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Anyone sitting through an unexpected traffic backup is experiencing one of transportation engineering’s most difficult problems: predicting how people will move through a changing transportation network. As communities grow and travel patterns evolve, planners must make critical decisions about roads, infrastructure investments, and future development based on forecasts that are often years in the making.
To help improve those forecasts, George Mason University Associate Professor Shanjiang Zhu received three awards totaling more than $290,000 from the Virginia Transportation Research Council (VTRC), the research division of the Virginia Department of Transportation (VDOT). The projects explore how emerging vehicle data and advanced modeling techniques can help transportation agencies better understand and anticipate future travel demand.
One award, valued at $149,999, addresses a long-standing methodological gap in traffic impact analysis: the potential duplication of future traffic growth embedded in background-growth forecasts and development-generated traffic estimates. Because these two components may reflect overlapping underlying sources of growth, conventional approaches that estimate and add them independently can lead to double counting and biased traffic forecasts. By integrating and synthesizing data from multiple sources, the research develops new methods to identify, quantify, and correct for this overlap, improving the accuracy and defensibility of future traffic projections.
The other two VTRC awards of $142,374 focuses on the use of emerging connected vehicle data to support travel demand modeling. Detailed vehicle trajectory data, with observations as frequent as once every three seconds, offer unprecedented insights into travel behavior and transportation system performance. However, these data are often “fuzzed,” with the beginning and ending portions of individual trips obscured to protect traveler privacy. These two projects develop methodologies that enable transportation planners to recover meaningful behavioral and planning insights from privacy-protected trajectory data while continuing to safeguard personally identifiable information.
“We want to protect privacy. But at the same time, we want to keep the data useful,” Zhu said. “We want to keep the capability to derive information that is important for planners but at the same time will not reveal personal information through methodological innovations.”
Together, the projects build on the Sid and Reva Dewberry Department of Civil, Environmental, and Industrial Engineering’s reputation for addressing real-world transportation challenges through data-driven research. Zhu’s work aims to provide VDOT with practical guidance for applying connected vehicle data in planning and policy, helping agencies make more informed decisions about Virginia’s transportation future.