Filling in the Gaps of Connected Car Data Helps Transportation Planners

Kuilin Zhang, assistant professor of civil and environmental engineering and affiliated assistant professor of computer science at Michigan Technological University, has developed a way to fill in the gaps, as presented in a recent study published in Transportation Research Part C: Emerging Technologies. In the future, Zhang believes this will be a cost-effective way to allow transportation planners to do everything from make more effective traffic congestion mitigation strategies to know where to build wider or new roads.

In this study, researchers used included two-months of connected vehicle data from 2,800 cars, provided by the Safety Pilot Model Deployment Program in Ann Arbor, Michigan. From it, they created a data-driven optimization approach to reconstructing the missing location-duration-path choices those cars make. The reconstructed choices can be used to improve the validation and calibration of the models.