Download or read book Trajectory-level Weather Detection and Behavior Investigation with Big-data Analytics and Machine Learning written by Md Nasim Khan and published by . This book was released on 2021 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main focus of this study was to develop reliable weather detection systems as well as to investigate driver behavior in adverse weather through big data analysis, image processing, and advanced machine learning techniques utilizing the trajectory-level naturalistic driving data collected by the second Strategic Highway Research Program (SHRP2), and explore their potential readily implementable applications in Wyoming. This study first developed several affordable in-vehicle detection systems, including a novel detection architecture, named “RoadweatherNet”, capable of identifying seven levels of adverse weather with more than 90% accuracy. The study then concentrated on the transferability of the findings in the state of Wyoming and successfully developed Wyoming specific weather and surface condition detection systems with an accuracy of about 94%. Finally, this research investigated driver speed selection behavior in adverse weather, which revealed that drivers reduced their speeds up to 30%, 10%, and 7% due to snow, rain, and fog, respectively. The trajectory weather detection models can be integrated on smartphones of regular road users, thus making it a cost-effective way of collecting real-time road weather information. In addition, the detection system can provide reliable spatial and temporal variations of road surface conditions which is beneficial for route optimizations of snowplows and for choosing road maintenance priorities. The findings from driver speed selection analysis can be integrated into the Variable Speed Limit (VSL) logics, which might result in better compliance with the posted speed limits. Hence, they could potentially reduce roadway injuries and fatalities.