Therefore, mapping the slippery pavement conditions in real time will help deploy proper countermeasures, such as snowplowing, spreading of deicing chemicals and shutting down roads, so as to avoid traffic jams and accidents. To address these problems, approximately 20% of the maintenance budget of US Department of Transportation (DOT) is spent on winter road maintenance. For example, 24% of weather-related vehicle crashes occur on snowy or icy pavements, causing more than 1,300 deaths and 116,800 injuries each year. Meanwhile, accident rates rise dramatically. Under the effect of poor visibility and snowy or icy pavements, average arterial speeds are reduced by 30 to 40%, while freeway speeds are reduced by 5 to 40% (Pisano et al., 2008). Ice and snow would diminish pavements' friction and vehicles' maneuverability, thus resulting in reduced vehicle speeds, lowered roadway capacities, and increased accident risks. In addition, nearly 70% of the American population resides in regions with snows. More than 70% of the roads in the United States are located in snowy areas, with an average annual snowfall of over 5 inches. In the winter time, slippery road conditions, such as snowy, icy or slushy pavements, become a latent danger for road safety. Simulation results indicate that the proposed slippery detection system and the information sharing function based on the CV technology and deep learning algorithm (i.e., the LSTM network implemented in this study) are expected to deliver real-time detection of slippery pavement conditions, thus significantly eliminating the potential risk of accidents. In addition, it is observed that potential accidents can be reduced by more than 90% if CVs can adjust their driving speed and maintain a greater distance from the leading vehicle after receiving a warning signal. The system can achieve 100%, 99.06% and 98.02% prediction accuracy for dry pavement, snowy pavement and icy pavement, respectively. The LSTM model is trained with simulated CV data in VISSIM and optimized with a Bayesian algorithm. The classification algorithm adopted in this study is Long Short-Term Memory (LSTM), which is an artificial Recurrent Neural Network (RNN). In practice, more attention should be paid to the detected icy and snowy pavements when driving or implementing pavement maintenance and road operation in winter. Different pavement conditions reflect different levels of slipperiness: dry surface corresponds to the least slippery condition, and icy surface to the most slippery condition. The system classifies pavement conditions into three major categories: dry, snowy and icy. This study proposes a CV-based slippery detection system that uses vehicles to acquire data and implements deep learning algorithms to predict pavements' slippery conditions. The emerging connected vehicle (CV) technology offers the opportunity to map slippery road conditions in real time. Most existing studies have mainly explored indirect estimates based on pavement images and weather forecasts. However, despite extensive research, it remains a challenging task to monitor pavement conditions and detect slippery roadways in real time. Department of Transportation (USDOT) spends over 20% of its maintenance budget on pavement maintenance in winter. Slippery road conditions, such as snowy, icy or slushy pavements, are one of the major threats to road safety in winter.
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