Employment of instrumented vehicles to identify real-time snowy weather conditions on freeways using supervised machine learning techniques – A naturalistic driving study. (December 2022)

• Record Type: Journal Article
• Authors: Ali, Elhashemi M.; Ahmed, Mohamed M.
• Is Part Of: IATSS research. Volume 46:Number 4(2022)
• Page Start: 525
• Page End: 536
• Publication Date: 2022-12
• Subjects: Snowy weather -- Unsupervised machine learning -- Sensors data -- CANbus data -- Weather estimation; Traffic safety -- Periodicals; Transportation and state -- Periodicals; Verkeersveiligheid; Internationale organisaties; Traffic safety; Transportation and state; Periodicals; 363.1256
• DOI: 10.1016/j.iatssr.2022.09.001
• Languages: English
• ISSNs: 0386-1112

Abstract

Abstract: This study introduces the idea of using vehicles as weather sensors to identify real-time weather on freeways in the same context as Road Weather Information System (RWIS) but in a continuous, trajectory-level, and for road segments allocated in the vehicles paths. The study developed a novel approach to detect snowy and clear weather conditions by utilizing real-time data collected from vehicles' external sensors and CANbus. The proposed approach used time series datasets from the SHRP2 Naturalistic Driving Study (NDS), collected during normal driving conditions on freeways. Trips occurring in snowy weather alongside matched trips in clear weather were segmented into time- and distance-based segments such as a one-minute, one-mile, and half a mile. Three assemblies of the input data are considered in the modeling step: data collected from external sensors, CANbus data, and these two data combined. Data analysis was implemented using the Deep Learning Artificial Neural Network, Decision Tree, Random Forest, and Gradient Boosted Trees models. The results indicate that using different segmentation levels provides decent results in detecting snowy weather. The accuracy in estimating the real-time snowy weather was in ranges of 80% to 85%, 71% to 79%, and 73% to 83% for the one-minute, one-mile, and half mile segmentation types, respectively. The GBT model performed the best among all models based on the area under the Receiver Operating Characteristics (ROC) curve, the highest cumulative percentage in estimating the snowy weather using the lower portion of the population, and the highest overall accuracy. Results indicated that an accuracy of 83% in estimating snowy weather conditions could be accomplished using the data collected from external sensors only without accessing CANbus data. Highlights: This study developed a novel approach to detect snowy and clear weather conditions by utilizing real-time data collected from vehicles' external sensors and CANbus. It aims to illustrate the importance of employing vehicles as weather sensors in Connected and Autonomous Vehicle applications to improve traffic safety on freeways. The results of this study indicate that using different segmentation levels provides decent results in detecting snowy weather. Study results proved that using sensors to collect data would provide similar results to these results derived from using CANbus data and sensors data. The study findings suggest that valuable results can still be attained using external vehicle-based sensors.