous empirical research studies on the evacuation behavior of the general population. In this paper, we provide these long-overdue insights using data from a large-scale phone survey (over 7000 samples) conducted in the aftermath of hurricane Irene in the Hampton Roads region in Virginia. Via aggregate and disaggregate analyses, we start to unravel the behavior of this heavily understudied, and potentially vulnerable population group. Special emphasis will be placed on the differences between the medically fragile and non-medically fragile population. Two alternative definitions for what constitutes medically fragile are examined in this paper. Using the broader definition, it was found that a key difference between these two groups relates to the importance of having a strong network of family members in the area. When considering a more narrow definition, we found that being a single parent household, likelihood of neighborhood flooding and knowing most of the names of one’s neighbors have significantly different impacts on the two population groups.     

Compromising system and user interests in shelter location and evacuation planning

Traffic management during an evacuation and the decision of where to locate the shelters are of critical importance to the performance of an evacuation plan. From the evacuation management authority’s point of view, the desirable goal is to minimize the total evacuation time by computing a system optimum (SO). However, evacuees may not be willing to take long routes enforced on them by a SO solution; but they may consent to taking routes with lengths not longer than the shortest path to the nearest shelter site by more than a tolerable factor. We develop a model that optimally locates shelters and assigns evacuees to the nearest shelter sites by assigning them to shortest paths, shortest and nearest with a given degree of tolerance, so that the total evacuation time is minimized. As the travel time on a road segment is often modeled as a nonlinear function of the flow on the segment, the resulting model is a nonlinear mixed integer programming model. We develop a solution method that can handle practical size problems using second order cone programming techniques. Using our model, we investigate the importance of the number and locations of shelter sites and the trade-off between efficiency and fairness.     

深远海设施人员应急撤离路径优化模型

针对深远海设施人员应急撤离路线优化问题,基于深水半潜式支持平台船体实际环境信息,考虑各可撤离路径的通行能力,引入拥挤度和撤离时间来计算人员路阻函数,构建人员撤离路径优化模型.以一个小型多源多汇拓扑网络的人员撤离方案为例,分别求解人员撤离路径优化模型和传统最短撤离路径模型,并对计算结果进行对比分析,验证人员撤离路径优化模型的有效性.     

Leveraging social networks for efficient hurricane evacuation

One of the important factors affecting evacuation performance is the departure time choices made by evacuees. Simultaneous departures of evacuees can lead to overloading of road networks causing congestion. We are especially interested in cases when evacuees subject to little or no risk of exposure evacuate along with evacuees subject to higher risk of threat (also known as shadow evacuation). One of the reasons for correlated evacuee departures is higher perceived risk of threat spread through social contacts. In this work, we study an evacuation scenario consisting of a high risk region and a surrounding low risk area. We propose a probabilistic evacuee departure time model incorporating both evacuee individual characteristics and the underlying evacuee social network. We find that the performance of an evacuation process can be improved by forcing a small subset of evacuees (inhibitors) in the low risk area to delay their departure. The performance of an evacuation is measured by both average travel time of the population and total evacuation time of the high risk evacuees. We derive closed form expressions for average travel time for ER random network. A detailed experimental analysis of various inhibitor selection strategies and their effectiveness on different social network topologies and risk distribution is performed. Results indicate that significant improvement in evacuation performance can be achieved in scenarios where evacuee social networks have short average path lengths and topologically influential evacuees do not belong to the high risk regions. Additionally, communities with stronger ties improve evacuation performance.