Before joining OSU, Carrel was a postdoctoral associate at the MIT Center for Transportation and Logistics, where worked on the use of large-scale data analytics to gain insights into product flows within supply chains and on the development of overarching strategies for the acquisition, management and sharing of supply chain related data. He obtained his Ph.D. from the University of California at Berkeley, where he was a member of the multi-department behavior modeling and mobile computing group. He was the lead researcher of the San Francisco Travel Quality Study, a pioneering, large-scale study that leveraged automatically collected GPS and sensor data from smartphones and transit vehicles along with app-based surveys from 856 participants to better understand the dynamics of demand in public transportation and the link between level of service and customer decision-making. He has been a member of several other transportation research teams at UC Berkeley, the Massachusetts Institute of Technology, and the Swiss Federal Institute of Technology, and he has managed or contributed to successful collaborations with industry and government partners, including Transport for London, the City and County of San Francisco, and Starbucks Corporation. In addition, he held a junior project manager position at Deutsche Bahn AG, a global transportation and logistics provider, where he co-led a study on behalf of the European Commission and the International Union of Railways to evaluate the economic impacts of proposed EU legislation on the rail sector. He has won a number of awards and fellowships, including UPS foundation, UCCONNECT and USDOT Eisenhower fellowships, the Eno foundation’s C.W. Koch award for future leaders in transportation, and the University of California multi-campus transportation center’s graduate student of the year award.
Dr. Carrel's research interests include:
- Understanding the influence of autonomous, shared, and alternative-fuel transportation technologies on travel behavior
- Incorporating the effect of past experiences and habits into travel behavior and demand models
- Leveraging new data collection technologies to improve the collection of travel behavior data
- Understanding and mitigating the effect of unreliability on public transportation passengers and operations
- Designing new operating models for public transportation, shared mobility systems and freight systems that make use of connected vehicle and automation technologies
- Understanding the interaction between supply chain management strategies, shopping behavior, and travel demand
- Acquisition and statistical analysis of large-scale data sets from emerging data sources to understand and influence traveler choices and facilitate the design, operations and monitoring of transportation and logistics systems.