October 8, 2018
Transportation leaders gather for a panel discussion in September 2018.
Transportation is poised for a big transformation in coming years, due to shared mobility, autonomous and connected vehicles, and virtual reality. As these new and emerging technologies begin to shape the future urban landscape, mobility related questions begin to surface, such as where to live, work and how to best get from place to place. While emerging big data from smart phones, social media and vehicle navigation systems is increasingly being used to answer such questions, concerns over the quality of data are beginning to emerge.
Without a scientifically controlled process to generate data, the quality of big data can be compromised. This includes not representing certain populations such as the low-incomes and elderly, missing certain trips and lack of details such as who is traveling. Incomplete data can have dire consequences in transportation investment decisions, affecting many neighborhoods whose residents are not well captured in the data.
|Jeff Ban||Cynthia Chen|
To discuss these issues, UW CEE faculty Jeff Ban and Cynthia Chen hosted a panel discussion in September 2018. Their goal was to better understand and leverage the strengths of emerging big data for a more efficient, equitable and proactive transportation planning process. The panel involved diverse stakeholders and researchers from the Federal Highway Administrations (FHWA), Washington State Department of Transportation (WSDOT), Puget Sound Regional Council (PSRC), City of Bellevue, Pierce Transit, data providers such as TrafficCast and Cuebiq, and faculty and students from UW and Carnegie Mellon University (CMU). The future of transportation is important to federal, state, and local government agencies that support jobs and aid growth in the manufacturing and service sector.
Panelists provided their perspectives on the role of big data in moving society forward in a highly exciting, yet also uncertain time in transportation, and pointed out the need for understanding the limitations of big data. Panelists also highlighted the need for leveraging the strengths of big data to aid the planning process. In particular, Antonio Tomarchio from Cuebiq provided an overview of the company’s app-based location data; associate professor Bill Howe from UW’s Information School and assistant professor Sean Qian from CMU presented their state-of-the-art research on new big data sharing methods and big data assisted transportation modeling.
This panel discussion is part of a research project led by Ban and Chen on understanding diverse big data sources in the Puget Sound Region, funded by FHWA and WSDOT. Ban shared a presentation on UW’s current progress and major findings of the project. Chen moderated the afternoon discussion session on two applications: origin-destination travel demand estimation and corridor-level traffic management. The discussion identified key performance measures and gaps in current practice and participants brainstormed the many potentials of emerging big data to answer policy questions. Results from the panel discussion also provided guidance for future planning and modeling practice involving big data.