AI/ML

Simulation Pipeline for Traffic Evacuation in Urban Areas and Emergency Traffic Management Policy Improvements. (arXiv:2002.06198v1 [cs.AI])



Traffic evacuation plays a critical role in saving lives in devastating
disasters such as hurricanes, wildfires, floods, earthquakes, etc. An ability
to evaluate evacuation plans in advance for these rare events, including
identifying traffic flow bottlenecks, improving traffic management policies,
and understanding the robustness of the traffic management policy are critical
for emergency management. Given the rareness of such events and the
corresponding lack of real data, traffic simulation provides a flexible and
versatile approach for such scenarios, and furthermore allows dynamic
interaction with the simulated evacuation. In this paper, we build a traffic
simulation pipeline to explore the above problems, covering many aspects of
evacuation, including map creation, demand generation, vehicle behavior,
bottleneck identification, traffic management policy improvement, and results
analysis. We apply the pipeline to two case studies in California. The first is
Paradise, which was destroyed by a large wildfire in 2018 and experienced
catastrophic traffic jams during the evacuation. The second is Mill Valley,
which has high risk of wildfire and potential traffic issues since the city is
situated in a narrow valley.

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