Predicting Traffic Performance During a Wildfire Using Machine Learning. Issue 3 (March 2023)

• Record Type: Journal Article
• Authors: Hou, Zenghao; Darr, Justin; Zhang, Michael
• Is Part Of: Transportation research record. Volume 2677:Issue 3(2023)
• Page Start: 1625
• Page End: 1636
• Publication Date: 2023-03
• Subjects: sustainability and resilience -- disaster response -- recovery -- and business continuity -- disaster response -- natural hazards and extreme weather events -- extreme weather events; Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/03611981221126509
• Languages: English
• ISSNs: 0361-1981

Abstract

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.