  Hurricane evacuation, ordered to save lives of people of coastal regions,
generates high traffic demand with increased crash risk. To mitigate such risk,
transportation agencies need to anticipate highway locations with high crash
risks to deploy appropriate countermeasures. With ubiquitous sensors and
communication technologies, it is now possible to retrieve micro-level
vehicular data containing individual vehicle trajectory and speed information.
Such high-resolution vehicle data, potentially available in real time, can be
used to assess prevailing traffic safety conditions. Using vehicle speed and
acceleration profiles, potential crash risks can be predicted in real time.
Previous studies on real-time crash risk prediction mainly used data from
infrastructure-based sensors which may not cover many road segments. In this
paper, we present methods to determine potential crash risks during hurricane
evacuation from an emerging alternative data source known as connected vehicle
data. Such data contain vehicle location, speed, and acceleration information
collected at a very high frequency (less than 30 seconds). To predict potential
crash risks, we utilized a dataset collected during the evacuation period of
Hurricane Ida on Interstate-10 (I-10) in the state of Louisiana. Multiple
machine learning models were trained considering weather features and different
traffic characteristics extracted from the connected vehicle data in 5-minute
intervals. The results indicate that the Gaussian Process Boosting (GPBoost)
and Extreme Gradient Boosting (XGBoost) models perform better (recall = 0.91)
than other models. The real-time connected vehicle data for crash risks
assessment will allow traffic managers to efficiently utilize resources to
proactively take safety measures.
