Traffic managers strive to have the most accurate information on road conditions, normally by using sensors and cameras, to act effectively in response to incidents. The prevalence of crowdsourced traffic information that has become available to traffic managers brings hope and yet raises important questions about the proper strategy for allocating resources to monitoring methods. Although many researchers have indicated the potential value in crowdsourced data, it is crucial to quantitatively explore its validity and coverage as a new source of data. This research studied crowdsourced data from a smartphone navigation application called Waze to identify the characteristics of this social sensor and provide a comparison with some of the common sources of data in traffic management. Moreover, this work quantifies the potential additional coverage that Waze can provide to existing sources of the advanced traffic management system (ATMS). One year of Waze data was compared with the recorded incidents in the Iowa's ATMS in the same timeframe. Overall, the findings indicated that the crowdsourced data stream from Waze is an invaluable source of information for traffic monitoring with broad coverage (covering 43.2% of ATMS crash and congestion reports), timely reporting (on average 9.8 minutes earlier than a probe-based alternative), and reasonable geographic accuracy. Waze reports currently make significant contributions to incident detection and were found to have potential for further complementing the ATMS coverage of traffic conditions. In addition to these findings, the crowdsourced data evaluation procedure in this work provides researchers with a flexible framework for data evaluation.

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Matt Hagge is a Senior Lecturer at Iowa State University. He has spent his career talking to students to figure out how students think and learn. The result of these talks has been the development of a course-wide decision framework for a thermodynamics course that allows students to solve previously unseen problems while building their expertise. This pedagogy is called Decision Based Learning, and has received tremendous student feedback and results. Students are able to solve complex problems through understanding rather than memorization and copying. Learning how to think, how to self reflect, how to take personal responsibility for learning, and the development of expert problem solving skills are all reasons why this style of teaching is life changing for many students.Mr. Mostafa Amin-Naseri, Iowa State University Mostafa Amin-Naseri, is a masters student in industrial engineering in Iowa State University. He is interested in data mining and statistical analysis. He applies data analysis to educational data, building learner models and reporting tools for instructors, in order to evaluate and enhance educational systems and methods. Prof. Stephen B Gilbert, Iowa State UniversityStephen B. Gilbert received a BSE from Princeton in 1992 and PhD from MIT in 1997. He has worked in commercial software development and run his own company. He is currently an assistant professor in the Industrial and Manufacturing Systems Engineering department at Iowa State University, as well as Associate Director of ISU's Virtual Reality Application Center and its Graduate Program in Human Computer Interaction. His research focuses on technology to advance cognition, including interface design, intelligent tutoring systems, and cognitive engineering. Dr. John Jackman, Iowa State UniversityJohn Jackman is an associate professor of industrial and manufacturing systems engineering at Iowa State University. His research interests include engineering problem solving, computer simulation, web-based immersive learning environments, and data acquisition and control. Ms. Enruo GuoEnruo Guo is a Ph.D. candidate in computer science, co-majoring in human computer interaction at Iowa State University. Before that, she got her M.S. degree in computer science and chemistry. Her research interests include AI in education, educational data mining and human computer interaction. Decision based learning for a sophomore level thermodynamics course AbstractTo meet the challenges of today's engineers, educators need to better understand and utilize teaching methods. These challenges and possible solutions are explored in Felder et. al. [1,2,3] . In this paper, decision based learning (DBL) is presented as a new pedagogy in an attempt to address some of these challenges. In decision based learning, a student is given a problem that they have never seen before. The student is asked to make a set of instructor decisions. When the student is unable to make a decision correctly, the instructor attempts to improve student understanding. This process cont...

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