Large Scale Microscopic Evacuation Simulation

Lämmel, Gregor; Rieser, Marcel; Nagel, Kai

doi:10.1007/978-3-642-04504-2_48

Cited by 27 publications

(15 citation statements)

References 13 publications

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“…It has been stressed that from an application point of view a realistic simulation of situations where smallest travel time is balanced against shortest path is highly relevant while at the same time available models usually do not offer good solutions [40]. Travel time as determinant of the motion has been discussed and modeled in a number of previous works, sometimes for macro-or mesoscopic approaches [13,14,32,8,44], but more often within the scope of microscopic models. Of these the majority are field-based as the dynamic potential approach [12,49,46,26,27,23,47,29,28,7,24], but there exist also other methods which are based on an additional routing network, discrete choice or heuristics [5,3,11,21,34].…”

Section: Related Workmentioning

confidence: 99%

Calibrating dynamic pedestrian route choice with an Extended Range Telepresence System

Kretz

Hengst

Roca

et al. 2011

2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)

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In this contribution we present the results of a pilot study in which an Extended Range Telepresence System is used to calibrate parameters of a pedestrian model for simulation. The parameters control a model element that is intended to make simulated agents walk in the direction of the estimated smallest remaining travel time. We use this to, first, show that that an Extended Range Telepresence System can serve for such a task in general and second to actually find simulation parameters that yield realistic results.

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Section: Related Workmentioning

confidence: 99%

Calibrating dynamic pedestrian route choice with an Extended Range Telepresence System

Kretz

Hengst

Roca

et al. 2011

2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)

View full text Add to dashboard Cite

show abstract

“…Evacuation modelling has most recently been carried out using existing well-established traffic simulators, including MATSIM [10] and PARAMICS [4]. Within these simulators, a driver's route choice behaviour is determined using a user-equilibrium assignment model.…”

Section: Introductionmentioning

confidence: 99%

An agent-based social forces model for driver evacuation behaviours

Handford

Rogers

2012

Prog Artif Intell

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Realistic modelling of driver behaviour during evacuation scenarios is vitally important for creating effective training environments for disaster management. However, few current models have satisfactorily incorporated the level of complexity required to model the unusual driver behaviours which occur in evacuations. In particular, few state-of-the-art traffic simulators consider desires of a driver other than to travel the quickest route between two points. Whereas in real disaster settings, empirical evidence suggests other key desires such as that of being near to other vehicles. To address this shortcoming, we present an agentbased behaviour model based on the social forces model of crowds, which explicitly includes these additional factors. We demonstrate, by using a metric of route similarity, that our model is able to reproduce the real-life evacuation behaviour whereby drivers follow the routes taken by others. The model is compared to the two most commonly used route choice algorithms, that of quickest route and real-time re-routing, on three road networks: an artificial "ladder" network, and those of Louisiana, USA and Southampton, UK. When our route choice forces model is used our measure of route similarity increases by 21-169 %. Furthermore, a qualitative comparison demonstrates that the model can reproduce patterns of behaviour observed in the 2005 evacuation of the New Orleans area during Hurricane Katrina.

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“…However, route choice behaviour in current state-of-the-art evacuation simulations (such as MATSIM [9] or PARAMICS [3]) incorporate limited driver behaviours and thus it is difficult to reproduce real-life patterns of traffic behaviour. In PARAMICS, the behaviour of drivers who are "unfamiliar" with an area is to take the route that they believe to be quickest to the exit with a preference for using major roads over minor roads [11].…”

Section: Introductionmentioning

confidence: 99%

Modelling Driver Interdependent Behaviour in Agent-Based Traffic Simulations for Disaster Management

Handford

Rogers

2011

Advances in Intelligent and Soft Computing

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Accurate modelling of driver behaviour in evacuations is vitally important in creating realistic training environments for disaster management. However, few current models have satisfactorily incorporated the variety of factors that affect driver behaviour. In particular, the interdependence of driver behaviours is often seen in real-world evacuations, but is not represented in current state-of-the art traffic simulators. To address this shortcoming, we present an agent-based behaviour model based on the social forces model of crowds. Our model uses utility-based path trees to represent the forces which affect a driver's decisions. We demonstrate, by using a metric of route similarity, that our model is able to reproduce the real-life evacuation behaviour whereby drivers follow the routes taken by others. The model is compared to the two most commonly used route choice algorithms, that of quickest route and real-time re-routing, on three road networks: an artificial "ladder" network, and those of Lousiana, USA and Southampton, UK. When our route choice forces model is used our measure of route similarity increases by 21%-93%. Furthermore, a qualitative comparison demonstrates that the model can reproduce patterns of behaviour observed in the 2005 evacuation of the New Orleans area during Hurricane Katrina.

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Large Scale Microscopic Evacuation Simulation

Cited by 27 publications

References 13 publications

Calibrating dynamic pedestrian route choice with an Extended Range Telepresence System

Calibrating dynamic pedestrian route choice with an Extended Range Telepresence System

An agent-based social forces model for driver evacuation behaviours

Modelling Driver Interdependent Behaviour in Agent-Based Traffic Simulations for Disaster Management

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