Development of Dust Storm Modeling for Use in Freeway Safety and Operations Management: An Arizona Case Study

Amin Mohebbi, Gabriel T. Green, Simin Akbariyeh, Fan Yu, Brendan J. Russo, Edward J. Smaglik

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Extreme weather conditions such as strong winds, hail, heavy rainfall, heavy snowfall, and high air temperature impact roads, traffic, and operational decisions. Strong winds in arid regions may pick up fine dust particles and create massive blowing plumes dramatically reducing the visibility. This reduced visibility severely impairs driving ability causing catastrophic crashes. The purpose of this research was to investigate the impacts of dust storms on freeway safety and operations. Interstates 8, 10, 15, 17, 19, and 40 running through Arizona were studied in relation to dust loading and crash risks. To achieve this, nine severe Arizona dust storms (2009–2016) were modeled using Weather Research and Forecasting (WRF) model coupled with a chemistry module (WRF-Chem). WRF is a mesoscale numerical weather prediction system with a software architecture allowing for parallel computation. When coupled with a chemistry module (WRF-Chem), it could be used to model the fate and transport of the particulate matter. Dust hot spots were calculated based on Getis-Ord Gi* statistical method and were correlated to dust storm caused crashes. It was shown that a positive Gi* accompanied by dust loading of at least 50 kgm–2 will result in a crash with a 90% confidence level. The outcome of this research could be used by local and federal transportation agencies to communicate warnings to drivers for improved safety.

Original languageEnglish (US)
Pages (from-to)175-187
Number of pages13
JournalTransportation Research Record
Issue number5
StatePublished - May 1 2019

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering


Dive into the research topics of 'Development of Dust Storm Modeling for Use in Freeway Safety and Operations Management: An Arizona Case Study'. Together they form a unique fingerprint.

Cite this