An agent-based modeling system for travel demand simulation for hurricane evacuation (original) (raw)

Comparison of Alternative Trip Generation Models for Hurricane Evacuation

Natural Hazards Review, 2004

The purpose of this study was to compare the relative accuracy of alternative forms of trip generation of evacuation traffic. Conventional participation rate, logistic regression, and various forms of neural network models were estimated and tested using a data set of evacuation behavior collected in southwest Louisiana following Hurricane Andrew. The data set was divided into a 350-household data base on which the logistic regression and neural network models were estimated, and a separate 60-household data base on which all models were tested. Limited and comprehensive model inputs were tested among the neural network models to determine whether more comprehensive specifications enhance the performance of the models. It was found that the limited specification performed almost as well as the more detailed specification. Comparison of the performance of the models considered in this study showed that the logistic regression and neural network models were able to predict evacuation more accurately than the participation rate model.

Modeling Evacuation Behavior Under Hurricane Conditions

Transportation Research Record: Journal of the Transportation Research Board, 2016

The understanding of evacuation behavior is critical to establishing policies, procedures, and organizational structure for an effective response to emergencies. This study specifically investigated the evacuation behavioral responses under hurricane conditions. The study aimed to explore the association between contributing factors and the evacuation decision choices as well as evacuation destination choices. Unlike previous studies that modeled each response behavior separately, this study proposed to use the structural equation modeling approach to examine the interrelationship between response behaviors. A case study was performed with the data set from a survey conducted in New Jersey. With Bayesian estimation approaches, the proposed structural equation models were estimated, and the effect of each predictive variable was captured. An important finding is that individuals’ preference to evacuate did not significantly affect their choices of evacuation destinations. In addition...

Integrated travel demand models for evacuations: a bridge between social science and engineering

International Journal of Safety and Security Engineering, 2014

Since 9/11, the Indian Ocean tsunami and hurricane Katrina, the number of papers that are being published related to mobility simulation in evacuation conditions has signifi cantly increased. Though several topics have been developed, they tend to be implemented with an isolated and non-system approach and for specifi c kinds of dangerous events. This work aims to present a classifi cation and specifi cation of demand models for mobility simulation in evacuation conditions under different evacuation scenarios, in respect to different temporal conditions. A general framework is proposed to support the analysis of dangerous events, in respect of type and effects, especially in time. Three different temporal evolutions are identifi ed and systematized: event developments and the relative conditioning on the system; user modifi cation of behavior; and planning and management evolution. Leaving from the integrated temporal evolutions, the user behavior in the system context is analyzed and specifi c models are developed. The importance of SP surveys to analyze user behavior in evacuation conditions is highlighted and a hybrid class of surveys, termed SP with a physical check, is introduced. An integrated demand model is specifi ed and calibrated for a dangerous event with effects on travel demand, with diffuse effects in space and delayed in time, according a behavioral approach.

Joint modeling of evacuation departure and travel times in hurricanes

Hurricanes are costly natural disasters periodically faced by households in coastal and to some extent, inland areas. A detailed understanding of evacuation behavior is fundamental to the development of efficient emergency plans. Once a household decides to evacuate, a key behavioral issue is the time at which individuals depart to reach their destination. An accurate estimation of evacuation departure time is useful to predict evacuation demand over time and develop effective evacuation strategies. In addition, the time it takes for evacuees to reach their preferred destinations is important. A holistic understanding of the factors that affect travel time is useful to emergency officials in controlling road traffic and helps in preventing adverse conditions like traffic jams. Past studies suggest that departure time and travel time can be related. Hence, an important question arises whether there is an interdependence between evacuation departure time and travel time? Does departing close to the landfall increases the possibility of traveling short distances? Are people more likely to depart early when destined to longer distances? In this study, we present a model to jointly estimate departure and travel times during hurricane evacuations. Empirical results underscore the importance of accommodating an interrelationship among these dimensions of evacuation behavior. This paper also attempts to empirically investigate the influence of social ties of individuals on joint estimation of evacuation departure and travel times. Survey data from Hurricane Sandy is used for computing empirical results. Results indicate significant role of social networks in addition to other key factors on evacuation departure and travel times during hurricanes.

