Over the past two years, the USDOT/Research and Innovative Technology Administration funded an IntelliDriveSM vehicle probe data collection testbedin the Northwest Detroit area (the Detroit Testbed). 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). During April 2009, a weather specific field study was performed over an 11-day period. The resulting dataset was processed by a Vehicle Data Translator (VDT), which parsed, quality controlled, and combinedthese 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 operated during the study time period. Results show that the vehicles accurately measure the temperature (compared with a nearby fixed weather station; KDTW), but are not as accurate at measuring the barometric pressure. In addition, stratification by speed, vehicle type, timeof day, and occurrence of precipitation do not affect the accuracy of thetemperature and barometric pressure measurements
Abstract