Complementary methodologies to identify weather conditions in naturalistic driving study trips: Lessons learned from the SHRP2 naturalistic driving study & roadway information database

摘要

Adverse weather conditions play a considerable role in the safety and efficiency of the transportation network. Many studies have aimed to quantify the impact that different weather conditions have on transportation safety and mobility; however, most studies have evaluated the network capacity, average speed, and other macroscopic measures without capturing specific driving characteristics. In order to understand specific driving behavior and performance characteristics that exist during different environmental conditions, high resolution vehicle data and video footage are required. The SHRP2 sponsored the generation of a large Naturalistic Driving Study (NDS) database - which provides vehicle time series data, front and rear video, driver video, external sensor readings, and driver surveys - and the Roadway Information Database (RID) - which is a complementary database with geospatial data for commonly driven roads in the NDS and other ancillary data sources, including annual traffic, roadway geometry, accident reports, weather conditions, and 511 alerts. The purpose of this study is to leverage these SHRP2 databases and weather data from the National Climatic Data Center (NCDC) to extract trips that occur during adverse weather conditions. The extraction of weather-related trips from a NDS is unprecedented, and this study presents three complementary methodologies used in parallel to acquire relevant trips from the SHRP2 NDS database. A semi-automated data reduction procedure was developed to process the raw trip files into a format that further analysis and modeling could be completed. This novel approach to NDS trip acquisition and reduction could be extended to other naturalistic driving studies worldwide.