Traffic models can be used to study evacuation scenarios during wildland-urban interface fires and identify the ability of a community to reach a safe place. In those scenarios, wildfire smoke can reduce visibility conditions on the road. This can have serious implications on the evacuation effectiveness since drivers would reduce their speed in relation to the optical density on the road. To date, there is no traffic model which explicitly represents the impact of reduced visibility conditions on traffic evacuation flow. This paper makes use of an experimental dataset collected in a virtual reality environment to calibrate two widely used macroscopic traffic models (the Lighthill-Whitham-Richards and the Van Aerde models) in order to account for the impact of reduced visibility conditions on driving speed. An application of the calibrated traffic model considering the impact of smoke has been performed using the WUI-NITY platform, an open multi-physics platform which includes wildfire spread, pedestrian response and traffic modelling. A dedicated verification test has been developed and performed considering different values of optical densities of smoke and traffic densities to ensure the model has been implemented correctly in WUI-NITY. A case study that demonstrates the applicability of the model to real life scenarios was also implemented, based on data from an evacuation drill. This paper shows that the presence of smoke on the road can significantly decrease movement speed and increase evacuation times thus highlighting the need for inclusion of this factor in traffic evacuation models applied for wildland-urban interface fire scenarios.

Modelling the impact of wildfire smoke on driving speed

Intini, P;
2022

Abstract

Traffic models can be used to study evacuation scenarios during wildland-urban interface fires and identify the ability of a community to reach a safe place. In those scenarios, wildfire smoke can reduce visibility conditions on the road. This can have serious implications on the evacuation effectiveness since drivers would reduce their speed in relation to the optical density on the road. To date, there is no traffic model which explicitly represents the impact of reduced visibility conditions on traffic evacuation flow. This paper makes use of an experimental dataset collected in a virtual reality environment to calibrate two widely used macroscopic traffic models (the Lighthill-Whitham-Richards and the Van Aerde models) in order to account for the impact of reduced visibility conditions on driving speed. An application of the calibrated traffic model considering the impact of smoke has been performed using the WUI-NITY platform, an open multi-physics platform which includes wildfire spread, pedestrian response and traffic modelling. A dedicated verification test has been developed and performed considering different values of optical densities of smoke and traffic densities to ensure the model has been implemented correctly in WUI-NITY. A case study that demonstrates the applicability of the model to real life scenarios was also implemented, based on data from an evacuation drill. This paper shows that the presence of smoke on the road can significantly decrease movement speed and increase evacuation times thus highlighting the need for inclusion of this factor in traffic evacuation models applied for wildland-urban interface fire scenarios.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/244820
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