by Joshua Chang, Curtis Louis Walker,
15 Aug, 2022
Millions of motor vehicle crashes and tens of thousands of resulting deaths occur each year in the United States. While it is well known that wintry conditions make driving more difficult and dangerous, it is difficult to quantify and communicate the threat to motorists, especially in real time. This proof-of-concept research uses machine learning (ML) to approach this problem in a new way by creating a ML model that can identify snow on the road from a traffic camera image. This information is coupled with the number of coincident vehicular crashes to provide detailed consideration of the impact of snow on the road to motorists and transportation agency decision-makers. It was found that, during meteorological winter, when the ML model determined there to be snow on the road in a traffic camera image, the chance of a vehicular crash pairing with that traffic camera increased by 61%. The systems developed as part of this research have potential to assist roadway officials in assessing risk in real time and making informed decisions about snow removal and road closures. Moreover, the implementation of in-vehicle weather hazard information could promote driver safety and allow motorists to adjust their driving behavior and travel decision making as well.
This study presents the analysis of seismic signatures generated during passage of hurricanes Newton (September 2016) and Willa (October 2018), recorded on the daily helicorders of a short-period seismic station at the distances about 450 km from the tracks of events. This view from seismic station allowed to obtain the following results. Periods of passage of these tropical storms and hurricanes were identified. Analysis of the dynamics of sequences of these seismic signals allowed to separate the time intervals of increase and decrease in the development of atmospheric disturbances. The spectral analysis of the signals of tropical storm Newton and hurricane Willa showed that the spectral amplitudes of signals, recorded during the maximum stage of activity of the tropical storm, were larger than the same for the maximum stage of activity of the hurricane. This may be related to the presence of intensive hailstorms during tropical storm.