Using passenger vehicles to diagnose road weather conditions—results from the Detroit Intellidrive (SM) testbed

Over the past two years the USDOT/Research and Innovative Technology Administration's IntelliDriveSM initiative was responsible for funding a vehicle probe data collection testbed in the Northwest Detroit area. The purpose of the testbed was to provide the infrastructure for both public and private organizations to collect, process, and generate a robust observation dataset for multiple purposes (e.g. crash avoidance, automated toll services, weather diagnostics, etc.). During April 2009, a weather specific field study was performed over an 11-day period. The dataset that was collected was processed by a Vehicle Data Translator (VDT), which was designed to parse, quality control, and combine these data (with ancillary weather data) in the generation of road-weather specific algorithms. This paper briefly describes the VDT concept and then examines the accuracy of the quality-controlled temperature and pressure data (for several different stratifications) collected from 11 specially-equipped vehicles operating during the study time period. Results show that the vehicles accurately measure the temperature (compared with a nearby fixed weather station; KDTX), but are not as accurate at measuring the barometric pressure. In addition, stratification by speed, vehicle type, time of day, and occurrence of precipitation don't appear to affect the accuracy of the temperature and barometric pressure measurements.