01863299

Component

https://doi.org/10.1177/03611981221126509

Many places around the world periodically suffer from wildfires that threaten lives and disrupt normal traffic operations. Poor traffic performance during wildfires can inhibit the effectiveness of evacuations. Understanding traffic performance during a wildfire would therefore help transportation operators develop emergency traffic control plans. In this study, we developed a traffic speed and flow prediction model that uses support vector regression (SVR), for use during wildfire incidents. This was constructed using historical data for wildfires in California from 2010 to 2019, which were paired with records of the traffic speed and flow on adjacent highways and the prevailing weather conditions during the wildfire events. The results showed that traffic performance during a wildfire could be predicted using the SVR model. Based on our prediction results, we recommend that policies be implemented to encourage or mandate more detailed data collection of wildfire events, such as the fire’s boundary over time, to facilitate better prediction results in models like the one proposed in this paper. This paper should inspire further work on the topic to improve the model and provide a reliable prediction tool for transportation operators in the future.

Zenghao Hou https://orcid.org/0000-0002-1787-9459© National Academy of Sciences: Transportation Research Board 2022.

English

0000-0002-1787-9459

0000-0002-1888-2537

0000-0002-4647-3888

pp 1625-1636

2023-3

Transportation Research Record: Journal of the Transportation Research Board

Web

References (35)

Evacuation; Machine learning; Mathematical prediction; Traffic flow; Traffic speed; Wildfires

Highways; Operations and Traffic Management; Security and Emergencies

TRIS, TRB, ATRI

Oct 28 2022 3:02PM