Date of Award

January 2013

Document Type


Degree Name

Master of Science (MS)


Atmospheric Sciences

First Advisor

Leon F. Osborne


Today State Departments of Transportation rely more and more on road weather data to make maintenance decisions. Inaccurate data can result in wrong treatment applications or inadequate staffing levels to maintain the roadway at the desired level of service.

Previous methods of road condition data reporting have been limited to static in situ sensor stations. These road weather information systems (RWIS) provide varied data about precipitation, winds, temperature, and more, but their siting does not always provide an accurate representation of weather and road conditions along the roadway. The use of mobile data collection from vehicles travelling the highway corridors may assist in the locations where RWIS sitings are sparse or non-existent.

The United States Department of Transporation's "Connected Vehicle" (formally IntelliDrive) research project is designed to create a fully connected transportation system providing road and weather data collection from an extensive array of vehicles. While the implementation of Connected Vehicle is in the future, some of the theories and technologies are already in place today. Several states, as a part of the Pooled Fund Study Maintenance Decision Support System (MDSS), have equipped their winter maintenance vehicles with Mobile Data Collection Automated / Vehicle Location (MDC/AVL) systems. In addition, since 1996, automobiles sold in the United States are required to be equipped with an Onboard Diagnostic Version 2 (OBDII) port that streams live data from sensors located in and around the vehicle. While these sensors were designed for vehicle diagnostics, some of the data can be used to determine weather characteristics around the vehicle. The OBDII data can be collected by a smartphone and sent to a server in real time to be processed. These mobile systems may fill the information gap along the roads that stationary environmental sensor stations are not able to collect.

Particular concern and care needs to be focused on data quality and accuracy, requiring the development of quality checks for mobile data collection. Using OBDII-equipped automobiles and mobile collection methods, we can begin to address issues of data quality by understanding, characterizing, and demonstrating the quality of mobile system observations from operational and research environments. Several forms of quality checking can be used, including range checks, Barnes spatial checks, comparing vehicle data to road weather models, and applying Clarus quality check methodologies and algorithms to mobile observations. Development of these quality checks can lead to the future integration of mobile data into the Clarus system, data implementation for improved forecasting, maintenance decision support, and traveler safety.

This paper will discuss the benefits and challenges in mobile data collection, along with how the development and implementation of a system of quality checks will improve the quality and accuracy of mobile data collection.