Comparison of traffic assignments in evacuation modeling

Hobeika, Antoine G.; Kim, Changkyun

doi:10.1109/17.669768

Cited by 130 publications

(44 citation statements)

References 2 publications

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“…In many of the early studies, evacuation is recognized as an exceptional event regarding different travel demand patterns, driver behaviour, traffic management, etc., resulting in new traffic models being developed specifically for evacuation studies. A few examples are NETVAC (Sheffi et al 1980), DYNEV (KLD 1984), MASSVAC (Hobeika and Jamei 1985;Hobeika and Kim 1998), TEDSS (Sherali et al 1991), IMDAS (Franzese and Han 2001), OREMS (Rathi and Solanki 1993), and CEMPS (Pidd et al 1993). Some basic model characteristics and applications of these models are listed in Table 1.…”

Section: Past and Current Evacuation Traffic Simulation Modelsmentioning

confidence: 99%

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: Past and Current Evacuation Traffic Simulation Modelsmentioning

confidence: 99%

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

2011

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“…Hence, if the total number of stages is three (i.e., T D 3), both stages 2 and 3 are assumed to last for 36 h, and the ı k t 's are determined accordingly. Furthermore, in the literature the number of evacuees E. Angün : Unit holding cost of commodity k at stage T due to spoilage accumulated in time is usually modeled through an S-shaped curve (see [27]). Hence, we assume a normal distribution-whose cumulative distribution function is an S-shaped curve-and a lognormal distribution-whose right tail is thicker than the one of a normal distribution-for Q & t;j .…”

Section: Disaster Preparedness and Short-term Relief Distribution Promentioning

confidence: 99%

Stochastic Dual Dynamic Programming Solution of a Short-Term Disaster Management Problem

Angün

2015

Uncertainty Management in Simulation-Optimization of Complex Systems

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Every year, more than 500 disasters are estimated to strike our planet, causing the deaths of around 75,000 people, and impacting more than 200 million others [1]. To decrease the number of deaths and disaster-affected people, the design of an efficient and effective emergency logistics system is vital. The inefficiency in such a system in Haiti after the 2010 earthquake had the following results: Various on-field journalists [2,3] reported that relief efforts were stalling in the logistics network and that, therefore, much aid remained undelivered. This caused more than 3 million affected people, 200,000 deaths, and more than one million wounded.Disaster operations represent the set of all activities performed before, during, and after a disaster in order to diminish its impacts. Traditionally, the life cycle of disaster operations is divided into four stages, namely risk mitigation, preparedness, response, and recovery. The risk mitigation operations are performed before the disaster occurs, and they aim at reducing vulnerability to the disaster impacts such as injuries, and loss of life and property. The preparedness operations involve those activities performed before the disaster occurs in order to accomplish a more efficient response. The response operations are performed upon the occurrence of the disaster, and they address immediate threats presented by the disaster, including saving lives, meeting humanitarian needs (food, shelter, clothing, public health and safety), evacuating people from the disaster affected area, etc. Finally, recovery operations are performed after a disaster occurs, and they refer to short-term or

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“…For example, Cova and Church (1997) employed an integer programming model to assess vulnerability of evacuation in communities. Hobeika and Kim (1998) proposed a mass evacuation computer program (MASSVAC 4.0) to simulate traffic movements within an evacuation process based on a user equilibrium assignment algorithm. Jha et al (2004) formulated a microscopic simulation model (MITSIMLab) to model traffic operations, and presented a laboratory-like setup to evaluate a large range of evacuation scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Evacuation Management Model Oriented Toward the Mitigation of Emissions

Tan¹

2015

J ENV INFORM

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ABSTRACT.Although the transportation sector is a major contributor to urban air pollution and global climate change due to its substantial energy consumptions, previous studies for evacuation practices in this sector seldom took environmental consequences into account. As an attempt in event-related evacuation planning under uncertainty, this study proposed an emission-mitigation-oriented fuzzy evacuation management (emoFEM) model. Comprehensive considerations over system efficiency, environmental protection, economic cost and resource availability were incorporated within a general modeling formulation to facilitate evacuation management in a systematic and compromise manner. Vague and ambiguous information embedded within evacuation problems could be quantified and directly communicated into the optimization process, greatly improving conventional tools for evacuation management under uncertainty. The proposed emoFEM model was then applied to a hypothetic but representative case. Useful solutions were generated, which could help identify timely, safe and cost-effective evacuation schemes without significant disturbances over normal municipal traffic and environmental quality. The advantages of emoFEM were further revealed through comparing its solutions with those from its deterministic counterpart.

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Comparison of traffic assignments in evacuation modeling

Cited by 130 publications

References 2 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

Stochastic Dual Dynamic Programming Solution of a Short-Term Disaster Management Problem

A Fuzzy Evacuation Management Model Oriented Toward the Mitigation of Emissions

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