A methodology for estimating emergency evacuation times

Tweedie, Stephen W.; Rowland, James; Walsh, Stephen J.; Rhoten, Ronald P.; Hagle, Paul I.

doi:10.1016/0362-3319(86)90035-2

Cited by 82 publications

(34 citation statements)

References 3 publications

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“…For the case of hurricanes, Baker (1991) listed five attributes determining the decision to evacuate: the risk level within the area, actions by public authorities, type of housing, prior perception of personal risk, and a storm specific threat factor. In the past, these attributes have served for an empirically based approach to predict evacuation demand by Tweedie et al (1986). Later, PBS&J (2000b) developed a cross-classification type of trip generation model based on survey data collected in the south-eastern states of the US where the evacuation participation specified by county depended on the hurricane category and speed, tourist occupancy, and type of housing in that area.…”

Section: Sequential Travel Demand Modelmentioning

confidence: 99%

“…The departure response curve has been assumed to follow many different distributions. Some examples are instantaneous departure (Lewis 2001;Chen and Zhan 2004;Chiu et al 2006), a Uniform distribution (Liu et al 2006;Yuan et al 2006), a Rayleigh distribution (Tweedie et al 1986), a Poisson distribution (Cova and Johnson 2002), a Weibull distribution (Jonkman 2007;Lindell 2008) or sigmoid curve (Kalafatas and Peeta 2009;Xie et al 2010). The Weibull distribution and sigmoid curve are most often used and claimed to be most realistic.…”

Section: Sequential Travel Demand Modelmentioning

confidence: 99%

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A review on travel behaviour modelling in dynamic traffic simulation models for evacuations

2011

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Dynamic traffic simulation models are frequently used to support decisions when planning an evacuation. This contribution reviews the different (mathematical) model formulations underlying these traffic simulation models used in evacuation studies and the behavioural assumptions that are made. The appropriateness of these behavioural assumptions is elaborated on in light of the current consensus on evacuation travel behaviour, based on the view from the social sciences as well as empirical studies on evacuation behaviour. The focus lies on how travellers' decisions are predicted through simulation regarding the choice to evacuate, departure time choice, destination choice, and route choice. For the evacuation participation and departure time choice we argue in favour of the simultaneous approach to dynamic evacuation demand prediction using the repeated binary logit model. For the destination choice we show how further research is needed to generalize the current preliminary findings on the location-type specific destination choice models. For the evacuation route choice we argue in favour of hybrid route choice models that enable both following instructed routes and en-route switches. Within each of these discussions, we point at current limitations and make corresponding suggestions on promising future research directions.

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Section: Sequential Travel Demand Modelmentioning

confidence: 99%

Section: Sequential Travel Demand Modelmentioning

confidence: 99%

A review on travel behaviour modelling in dynamic traffic simulation models for evacuations

2011

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“…First, we assume mobilization time is a constant and equal to four hours based on prior research that suggests mobilization time ranges between two and four hours (Lindell et al, 2011;Stone, 1983;Tweedie, Rowland, Walsh, Rhoten, & Hagle, 1986). For the purposes of this study, clearance time is defined as the total time required for all residents of a given Census tract to move to a 'safe' destination along an evacuation route at a distance of 250 miles (402 km) using private vehicles, consistent with travel distances reported by previous studies (Lindell et al, 2011;Zhang et al, 2007).…”

Section: Modeling Clearance Timementioning

confidence: 99%

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

Berg

Wilson

2013

Environmental Hazards

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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.

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“…Those curves are symmetric, indicating an increasing hourly arrival rate for the first 12 h and a decreasing hourly arrival rate for the following 12 h; however, the shape and peak values vary based on the chosen response time rate. The response time curve is also expressed as a deformation of Rayleigh's cumulative function (Tweedie et al 1986). The cumulative function estimated the response percentage as below:…”

Section: Previous Behavior Studies As An Input To the Proposed Frameworkmentioning

confidence: 99%

A simulation modeling framework for community-wide evacuation planning

2010

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Simulation is a useful and cost effective tool for evacuation planning. However, extensive data collection and preparation is necessary to build a traffic evacuation simulation model that can closely replicate real life conditions. In a community-wide evacuation process during an emergency, which covers hundreds of miles, input data related to simulation of traffic evacuations include (1) Traffic and roadway geometry, (2) Geographic distribution of the affected area, (3) Travel demand modeling, and (4) Behavioral analysis of potential evacuees. This paper presents a framework for preparing simulation inputs and ultimately developing a simulation model. Brief excerpts from a case study on the evacuation of Charleston, South Carolina are also included. An accurate input analysis is very important to the success of a simulation project since without correct input data, the output of a simulation cannot contribute to more effective decision making. This paper presents a simple and efficient methodology for data preparation regarding a large scale city evacuation simulation involving long distance trips.

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A methodology for estimating emergency evacuation times

Cited by 82 publications

References 3 publications

A review on travel behaviour modelling in dynamic traffic simulation models for evacuations

A review on travel behaviour modelling in dynamic traffic simulation models for evacuations

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

A simulation modeling framework for community-wide evacuation planning

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