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

Networks and Spatial Economics, 2016

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 Netw Spat Econ

Modeling Emergency Managers’ Hurricane Evacuation Decisions

Transportation Research Record: Journal of the Transportation Research Board, 2017

Emergency management and decision support system (EMDSS) tools play an important role in assisting emergency managers with making important decisions about the movement of people to safety when a jurisdiction is threatened by a storm. One of the important components of an EMDSS is an evacuation demand model that predicts whether and when households will evacuate when they are threatened by a storm. A critical input to that model is an emergency manager's decision to issue an evacuation notice. No existing mathematical models predict whether and when an emergency manager will issue an evacuation notice on the basis of a hurricane forecast and other contextual factors. To fill this gap, this research study sought to develop a model that would predict if and when an emergency manager would issue an evacuation notice when a jurisdiction was threatened by a storm. Data from poststorm assessment surveys and newspaper archives were used to retrieve past decisions made by evacuation man...

A destination choice model for hurricane evacuation

87th Annual Meeting of the …, 2008

A disaggregate destination choice model for hurricane evacuation was developed with post hurricane Floyd survey data collected in South Carolina in 1999. Because destination choice is a choice between discrete, independent alternatives, the multinomial logit model was selected as a convenient model form. It was used to investigate the effect of risk areas in the path, or projected path, of a hurricane, and destination socioeconomic and demographic characteristics on destination choice behavior. Models were developed for persons evacuating to friends or relatives, or hotels or motels separately. The models were tested by comparing the observed destination choices with predicted values. No significant difference was found.

A spatial agent-based model for preemptive evacuation decisions during typhoon

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2021

Natural disasters continue to cause tremendous damage to human lives and properties. The Philippines, due to its geographic location, is considered a natural disaster-prone country experiencing an average of 20 tropical cyclones annually. Understanding what factors significantly affect decision making during crucial evacuation stages could help in making decisions on how to prepare for disasters, how to act appropriately and strategically respond during and after a calamity. In this work, an agent-based model for preemptive evacuation decisions during typhoon is presented. In the model, civilians are represented by households and their evacuation decisions were based from calculated perceived risk. Also, rescuer and shelter manager agents were included as facilitators during the preemptive evacuation process. National and municipal census data were employed in the model, particularly for the demographics of household agents. Further, geospatial data of a village in a typhoon-susceptible municipality was used to represent the environment. The decision to evacuate or not to evacuate depends on the agent's perceived risk which also depends on three decision factors: characteristics of the decision maker (CDM); capacity related factors (CRF); and hazard related factors (HRF). Finally, the number of households who decided to evacuate or opted to stay as influenced by the model's decision factors were determined during simulations. Sensitivity analysis using linear regression shows that all parameters used in the model are significant in the evacuation decision of household agents.

Household-Level Model for Hurricane Evacuation Destination Type Choice Using Hurricane Ivan Data

Natural Hazards Review, 2013

Hurricanes are costly natural disasters periodically faced by households in coastal and, to some extent, inland areas. Public agencies must understand household behavior to develop evacuation plans that align with evacuee choices and behavior. This paper presents a previously unknown household-level hurricane evacuation destination type choice model. The discrete choice of destination type is modeled using a nested logit model. Although previous literature considers only houses of friends and relatives and hotels for modeling purposes, this paper incorporates public shelters, churches, and an aggregated destination type denoted other. This research found that the variables influencing this choice include hurricane position at evacuation time, household geographic location, race, income, preparation time, changes in evacuation plans, previous experiences with major hurricanes, household members working during the evacuation, and evacuation notices. The findings of this paper are useful to understand the competition among destination types and how the characteristics of the demand can be used to develop evacuation strategies, such as increasing and/or decreasing use of public shelters, and measuring the effect of evacuation notices in areas with high accessibility to hotels.